Analytics Modules

Installation and Porting
Guide
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CONTENTS
Book Overview and
Additional Resources
Description of this guide............................................................ ix
About the Analytics Modules ..........................................................x
About this book ..............................................................................xi
Additional formats ....................................................................xi
How to find business scenarios and examples ....................... xii
Prerequisites ........................................................................... xii
Who should use this guide..................................................... xiii
Resources.................................................................................... xiii
Documentation....................................................................... xiii
Education .............................................................................. xxii
Consulting ............................................................................. xxii
International support ............................................................. xxii
Technical Support ................................................................ xxiii
Feedback ..................................................................................xxviii
1. Introduction to
MicroStrategy
Analytics Modules
Introduction.................................................................................. 1
2. Installation
Introduction.................................................................................. 9
Documentation............................................................................... 2
MicroStrategy Analytics Modules................................................... 2
An analysis of portable analytical applications......................... 4
Analytics Modules as best practices examples........................ 6
About building analytical applications ...................................... 7
Installation prerequisites ................................................................ 9
System hardware recommendations ..................................... 10
System software requirements .............................................. 10
© 2015 MicroStrategy, Inc.
v
Contents
Installation and Porting Guide
Installation procedures................................................................. 11
The Setup Wizard .................................................................. 11
Installation verification ........................................................... 17
Uninstalling a MicroStrategy component ..................................... 18
3. Configuration
Introduction................................................................................ 21
Configuring your software............................................................ 21
Configuration preparation ...................................................... 21
Configuration prerequisites .................................................... 23
Configure the production metadata repository....................... 24
Duplicate the Analytics Modules ............................................ 25
Configure the data warehouse connection ............................ 26
4. Using the Analytics
Modules
Introduction................................................................................ 31
Using the Analytics Modules as an initial design ......................... 32
Initial data warehouse ............................................................ 33
Customize and extend the Analytics Modules ............................. 34
Customization scenarios........................................................ 34
Scenario: Adding customer characteristics to CAM............... 36
Customization steps .............................................................. 37
Roll out to production................................................................... 37
Tuning and performance........................................................ 38
MicroStrategy users ............................................................... 38
Security .................................................................................. 38
Direct and server connections ............................................... 38
5. Porting the Analytics
Modules
Introduction................................................................................ 41
Introduction to portability.............................................................. 42
Portability methodology ............................................................... 43
Portability design drivers........................................................ 43
General porting prerequisites ...................................................... 46
MicroStrategy tools ................................................................ 46
Documentation....................................................................... 47
Other prerequisites ................................................................ 47
Port an Analysis Module .............................................................. 48
Porting overview .................................................................... 48
Perform a gap analysis ................................................................ 48
Gap analysis prerequisites..................................................... 49
vi
© 2015 MicroStrategy, Inc.
Installation and Porting Guide
Contents
Map the target data warehouse to the module’s physical
schema .................................................................................. 49
Map the module’s logical data model to the target schema... 54
Identify application objects..................................................... 62
Identify additional requirements ............................................. 73
Map the module ........................................................................... 73
Mapping prerequisites ........................................................... 74
Map the module with Object Manager ................................... 74
Map the module without Object Manager .............................. 90
Customize and extend the module ............................................ 103
6. Creating Portable
Analytical
Applications
Introduction.............................................................................. 105
Architecture of portable analytical applications.......................... 105
Best practices for building portable applications........................ 107
Design and development premises...................................... 108
Development process overview ........................................... 109
Phase I: Define the analytical domain.................................. 109
Phase II: Develop the logical data model............................. 115
Phase III: Define the report library ....................................... 123
Phase IV: Develop and test the reports ............................... 127
Portability rules and recommendations...................................... 131
Schema objects ................................................................... 131
Application objects ............................................................... 136
Glossary ................................................................................... 145
Index ......................................................................................... 157
© 2015 MicroStrategy, Inc.
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Contents
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Installation and Porting Guide
© 2015 MicroStrategy, Inc.
BOOK OVERVIEW AND
ADDITIONAL RESOURCES
Description of this guide
This guide is a technical reference for the MicroStrategy Analytics Modules
that come with MicroStrategy Architect. This guide provides installation and
configuration steps for setting up the Analytics Modules. It also provides a
detailed explanation of the MicroStrategy porting methodology to let you
implement the Analytics Modules with an existing data warehouse;
customization and extension details; and procedures to create your own
analytical applications based on the best practices represented by the
Analytics Modules components:
•
Chapter 1, Introduction to MicroStrategy Analytics Modules presents an
introduction to the Analytics Modules, and provides an analysis of
analytical applications as well as a detailed description of the
MicroStrategy Analytics Modules components.
•
Chapter 2, Installation presents steps to install and uninstall the
Analytics Modules.
•
Chapter 3, Configuration presents steps to configure your Analytics
Modules software.
•
Chapter 4, Using the Analytics Modules presents methods to use the
Analytics Modules as your initial data warehouse when you do not have
© 2015 MicroStrategy, Inc.
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Book Overview and Additional Resources
Installation and Porting Guide
an existing data warehouse in the given analysis area. It also provides
steps and scenarios to customize and extend your Analytics Modules
implementation.
•
Chapter 5, Porting the Analytics Modules presents an introduction to
portability, a description of the MicroStrategy portability methodology,
and detailed procedures to port an Analysis Module to an existing data
warehouse.
•
Chapter 6, Creating Portable Analytical Applications presents a
description of the benefits of building portable analytical applications,
provides a discussion of the architecture and best practices for designing
and developing analytical applications, and presents detailed procedures
for using the Analytics Modules as a template to build an analytical
application.
Technical terms that need more clarification are defined in the glossary
section of this guide.
Consult each Analysis Module’s individual reference guide for detailed
descriptions of the given module’s analysis area, scorecards and reports,
logical data model, physical schema, and data dictionary.
About the Analytics Modules
MicroStrategy helps you build analytical applications by offering a rapid
application development framework consisting of analytic starter kits,
development products, and methodologies. There are five Analytics Modules
that are built to be portable. You can choose to deploy the analytical
applications against your existing data warehouse, use the packaged schema
as the basis of a new data warehouse, or use the modules as templates to
build analytical applications.
The components are:
•
Analytics Modules


Prepackaged metadata: Best practices reports, scorecards and
dashboards, key performance indicators, attributes, business metrics,
filters, and custom groups
Default physical and logical data model: Analytics that are designed to
work with your physical schemas and data model or with the module’s
packaged data warehouse schema
x About the Analytics Modules
© 2015 MicroStrategy, Inc.
Installation and Porting Guide

•
Book Overview and Additional Resources
Reference guides: Documentation on each analysis module’s data
model, the individual analysis areas, metadata object definitions, data
dictionary, and individual report use scenarios
Implementation methodology



Documentation that guides you step-by-step through implementing
analytic modules against existing data warehouses (known as porting)
Design rules and tenets for designing and developing portable
analytical applications
MicroStrategy Architect: A development tool that allows you to map
Analytics Modules to existing data warehouses
The Advanced Reporting Guide and other documentation for MicroStrategy
Developer and MicroStrategy Architect is available on your MicroStrategy
disk (see Resources, page xiii). See each Analysis Module’s reference guide
for a description of the given module’s analysis area, packaged reports,
logical and physical models, and data dictionary.
About this book
This book is divided into chapters that begin with a brief overview of the
chapter’s content.
The following sections provide the location of examples, list prerequisites for
using this book, and describe the user roles the information in this book was
designed for.
in the MicroStrategy Tutorial project are updated to reflect the
 Dates
current year. The sample documents and images in this guide, as well
as the procedures, were created with dates that may no longer be
available in the Tutorial project. Replace them with the first year of
data in your Tutorial project.
Additional formats
This book is also available as an electronic publication in the Apple
iBookstore, and can be read on an iPhone or iPad with the iBooks app
© 2015 MicroStrategy, Inc.
About this book
xi
Book Overview and Additional Resources
Installation and Porting Guide
installed. To download this book, search for the book’s title in the iBookstore
search bar, or scan the QR code below using your device’s camera.
How to find business scenarios and examples
For examples of report features and a basic introduction to the
MicroStrategy business intelligence system, use the MicroStrategy Tutorial,
which is MicroStrategy’s sample warehouse, metadata, and project.
Information about the MicroStrategy Tutorial can be found in the
MicroStrategy Basic Reporting Guide.
For extensive examples of metrics, filters, and other report objects, see the
MicroStrategy Advanced Reporting Guide.
Prerequisites
How you use this document depends on the type of user you are and your
goals for working with the Analytics Modules.
If you intend to evaluate the business value of the modules, you should have:
•
Experience with MicroStrategy reports and metrics using MicroStrategy
technology
If you intend to implement and customize the modules, you should have:
•
Experience with logical data modeling and creating business intelligence
applications using MicroStrategy technology
•
A basic understanding of RDBMS concepts and data modeling
xii About this book
© 2015 MicroStrategy, Inc.
Installation and Porting Guide
Book Overview and Additional Resources
Who should use this guide
This document is designed for:
•
Advanced users and administrators evaluating the business value of the
Analytics Modules
•
Consultants and developers implementing and customizing the Analytics
Modules
Resources
Documentation
MicroStrategy provides both manuals and online help; these two information
sources provide different types of information, as described below:
•
Manuals: In general, MicroStrategy manuals provide:

Introductory information and concepts

Examples and images

Checklists and high-level procedures to get started
The steps to access the manuals are described in Accessing manuals and
other documentation sources, page xx.
Most of these manuals are also available printed in a bound, soft cover
format. To purchase printed manuals, contact your MicroStrategy
Account Executive with a purchase order number.
•
Help: In general, MicroStrategy help provides:

Detailed steps to perform procedures

Descriptions of each option on every software screen
Translations
For the most up-to-date translations of MicroStrategy documentation, refer
to the MicroStrategy Knowledge Base. Due to translation time, manuals in
© 2015 MicroStrategy, Inc.
Resources
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Book Overview and Additional Resources
Installation and Porting Guide
languages other than English may contain information that is one or more
releases behind. You can see the version number on the title page of each
manual.
Finding information
You can search all MicroStrategy books and Help for a word or phrase, with a
simple Google™ search at www.google.com. For example, type
“MicroStrategy derived metric” or “MicroStrategy logical table” into a Google
search. As described above, books typically describe general concepts and
examples; Help typically provides detailed steps and screen options. To limit
your search to MicroStrategy books, on Google’s main page you can click
More, then select Books.
Manuals for MicroStrategy overview and evaluation
•
Introduction to MicroStrategy: Evaluation Guide
Instructions for installing, configuring, and using the MicroStrategy
Evaluation Edition of the software. This guide also includes a detailed,
step-by-step evaluation process of MicroStrategy features, where you
perform reporting with the MicroStrategy Tutorial project and its sample
business data.
•
MicroStrategy Evaluation Edition Quick Start Guide
Overview of the installation and evaluation process, and additional
resources.
•
MicroStrategy Suite: Quick Start Guide
Evaluate MicroStrategy as a departmental solution. Provides detailed
information to download, install, configure, and use the MicroStrategy
Suite.
Resources for Identity and Loyalty
•
Alert Commerce Management System (CMS) Guide and Alert API
Reference
Content resources providing steps to deliver and manage marketing and
commerce content through the Alert mobile applications.
xiv Resources
© 2015 MicroStrategy, Inc.
Installation and Porting Guide
•
Book Overview and Additional Resources
Usher Administration Guide
Steps to perform mobile identity validation using the Usher mobile
identity network to issue electronic badges for identifying users.
Manuals for query, reporting, and analysis
•
MicroStrategy Installation and Configuration Guide
Information to install and configure MicroStrategy products on
Windows, UNIX, Linux, and HP platforms, as well as basic maintenance
guidelines.
•
MicroStrategy Upgrade Guide
Instructions to upgrade existing MicroStrategy products.
•
MicroStrategy Project Design Guide
Information to create and modify MicroStrategy projects, and
understand facts, attributes, hierarchies, transformations, advanced
schemas, and project optimization.
•
MicroStrategy Basic Reporting Guide
Instructions to get started with MicroStrategy Developer and
MicroStrategy Web, and how to analyze data in a report. Includes the
basics for creating reports, metrics, filters, and prompts.
•
MicroStrategy Advanced Reporting Guide: Enhancing Your Business
Intelligence Application
Instructions for advanced topics in the MicroStrategy system, building on
information in the Basic Reporting Guide. Topics include reports,
Freeform SQL reports, Query Builder reports, filters, metrics, Data
Mining Services, custom groups, consolidations, and prompts.
•
Document and Dashboard Analysis Guide
Instructions for a business analyst to execute and analyze a document in
MicroStrategy Developer and MicroStrategy Web, building on basic
concepts about projects and reports presented in the MicroStrategy
Basic Reporting Guide.
•
MicroStrategy Report Services Document Creation Guide: Creating
Boardroom Quality Documents
Instructions to design and create Report Services documents, building on
information in the Document and Dashboard Analysis Guide. It is
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Book Overview and Additional Resources
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organized to help guide you through creating a new document, from
creating the document itself, to adding objects to the new document, and
formatting the document and its objects.
•
MicroStrategy Dashboards and Widgets Creation Guide: Creating
Interactive Dashboards for your Data
Instructions for designing and creating MicroStrategy Report Services
dashboards, a type of document that is optimized for viewing online and
for user interactivity. It builds on the basic concepts about documents
presented in the MicroStrategy Report Services Document Creation
Guide.
•
MicroStrategy OLAP Services Guide
Information on MicroStrategy OLAP Services, which is an extension of
MicroStrategy Intelligence Server. OLAP Services features include
Intelligent Cubes, derived metrics, derived elements, dynamic
aggregation, view filters, and dynamic sourcing.
•
MicroStrategy Office User Guide
Instructions for using MicroStrategy Office to work with MicroStrategy
reports and documents in Microsoft® Excel, PowerPoint, and Word, to
analyze, format, and distribute business data.
•
MicroStrategy Mobile Analysis Guide: Analyzing Data with
MicroStrategy Mobile
Information and instructions for using MicroStrategy Mobile to view and
analyze data, and perform other business tasks with MicroStrategy
reports and documents on a mobile device.
•
MicroStrategy Mobile Design and Administration Guide: A Platform for
Mobile Intelligence
Information and instructions to install and configure MicroStrategy
Mobile, as well as instructions for a designer working in MicroStrategy
Developer or MicroStrategy Web to create effective reports and
documents for use with MicroStrategy Mobile.
•
MicroStrategy System Administration Guide: Tuning, Monitoring, and
Troubleshooting your MicroStrategy Business Intelligence System
Concepts and high-level steps to implement, deploy, maintain, tune, and
troubleshoot a MicroStrategy business intelligence system.
xvi Resources
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Installation and Porting Guide
•
Book Overview and Additional Resources
MicroStrategy Supplemental Reference for System Administration:
VLDB Properties, Internationalization, User Privileges, and other
Supplemental Information for Administrators
Information and instructions for MicroStrategy administrative tasks such
as configuring VLDB properties and defining data and metadata
internationalization, and reference material for other administrative
tasks.
•
MicroStrategy Functions Reference
Function syntax and formula components; instructions to use functions
in metrics, filters, attribute forms; examples of functions in business
scenarios.
•
MicroStrategy MDX Cube Reporting Guide
Information to integrate MicroStrategy with MDX cube sources. You can
integrate data from MDX cube sources into your MicroStrategy projects
and applications.
Manuals for Analytics Modules
•
Analytics Modules Installation and Porting Guide
•
Customer Analysis Module Reference
•
Sales Force Analysis Module Reference
•
Financial Reporting Analysis Module Reference
•
Sales and Distribution Analysis Module Reference
•
Human Resources Analysis Module Reference
Manuals for Narrowcast Services products
•
MicroStrategy Narrowcast Server Getting Started Guide
Instructions to work with the tutorial to learn Narrowcast Server
interfaces and features.
•
MicroStrategy Narrowcast Server Installation and Configuration Guide
Information to install and configure Narrowcast Server.
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Book Overview and Additional Resources
•
Installation and Porting Guide
MicroStrategy Narrowcast Server Application Designer Guide
Fundamentals of designing Narrowcast Server applications.
•
MicroStrategy Narrowcast Server System Administrator Guide
Concepts and high-level steps to implement, maintain, tune, and
troubleshoot Narrowcast Server.
•
MicroStrategy Narrowcast Server Upgrade Guide
Instructions to upgrade an existing Narrowcast Server.
Software Development Kits
•
MicroStrategy Developer Library (MSDL)
Information to understand the MicroStrategy SDK, including details
about architecture, object models, customization scenarios, code
samples, and so on.
•
MicroStrategy Web SDK
Web SDK is available in the MicroStrategy Developer Library,
 The
which is part of the MicroStrategy SDK.
•
Narrowcast Server SDK Guide
Instructions to customize Narrowcast Server functionality, integrate
Narrowcast Server with other systems, and embed Narrowcast Server
functionality within other applications. Documents the Narrowcast
Server Delivery Engine and Subscription Portal APIs, and the Narrowcast
Server SPI.
Documentation for MicroStrategy Portlets
•
Enterprise Portal Integration Help
Information to help you implement and deploy MicroStrategy BI within
your enterprise portal, including instructions for installing and
configuring out-of-the-box MicroStrategy Portlets for several major
enterprise portal servers.
This resource can be accessed from the MicroStrategy Product Manuals
page, as described in Accessing manuals and other documentation
sources, page xx.
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Documentation for MicroStrategy GIS Connectors
•
GIS Integration Help
Information to help you integrate MicroStrategy with Geospatial
Information Systems (GIS), including specific examples for integrating
with various third-party mapping services.
This resource can be accessed from the MicroStrategy Product Manuals
page, as described in Accessing manuals and other documentation
sources, page xx.
Help
Each MicroStrategy product includes an integrated help system to
complement the various interfaces of the product as well as the tasks that can
be accomplished using the product.
Some of the MicroStrategy help systems require a web browser to be viewed.
For supported web browsers, see the MicroStrategy Readme.
MicroStrategy provides several ways to access help:
•
Help button: Use the Help button or ? (question mark) icon on most
software windows to see help for that window.
•
Help menu: From the Help menu or link at the top of any screen, select
MicroStrategy Help to see the table of contents, the Search field, and the
index for the help system.
•
F1 key: Press F1 to see context-sensitive help that describes each option in
the software window you are currently viewing.
MicroStrategy Web, MicroStrategy Web Administrator, and
 For
MicroStrategy Mobile Server, pressing the F1 key opens the
context-sensitive help for the web browser you are using to access
these MicroStrategy interfaces. Use the Help menu or ? (question
mark) icon to access help for these MicroStrategy interfaces.
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Book Overview and Additional Resources
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Accessing manuals and other documentation sources
The manuals are available from http://www.microstrategy.com/
producthelp, as well as from your MicroStrategy disk or the machine where
MicroStrategy was installed.
Acrobat Reader is required to view these manuals. If you do not
 Adobe
have Acrobat Reader installed on your computer, you can download it
from http://get.adobe.com/reader/.
The best place for all users to begin is with the MicroStrategy Basic
Reporting Guide.
To access the installed manuals and other documentation sources, see the
following procedures:
•
To access documentation resources from any location, page xx
•
To access documentation resources on Windows, page xx
•
To access documentation resources on UNIX and Linux, page xxi
To access documentation resources from any location
1 Visit http://www.microstrategy.com/producthelp.
To access documentation resources on Windows
1 From the Windows Start menu, choose Programs (or All Programs),
MicroStrategy Documentation, then Product Manuals. A page opens in
your browser showing a list of available manuals in PDF format and other
documentation sources.
2 Click the link for the desired manual or other documentation source.
3 If you click the link for the Narrowcast Services SDK Guide, a File
Download dialog box opens. This documentation resource must be
downloaded. Select Open this file from its current location, and click
OK.
bookmarks are not visible on the left side of an Acrobat (PDF)
 Ifmanual,
from the View menu click Bookmarks and Page. This step
varies slightly depending on your version of Adobe Acrobat Reader.
xx Resources
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Book Overview and Additional Resources
To access documentation resources on UNIX and Linux
1 Within your UNIX or Linux machine, navigate to the directory where you
installed MicroStrategy. The default location is /opt/MicroStrategy,
or $HOME/MicroStrategy/install if you do not have write access to
/opt/MicroStrategy.
2 From the MicroStrategy installation directory, open the Help folder.
3 Open the Product_Manuals.htm file in a web browser. A page opens in
your browser showing a list of available manuals in PDF format and other
documentation sources.
4 Click the link for the desired manual or other documentation source.
5 If you click the link for the Narrowcast Services SDK Guide, a File
Download dialog box opens. This documentation resource must be
downloaded. Select Open this file from its current location, and click
OK.
bookmarks are not visible on the left side of an Acrobat (PDF)
 Ifmanual,
from the View menu click Bookmarks and Page. This step
varies slightly depending on your version of Adobe Acrobat Reader.
Documentation standards
MicroStrategy online help and PDF manuals (available both online and in
printed format) use standards to help you identify certain types of content.
The following table lists these standards.
standards may differ depending on the language of this manual;
 These
some languages have rules that supersede the table below.
Type
Indicates
bold
• Button names, check boxes, options, lists, and menus that are the focus of actions or
part of a list of such GUI elements and their definitions
Example: Click Select Warehouse.
italic
• Names of other product manuals and documentation resources
• When part of a command syntax, indicates variable information to be replaced by the
user
Example: The aggregation level is the level of calculation for the metric.
Example: Type copy c:\filename d:\foldername\filename
© 2015 MicroStrategy, Inc.
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Book Overview and Additional Resources
Type
Indicates
Courier
font
•
•
•
•
•
•
•
Installation and Porting Guide
Calculations
Code samples
Registry keys
Path and file names
URLs
Messages displayed in the screen
Text to be entered by the user
Example: Sum(revenue)/number of months.
Example: Type cmdmgr -f scriptfile.scp and press Enter.
+
A keyboard command that calls for the use of more than one key (for example,
SHIFT+F1).


A note icon indicates helpful information for specific situations.
A warning icon alerts you to important information such as potential security risks; these
should be read before continuing.
Education
MicroStrategy Education Services provides a comprehensive curriculum and
highly skilled education consultants. Many customers and partners from
over 800 different organizations have benefited from MicroStrategy
instruction. For a detailed description of education offerings and course
curriculums, visit http://www.microstrategy.com/Education.
Consulting
MicroStrategy Consulting Services provides proven methods for delivering
leading-edge technology solutions. Offerings include complex security
architecture designs, performance and tuning, project and testing strategies
and recommendations, strategic planning, and more. For a detailed
description of consulting offerings, visit http://www.microstrategy.com/
Services.
International support
MicroStrategy supports several locales. Support for a locale typically includes
native database and operating system support, support for date formats,
xxii Resources
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Book Overview and Additional Resources
numeric formats, currency symbols, and availability of translated interfaces
and certain documentation.
MicroStrategy is certified in homogeneous configurations (where all the
components lie in the same locale) in the following languages—English (US),
French, German, Italian, Japanese, Korean, Portuguese (Brazilian), Spanish,
Chinese (Simplified), Chinese (Traditional), Danish, and Swedish. A
translated user interface is available in each of the above languages. For
information on specific languages supported by individual MicroStrategy
system components, see the MicroStrategy readme.
MicroStrategy also provides limited support for heterogeneous
configurations (where some of the components may lie in different locales).
Please contact MicroStrategy Technical Support for more details.
Technical Support
If you have questions about a specific MicroStrategy product, you should:
1 Consult the product guides, Help, and readme files. Locations to access
each are described above.
2 Consult the MicroStrategy Knowledge Base online at https://
resource.microstrategy.com/support.
administrator in your organization may be able to help
 Ayoutechnical
resolve your issues immediately.
3 If the resources listed in the steps above do not provide a solution,
contact MicroStrategy Technical Support directly. To ensure the most
productive relationship with MicroStrategy Technical Support, review the
Policies and Procedures document in your language, posted at http://
www.microstrategy.com/Support/Policies. Refer to the terms of your
purchase agreement to determine the type of support available to you.
MicroStrategy Technical Support can be contacted by your company’s
Support Liaison. A Support Liaison is a person whom your company has
designated as a point-of-contact with MicroStrategy’s support personnel. All
customer inquiries and case communications must come through these
named individuals. Your company may designate two employees to serve as
their Support Liaisons, and can request to change their Support Liaisons two
times per year with prior written notice to MicroStrategy Technical Support.
© 2015 MicroStrategy, Inc.
Resources
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Book Overview and Additional Resources
Installation and Porting Guide
It is recommended that you designate Support Liaisons who have
MicroStrategy Administrator privileges. This can eliminate security conflicts
and improve case resolution time. When troubleshooting and researching
issues, MicroStrategy Technical Support personnel may make
recommendations that require administrative privileges within
MicroStrategy, or that assume that the designated Support Liaison has a
security level that permits them to fully manipulate the MicroStrategy
projects and has access to potentially sensitive project data such as security
filter definitions.
Ensure issues are resolved quickly
Before logging a case with MicroStrategy Technical Support, the Support
Liaison may follow the steps below to ensure that issues are resolved quickly:
1 Verify that the issue is with MicroStrategy software and not a third party
software.
2 Verify that the system is using a currently supported version of
MicroStrategy software by checking the Product Support Expiration
Schedule at http://www.microstrategy.com/Support/Expiration.asp.
3 Attempt to reproduce the issue and determine whether it occurs
consistently.
4 Minimize the complexity of the system or project object definition to
isolate the cause.
5 Determine whether the issue occurs on a local machine or on multiple
machines in the customer environment.
6 Discuss the issue with other users by posting a question about the issue
on the MicroStrategy Customer Forum at https://
resource.microstrategy.com/forum/.
The following table shows where, when, and how to contact MicroStrategy
Technical Support. If your Support Liaison is unable to reach MicroStrategy
Technical Support by phone during the hours of operation, they can leave a
voicemail message, send email or fax, or log a case using the Online Support
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Book Overview and Additional Resources
Interface. The individual Technical Support Centers are closed on certain
public holidays.
North America
Email: [email protected]
Web: https://resource.microstrategy.com/support
Fax: (703) 842–8709
Phone: (703) 848–8700
Hours: 9:00 A.M.–7:00 P.M. Eastern Time, Monday–Friday except holidays
EMEA:
Europe
The Middle East
Africa
Email: [email protected]
Web: https://resource.microstrategy.com/support
Fax: +44 (0) 208 711 2525
The European Technical Support Centre is closed on national public holidays in each country.
Phone:
• Belgium: + 32 2792 0436
• France: +33 17 099 4737
• Germany: +49 22 16501 0609
• Ireland: +353 1436 0916
• Italy: +39 023626 9668
• Poland: +48 22 459 52 52
• Scandinavia & Finland: +46 8505 20421
• Spain: +34 91788 9852
• The Netherlands: +31 20 794 8425
• UK: +44 (0) 208 080 2182
• International distributors: +44 (0) 208 080 2183
Hours:
• United Kingdom: 9:00 A.M.–6:00 P.M. GMT, Monday-Friday except holidays
• EMEA (except UK): 9:00 A.M.–6:00 P.M. CET, Monday-Friday except holidays
Asia Pacific
Email: [email protected]
Web: https://resource.microstrategy.com/support
Phone:
• Australia: +61 2 9333 6499
• Korea: +82 2 560 6565 Fax: +82 2 560 6555
• Japan: +81 3 3511 6720 Fax: +81 3 3511 6740
• Singapore: +65 6303 8969 Fax: +65 6303 8999
• Asia Pacific (except Australia, Japan, Korea, and Singapore): +86 571 8526 8067 Fax: +86
571 8848 0977
Hours:
• Japan and Korea: 9:00 A.M.–6:00 P.M. JST (Tokyo), Monday-Friday except holidays
• Asia Pacific (except Japan and Korea): 7 A.M.-6 P.M. (Singapore) Monday-Friday except
holidays
Latin America
Email: [email protected]
Web: https://resource.microstrategy.com/support
Phone:
• LATAM (except Brazil and Argentina): +54 11 5222 9360 Fax: +54 11 5222 9355
• Argentina: 0 800 444 MSTR Fax: +54 11 5222 9355
• Brazil: +55 11 3054 1010 Fax: +55 11 3044 4088
Hours:
• Latin America (except Brazil): 9:00 A.M.–7:00 P.M. (Buenos Aires), Monday-Friday except
holidays
• Brazil: 9 A.M. - 6 P.M. (São Paulo), Monday–Friday except holidays
© 2015 MicroStrategy, Inc.
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Book Overview and Additional Resources
Installation and Porting Guide
Support Liaisons should contact the Technical Support Center from which
they obtained their MicroStrategy software licenses or the Technical Support
Center to which they have been designated.
Required information when calling
When contacting MicroStrategy Technical Support, please provide the
following information:
•
•
Personal information:

Name (first and last)

Company and customer site (if different from company)

Contact information (phone and fax numbers, e-mail addresses)
Case details:


•
Configuration information, including MicroStrategy software
product(s) and versions
Full description of the case including symptoms, error messages(s),
and steps taken to troubleshoot the case thus far
Business/system impact
If this is the Support Liaison’s first call, they should also be prepared to
provide the following:
•
Street address
•
Phone number
•
Fax number
•
Email address
To help the Technical Support representative resolve the problem promptly
and effectively, be prepared to provide the following additional information:
•
Case number: Please keep a record of the number assigned to each case
logged with MicroStrategy Technical Support, and be ready to provide it
when inquiring about an existing case
•
Software version and product registration numbers of the MicroStrategy
software products you are using
xxvi Resources
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Installation and Porting Guide
•
Case description:


What causes the condition to occur?
Does the condition occur sporadically or each time a certain action is
performed?

Does the condition occur on all machines or just on one?

When did the condition first occur?



•
Book Overview and Additional Resources
What events took place immediately prior to the first occurrence of
the condition (for example, a major database load, a database move,
or a software upgrade)?
If there was an error message, what was its exact wording?
What steps have you taken to isolate and resolve the issue? What were
the results?
System configuration (the information needed depends on the nature of
the problem; not all items listed below may be necessary):

Computer hardware specifications (processor speed, RAM, disk space,
and so on)

Network protocol used

ODBC driver manufacturer and version

Database gateway software version


(For MicroStrategy Web-related problems) browser manufacturer
and version
(For MicroStrategy Web-related problems) Web server manufacturer
and version
If the issue requires additional investigation or testing, the Support Liaison
and the MicroStrategy Technical Support representative should agree on
certain action items to be performed. The Support Liaison should perform
any agreed-upon actions before contacting MicroStrategy Technical Support
again regarding the issue. If the Technical Support representative is
responsible for an action item, the Support Liaison may call MicroStrategy
Technical Support at any time to inquire about the status of the issue.
© 2015 MicroStrategy, Inc.
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Installation and Porting Guide
Feedback
Please send any comments or suggestions about user documentation for
MicroStrategy products to:
[email protected]
Send suggestions for product enhancements to:
[email protected]
When you provide feedback to us, please include the name and version of the
products you are currently using. Your feedback is important to us as we
prepare for future releases.
xxviii Feedback
© 2015 MicroStrategy, Inc.
1
1.
INTRODUCTION TO
MICROSTRATEGY ANALYTICS
MODULES
Introduction
The MicroStrategy Analytics Modules are analytical starter kits designed to
solve business problems. Each of the modules ships with a sample data
model and numerous reports and analytics. The MicroStrategy Analytics
Modules are designed with a portability paradigm, which means they can
work against existing data warehouses and allow businesses to make use of
data investments without the need for extensive data extraction and loading
concerns.
MicroStrategy helps you build analytical applications by offering a rapid
application development framework consisting of analytic starter kits,
development products, and design and development methodologies. The
development framework includes five analytics modules that are built to be
portable. You can choose to:
•
Deploy the analytical applications against your existing data warehouse
•
Use the packaged physical schema as the basis of your data warehouse
•
Use the modules as templates to build analytical applications
© 2015 MicroStrategy, Inc.
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Introduction to MicroStrategy Analytics Modules
Installation and Porting Guide
Documentation
Documentation includes a separate reference guide for each Analysis
Module, and this Installation and Porting Guide. Documentation for
Developer Designer and Architect is available to help you use these
development tools. The MicroStrategy Advanced Reporting Guide contains
most of the information pertinent to the procedures required in this
document.
MicroStrategy Analytics Modules
The MicroStrategy Analytics Modules are a set of packaged analytical
components built using the MicroStrategy platform. The modules can be
mapped to a different data warehouse or used as starter kits to develop
custom applications. This release includes Analysis Modules for customer
analysis, sales analysis, financial reporting analysis, sales distribution
analysis, and human resources analysis.
Each Analysis Module consists of a MicroStrategy project in a metadata
repository and comes with a default physical data model and documentation
covering that module’s reports and metadata. Each module contains
approximately 50-60 reports, a set of key performance indicators (KPIs) and
business metrics, and sample scorecards. You can use these reports and KPIs
as building blocks to create and deploy additional reports and performance
measurements. MicroStrategy provides report-building wizards that anyone
can use to design and build reports. Developers have access to over 150
functions (arithmetic, aggregate, statistical, financial, mathematical, and
OLAP) that they can use to build new KPIs and business metrics.
For example, a developer can use the Revenue metric included in the
Customer Analysis Module to build additional metrics such as the running
sum, moving average, and standard deviation of revenue. Developers can
also customize the areas of analysis and navigation paths for end users, and
create workflow wizards for end users to build their own reports and analyze
information.
You can take advantage of the Analytics Modules in different ways:
•
2 Documentation
If you have an existing data warehouse containing data pertinent to a
given Analysis Module’s analytical area, you can “map” the Analysis
Module and its reports to work with your data warehouse’s existing data
structures.
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Installation and Porting Guide
Introduction to MicroStrategy Analytics Modules
1
•
If you do not have a data warehouse containing data pertinent to a given
Analysis Module’s analytical area, you can use the physical schema that
comes with each of the Analytics Modules, as well as the logical data
model and reports, to serve as a starter design for a new data warehouse.
•
You can use the components of the Analytics Modules as best practices
examples in a given analytical area. You can use the packaged
components, documentation, and best practices scenarios and examples
as templates to build analytical applications.
The Analytics Modules:
•
Provide best practices analysis and MicroStrategy expertise in the areas
of customer analysis, sales analysis, sales and distribution analysis,
human resources analysis, and financial reporting analysis
•
Can be integrated into an existing data warehouse, taking advantage of
MicroStrategy platform flexibility, schema support, and database
independence while protecting your data warehouse investments
•
Use an easily extendable and modifiable architecture
•
Are developed with MicroStrategy standard tools so an additional
application framework is not required; standard MicroStrategy tools are
used to implement and extend the application
•
Are modular by nature, allowing you to implement only parts of a module
or to combine several modules together, while providing a seamless
experience for users
The MicroStrategy Analytics Modules include metadata content and
documentation, as described below.
•
Metadata content is the definition of the logical data model and reports in
the MicroStrategy metadata repository.
•
Documentation includes:


Business content, such as business concept (attribute) definitions and
report usage scenarios located in each Analysis Module’s reference
guide
Technical content, such as the chapters in this guide that describe the
portability methodology and explain how to implement the modules
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MicroStrategy Analytics Modules
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Introduction to MicroStrategy Analytics Modules
Installation and Porting Guide
Analytics Modules documentation
The documentation is an essential component of the modular analytical
applications, providing both business and technical content.
Documentation
Description
Reference Guides
Each of the Analytics Modules has its own reference guide, containing all the
definitions for that module. Each reference guide includes:
• An introduction to each module’s analysis area and reporting challenges
• Business value information and a complete definition, with a report screen
shot, for each packaged report
• A complete list of all metrics and their descriptions
• A logical data model definition with all hierarchies and attributes, including
metadata definitions
• The default physical schema provided with the module and the data
dictionary (tables and columns)
Installation and Porting
Guide
This guide contains
• Installation information specific to the Analytics Modules
• Configuration steps for the Analytics Modules
• Information for customizing and extending the Analytics Modules to suit your
business requirements
• The MicroStrategy porting methodology, along with detailed steps and
examples for porting an Analysis Module to your existing data warehouse
• Best practices for using the Analytics Modules as a template for designing
and building analytical applications
An analysis of portable analytical applications
Prepackaged analytical applications like the MicroStrategy Analytics
Modules provide two major benefits:
•
They deliver best practices reports for a given domain area.
•
They allow reduced time for implementation.
Drawbacks of traditional analytical applications
Unfortunately, many prepackaged analytical applications also come with
costs, like the following:
•
A fixed physical schema and reports force you to model your business
around packaged components instead of around your own business
requirements.
4 MicroStrategy Analytics Modules
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Installation and Porting Guide
Introduction to MicroStrategy Analytics Modules
1
•
Fixed metadata components are difficult to customize and extend to
support your business requirements.
•
Data must be extracted to a new database schema, requiring an extraction
tool and very intensive services.
•
They have a high cost entry point. Additional costs include a required
ETL tool, end-user licenses, and implementation services.
Traditional Packaged
Analytics Apps
Existing Analytics Apps and
Data Warehouse
Existing
Reports
Fixed
packaged
Reports
Existing
Reports
Hard
Wired
Fixed Schema
New
ETL
New
ETL
Existing Data
Warehouse
ETL
ETL
ETL
OLTP
CRM
ERP
Advantages of MicroStrategy analytical applications
The MicroStrategy Analytics Modules let you benefit from the advantages of
using a prepackaged analytical application and provide additional benefits
without the costs inherent in traditional packaged analytical applications.
MicroStrategy advantages include:
•
Best practices reports for a given domain area, developed with over a
decade of MicroStrategy business intelligence implementation experience
and best practices methodologies
•
Integration into your existing data warehouse, which is critical to make
use of existing data warehouse investments
•
Rapid deployment, providing packaged components without additional,
extensive data extraction
© 2015 MicroStrategy, Inc.
MicroStrategy Analytics Modules
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Introduction to MicroStrategy Analytics Modules
Installation and Porting Guide
•
No fixed components, making it easy to extend and customize the
analytical applications to address your business and analysis
requirements
•
Modularity, which allows you the flexibility to implement some analysis
areas within an Analysis Module or to combine several Analytics Modules
•
A low cost entry point; the five Analytics Modules plus standard project
development tools have a low per-developer price, while end users only
require standard licenses
using an existing data warehouse, the value obtained from the
 When
Analytics Modules depends on existing data structures and data
elements. If your data warehouse has non-optimal schemas or
missing key data elements, it can limit the value obtained from the
MicroStrategy Analytics Modules.
Analytics Modules as best practices examples
The MicroStrategy Analytics Modules reflect best practices for analytical
applications. Because of this, you can use the Analytics Modules and their
components as templates on every level.
6 MicroStrategy Analytics Modules
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Introduction to MicroStrategy Analytics Modules
1
Examples of best practices templates include
•
Best practices reports: Use the documentation and metadata definitions
to reproduce your favorite Analysis Module reports in an existing
MicroStrategy project. You can use the Analytics Modules reports as
templates, reflecting best practices examples for customer analysis, sales
analysis, financial analysis, human resources analysis, and sales
distribution analysis.
•
Best practices schema design: Use portions of the physical schema design
to enhance a data warehouse that has no existing data in the given
analysis area.
•
Best practices documentation: Use the documentation as a template to
document the processes and tasks required to define any packaged
analytical application.
•
Best practices analytical applications: Use the Analytics Modules as a
template to design and develop your own portable analytical applications.
About building analytical applications
Many of the benefits of implementing portable analytical applications are
described in An analysis of portable analytical applications, page 4. But
your design and development efforts can also benefit when you use the
portability paradigm and the information in Chapter 6, Creating Portable
Analytical Applications, whether you are building internal applications,
developing packaged applications, or performing integration and consulting
services for portable analytical applications.
Building internal applications
If you are building internal analytical applications and implementing them in
different sites, there are many ways to benefit from the MicroStrategy
Analytics Modules:
•
Define best analytical practices for reports, metrics, the logical data
model, and so on; and save time implementing them through different
database engines and data warehouses.
•
Protect your investment in existing data warehouses across the company.
•
Take advantage of MicroStrategy best practices and documentation.
© 2015 MicroStrategy, Inc.
MicroStrategy Analytics Modules
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Introduction to MicroStrategy Analytics Modules
Installation and Porting Guide
For example, a global telecommunications company implements a customer
analysis application based on the MicroStrategy Customer Analysis Module
for its American operations. The portable nature of the MicroStrategy
application allows the company to implement analytical best practices in
their international operations as well, even if they use different physical
schemas or database engines. The application is also easy to customize to the
needs of each site.
Developing packaged applications
For software companies developing packaged analytical applications,
benefits are numerous:
•
Increase your market by targeting companies with existing data
warehouses in a given domain area.
•
Focus on making the best use of intellectual property in the form of best
practices analyses instead of creating physical schemas and performing
extensive data extraction.
•
Take advantage of MicroStrategy’s best practices and documentation.
•
Save time during implementation and deployment because
MicroStrategy’s Analytics Modules are the easiest to customize and
extend.
Integrating and consulting
For MicroStrategy’s system integrators and consultants, they can:
•
Reuse best practices implementations and proofs of concept, saving time
and providing consistency for future implementations
•
Package intellectual property in the form of the logical data model,
metrics, reports, and so on without requiring a fixed physical schema
•
Implement, customize, and extend analytical applications easily
8 MicroStrategy Analytics Modules
© 2015 MicroStrategy, Inc.
2
2.
INSTALLATION
Introduction
This chapter describes the prerequisites and procedures for installing the
MicroStrategy Analytics Modules and their related packaged components.
Developer and MicroStrategy Architect are the
 MicroStrategy
additional products that work with the Analytics Modules. Developer
and Architect are necessary to make use of the Analytics Modules. If
you have already installed Developer and Architect, you can proceed
to install the Analytics Modules. However, if you have not yet installed
Developer and Architect, see the MicroStrategy Installation and
Configuration Guide to install them either before or at the same time
as you install the Analytics Modules.
Installation prerequisites
Before installing the Analytics Modules, review the requirements below.
© 2015 MicroStrategy, Inc.
Installation prerequisites
9
2
Installation
Installation and Porting Guide
System hardware recommendations
Make sure you have sufficient hard drive space to install all of the Analytics
Modules and their related documentation.
with version 7.5.1, depending on the license you have
 Beginning
purchased and the installation options you choose, you may install
files within one of several scenarios, as follows:
•
A default (clean) installation of Analytics Modules files
automatically installs the MicroStrategy Tutorial files along with
the Analytics Modules files, and both products share a single
metadata.
•
If you have previously installed either the Analytics Modules or the
Tutorial, the installation process prompts you as to whether you
wish to overwrite your old Analytics Modules or Tutorial files. The
options for each decision are noted in the appropriate places in the
installation steps below.
There are no additional system hardware requirements to install the
Analytics Modules, other than those required for your other MicroStrategy
products.
See the MicroStrategy Installation and Configuration Guide for a complete
list of recommended and required hardware, software, and other installation
prerequisites before you install any MicroStrategy product.
System software requirements
If you are installing the Analytics Modules at a different time than any other
MicroStrategy products, make sure MicroStrategy Developer and
MicroStrategy Architect have been installed before you install the Analytics
Modules.
If you have already installed Developer and Architect, there are no additional
system software requirements to install the Analytics Modules outside of
those required for your other MicroStrategy products. See the MicroStrategy
Installation and Configuration Guide for a complete list of recommended
and required hardware, software, and other installation prerequisites before
you install any MicroStrategy product.
10 Installation prerequisites
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Installation and Porting Guide
Installation
2
Installation procedures
For information about installing the Analytics Modules in a manner different
than that listed in this section, see the MicroStrategy Installation and
Configuration Guide for advanced installation functionality.
You can also install the Analytics Modules by following the standard
installation procedure for the suite of MicroStrategy products in the
MicroStrategy Installation and Configuration Guide. Select the
MicroStrategy Analytics Modules along with MicroStrategy Developer, and
MicroStrategy Architect during the installation.
The Setup Wizard
The MicroStrategy Setup Wizard provides step-by-step instructions to guide
you through the installation process.
To access the Setup Wizard
1 Log on to the machine where you are installing the Analytics Modules.
You must log on using a domain account with Windows administrative
privileges for the domain or target machine. The domain must include
your database servers.
2 Exit all Windows applications before beginning the installation process.
3 Run the setup program to begin the installation:
•
If you are installing the Analytics Modules from a CD, insert the CD
into the CD-ROM drive and wait a few moments for the MicroStrategy
Main Menu window to automatically display. If the MicroStrategy
Main Menu does not display, locate and run the Setup.exe file on
the CD.
•
If you are installing the Analytics Modules from a set of downloaded
files, locate and run the Setup.exe file.
4 Click Install Software.
5 Click Install MicroStrategy Platform.
© 2015 MicroStrategy, Inc.
Installation procedures
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Installation
Installation and Porting Guide
6 If this is the first time you are running this installation, you are prompted
to choose the language for the wizard. Select the appropriate language
from the drop-down list and click OK.
7 The Setup Wizard opens and walks you through the rest of the
installation process. The following sections of this guide describe the
actions you need to take for each page in the wizard.
 Note the following:
•
The installation screens you see depend on what products you
choose to install. These instructions describe only those screens
necessary to install the Analytics Modules.
•
At any time during the setup, click Cancel to quit the installation.
Welcome
Page Content
Options
Welcome statement
Click Next to proceed.
If any Windows services are running for previously installed MicroStrategy
products, you are prompted to stop them. Click Yes to proceed. If you click
No, you cannot install any MicroStrategy products until you stop all
MicroStrategy services.If you opened the Setup Wizard through the
12 Installation procedures
© 2015 MicroStrategy, Inc.
Installation and Porting Guide
Installation
2
Microsoft Control Panel Add/Remove Programs option, the wizard opens the
Welcome page in maintenance mode.
Page Content
Options
Welcome statement
• Click Modify to add new program
components or to remove currently installed
components. The remaining pages are the
same as for a first-time installation.
• Click Repair to reinstall program components
if you have problems with previously installed
components. Your program components are
returned to their original installation state.
• Click Remove to quickly remove all installed
MicroStrategy components without going
through all the Setup Wizard pages.
• Click Next to proceed.
you installed using a CD, you need your original installation CD to
 Ifrepair
the installation by reinstalling.
License Agreement
Page Content
Options
License-related terms • Read the license agreement.
and conditions
• Select to accept or to decline the agreement.
If you choose to decline, you cannot install
MicroStrategy products.
• Click Next to proceed.
• Click Back to return to the previous page.
Customer Information
Page Content
Options
Boxes for name,
company name, and
product license key
•
•
•
•
•
© 2015 MicroStrategy, Inc.
Enter your name.
Enter the name of your company.
Enter the license key.
Click Next to proceed.
Click Back to return to the previous page.
Installation procedures
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Setup Type
Page Content
Options
Options to select either
a typical or advanced
setup
• Select Typical to place the Analytics
Modules in a given root directory.
• Select Advanced to specify a different
directory for each MicroStrategy product
you install.
• Click Next to proceed.
• Click Back to return to the previous page.
There is one significant difference between a typical setup and an advanced
one. With the advanced setup option, you can select a different location for
each product selected in the Select Components window; with the typical
option, the Analytics Modules are placed by default in C:\
Program Files (x86)\MicroStrategy\Analytics Modules\
Analytics_Metadata.mdb.
Choose Destination Location
Page Content
Options
Location where the
MicroStrategy products will be
installed
• Click Browse to select a location
different from the default value (see
the following Notes).
• Click Next to proceed.
• Click Back to return to the previous
page.
An MS Access file called Analytics_Metadata.mdb will be installed. It
contains the production version of the Customer Analysis Module (CAM),
Sales Force Analysis Module (SFAM), Financial Reporting Analysis Module
(FRAM), Sales and Distribution Analysis Module (SDAM), and Human
Resources Analysis Module (HRAM) projects, as well as three tutorial
projects.
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Installation
2
By default the Analytics_Metadata.mdb file is installed in the following
location: C:\Program Files (x86)\MicroStrategy\
Analytics Modules\Analytics_Metadata.mdb.
 Note the following:
•
While this setting determines the default root directory for the
installed Analytics Modules, you can change the destination of a
product later if you chose an advanced setup.
•
With both typical and advanced setup types, you can choose the
directory for a product only if that product is not already installed
on the server machine. Otherwise, the product can only be
installed in the same directory in which it already exists and you
will not see this page.
Select Components
Page Content
Options
• A list of MicroStrategy products
• Space Required: Space
needed for the MicroStrategy
products selected; the count
changes dynamically as check
boxes are selected and cleared
• Description: Details about each
MicroStrategy product
• Select or clear the appropriate
check boxes.
• Click Next to proceed.
• Click Back to return to the
previous page.
If you have not uninstalled previous versions of MicroStrategy products, you
are prompted to overwrite them. If you have an old metadata repository and
warehouse for MicroStrategy Tutorial for example, perhaps from an
evaluation, and you want to keep them, you must rename them or move
them to another location; otherwise, they will be overwritten. If you are
prompted to overwrite existing files, click Yes to ensure that all products and
product tutorials will work properly.
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Select Program Folder
Page Content
Options
• Box to specify the name of the
program folder in your Windows
Start menu from which
MicroStrategy products will be
accessed
• List of the existing program
folders found under the
Windows Start menu
• If you want, type a folder name
different from the default or
select an existing folder;
otherwise leave as is
(recommended).
• Click Next to proceed.
• Click Back to return to the
previous page.
Start Copying Files
Page content
Options
Current Settings:
• Products that will be installed or updated
• Locations in which the products will be
installed (target directories)
• Name of the Windows Start menu
program folder
• Virtual directories for Web and
Subscription Portal
• Service account for MicroStrategy
Narrowcast Server
• Location of the installation log file
• License details
• Click Next to proceed.
• Click Back to return to
the previous page.
Click Next and the installation process begins, which can take several
minutes depending on your computer’s hardware configuration.
When the installation process has finished, you are prompted to either view
the ReadMe file (click Yes or No) or go directly to the InstallShield Wizard
Complete page.
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Installation
2
InstallShield Wizard Complete
Page Content
Options
• Message confirming installation
completion
• Options (yes/no) to restart the
machine (if necessary)
• Check box to open the ReadMe
file (if restarting is not required)
• Instructions to empty drives and
click Finish
• Click Yes to restart the machine.
• Click No to continue without
restarting.
• Select the check box to open
the ReadMe file (if restarting is
not required).
• Click Finish to complete the
setup.
the option to restart your machine appears, to ensure that the
 Ifinstallation
process finishes correctly you should select Yes I want to
restart my computer now.
After installation is complete, you are ready to configure the MicroStrategy
components you have installed. This will ensure that the software can be
used immediately. To configure your MicroStrategy software with the
Configuration Wizard, see Configuration, page 21.
Installation verification
During the installation process, the Setup Wizard gathers and records
information about your system and your installation selections. You can
verify installation setup information through the installation log file
(install.log), located by default in C:\Program Files (x86)\
Common Files\MicroStrategy.
The installation log file includes the following information:
•
Installation date
•
Target directories
•
Program folder name
•
Operating system identification
•
Hardware specifications
•
Selected installation options
•
Registry paths
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•
Installation and Porting Guide
List of registered files
installation log file can be particularly helpful if you encounter
 The
errors during the installation process. For example, the log can tell
you if a registry key or path was not added or if a critical file was not
registered successfully.
Uninstalling a MicroStrategy component
You might choose to uninstall one or more MicroStrategy components,
perhaps to install those components on a different machine. The uninstall
function:
•
Unregisters and removes selected files, registry entries, and shortcuts
logged in the Uninst.isu log file
•
Calls a custom DLL to handle unlogged items such as registry entries and
files
Before uninstallation begins, the DLL file
•
Checks for user privileges. If they are not valid, uninstallation stops.
•
Checks for running components. If one is found, uninstallation stops.
•
Stops and deletes the MicroStrategy Intelligence Server service (only
when uninstalling Intelligence Server).
•
Deletes application-created files such as *.log, *.gid, *.ldb, and
*.tb.
To uninstall from the Control Panel
1 Close all installed MicroStrategy products.
2 From the Windows Start menu, point to Settings and select Control
Panel.
3 In the Control Panel, double-click the Add/Remove Programs icon. The
Add/Remove Programs dialog box opens.
4 Select MicroStrategy and click Change/Remove (or Add/Remove in
Windows NT). The MicroStrategy Setup/Maintenance program opens.
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Installation
2
5 Select Modify and click Next.
you want to remove all MicroStrategy components, select
 IfRemove.
Click Yes for any prompts that appear, then click Finish
when maintenance is complete to close the maintenance program.
6 Select to accept the license agreement and click Next.
7 Verify your customer information and click Next.
8 Verify your setup type and click Next.
9 Currently installed components appear with a check mark. Clear the
check boxes for the components to uninstall and click Next.
10 If you are prompted about stopping your Web server, click Yes to stop it
and continue with the uninstallation.
11 Verify the settings and click Next to begin removing files.
12 When the uninstall routine is complete, select Yes to restart your
computer or No to restart it later. Then click Finish to close the
maintenance program.
 You should restart the computer to achieve a clean uninstall.
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3.
CONFIGURATION
Introduction
Once you have installed your MicroStrategy Analytics Modules software, you
complete a few configuration tasks before you can begin using them with
your own data. This chapter provides steps to configure the installed
Analytics Modules projects.
Configuring your software
Follow the sections below to prepare your system. Then perform the
procedures that follow to configure your system to use the Analytics
Modules.
Configuration preparation
Make the following decisions about product setup and use.
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Project sources
Each Analysis Module consists of a MicroStrategy project in a metadata
repository. A project source contains the information necessary for
MicroStrategy to connect to the metadata in which your projects are stored.
The project source stores the location of the metadata repository or the
MicroStrategy Intelligence Server that is used to run the project. There are
two types of project sources:
•
Direct project sources connect directly to the metadata through ODBC.
This is known as a direct connection.
•
Server project sources connect to the metadata via a MicroStrategy
Intelligence Server. This is known as a server connection.
The Configuration Wizard guides you through the process of creating both
types of project sources. You will use the Configuration Wizard during the
configuration procedures described below.
Connection types
You can configure the Analytics Modules projects to run with a direct
connection or a server connection.
•
A direct connection connects the project source to the metadata
repository directly through ODBC.
•
A server connection connects the project source to the metadata
repository through MicroStrategy Intelligence Server. A server
connection is the most commonly used connection type. See the
MicroStrategy Installation and Configuration Guide for more detailed
information on connection types.
use the Analytics Modules documents (for example, for
 Todashboards
and scorecards), you must be connected through a server
connection using MicroStrategy Intelligence Server.
With or without a data warehouse
The Analytics Modules can be used with or without an existing data
warehouse. If you have an existing data warehouse in a given analytical area,
you can take advantage of best practices reports and metrics, while making
the best use of your existing data warehouse investments. If you have no data
warehouse storing data for a given analysis area, you can use the Analytics
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Configuration
3
Modules’ default physical schema and packaged metadata components as an
initial design for your own data warehouse and analytical application.
you do not have an existing data warehouse in the given analytical
 Ifarea,
you can follow the procedures below to create a data warehouse.
For your new warehouse, you must create the required tables using
the scripts generated from the Erwin files that come with the
MicroStrategy Analytics Modules.
The configuration procedures below include steps important for both uses.
These include:
•
Configuring a production metadata repository
•
Duplicating selected Analytics Modules projects into the production
metadata repository
•
Configuring the chosen MicroStrategy projects to point to the target data
warehouse
Configuration prerequisites
Before you begin using the Configuration Wizard you should satisfy the
following requirements:
•
Install the necessary MicroStrategy products. At a minimum, you should
have MicroStrategy Architect and MicroStrategy Developer (Architect is a
subcomponent of Developer) and the MicroStrategy Analytics Modules
installed. For steps to install these products, see Chapter 2, Installation.
•
During the Analytics Modules configuration procedures below, you will
create project sources, when you can set up the Analytics Modules
projects to run through either a direct connection or a server connection.
To run the Analytics Modules projects through a server connection, you
must have MicroStrategy Intelligence Server installed. See the
Installation and Configuration Guide for details to install and configure
Intelligence Server.
•
Be sure you have access to an empty database location certified to house
the metadata repository. For a list of certified metadata platforms, see the
MicroStrategy Readme file. From the Windows Start menu, point to
Programs, then MicroStrategy Documentation, and then select
Product Manuals. Click the MicroStrategy ReadMe link.
products must be configured on the machine on which
 MicroStrategy
they are installed. You cannot configure remotely.
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Configure the production metadata repository
In this procedure, you create a DSN for the metadata repository that comes
with the Analytics Modules, connect it to a project source, and then create a
space to serve as your production metadata repository.
To configure the production metadata repository
1 On a machine with a MicroStrategy installation, create a data source
name (DSN) using an Access ODBC driver that points to the
Analytics_Metadata.mdb file. Name the DSN
“Analytics_Metadata”.
2 Create a direct connection (a 2-tier project source) pointing to the
Analytics_Metadata DSN you created in the step above. You can
create a project source in the following ways:
•
If you need guidance, use the MicroStrategy Configuration Wizard.
See the MicroStrategy Installation and Configuration Guide for steps
to open the wizard and create a direct (2 tier) project source.
•
If you are familiar with creating project sources, use MicroStrategy
Developer. From Developer’s Tools menu, select Project Source
Manager.
3 Connect to this metadata repository by logging in as Administrator with
no password. The metadata repository should display five different
projects for the Analytics Modules, plus three projects for Tutorial.
in these projects do not run because the data warehouses
 Reports
are not yet set up. However, you can browse through the metadata
objects.
4 In your database of choice, create a space for storing the project
metadata. See the MicroStrategy Readme for supported databases.
5 Create a DSN that points to the new space created in the step above.
it is possible to use a Microsoft Access database for the
 While
metadata repository, it is not a suitable metadata repository for a
production project. Use Access only for a proof-of-concept type of
application.
6 Using the MicroStrategy Configuration Wizard, create a metadata shell
for this new space by running appropriate table and trigger creation
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Configuration
3
scripts. See the MicroStrategy Installation and Configuration Guide for
steps to open and use the Configuration Wizard.
Once the scripts are run, an empty metadata shell is created in this
location. This metadata repository is your production metadata
repository and will be called the production metadata through the rest of
the configuration procedures.
7 Using either the Configuration Wizard or MicroStrategy Developer,
create a direct connection (a 2-tier project source) pointing to the DSN
created in step 5 above. You can name this new direct connection
anything you want, but it is called PROD_MD (or production metadata)
throughout the rest of this chapter.
8 Connect to the production metadata by logging in as Administrator with
no password. There will be no projects in this shell.
Duplicate the Analytics Modules
In this procedure, you duplicate selected Analytics Modules into the
production metadata created in the procedure above.
To duplicate the Analytics Modules
1 In Developer, from the Schema menu, select Duplicate Project. Follow
each step in the Project Duplication Wizard, using the following
information:
•
Step 1 - Select source project location: Use Analytics_Metadata as
the source project location.
•
Step 2 - Select project to duplicate: Select the Analysis Module you
want to duplicate.
•
Step 3 - Select a location for the duplicated project: Use PROD_MD as
the target location.
•
Steps 4 - 9: Accept the default settings.
2 Depending on your system configuration, the project duplication process
can take several minutes. When the process is complete, examine the logs
and make sure the duplication ran error-free.
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Configure the data warehouse connection
Use this procedure to configure the production metadata to point to the
target data warehouse.
To configure the data warehouse connection
1 Create a DSN that points to the target data warehouse.
•
If you intend to use an existing data warehouse with the Analytics
Modules, the target data warehouse will be your existing data
warehouse.
•
If you do not have an existing data warehouse in the given analytical
area, the target data warehouse will be a data warehouse created using
the physical schema that comes with the Analytics Modules. This data
warehouse must have the required tables created using the scripts
generated from the Erwin files that come with the MicroStrategy
Analytics Modules.
2 Open the Database Instance Manager. (From MicroStrategy Developer,
select Analytics Modules, select Administration, and then select
Database Instance Manager.)
3 Edit the database instance for the duplicated project, and point it to the
new data warehouse DSN. Select an appropriate database connection
type. Changes to the database instance automatically update the project
level VLDB settings. Any additional changes to VLDB settings can be
made in the project’s Project Configuration menu.
4 Depending on the type of data warehouse to which you port a module and
the Analysis Module you choose to port, you may need to change certain
settings in your system so the module recognizes the new data
warehouse. See Database settings, page 26 to locate your database type
and required project and other object settings.
5 After making appropriate changes to the database instance, disconnect
and reconnect to the project source so your changes can take effect.
Database settings
Settings for the following certified database types are provided below:
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•
Oracle 8i2 and later versions
•
SQL Server 2000
•
IBM DB2 UDB 5.2 and later versions
•
Teradata v2R4 and later versions
Configuration
3
the Supplemental Reference for System Administration for a
 See
complete list of default VLDB settings and an explanation of Join
Type settings. See the online help for steps to change join types.
Oracle databases
For porting to an Oracle 8i2 or later database, use the following table to
locate the Analysis Module you are porting and identify the necessary
changes to make.
Analysis Module
Port from MS Access to Oracle 8i2 (or later) database
CAM
Change the value of the Join Type VLDB property to Join 89.
FRAM
Change the value of the Join Type VLDB property to Join 89.
HRAM
1 Change the project level join type to Join 89.
2 Change the join type to Join 89 for the Current Headcount and Average Tenure
Summary report, located by default in the Human Resources Analysis
Module project under Public Objects\Reports\Scorecards\
HR Summary Scorecard Base Reports\
Current Headcount and Average Tenure Summary.
SFAM
No changes are required.
SDAM
Change the project level join type to Join 89.
SQL Server database
For porting to a SQL Server 2000 database, use the following table to locate
the Analysis Module you are porting and identify the necessary changes to
make.
Analysis Module
Port from MS Access to SQL Server 2000 database
CAM
No changes are required.
FRAM
No changes are required.
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Analysis Module
Port from MS Access to SQL Server 2000 database
HRAM
Calculating dates: For the Date metrics located by default in the Human
Resources Analysis Module project under Public Objects\Metrics\
Dates, you may need to modify the default metric definition for the metric you want
to use, to comply with the SQL Server syntax requirements. For example, for the
metric Current Tenure, you must change the definition to:
Applysimple(‘Avg(abs(datediff(day,CurrentDate(),#0)))’, [Hire Date]).
SFAM
No changes are required.
SDAM
• Change the definition to:
([NET_ITEM_AMOUNT] * (1 - 1.0001*
[ITEM_QTY_DELIV_BU] / 1.0001*
[ITEM_QTY_BU])))
for the Backlog Net Item Amount fact, located by default in the Sales and
Distribution Analysis Module project under Schema Objects\
Facts\Sales Documents\Backlog New Item Amount.
• Calculating dates: You may be required to modify the default fact expressions
for the fact you want to use. See Calculating dates and times in the SDAM
Reference for complete information.
• Calculating times: You may want to calculate time differences using a different
time scale than that used in the project. See Calculating dates and times in the
SDAM Reference for complete information.
• Contribution metrics are handled differently in different databases, due to the
way integers are truncated. See Contribution metrics ported to a database in the
SDAM Reference for complete information.
DB2 databases
For porting to a DB2 UDB 5.2 or later database, use the following table to
locate the Analysis Module you are porting and identify the necessary
changes to make.
Analysis Module Port from MS Access to DB2 UDB 5.2 (or later) database
CAM
No changes are required.
FRAM
No changes are required.
HRAM
Change the formula to: ApplyAgg(“Max(DAYS(Current Date) - DAYS(#0))”, [Hire
Date]) {~+}
for the Current Tenure metric, located by default in the Human Resources
Analysis Module project under Public Objects\Metrics\Dates\
Current Tenure.
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3
Analysis Module Port from MS Access to DB2 UDB 5.2 (or later) database
SFAM
No changes are required.
SDAM
• Change the definition to:
([NET_ITEM_AMOUNT] * (1 - 1.0001*
[ITEM_QTY_DELIV_BU] / 1.0001*
[ITEM_QTY_BU])))
for the Backlog Net Item Amount fact, located by default in the Sales and
Distribution Analysis Module project under Schema Objects\Facts\
Sales Documents\Backlog New Item Amount.
• Calculating dates: You may be required to modify the default fact expressions for
the fact you want to use. See Calculating dates and times in the SDAM Reference
for complete information.
• Calculating times: You may want to calculate time differences using a different
time scale than that used in the project. See Calculating dates and times in the
SDAM Reference for complete information.
• Contribution metrics are handled differently in different databases, due to the way
integers are truncated. See Contribution metrics ported to a database in the
SDAM Reference for complete information.
Teradata databases
For porting to a Teradata V2R4 or later database, use the following table to
locate the Analysis Module you are porting and identify the necessary
changes to make.
Analysis Module
Port from MS Access to Teradata V2R4 (or later) database
CAM
No changes are required.
FRAM
Not supported.
HRAM
No changes are required.
SFAM
No changes are required.
SDAM
• Calculating dates: You may be required to modify the default fact expressions for
the fact you want to use. See Calculating dates and times in the SDAM
Reference for complete information.
• Calculating times: You may want to calculate time differences using a different
time scale than that used in the project. See Calculating dates and times in the
SDAM Reference for complete information.
• Contribution metrics are handled differently in different databases, due to the
way integers are truncated. See Contribution metrics ported to a database in the
SDAM Reference for complete information.
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4.
USING THE ANALYTICS
MODULES
Introduction
The MicroStrategy Analytics Modules are designed to let you take advantage
of best practices analysis for a given analysis area, while making the best use
of your existing data warehouse investments by porting an Analysis Module
to your target data warehouse. For more details on porting, see Chapter 5,
Porting the Analytics Modules.
If you have no data warehouse storing data for a given analysis area, you can
take advantage of the Analytics Modules by using the physical schema and
packaged metadata components that come with the modules as an initial
design for your own data warehouse. You can also use the Analytics Modules
as templates to design and develop analytical applications.
This chapter explains how to use the Analytics Modules when you do not
have an existing data warehouse in a given analysis area. It also provides
information on customizing and extending the Analytics Modules. For
information on using the Analytics Modules with an existing data warehouse,
see Chapter 5, Porting the Analytics Modules. For information on designing
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and creating your own analytical applications using the Analytics Modules as
a template, see Chapter 6, Creating Portable Analytical Applications.
 Be aware of the following:
•
MicroStrategy provides the Analytical Modules and their related
components, including the default physical schema, as examples.
If you decide to populate, customize, enhance, or otherwise change
the Analytics Modules or any individual components, these
become the property of your company and you or your company
are responsible for any additions, modifications, enhancements, or
customizations made.
•
MicroStrategy does not provide ETL routines or tools to populate
the physical schemas that come with the Analytics Modules; they
are simply provided as an example. If you choose to use the
physical schemas, you must identify data sources and create the
ETL routines necessary to populate the database schema.
Using the Analytics Modules as an initial
design
MicroStrategy Analytics Modules provide the key data elements necessary to
define a given analytical area.
If you do not have an existing data warehouse in the sales, financial
reporting, human resources, sales distribution, or customer analysis areas,
you can use the default physical schema and prepackaged metadata
components in the Analytics Modules as an initial design for your own new
data warehouse.
There are two approaches to using the default physical schema that comes
with each Analysis Module as an initial design for your own data warehouse:
•
Initial Data Warehouse: You can take immediate advantage of the default
physical schema for the data warehouse that comes with each module,
populating it with your own data. Once you have established your own
data warehouse using the modules, you can expand and customize the
application as you identify additional business and analytical
requirements.
•
Initial Customization: Alternatively, you can enhance the default physical
schema initially with your own customizations, to reflect immediate
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Using the Analytics Modules
4
business and analytical requirements you may have. Once the default
schema has been customized, you can then populate it with your data.
For customization scenarios and steps to customize and extend the Analytics
Modules, see Customize and extend the Analytics Modules, page 34.
Initial data warehouse
You can use a given Analysis Module as an initial design for your analysis
solutions in the module’s analytical area and then add your company’s
specific business and analysis requirements by customizing and extending
the module.
You begin this approach by selecting an Analysis Module and then
populating the physical schema that comes with your chosen module. Each
Analysis Module’s reference guide contains the logical and physical view of
the default data warehouse schema, as well as a complete data dictionary
with all tables and columns, and how to populate them.
Each Analysis Module’s metadata components provide best practices
analytics that can be used to define reports addressing any additional
requirements of your company. As you continue to add data to the default
physical schema, you can decide at any point to enhance the module by
customizing or extending the application. Each Analysis Module provides its
physical schema definition in an Erwin format, allowing you to edit
definitions and then generate table creation scripts for any database
platform.
See Customize and extend the Analytics Modules, page 34 for more
information on enhancing the default physical schema.
provides the Analytics Modules and their related
 MicroStrategy
components, including the default physical schema, as examples. If
you decide to populate, customize, enhance, or otherwise change the
Analytical Modules or any individual components, these become the
property of your company and you or your company are responsible
for any additions, modifications, enhancements, or customizations
made.
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Customize and extend the Analytics Modules
MicroStrategy Analytics Modules can be customized and extended to address
your specific business needs. Whether you are using the Analytics Modules
as your initial data warehouse (see above), porting them to an existing data
warehouse (see Chapter 5), or using them as a template to create your own
analytical applications (see Chapter 6), you can customize and extend the
metadata objects (attributes and facts) to address additional business and
reporting requirements.
When you customize or extend the Analytics Modules, you are customizing
existing data elements to reflect your company’s requirements or adding
data elements that are specific to your company. Typical enhancements
include the following:
•
Adding additional customer characteristics to the Customer Analysis
Module, which is described in detail in Customization scenarios, page 34
•
Converting some prompted reports to static selections for end users
•
Changing a Sales Force Analysis Module (SFAM) report’s Time analysis
to Month instead of Quarter
Customization and extension are accomplished using the standard
MicroStrategy design and development tools, MicroStrategy Architect and
MicroStrategy Developer. Both of these products come with the Analytics
Modules. For more information, see Chapter 1, About the Analytics
Modules.
does not provide ETL routines or tools to populate the
 MicroStrategy
physical schemas provided with the Analytics Modules; they are
provided as an example. If you choose to use them, you must identify
data sources and create the ETL routines required to populate the
database’s physical schema.
Customization scenarios
There are two general types of scenarios when you customize or extend an
Analysis Module, depending on what elements you want to change. You can
make changes or additions to the application objects (such as reports,
metrics, and filters, located in the Public Objects folder). Or you can make
changes or additions to the schema objects (such as attributes and facts,
located in the Schema Objects folder).
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Knowing the different efforts required for each of these approaches can help
you decide whether and how to make a modification. The following list
presents examples of typical changes based on the two general scenarios and
any related updates necessary based on the change.
Application objects
These scenarios detail changes to application objects only. It does not
include changes to logical data models.
Examples of changes to the application objects include:
•
Converting prompts, which enable dynamic selections, of packaged
reports to static selections.
•
Modifying a metrics definition. For example, you might modify Quarter
Comparison metrics to compare data at the Month level, using the
Previous Month transformation instead of the Previous Quarter.
•
Updating filter conditions to use values in the target data warehouse.
Since packaged filters can include conditions based on values existing in
the default database, you may need to do this when you port an Analysis
Module to an existing data warehouse.
•
Modifying report templates to include different attributes or metrics. For
example, SFAM reports analyze data at the Quarter level. If you want to
analyze data at the Month level, substitute Quarter by Month in the
reports.
Examples of creating new application objects include:
•
Creating new reports based on existing attributes and metrics. The
prepackaged reports can be used as best practices examples to create new
reports.
•
Creating new metrics, such as Percent to all Contributions and Month to
Month comparisons, that are not included in the prepackaged modules.
Schema objects
These scenarios include changes to the schema objects. These changes
require updates to the physical database schema, and may drive changes to
the application objects (reports and metrics) as well.
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Examples of changes to the logical data model include:
•
Changing attribute keys for fact tables. For example, in SFAM, the
Opportunity fact table does not include a product key. To allow users to
calculate product information at the Opportunity level, you would modify
the fact table and then update it in the project.
•
Modifying a hierarchy to include additional levels. For example, the
packaged Product hierarchy includes two levels, Product and Product
Group. If you require additional levels, you would add corresponding
attributes and relationships. This type of change also requires you to
update the physical schema.
Examples of additions to the logical data model include:
•
Adding additional characteristic attributes to an existing attribute. For
example, SFAM includes a number of characteristics for the Account
attribute. You may require additional attributes to reflect your company’s
Account data. This addition requires you to update the structure of
lookup tables and add attributes and relationships to the project.
•
Adding facts to existing tables. For example, SFAM includes revenue
data. You might update the table to include Cost and Number of Units,
and add them to the MicroStrategy project.
Examples of enhancements to the application objects, driven by changes or
additions to the schema objects, such as those described above, include:
•
Adding new Product attributes to existing reports
•
Creating new metrics and reports to analyze Cost and Profit data
Scenario: Adding customer characteristics to CAM
The Customer Analysis Module (CAM) provides a limited number of
common customer characteristics, such as Customer Name, Customer
Address, and so on. This is a common area to add company-specific data
elements to reflect information your company needs to store and use.
To customize CAM by adding additional customer characteristics, you add
the additional attributes as new columns in the Customer lookup table. You
use the Erwin file that comes with CAM to edit the default physical schema
and then generate the table script.
may have to define additional lookup tables to store descriptions
 You
and relationships with other attributes.
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Once the table is created, you use MicroStrategy Architect to add the new
attributes to the project, thus linking the project to the new data structures.
For the end user, the additions cannot be distinguished from the existing
packaged components and the existing components can be enhanced to take
advantage of the new data elements.
Customization steps
You usually identify necessary additions and modifications for the Analytics
Modules during a gap analysis, as described in Port an Analysis Module,
page 48.
After you complete a gap analysis, you enhance the logical data model for
your chosen Analysis Module to include any additional attributes and facts
you require. This may include adding data elements in the data warehouse.
Finally, you modify the module’s packaged components or add new ones,
such as metrics, prompts, reports, and so on. For details on adding or
modifying attributes, facts, metrics, prompts, reports, and so on, see the
MicroStrategy Advanced Reporting Guide.
The comprehensive documentation and packaged components that come
with the Analytics Modules can be used as templates for your customization
and enhancements. Developers can access and reverse-engineer all packaged
components using MicroStrategy developer tools.
Roll out to production
Before the final version of the Analytics Modules is made available to end
users, you must complete some preparation tasks. Each section listed below
describes aspects of your Analytics Modules implementation that can cause
performance issues for users. Each section also suggests actions to be taken
that can lessen or eradicate the potential issue.
You must consider the tasks listed below when identifying resources and
determining a schedule for rolling out the Analytics Modules to your
production environment and making them available to users.
You can find details for many of the MicroStrategy-specific procedures in the
MicroStrategy System Administration Guide.
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Tuning and performance
The Analytics Modules do not include any type of tuning or performance
optimization. Consider using the following techniques to optimize
performance for your users:
•
aggregate tables (details available in the MicroStrategy System
Administration Guide)
•
partitioning (details available in the MicroStrategy System
Administration Guide)
•
indexes
MicroStrategy users
Different users and user profiles may be required to allow or restrict access
to areas of the Analytics Modules. Users and user profiles are defined based
on your company’s requirements, which include configuring permissions to
access MicroStrategy functionality.
default, the Analytics Modules include a single user
 By(Administrator)
with all privileges.
Security
Your company may require you to limit access to certain objects, and to
implement data level security with security filters. See the MicroStrategy
System Administration Guide for information on implementing these
security features.
default, the Analytics Modules include a single user
 By(Administrator)
with full control for all objects. Additionally, all
objects are set up with View access for all users.
Direct and server connections
The Analytics Modules are packaged as direct connection projects (also
known as 2-tier projects), which means they run in two architectural
layers—the business logic/user interface layer and the database layer. For
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optimum performance, they should run on a server connection (also known
as 3-tier mode), through MicroStrategy Intelligence Server.
A given Analysis Module can be easily set up to run on a server connection,
through MicroStrategy Intelligence Server, by making the appropriate
changes to the project configuration. More details are available in the
MicroStrategy System Administration Guide, and also in Chapter 3,
Configure the production metadata repository.
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5
5.
PORTING THE ANALYTICS
MODULES
Introduction
The packaged MicroStrategy Analytics Modules applications can be used in
many ways. The modules can be integrated into your existing data
warehouse, serve as an initial design for a new data warehouse, and facilitate
the deployment of enterprise-class portable analytical applications. For any
of these scenarios, you can also customize and extend the Analytics Modules
to meet changing reporting and business requirements.
•
Existing data warehouse: If you have a data warehouse already holding
data for a given analytical area, you can reconfigure the packaged logical
data model and analytical reports to work with your existing data
warehouse structures. This chapter describes the reconfiguration process,
known as porting.
•
No existing data warehouse: If you do not have a data warehouse
containing data for a given analysis area, you can use the default logical
data model, physical schema, and analytical reports as a starter design for
a new data warehouse. For more information, see Chapter 4, Using the
Analytics Modules.
•
Building analytical applications: If you want to build analytical
applications, you can use the Analytics Modules as best practices
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examples for each analysis area by treating the Analytics Modules
components as a template. For more information, see Chapter 6,
Creating Portable Analytical Applications.
•
Customize and extend the Analytics Modules: You can easily customize
and extend the Analytics Modules for any of the scenarios above, to meet
changing reporting and business requirements. For more information,
see Customize and extend the Analytics Modules, page 34.
This chapter explains how to port the Analytics Modules so they work with
an existing data warehouse.
does not provide ETL routines or tools to populate data
 MicroStrategy
warehouses. You must identify data sources and create the ETL
routines necessary to populate your particular database schema.
Introduction to portability
Portability is the ability of an analytical application to be integrated into an
existing data warehouse. Porting a given analytical application involves
mapping the application to an existing physical data warehouse schema
while retaining the logical data model definition.
A portable analytical application such as a MicroStrategy Analysis Module is
a prepackaged project which has been built using portability design rules.
These rules are defined to minimize the changes required to map an
application’s logical data model to a target data warehouse’s physical
schema. To achieve this, MicroStrategy technology provides an abstraction
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layer between the logical data model and the physical data warehouse
schema, making it possible to map the existing links to a physical schema
with a similar data set. By retaining the logical data model definition, all
MicroStrategy object definitions such as attributes, facts, metrics, and
reports are also retained.
MicroStrategy has developed a detailed methodology to support the mapping
of metadata objects from the logical data model to physical database objects
in a target data warehouse’s physical schema using MicroStrategy tools.
Portability methodology
When you port an Analysis Module to an existing data warehouse, you map
the facts and attributes in the logical data model to the structure of the
physical schema in an existing data warehouse, allowing the analytical
reports to reflect the data stored in your data warehouse. Portability centers
on the concept of “Metadata Only” analytical applications. By packaging
Analytics Modules with reports, metrics, facts, and attributes, but no
hard-wired data warehouse, MicroStrategy provides you with the tools to
perform best practices analysis in each of the given analysis areas, based on
the data in your existing data warehouse.
Portability design drivers
The MicroStrategy Analytics Modules were designed to be portable. This
section describes design drivers that are built into the Analytics Modules
architecture and that are reflected in the porting procedures that follow. For
a complete description of the design rules, see Portability rules and
recommendations, page 131.
Well-known analysis area
Each Analysis Module focuses on a well-known analysis area. Analysis areas
include customer analysis, sales analysis, financial reporting analysis,
human resources analysis, and sales distribution analysis. Different analysis
areas increase the chance that companies will have a data warehouse for such
analysis.
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Generic data elements
The logical data model is built based on generic data elements. Using generic
data elements maximizes the probability that data elements will exist in the
target data warehouse. For example:
•
Sales analysis is defined based on generic elements such as Opportunity,
Order, Day, Product, Revenue, and the like.
•
Customer analysis includes generic customer characteristics such as
Gender or Age Range, but not others that are specific to a company (Head
of Household, Pet Owner Flag, and so on). Specific attributes can be
added later.
No specific physical structure
The logical data model should not be dependent on any specific physical
structure.
•
The MicroStrategy metadata repository allows the definition of the logical
data model, that is, the business representation, to be independent from
the physical schema, that is, the storage structure.
•
The Analytics Modules are not hard-coded to a specific physical schema;
therefore, they can be mapped to different schemas for different data
warehouses. For example, the modules avoid using many-to-many
relationships because these require a specific table structure.
Simplicity
The logical data model is simple but complete.
•
Simplicity is achieved through a manageable number of data elements;
each module includes 20 to 30 attributes and 5 to 10 facts.
•
Completeness is achieved by including the key elements that define a
given analytical area and by providing best practices reports.
can extend the model to add specific details required by your
 You
organization; see Customize and extend the Analytics Modules,
page 34.
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Common hierarchies
The modular approach provides clear, defined analysis areas based around
fact tables and common hierarchies across Analysis Modules.
•
Modularity minimizes the impact when a fact table does not exist in the
target data warehouse. Each module includes 5 to 10 facts; these are
typically stored in 2 or 3 fact tables. If all reports are based on facts from
different fact tables, a missing fact table can impact all reports.
Modularity ensures that the maximum number of reports can be used.
•
A report usually focuses on one analysis area, although when required it
can include several areas. This does not limit the value of the reports.
•
Each Analysis Module was designed to be combined with other modules.
Common analysis hierarchies exist across different Analysis Modules.
Best practices reports
Analytics Module reports provide best practices analysis.
•
Each Analysis Module provides 40 to 50 advanced reports using a total of
75-100 metrics, representing best practices analysis for each analytical
area.
•
Packaged elements can be used as templates when defining new reports.
•
Reports can be modified to address additional reporting requirements.
For example, the Customer Segmentation by Customer Characteristics
report can be easily modified to add additional attributes reflecting
company-specific customer characteristics.
Portability design rules
All metadata objects are defined based on a number of rules that ensure
portability. For example:
•
Database functions were avoided if they were tied to a specific database
engine.
•
Filters were avoided when possible. Most reports provide prompts that
can be modified to meet custom requirements.
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•
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Base formulas were used to isolate metric definition of facts.
can read the complete list of design rules; see Chapter 6, Creating
 You
Portable Analytical Applications.
General porting prerequisites
MicroStrategy tools
To port any of the Analytics Modules to your existing data warehouse, you
only need MicroStrategy Developer and MicroStrategy Architect. Developer
and Architect allow you to access metadata objects such as attributes, facts,
metrics, and reports.
MicroStrategy Object Manager is an optional tool that can make the porting
process more efficient. Object Manager allows you to implement only those
portions of the Analytics Modules that are relevant to a company’s
requirements. Object Manager also provides enhanced searching
functionality that facilitates the gap analysis. See the MicroStrategy System
Administration Guide for information about using Object Manager.
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Documentation
In addition to the tools listed above, you must also have all necessary
documentation:
Documentation
Description
Analytics Modules
reference guides
Each Analysis Module has its own reference guide which includes:
• An introduction to the module’s analysis area and reporting challenges
• A list of packaged reports, and for each report, the business value, report
definition, and report screen shots
• A complete list of all metrics and descriptions
• The logical data model definition, with all hierarchies and attributes, including
metadata definitions
• The default physical schema provided with the module and a data dictionary
Physical model, in
Erwin format
This file contains a definition of the physical schema provided with the module, in
an Erwin format:
• This file can be used to generate database creation scripts for any database
platform.
• This file can be used as an initial design for your company’s data warehouse.
Portability
methodology and
porting process
This content appears in this Porting chapter, and includes:
• The Portability methodology section with portability design drivers, necessary
tools, and overview information for porting a module.
• The Port an Analysis Module section with a detailed explanation of how to
implement the Analytics Modules using an existing data warehouse.
Best practices for
portable analytical
applications
This content appears in Chapter 6, and includes:
• A comprehensive explanation of how to design and build applications using the
portability paradigm
• Best practices used by MicroStrategy to develop the Analytics Modules
• Development project guidelines (task and plan)
• Recommendations for creating MIcroStrategy objects
Other prerequisites
Drill maps must be removed from your chosen module before the module
can be ported. A drill map is essentially a customized drill path for the
attributes and metrics in reports. For some of the reports within the
Analytics Modules, the drill paths have been customized by assigning a drill
map, so that only selected attributes can be drilled on within given reports.
A common example is the quarterly reports that are available in many of the
modules. The data in quarterly reports is, logically, only available for the
Quarter level. Therefore, users are restricted within these quarterly reports
by an associated drill map so that they cannot drill down to Month.
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Port an Analysis Module
To port a given MicroStrategy Analysis Module, you map the module to the
physical schema of an existing data warehouse. However, you keep the
logical data model and analytics library for the Analysis Module. This allows
you to use all the related MicroStrategy objects, such as attributes
(analysis-friendly business concepts), hierarchies (relationships between
attributes), facts (measures), metrics (business calculations), and reports.
Porting overview
Porting involves the following general steps:
1 Perform a gap analysis: Identify the overlap between the chosen Analysis
Module, your existing data warehouse, and your company’s reporting
requirements. Once you determine the overlap, you can identify any gaps.
2 Map the module: Using the overlap information from the step described
above, map the Analysis Module to the data warehouse.
3 Customize and extend the module: Using the gap information from the
step described above, customize the module to accommodate additional
business and reporting requirements.
Complete the steps below in the order they appear.
Perform a gap analysis
A gap analysis identifies which areas of a given Analysis Module’s logical data
model and reports can be implemented using the structures of your existing
data warehouse.
The gap analysis is broken into the following procedures:
1 Map the target data warehouse to the module’s physical schema.
2 Map the module’s logical data model to the target schema.
3 Identify application objects.
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4 Optionally, identify additional business requirements.
Each procedure is presented below with a description, examples, and many
alternative scenarios to support your own gap analysis process.
Gap analysis prerequisites
To complete a successful gap analysis, make sure you have access to the
following:
Skills
Good knowledge of your company’s target data warehouse schema and your company’s
reporting requirements, if these exist.
Experience using MicroStrategy tools and with logical data modeling and physical data
modeling.
Physical schema and data dictionary for the target data warehouse and logical data
model, if available.
The MicroStrategy Analytics Modules reference guides, each of which provides a given
module’s default physical schema, data dictionary, and logical data model.
Tools
Erwin file for the Analysis Module; contains the module’s default physical schema and
production metadata.
MicroStrategy Developer and MicroStrategy Architect, to access definitions of attributes,
facts, reports, metrics, and so on; and to search for dependencies. For example, if a fact
does not exist in the target data warehouse schema, dependencies identify which reports,
metrics, and so on will be impacted.
(Optional) MicroStrategy Object Manager, to search for child-parent and parent-child
dependencies. This tool provides a thorough impact analysis.
Map the target data warehouse to the module’s physical
schema
In this first procedure of the gap analysis, you identify whether each data
element from the module’s physical schema exists in the target data
warehouse. For elements that do not exist, you check whether they can be
replaced with data elements that are present in the target data warehouse.
The result is a document presenting a complete mapping that focuses on
available data elements and any proposed changes needed.
An example follows this procedure, and a list of typical scenarios and actions
follows the example.
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To map the target schema to the module’s schema
1 Prepare a document to record the information you will identify in this
procedure. The document should include space for a list of data elements
(tables and columns) and for comments.
example document can be found at the end of this procedure;
 An
see Example: Mapping physical schemas, page 52.
2 Compare the target data warehouse’s physical schema (target schema)
with the selected module’s physical schema (module’s schema). Identify
the list of tables in the target schema that are relevant to the module’s
analysis area. Record this information in the document you created
above.
3 In the module’s physical schema, for each table, identify whether there is
an equivalent table in the target schema. Begin with the lookup tables,
then move on to the fact tables. Add this information to your document.
4 For each matching table identified in the previous step, map the module’s
schema columns to the corresponding ones in the target schema, and add
this information to your document.
some cases, you can map a data element from the module’s
 Inschema
to a similar but not identical data element in the target
schema; the target schema’s data element will replace the data
element from the module’s schema. For example, assume the AGE
data element does not exist in the target schema but AGE_RANGE
does. You can map the AGE column from the module’s schema
with the AGE_RANGE column in the target schema.
5 Identify areas in the target schema where a given hierarchy is stored in a
different number of tables than in the module’s schema.
For example, the target schema might have several lookup tables for the
Time hierarchy:
50 Perform a gap analysis
L_YEAR
L_QUARTER
L_MONTH
year_id
quarter_id
month_id
year_desc
quarter_desc
month_desc
year_id
quarter_id
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But the module’s schema might have a single lookup table for the Time
hierarchy:
L_TIME
year_id
year_desc
quarter_id
quarter_desc
month_id
month_desc
In the example above, the Time hierarchy in the target schema is stored
in more tables than the Time hierarchy in the module’s schema.
6 Identify areas in the target schema where a given hierarchy has a
different number of levels than in the module’s schema.
For example, referring to the L_TIME table in the step above, the
module’s schema has three levels in the Time hierarchy: year, quarter,
and month. But the target schema might only have two levels in its Time
hierarchy (quarter and month), or it might have four (year, quarter,
month, and date), and so on.
7 Identify relevant aggregates and fact tables in the target schema that have
information at different levels than that in the module’s schema.
8 Review your document to ensure that:
•
For all tables and columns in the module’s physical schema, you have
a list of corresponding tables and columns from the target schema.
•
You have marked or otherwise indicated any data elements from the
module’s schema that cannot be used because there are no
corresponding data elements in the target schema.
•
(Optional) You have included a list of tables from the module’s
schema that do not exist in the target schema but will be added to
support packaged reports.
•
(Optional) You have included a list of tables from the target schema
that have no equivalent in the module’s schema but will be added to
support additional analysis requirements.
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Example: Mapping physical schemas
The following table records example information discovered using the
procedure above. The table explains how an (imaginary) existing data
warehouse’s structures map with an Analysis Module’s default physical
schema.
module used in this example is the Sales Force Analysis Module
 The
(SFAM); see the SFAM Reference guide for the physical schema and
data dictionary.
Table in imaginary
target schema
Columns in imaginary
target schema
Results of mapping target schema with SFAM’s
physical schema, and comments
L_CUSTOMER
CUSTOMER_ID
Lookup table for
Customer
CUSTOMER_NAME
This maps with L_ACCOUNT in the module’s
schema.
• Account is Customer in the module’s schema.
• Different names, similar structure.
• Module’s concept of Company does not exist but
can be replaced by target schema’s Customer
Group.
CUST_GROUP_ID
CUST_GROUP_NAME
INDUSTRY_ID
INDUSTRY_NAME
EMPLOYEE_COUNT
ADDRESS
ZIP
A number of additional target schema columns are
not present in the module’s schema. These can be
added later to enhance the Analysis Module and
provide additional objects for reporting.
CITY_ID
REGION_ID
COUNTRY_ID
VENDOR_FLAG
L_PRODUCT
PRODUCT_ID
Lookup table for
Product
PRODUCT_DESC
This is L_SALES _PROD in the module’s schema.
• Different names, same structure.
PRODUCT_GROUP_ID
PRODUCT_GROUP_DESC
L_TIME
CALENDAR_DATE
Lookup table for
Time hierarchy
CALENDAR_MONTH
CALENDAR_QUARTER
CALENDAR_YEAR
FISCAL_MONTH
FISCAL_YEAR
This table maps to several tables in the module’s
schema: L_CAL_DATE, L_CAL_ MNTH,
L_CAL_QTR, and L_CAL_YEAR.
• Different names.
• Provide same relationships.
• Last Period columns are missing in target
schema, so trend analysis is not supported.
DAY_IN_WEEK
L_SALES_PERSON
SALES_PERSON_ID
SALES_PERSON_NAME
52 Perform a gap analysis
This maps to L_SALES_REP in the module’s
schema.
• Region and District do not exist in this table in
the target schema.
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Table in imaginary
target schema
Columns in imaginary
target schema
Results of mapping target schema with SFAM’s
physical schema, and comments
L_SALES_OFFICE
SALES_OFFICE_ID
This table does not exist in the module’s schema.
SALES_OFFICE_NAME
However, it can be mapped to the module’s Sales
District concept, although the relationship with
Sales Representative is broken.
ADDRESS
ZIP
CITY_ID
REGION_ID
COUNTRY_ID
L_CITY
CITY_ID
CITY_NAME
REGION_ID
L_REGION
REGION_ID
REGION_NAME
COUNTRY_ID
L_COUNTRY
COUNTRY_ID
COUNTRY_NAME
F_SALES_ORDER
Fact table with sales
order data
ORDER_ID - unique
identifier for the order
ORDER_LINE_ITEM_ID an order can contain
several different products;
this ID identifies different
products within the same
order
CUSTOMER_ID
SALES_PERSON_ID
A number of additional columns are not present in
the module’s schema. They can be added later to
enhance the Analysis Module and provide
additional objects for reporting.
This table does not exist in the module’s schema.
• It can be added to provide a new Sales
Geography hierarchy.
This table does not exist in the module’s schema.
• It can be added to provide a new Sales
Geography hierarchy.
This table does not exist in the module’s schema.
• It can be added to provide a new Sales
Geography hierarchy.
This maps with F_ORDER in the module’s
schema.
• Different table name and column names, but
similar structure and keys.
• The module’s Fact ORDER_AMT maps with the
target schema’s NET_LINE_AMOUNT.
A number of additional columns are not present in
the module’s schema. They can be added later to
enhance the Analysis Module and provide
additional objects for reporting.
SALES_OFFICE_ID
PRODUCT_ID
DATE_ID
NET_LINE_AMOUNT
NET_ORDER_AMOUNT
NUM_UNITS
UNIT_MEASURE
COST
Additional comments: Some tables in the module’s schema have a
corresponding table in the target schema, including F_LEAD_STATUS,
F_OPTY, L_OPTY, L_LEAD_SOURCE, and L_ORDER.
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Scenarios: Mapping physical schemas
The table below presents some typical scenarios you can encounter while
completing the physical schema mapping procedure above.
Object
Scenario
Action
Table
Different name
Map all related schema objects together.
Not available
Facts and attributes available only in this table should be
eliminated after resolving dependencies.
Missing one or more
columns
Update relevant attributes and facts that use the missing
column in their expressions.
Partitioned
Create a partition mapping table and add all partitioned
tables to the project.
Aggregates tables
present
Add aggregates tables to project.
Information distributed in
multiple tables
Add tables to the project and map each table to relevant
schema objects.
Different name
Map all related objects together.
Not available
Update relevant attributes and facts that use the missing
column in their expressions.
Column
Information distributed in
multiple columns
• in the same table
• in different tables
• Update attribute or fact expressions.
• Create additional attribute forms or add facts.
Different data types
Update the warehouse catalog to read in new data types.
Columns with same
information have same
names.
Use automated mapping for corresponding schema objects
and when adding tables to the project.
Columns with same
Use manual mapping for corresponding schema objects and
information have different when adding tables to the project.
names
Map the module’s logical data model to the target schema
In this second gap analysis procedure, you identify which of the module’s
logical data model objects can be mapped to the target data warehouse.
These objects, also called schema objects, include attributes, facts,
hierarchies, and so on. The results are high-level guidelines for mapping the
module’s logical data model to your target data warehouse schema (target
schema).
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An example follows this procedure, and a list of typical scenarios and actions
follows the example.
To map the logical data model to the target schema
1 Prepare a document to record the results of your analysis in this
procedure. The document will need space for a list of supported schema
objects and for guidelines to map those objects to the target schema.
documents (one each for attributes, facts, and other
 Example
schema objects) can be found at the end of this procedure; see
Example: Mapping the logical data model, page 56.
2 Identify the list of the Analysis Module’s logical data model objects that
are relevant to the target schema. For attributes, include all tables with
relevant information, unless there are good reasons to exclude certain
tables.
 Note the following:
•
You will be working with only a subset of the logical data model
objects, based on the fact that missing data elements were
identified in the previous procedure.
•
Refer to the reference guide for the Analysis Module you are
using, to find definitions for all objects in the logical data
model. Refer to the production metadata for relationships with
the module’s physical schema. You can also access object
definitions and physical structure links for attributes, facts,
and so on using MicroStrategy Architect.
3 Using the document completed in the procedure above (Map the target
data warehouse to the module’s physical schema, page 49), determine
whether each object is supported by the data elements that exist in the
target schema. Start by analyzing attributes, then facts, hierarchies, and
so on.
•
For attributes, identify the following for each form expression:

target schema columns

lookup table

other tables where the data element is present

type of relationship with other attributes
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
•
Installation and Porting Guide
relationship tables in the target schema
For facts, identify the following for each form expression:

target schema fact tables/columns and their entry level attributes
•
For hierarchies, identify constituent attributes and relationships.
•
For transformations, identify the following:
•

attribute

mapping type

member expression

target schema member table
(Optional) Use MicroStrategy Developer or Object Manager to
identify dependencies between objects. For example, for an
unsupported object, use these tools to identify other objects that
depend on it. See the MicroStrategy System Administration Guide
for details on using Object Manager.
4 For each supported object, identify changes required to map it to the
target schema.
 Note the following:
•
In some cases, schema objects might be partially mapped. For
example, the target schema might support only one of two
attribute forms or a relationship between two attributes might
not exist in the target schema.
•
Schema objects without matching data elements cannot be
implemented.
Example: Mapping the logical data model
The tables below present examples of a document that reflects the results of
mapping a module’s logical data model to a target data warehouse. This
example includes a list of logical data model objects that are supported in the
sample target schema, as well as actions required for mapping. The Sales
Force Analysis Module (SFAM) is used in this example.
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Attributes
This example table identifies the attributes that can be mapped to the sample
target data warehouse and the actions required to map them.
Attributes
Exists in target
data warehouse?
Account
Yes
• Can be mapped to L_CUSTOMER. Attribute name and
hierarchy name are now “Customer.”
• Different table and column names for the attribute forms;
requires updating attribute form expression to use new columns
(ID: CUSTOMER_ID, DESC: CUSTOMER_NAME) and new
tables (L_CUSTOMER and F_SALES_ORDER).
Company
Yes
• Can be mapped to Customer Group in the L_CUSTOMER
table.
• Requires updating attribute name to Customer Group and form
expressions (ID: CUST_GROUP_ID, DESC:
CUST_GROUP_NAME).
Industry
Yes
• Can be mapped to Industry in the L_CUSTOMER table.
• Requires updating attribute form expression (ID:
INDUSTRY_ID, DESC: INDUSTRY_NAME).
Sales
Representative
Yes
• Can be mapped to Sales Person in the L_SALES_PERSON
table, but relationships with Sales District and Sales Region do
not exist.
• Requires updating name to Sales Person, form expressions
(ID: SALES_PERSON_ID, DESC: SALES_PERSON_NAME),
tables (L_SALES_PERSON and F_SALES_ORDER), and
delete relationships.
Sales District
Yes
• This attribute can be mapped to Sales Office in the
L_SALES_OFFICE table, but relationships with Sales District
and Sales Region do not exist.
• Requires updating name to Sales Office, form expressions (ID:
SALES_PERSON_ID, DESC: SALES_PERSON_NAME),
tables (L_SALES_OFFICE and F_SALES_ORDER), and
delete relationships.
Sales Region
No
None. Data element does not exist in the target data warehouse.
Product
Yes
• Can be mapped to Product in L_PRODUCT.
• Requires updating form expressions to new tables
(L_PRODUCT and F_SALES_ORDER) and columns (ID:
PRODUCT_ID, DESC: PRODUCT_DESC).
Product Group
Yes
• Can be mapped to Product Group in L_PRODUCT.
• Requires updating form expressions to new table
(L_PRODUCT) and columns (ID: PRODUCT_GROUP_ID,
DESC: PRODUCT_GROUP_DESC).
Lead attributes
No
None of them exist in the target schema; eliminate them from the
MicroStrategy project.
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Attributes
Exists in target
data warehouse?
Installation and Porting Guide
Action
Opportunity
attributes
No
None of them exist in the target schema; eliminate them from the
MicroStrategy project.
Order
Yes
• Can be mapped to Order in the F_SALES_ORDER table.
There is not a lookup table but the fact table can be used
instead.
• Requires updating form expression to use new table
(F_SALES_ORDER) and column (ID: ORDER_ID).
Discount
Indicator
No
Eliminate it from the MicroStrategy project.
Date
Yes
• Can be mapped to Calendar Date in L_TIME.
• Requires updating form expressions to new tables (L_TIME
and F_SALES_ORDER) and column (ID: CALENDAR_DATE;
DATE_ID in fact table.)
Month
Yes
• Can be mapped to Calendar Month in L_TIME.
• However, target schema does not include Previous Month for
historical analysis. Table L_CAL_MONTH from module’s
schema will be added to target data warehouse to support this
type of analysis.
• Requires updating form expression to use new tables (L_TIME
and L_CAL_MONTH) and form expressions.
Quarter
Yes
• Can be mapped to Calendar Quarter in L_TIME.
• However, target schema does not include Previous Year for
historical analysis. Table L_CAL_QTR from module’s schema
will be added to target data warehouse to support this type of
analysis.
• Requires updating form expression to use new tables (L_TIME
and L_CAL_QTR) and form expressions.
Year
Yes
• Can be mapped to Calendar Year in L_TIME.
• However, target schema does not include Previous Year for
historical analysis. Table L_CAL_YEAR from module’s schema
will be added to target data warehouse to support this type of
analysis.
• Requires updating form expression to use new tables (L_TIME
and L_CAL_YEAR) and form expressions.
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Facts
The example table below identifies the facts that can be mapped to the
sample target data warehouse schema and the actions required to map them.
Facts
Exists in the target
data warehouse?
Action
Lead Size
No
None. Not supported in the target schema.
Opportunity Size
No
None. Not supported in the target schema.
Weighted Opportunity
Size
No
None. Not supported in the target schema.
Order Amount
Yes
Map to NET_LINE_AMOUNT in F_SALES _ORDER.
Sales Representative
Quota
No
For this example this concept will be added by creating
the table F_SALES_REP_QTA in the target schema.
Index for Lead Counts
No
None. Not supported in the target schema.
Index for Opportunity
Counts
No
None. Not supported in the target schema.
Index for Order Counts Yes
Requires updating to point to ORDER_ID column in
F_SALES_ORDER table.
Hierarchies and transformations
The example table below identifies actions for other schema objects.
Other schema objects
Exists in the target
data warehouse?
Action/Comments
Hierarchies
Time - Year to
Month
Yes
All attributes exist in the target schema.
Sales
Organization
No
Update it to eliminate Sales
Representative relationships with District
and Region.
Product
Yes
All attributes exist in the target schema.
Time
Yes
All attributes exist in the target schema.
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Other schema objects
Exists in the target
data warehouse?
Transformations
No
Previous Month
Action/Comments
The table L_CAL_MNTH will be added to
target schema to support this time
transformation.
Previous Quarter No
The table L_CAL_QTR will be added to
target schema to support this time
transformation.
Previous Year
The table L_CAL_YEAR will be added to
target schema to support this time
transformation.
No
Scenarios: Mapping the logical data model
The table below presents some typical scenarios you can encounter while
completing the logical data model mapping procedure above.
Object
Scenario
Action
Attribute
Different name
Update the name. All dependents read the change
automatically.
Not available
Remove after resolving dependencies.
Additional forms
Add forms if relevant to analysis and update the
display tab where appropriate.
Missing forms: ID or DESC
If no substitution is possible, consider eliminating the
attribute.
Remove the form and update the display as
appropriate.
Split columns for forms
Create an additional form expression.
Merged columns for forms
Remove the form expression or modify the form
expression.
Changed relationship with
parent or child
Update the parent-child relationship, hierarchies, and
dimensional metrics.
Additional parent or child
Add to relationships and update the hierarchy.
Missing parent or child
Update attribute relationships appropriately and modify
any relevant hierarchies.
Change in relate table
Choose a new relate table for the parent or child
attribute.
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Object
Scenario
Action
Fact
Different name
Update the name. All dependents read the change
automatically.
Not available
Remove after resolving dependencies.
Different column name
Update the fact.
Expression derived with
multiple columns
Update the fact expression and base formulas or
metrics.
Additional expressions:
• Information is at same
level
• Information is at different
level
Hierarchy
5
• Update the fact expression. No changes to the base
formulas or metrics are required.
• Update fact expressions to use the ApplySimple
function and update base formulas or metrics to use
ApplyAgg functions in the platform. Another option is
to create separate metrics and update dependent
application objects appropriately.
Missing expression
If there is only one expression, the fact is no longer
supported. In the case of multiple expressions, identify
metrics/reports that use the missing expression and
eliminate those. Checking on the report SQL may be
required.
Change in column data type
Update the warehouse catalog to read in new data
types.
Fact available in partitioned
tables
Add a partition mapping table and update the fact
appropriately.
Fact available in aggregates
Add aggregates to the list of other tables.
Fact available but at different
entry levels
Create additional expressions if appropriate. Update
related metrics and base formulas.
Table for dummy fact has
different keys
Update the fact and change the base formula/metric
appropriately.
Different name
Update the name. All dependents read the change
automatically.
Not available
Eliminate the hierarchy after resolving dependencies.
Metrics using the hierarchy for dimensionality change
in meaning and filters/prompts using the hierarchy are
removed.
Additional attribute
Add an attribute if it is a parent or child of other
attributes in the hierarchy. The attribute should also be
added if it has relationships (parent and/or child) and
meaning similar to those of other attributes in the
hierarchy.
Missing attribute
Update the hierarchy.
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Object
Scenario
Transformation Different name
Installation and Porting Guide
Action
Update the name. All dependents read the change
automatically.
Not available
Eliminate the transformation after resolving
dependencies. Any metrics using the transformation
are no longer supported.
Same mapping type not
available
Remove the transformation after resolving dependent
metrics.
New name for member
expression or table
Update the transformation.
Identify application objects
Using the results from the two procedures above, this third gap analysis
procedure helps you identify the Analysis Module’s components that can be
implemented with your target data warehouse. Components include
application objects (located by default in the Public Objects folder) such as
reports, metrics, filters, and so on. Results of this procedure include a list of
supported components, unsupported components, and those that require
changes to be supported.
An example follows this procedure, and a list of typical scenarios and actions
follows the example.
To identify application objects
1 Prepare a document to record the results of your analysis in this
procedure. The document will need space for a list of supported
application objects, unsupported objects, and objects requiring changes,
as well as space for the actions required.
documents (one for base formulas and metrics, one for
 Example
prompts, one for filters, and one for reports) can be found at the
end of this procedure; see Example: Identifying application
objects, page 63.
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2 Using the results from the procedure above (Map the module’s logical
data model to the target schema, page 54), identify the dependencies
between logical data model objects and application objects.
 Note the following:
•
See the appropriate Analysis Module’s reference guide for a list
of reports, metrics, and so on. You can use the production
metadata to determine dependencies between different
objects.
•
You can use also MicroStrategy Developer or Object Manager
to complete this task. Object Manager provides enhanced
functionality to identify child-parent and parent-child
dependencies.
3 Determine which application objects can be implemented using the target
data warehouse schema. Document the results of this analysis and be
sure to include:
•
A list of supported application objects
•
A list of application objects that require changes and how to
implement those changes
•
A list of application objects not supported
can complete the analysis either at the report level or at a
 You
more detailed level by taking into account the attributes, metrics,
filters, and custom groups that make up a report.
Example: Identifying application objects
The tables below present examples of analysis results from the procedure
above. They reflect the results of mapping a module’s application objects to a
target data warehouse. This example includes a list of application objects
that are supported in the target schema, as well as unsupported objects and
objects that require changes to be supported. In this example, the Sales Force
Analysis Module (SFAM) is used; only two of the analysis areas within SFAM
are supported by the sample target schema.
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Base formulas and metrics
This example table identifies the base formulas and metrics that can be
reused with the sample target data warehouse, and any actions required to
use them.
Base formulas and metrics
Base
Sum of Lead Size
Formulas
Can be implemented
in target warehouse?
Action/Comments
No
Underlying fact is not present in the target
schema.
Sum of
Opportunity Size
No
Underlying fact is not present in the target
schema.
Sum of Weighted
Opportunity Size
No
Underlying fact is not present in the target
schema.
Sum of Sales
Representative
Quota
Yes
For this example, the fact containing quota
data was added to the target data warehouse.
Sum of Order
Amount
Yes
A data element supporting this base formula
exists in the target data warehouse.
Count of Leads
No
Underlying fact is not present in the target
schema.
Count of
Opportunities
No
Underlying fact is not present in the target
schema.
Count of Orders
Yes
A data element supporting this base formula
exists in the target data warehouse. Update
the definition to point to the correct column for
counts.
Count of Sales
Representatives
Yes
A data element supporting this base formula
exists in the target data warehouse. Update
the definition to point to the correct column for
counts.
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Base formulas and metrics
Can be implemented
in target warehouse?
Metrics
All Lead Analysis
metrics
No
These metrics are not supported in the target
data warehouse.
All Pipeline
Analysis metrics
No
These metrics are not supported in the target
data warehouse.
Revenue
Yes
An underlying data element defining this
metric is present in the target data warehouse.
Revenue (Region
Filtered)
No
Sales Region data element does not exist in
the target data warehouse.
Action/Comments
Revenue
Yes
(Previous Quarter)
Underlying data elements defining this metric
are present in the target data warehouse.
% Contribution to
Total Revenues
No
Sales Region data element does not exist in
the target data warehouse.
% Revenue
Change vs.
Previous Quarter
Yes
Underlying data elements defining this metric
are present in the target data warehouse.
Number of Sales
Representatives
Yes
An underlying data element defining this
metric is present in the target data warehouse.
Revenue by Sales
Representative
Yes
Underlying data elements defining this metric
are present in the target data warehouse.
Target Quota
Yes
Quota data was added to the target data
warehouse.
% Quota Achieved Yes
All data elements that define this metric are
present in the target data warehouse (quota
data was added to the existing schema).
Opportunity Size
No
Data elements that define this metric are not
present in the target data warehouse.
% Conversion vs.
Opportunity Size
No
Data elements that define this metric are not
present in the target data warehouse.
Weighted
Opportunity Size
No
Data elements that define this metric are not
present in the target data warehouse.
Orders
Yes
An underlying data element defining this
metric is present in the target data warehouse.
Opportunities
No
A data element that defines this metric is not
present in the target data warehouse.
% Opportunities
Converted
No
Data elements that define this metric are not
present in the target data warehouse.
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Base formulas and metrics
Metrics
(cont.)
Installation and Porting Guide
Can be implemented
in target warehouse?
Action/Comments
Target Quota
Yes
(Previous Quarter)
All data elements that define this metric are
present in the target data warehouse (quota
data was added to the existing schema).
% Quota Achieved
(Previous Quarter)
All data elements that define this metric are
present in the target data warehouse (quota
data was added to the existing schema).
Wins vs.
Competitors
No
Data elements that define this metric are not
present in the target data warehouse.
% Wins vs.
Competitors
No
Data elements that define this metric are not
present in the target data warehouse.
Lost Opportunities
vs. Competitor
No
Data elements that define this metric are not
present in the target data warehouse.
% Lost vs.
Competitor
No
Data elements that define this metric are not
present in the target data warehouse.
Deals
No
A data element that defines this metric is not
present in the target data warehouse.
Deal Size
No
A data element that defines this metric is not
present in the target data warehouse.
Lost Deal Size
No
Data elements that define this metric are not
present in the target data warehouse.
Orders containing
Product
Yes
Underlying data elements defining this metric
are present in the target data warehouse.
Revenue
Yes
Underlying data elements defining this metric
are present in the target data warehouse.
Average Product
Yes
Revenue by Order
Underlying data elements defining this metric
are present in the target data warehouse.
Revenue
Yes
(Previous Quarter)
Underlying data elements defining this metric
are present in the target data warehouse.
Revenue (Product
Group)
Yes
Underlying data elements defining this metric
are present in the target data warehouse.
% Contribution to
Product Group
Revenue
Yes
Underlying data elements defining this metric
are present in the target data warehouse.
Revenue (Sales
District, Quarter)
Yes
Underlying data elements defining this metric
are present in the target data warehouse.
% Contribution to
Quarter Sales
District Revenue
Yes
Underlying data elements defining this metric
are present in the target data warehouse.
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Prompts
The example table below identifies prompts that can be reused with the
sample target data warehouse, and any actions required to use them.
Prompts
Can be implemented in
target warehouse?
Account List
Yes
Attribute is supported in the target warehouse
(Customer).
Company List
Yes
Attribute is supported in the target warehouse
(Customer Group).
Action/Comments
Lead Characteristics No
Attribute does not exist in the target data warehouse.
Lead Source Multiple selections
No
Attribute does not exist in the target data warehouse.
Product Group Multiple selections
Yes
Attribute is supported in the target data warehouse.
Quarter - Multiple
selections
Yes
Attribute is supported in the target data warehouse.
Quarter - One
selection only
Yes
Attribute is supported in the target data warehouse.
Sales District Multiple selections
Yes
Attribute is supported in the target data warehouse
(Sales Office).
Sales District - One
selection only
Yes
Attribute is supported in the target data warehouse
(Sales Office).
Sales Region Multiple selections
No
Attribute does not exist in the target data warehouse.
Sales Region - One
selection only
No
Attribute does not exist in the target data warehouse.
Time - Year to
Month hierarchy
Yes
Hierarchy is supported in the target data warehouse.
Year - One selection
only
Yes
Attribute is supported in the target data warehouse.
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Filters
The example table below identifies the filters that can be reused with the
sample target data warehouse, and any changes required.
Can be implemented
in target warehouse?
Filters
Action/Comments
Rank of Top 10 Companies by
Deal Size
Yes
If updated to use Revenue instead of Deal
Size.
Rank of Top Deals by Deal
Size
Yes
If updated to use Revenue instead of Deal
Size.
Rank of Top 10 Lost
Opportunities by Lost Deal
Size
No
Opportunity data is not present in the target
data warehouse.
Rank of Top 3 Deals by Deal
Size, break by Primary
Competitor
No
Opportunity data is not present in the target
data warehouse.
Rank of Top 3 Deals by Deal
Size, break by Sales Region
Yes
If updated to use Revenue instead of Deal
Size and another sales organization attribute
instead of Sales Region.
Rank of Top Lost
Opportunities by Lost Deal
Size, break by ...
No
Opportunity data is not present in the target
data warehouse.
Rank Top N Products by
Revenue
Yes
All objects required for this report are
supported by the target data warehouse.
All filters in folder: “Filters for
conditional metrics”
No
Data elements defining these filters are not
present in the target data warehouse.
Reports
The example table below identifies the reports that can be reused with the
sample target data warehouse, and any changes required. In this example, 11
out of the 28 reports in the Sales Performance and Product Sales analysis
areas are supported out of the box (39% initial set), with 9 additional reports
requiring simple modifications (totaling 71% of the initial set).
Reports
Can be implemented in
target warehouse?
Action/Comments
All reports: Lead
Analysis folder
No
These reports are not supported in the target data
warehouse.
All reports: Pipeline
Analysis folder
No
These reports are not supported in the target data
warehouse.
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Can be implemented in
target warehouse?
Action/Comments
Summary Sales
Performance by Sales
Region & Quarter
Yes
If the Opportunity metrics are removed from the
report it can be reused.
Revenue Distribution by
Sales Region
Yes
If Sales Region is replaced by another sales
organization attribute, the report can be reused.
Quarterly Revenue
Trend by Sales Region
Yes
If Sales Region is replaced by another sales
organization attribute, the report can be reused.
Quarterly Revenue &
Closure Trend
No
Required data elements do not exist in the target
data warehouse.
Quarterly Revenue &
Closure Trend by Sales
Region
Yes
If Sales Region is replaced by another sales
organization attribute and all Opportunity metrics
are removed, the report can be reused.
Quarterly Quota
Performance by Sales
District
Yes
In this example, a fact with quota performance
data will be added to the target data warehouse to
support this type of analysis.
Quota Achievement by
Quarter & Sales
Organization
Yes
If Sales Region is replaced by another sales
organization attribute and all Opportunity metrics
are removed, the report can be reused.
Competitive Activity by
Quarter
No
Required data elements (Primary Competitor) do
not exist in the target data warehouse.
Quarterly Competitive
Win Rate Trend
No
Required data elements (Primary Competitor) do
not exist in the target data warehouse.
Top 10 Companies by
Deal Size - Quarter
Yes
If report is modified to rank attributes by Revenue,
implying changing metrics and rank filter.
Top 10 Deals by Quarter
Yes
If report is modified to rank attributes by Revenue,
implying changing metrics and rank filter.
Top 3 Deals by Quarter
& Primary Competitor
No
Required data elements (Primary Competitor) do
not exist in the target data warehouse.
Top 3 Deals by Quarter
& Sales Region
Yes
If report is modified to rank attributes by Revenue,
implying changing metrics and rank filter, and
Sales Region replaced by any other sales
organization attribute.
Top 10 Lost
No
Opportunities by Quarter
Required data elements (Opportunity data) do not
exist in the target data warehouse.
Top 3 Lost Opportunities
by Quarter & Primary
Competitor
No
Required data elements (Opportunity data) do not
exist in the target data warehouse.
Top 3 Lost Opportunities
by Quarter & Sales
Region
No
Required data elements (Opportunity data) do not
exist in the target data warehouse.
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Reports
Can be implemented in
target warehouse?
Installation and Porting Guide
Action/Comments
Product Performance
Summary by Quarter
Yes
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Products with Increasing
Sales by Sales District
Yes
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Products with
Decreasing Sales by
Sales District
Yes
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Products with Increasing
Quarterly Sales
Yes
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Products with
Decreasing Quarterly
Sales
Yes
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Top N Products Sold Quarter Revenue Trend
Yes
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Top N Products Sold by
Sales District
Yes
If Sales Region is removed from the template and
prompt.
Quarter Product
Contribution to Sales
District Revenues
Yes
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Quarterly Orders Trend
by Product Group
Yes
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Product Revenue Trend
Yes
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Revenue Distribution by Yes
Product Group & Quarter
Data elements for the attributes and metrics (facts)
exist in the target data warehouse.
Quarterly Closure Rate
based on Product
Required data elements (Opportunity data) do not
exist in the target data warehouse.
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No
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Scenarios: Identifying application objects
The table below presents some typical scenarios you can encounter while
completing the application object mapping procedure above. The scenarios
are listed by object type.
Object
Scenario
Action
Metrics /
Base
Formula
Different name
Update the name. All dependents read the change
automatically.
Not available
Delete after resolving dependencies. Filters and custom groups
that use such a metric also have to be removed.
Fact used is not
available
Delete the base formula and its dependent metrics after
resolving dependencies.
Table used for counts
has different keys
Check the Count Distinct and Fact ID parameters.
Attribute or hierarchy
used for
dimensionality is not
available
Evaluate whether the metric is meaningful without the
dimensionality. If it is, change the name and use it only on
appropriate reports.
Filter used for
condition is not
available
Evaluate whether the metric is meaningful without the condition.
If it is, change the name and use it only on appropriate reports.
Transformation is not
available
Delete the metric after resolving dependencies, because the
metric meaning is completely changed.
Compound metric:
Constituent metric is
not available
Evaluate whether the remaining constituent metric combination
is useful. If it is, change the name and use it only on appropriate
reports.
Different name
Update the name. All dependents read the change
automatically.
One or more
embedded filters not
supported
Evaluate whether the remaining custom grouping is worth using
on the report.
Embedded filters not
supported
Eliminate the custom group after resolving dependencies.
Custom
Groups
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Object
Scenario
Action
Prompts
Different name
Update the name. All dependents read the change
automatically.
Embedded attribute or Delete the prompt and dependent filter after resolving
hierarchy not
dependencies.
available
Filters
For object prompts,
one or more of the
constituent list not
available
Evaluate whether the remaining list of objects is worth using in a
report. If a single object remains, place it on the report and
eliminate the object prompt.
Different name
Update the name. All dependents read the change
automatically.
Attribute Qualification: Eliminate the filter after resolving dependencies.
Attribute not
supported
Reports
Metric qualification:
Metric not supported
Eliminate the filter after resolving dependencies.
Relationship filters:
Output level, filter
qualification, or relate
by fact/table not
supported
Eliminate the filter after resolving dependencies.
Different name
Update the name.
Analysis not
supported
Eliminate related reports, or move to a separate folder if there
are plans for support at a later stage of the deployment.
Attribute on template
not available
Remove the report unless a suitable substitution can be found in
the target data model.
Metric on template not
available
Remove the metric and keep the report.
Filter or parts of it not
supported
If the entire filter is not supported:
• With relationship type set filters, the report may have to be
removed.
• With other filters, a substitution should be considered for
limiting the result set.
If part of a filter is not supported, a substitution should be
considered but may not be essential.
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Identify additional requirements
This procedure is optional.
The three gap analysis procedures performed above can include identifying
data elements from the target data warehouse that can be added to the
application metadata. This optional procedure is not described in this
methodology. For a list of typical scenarios to customize and extend the
Analytics Modules, see Customize and extend the Analytics Modules,
page 34.
Map the module
After you perform a gap analysis as described previously, you can map the
schema objects (attributes, facts, and so on) to the new target schema and
update application objects (reports, metrics, filters, and so on) to ensure they
work against the new schema.
You can choose from the following methods for the mapping, depending on
whether you want to use MicroStrategy Object Manager:
•
With Object Manager: Using MicroStrategy Architect and Developer,
duplicate the packaged project and map the schema objects. Then move
the application objects to the mapped project using MicroStrategy Object
Manager. If you decide to use Object Manager, see Map the module with
Object Manager, page 74.
•
Without Object Manager: Using MicroStrategy Architect and Developer,
make all changes in the original project. First update all application
objects to use only those schema objects supported in the target data
warehouse, and then map schema object links to use the new physical
schema. If you decide not to use Object Manager, see Map the module
without Object Manager, page 90.
Mapping with Object Manager is more efficient because it allows you to
implement only those portions of an Analysis Module that are relevant to
your company’s requirements.
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Mapping prerequisites
Before you map the Analysis Module, make sure you have access to the
following:
Experience creating analytical applications with MicroStrategy Developer and
MicroStrategy Architect.
Skills
The results from your gap analysis, performed above.
MicroStrategy Architect, to modify attribute and fact definitions so they use the tables and
columns from the target data warehouse.
MicroStrategy Developer, to modify definitions for the reports, metrics, filters, and so on
that may be impacted by the missing attributes and facts in the target data warehouse.
Tools
The appropriate MicroStrategy Analysis Module’s reference guide.
(Optional) MicroStrategy Object Manager, to move objects (such as reports and metrics)
between projects.
Map the module with Object Manager
If you decide to use MicroStrategy Object Manager to assist you in the
mapping part of the porting process, you will perform the following
procedures:
•
Use MicroStrategy Architect and Developer to duplicate the packaged
project and map schema objects.
•
Use MicroStrategy Object Manager to move application objects to the
mapped project.
If you decide not to use Object Manager, see Map the module without Object
Manager, page 90.
Overview: Mapping with Object Manager
This mapping method uses two MicroStrategy projects:
•
Master project: The original project for the Analytics Modules
•
Destination project: The project that will be mapped to the target
schema; created by duplicating the master project
Complete both mapping procedures below in the order they appear.
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Map schema objects
This first procedure uses MicroStrategy Architect to map schema objects in
the destination project to the target schema, based on results from your gap
analysis at the beginning of this chapter. For details on the gap analysis, see
Perform a gap analysis, page 48.
An example follows this procedure.
To map schema objects
1 Using MicroStrategy Architect, duplicate the project for the
MicroStrategy Analysis Module you are interested in. Use the project
provided in the production metadata, and duplicate it to a new location.
Use the option Duplicate only schema objects.
all actions taken during this mapping process to allow
 Document
you to backtrack if necessary.
2 Update the data warehouse catalog to point to the target schema, adding
relevant tables from the target schema to the destination project.
3 Map hierarchies based on the results from your gap analysis:
•
If a hierarchy exists in the destination project, proceed with mapping.
Actions can include changing the name, removing or adding
attributes, and so on.
•
If a hierarchy does not exist, remove it from the project.
can only be removed from the MicroStrategy metadata
 Objects
if no dependents exist in the project. The order for removing
objects throughout this procedure is determined based on that
principle.
4 Map transformations based on the results from your gap analysis:
•
If a transformation exists in the destination project, proceed with
mapping. Actions can include changing the name, updating the
definition to the target schema tables and columns, and so on.
•
If a transformation does not exist, remove it from the project.
5 Map facts based on the results from your gap analysis:
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•
If a fact exists in the destination project, proceed with mapping.
Actions can include changing the fact name, updating fact expressions
to point to the target schema tables and columns, removing or adding
fact expressions, and so on.
•
If a fact does not exist, remove it from the project.
6 Map attributes based on the results from your gap analysis:
•
Start with the lowest attributes in each hierarchy, and proceed
sequentially with all parents.
•
If an attribute exists in the destination project, proceed with mapping.
Actions can include changing the attribute name; updating forms to
point to the target schema tables and columns; adding or removing
forms; updating, removing or adding relationships; and so on.
•
If an attribute does not exist, remove it from the project.
7 Update the project schema using the default options to make your
changes available. Then follow the final mapping procedure, Update
application objects with Object Manager, page 79, to move all
application objects to the destination project.
Example: Mapping schema objects
This example summarizes the actions taken to map packaged schema objects
from the MicroStrategy Sales Force Analysis Module (SFAM) to the example
target data warehouse.
1 To update the connection to the target schema, begin by defining the
database connection to the target data warehouse.
example, some tables from the default physical schema are
 Inused,thissuch
as F_SALES_REP_QTA. Tables must be created before
this step is taken; the Erwin file containing the default physical
schema can be used to generate scripts for these tables.
2 Update the warehouse catalog, selecting the relevant tables based on the
gap analysis between physical schemas. For more details, see Example:
Mapping physical schemas, page 52.
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3 Map all schema objects in the destination project.
•
Hierarchies: The following table summarizes the example actions
taken to map SFAM hierarchies to the target data warehouse.
Order
Packaged
Object
Action
1
Time - Year to
Month
No changes are required.
2
Product
No changes are required.
3
Time
No changes are required.
4
Sales
Organization
Delete Sales District and Sales Region from
this hierarchy.
•
Transformations: No changes are required for transformations
because all base attributes and tables are the same.
•
Facts: The following table summarizes the example actions taken to
map the SFAM facts to the target data warehouse.
Order
Packaged
Object
1
Order Amount
Proceed with changes based on the gap
analysis; see Example: Mapping the logical
data model, page 56 for details.
2
Sales
Representative
Quota
Proceed with changes based on the gap
analysis; see Example: Mapping the logical
data model, page 56 for details.
3
Index for Order
Counts
Proceed with changes based on the gap
analysis; see Example: Mapping the logical
data model, page 56 for details.
4
Opportunity
Size
Delete the fact from the MicroStrategy
project.
5
Weighted
Opportunity
Size
Delete the fact from the MicroStrategy
project.
6
Lead Size
Delete the fact from the MicroStrategy
project.
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Packaged
Object
Action
7
Index for Lead
Counts
Delete the fact from the MicroStrategy
project.
8
Index for
Opportunity
Counts
Delete the fact from the MicroStrategy
project.
Order
•
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Attributes: The following table summarizes the example actions taken
to map SFAM attributes to the target data warehouse.
of the folders require name changes based on new attribute
 Some
names. For example, change the name of the Account folder to
Customer.
Order
Packaged
Object
1
Account
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
2
Company
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
3
Industry
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
4
Lead
Delete the attribute from the MicroStrategy project.
5
Lead Source
Delete the attribute from the MicroStrategy project.
6
Lead Status
Delete the attribute from the MicroStrategy project.
7
Lead Type
Delete the attribute from the MicroStrategy project.
8
Opportunity
Delete the attribute from the MicroStrategy project.
9
Current
Opportunity
Status
Delete the attribute from the MicroStrategy project.
10
Opportunity
Open Date
Delete the attribute from the MicroStrategy project.
11
Opportunity
Close Date
Delete the attribute from the MicroStrategy project.
12
Primary
Competitor
Delete the attribute from the MicroStrategy project.
13
Opportunity
Status
Delete the attribute from the MicroStrategy project.
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5
Order
Packaged
Object
14
Order
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
15
Discount
Indicator
Delete the attribute from the MicroStrategy project.
16
Product
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
17
Product Group
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
18
Sales
Representative
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
19
Sales District
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
20
Sales Region
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
21
Date
Delete the attribute from the MicroStrategy project.
22
Month
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
23
Quarter
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
24
Year
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
Action
4 Finally, after all schema objects have been mapped, remove all tables that
are not used. The project schema must be updated to reflect all of the
changes.
Update application objects with Object Manager
This second procedure uses MicroStrategy Object Manager to move all
application objects that can be implemented to the destination project, based
on the gap analysis completed earlier in this chapter.
The schema objects represent the building blocks on which application
objects are defined. Once schema object definitions are updated to use the
target schema, using the procedure above, application objects can be moved
to the destination project.
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Application objects must be moved in a certain order, based on which
application objects are used as building blocks for other application objects.
For example, to move a report with its attributes, metrics, and so on, the
defining report must be present in the destination project. The procedure
below will prompt you to move application objects in the appropriate order,
based on specific considerations. The example following this procedure
contains additional specifics on the appropriate order for moving objects.
 Be aware of the following:
•
Before you move any application objects, you must check on the
components (schema and application objects) in the master
project and determine whether these exist in the destination
project. During the procedure below, use MicroStrategy Object
Manager child-parent dependencies and its Search functionality to
identify object components for a given object in the master project
and determine whether all components exist in the destination
project.
•
Document in detail all actions taken during this procedure.
To update application objects with Object Manager
1 Move prompts and filters, based on the results from the gap analysis,
proceeding first with prompts and then filters.
•
If all components of the object exist in the destination project, move
the object to the destination project using the option Use existing
definition in the destination project. If a name change is required,
do that in the destination project.
•
If all components are not present, do one of the following:
– Move the necessary components to update the definition in the
master project and then move the object.
– Skip the object and update the definition to remove any
unavailable components.
 Note the following:
•
80 Map the module
Metrics can use filters in their definition, and filters can use
metrics. In this case, filters are considered before metrics
because the Analytics Modules minimize the use of metrics
qualification within filters, while many metrics include filters
in their qualifications. If a filter requires a metric, the metric
must be moved first.
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Filters qualifying on attribute or metric values must be updated
after the filter is moved to the destination project. They also
must use suitable new values in the existing data warehouse.
For example, a filter like Lost Deals qualifies Opportunities
when Opportunity Status=“Lost,” but if the value in the target
data warehouse is “Loss,” the filter must be updated
accordingly.
2 Move base formulas, based on the results from the gap analysis.
•
If all facts or attributes defining the base formula exist in the
destination project, move the object to the destination project using
the option Use existing definition in the destination project. If a
name change is required, do that in the destination project.
•
If all components are not present, do one of the following:
– Move the necessary components to update the definition in the
master project and then move the object.
– Skip the object and update the definition to remove any
unavailable components.
3 Move metrics, based on the results from the gap analysis. Start with
simple metrics, and then proceed with conditional, transformation,
dimensional, and compound metrics. This order is important because, for
example, compound metrics can be defined based on other metrics.
•
If all components of the object exist in the destination project, move
the object to the destination project using the option Use existing
definition in the destination project. If a name change is required,
do that in the destination project.
•
If all components are not present, do one of the following:
– Move the necessary components to update the definition in the
master project and then move the object.
– Skip the object and update the definition to remove any
unavailable components.
some components such as dimensionality or
 Changing
conditionality can have some unexpected results in the reports.
4 Move reports, based on the results from the gap analysis.
•
If all components (attributes, metrics, prompts, filters, and so on)
exist in the destination project, move the object to the destination
project using the option Use existing definition in the destination
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project. If a name change is required, do that in the destination
project.
•
If all components are not present, do one of the following:
– Move the necessary components to update the definition in the
master project and then move the object.
– Skip the object and update the definition to remove any
unavailable components.
5 Execute all reports to test the application objects and ensure that the
results of your work in this procedure are correct.
Example: Updating application objects with Object Manager
This example summarizes the actions taken to implement application objects
from the MicroStrategy Sales Force Analysis Module (SFAM) against the
example target data warehouse, based on the gap analysis. For details on the
gap analysis, see Example: Identifying application objects, page 63.
1 Prompts: The following table shows the example actions taken to move
SFAM prompts to the destination project.
Order Packaged Object
Action
1
This prompt can be moved because all objects defining this object are
present in the destination project.
Account List
Change the name to Customer List.
2
Company List
This prompt can be moved because all objects defining this object are
present in the destination project.
Change the name to Customer Group Account.
3
Lead Characteristics
No action; not supported in the target data warehouse. For details, see
Example: Identifying application objects, page 63
4
Lead Source Multiple Selections
No action; not supported in the target data warehouse. For details, see
Example: Identifying application objects, page 63.
5
Product Group Multiple Selections
This prompt can be moved because all objects defining this object are
present in the destination project.
6
Quarter - Multiple
Selections
This prompt can be moved because all objects defining this object are
present in the destination project.
7
Quarter - One
Selection Only
This prompt can be moved because all objects defining this object are
present in the destination project.
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Order Packaged Object
Action
8
This prompt can be moved because all objects defining this object are
present in the destination project.
Sales District Multiple Selections
5
Change the name to use Sales Office.
9
Sales District - One
Selection Only
This prompt can be moved because all objects defining this object are
present in the destination project.
Change the name to Sales Office.
10
Sales Region Multiple Selections
No action; not supported in the target data warehouse. For details, see
Example: Identifying application objects, page 63.
11
Sales Region - One
Selection Only
No action; not supported in the target data warehouse. For details, see
Example: Identifying application objects, page 63.
12
Time - Year to Month
Hierarchy
This prompt can be moved because all objects defining this object are
present in the destination project.
13
Year - One Selection
Only
This prompt can be moved because all objects defining this object are
present in the destination project.
2 Filters: The following table shows the example actions taken to move
SFAM filters to the destination project.
Order Packaged Object
Action
1
Note: This filter requires that the metric is present in the destination
project first, so move the Revenue metric before proceeding with this
filter.
Rank of Top 10
Companies by Deal Size
Proceed based on the gap analysis (see Example: Identifying
application objects, page 63). Modify the filter in the source project to
use the Revenue metric instead of Deal Size. Then move it and
change the name.
2
Rank of Top Deals by
Deal Size
Note: This filter requires that the metric is present in the destination
project first, so move the Revenue metric before proceeding with this
filter.
Proceed based on the gap analysis (see Example: Identifying
application objects, page 63). Modify the filter in the source project to
use the Revenue metric instead of Deal Size. Then move it and
change the name.
3
Rank of Top 10 Lost
Opportunities by Lost
Deal Size
No action; not supported in the target data warehouse. For details,
see Example: Identifying application objects, page 63.
4
Rank of Top 3 Deals by
Deal Size, break by
Primary Competitor
No action; not supported in the target data warehouse. For details,
see Example: Identifying application objects, page 63.
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Order Packaged Object
Action
5
Note: This filter requires that the metric is present in the destination
project first, so move the Revenue metric before proceeding with this
filter.
Rank of Top 3 Deals by
Deal Size, break by
Sales Region
Proceed based on the gap analysis (see Example: Identifying
application objects, page 63). Modify the filter in the source project to
use the Revenue metric instead of Deal Size, and use Sales District
instead of Sales Region. Then move it and change the name.
6
Rank of Top Lost
Opportunities by Lost
Deal Size, break by
Sales Region
No action; not supported in the target data warehouse. For details,
see Example: Identifying application objects, page 63.
7
Rank Top N Products by
Revenue
Note: This filter requires that the metric is present in the destination
project first, so move the Revenue metric before proceeding with this
filter.
All objects defining this object are present in the destination project;
move the filter.
8
All filters in Folder “Filter
for Conditional Metrics”
No action; not supported in the target data warehouse. For details,
see Example: Identifying application objects, page 63.
3 Base formulas: The following table shows the example actions taken to
move SFAM base formulas to the destination project.
Order
Packaged Object
Action
1
Sum of Lead Size
No action; not supported in the target data warehouse. For details, see
Example: Identifying application objects, page 63.
2
Sum of Opportunity
Size
No action; not supported in the target data warehouse. For details, see
Example: Identifying application objects, page 63.
3
Sum of Weighted
Opportunity Size
No action; not supported in the target data warehouse. For details, see
Example: Identifying application objects, page 63.
4
Sum of Sales
Representative Quota
This base formula can be moved because the fact used for this base
formula is present in the destination project.
5
Sum or Order Amount
This base formula can be moved because the fact used for this base
formula is present in the destination project.
6
Count of Leads
No action; not supported in the target data warehouse. For details, see
Example: Identifying application objects, page 63.
7
Count of Opportunities No action; not supported in the target data warehouse. For details, see
Example: Identifying application objects, page 63.
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Order
Packaged Object
Action
8
Count of Orders
This base formula can be moved because the fact used for this base
formula is present in the destination project.
9
Count of Sales
Representatives
This base formula can be moved because the fact used for this base
formula is present in the destination project.
5
4 Metrics: The following table shows the example actions taken to move
SFAM metrics to the destination project.
Order
Packaged Object
Action
Simple metrics
1
Revenue
The metric can be moved because the base formula was moved in the
previous step, so the base formula exists in the destination project.
2
Target Quota
The metric can be moved because the base formula was moved in the
previous step, so the base formula exists in the destination project.
3
Orders
The metric can be moved because the base formula was moved in the
previous step, so the base formula exists in the destination project.
4
Orders containing
Product
The metric can be moved because the base formula was moved in the
previous step, so the base formula exists in the destination project.
5
Opportunity Size
No action; the metric cannot be moved because the base formula
does not exist in the target data warehouse.
6
Weighted Opportunity No action; the metric cannot be moved because the base formula
Size
does not exist in the target data warehouse.
7
Opportunities
No action; the metric cannot be moved because the base formula
does not exist in the target data warehouse.
8
Number of Sales
Representatives
The metric can be moved because the attribute defining the metric
was moved in the previous step, so it exists in the destination project.
Name the updated metric to Number of Sales Persons.
Simple metrics with transformation
9
Revenue (Previous
Quarter)
The metric can be moved because both the base formula and
transformation exist in the destination project.
10
Target Quota
(Previous Quarter)
The metric can be moved because both the base formula and
transformation exist in the destination project.
Simple metrics with dimensionality
11
Revenue (Region
Filtered)
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No action; Sales Region attribute is not supported.
Note: This metric can be moved if it was updated earlier to use Sales
Office, although this may change the results in the reports that use the
metric.
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Order
Packaged Object
Action
12
Revenue (Product
Group)
The metric can be moved because both the base formula and metric
level attribute exist in the destination project.
13
Revenue (Sales
District, Quarter)
The metric can be moved because both the base formula and metric
level attributes exist in the destination project.
Name the updated metric to Revenue (Sales Office, Quarter).
Simple metrics with conditionality
14
Deals
No action; the underlying fact is not supported in the target data
warehouse.
15
Deal Size
No action; the underlying fact is not supported in the target data
warehouse.
16
Lost Deal Size
No action; the underlying fact is not supported in the target data
warehouse.
17
Opportunities with a
Competitor
No action; the underlying fact is not supported in the target data
warehouse.
18
Lost Opportunities vs.
Competitor
No action; the underlying fact is not supported in the target data
warehouse.
19
Wins vs. Competitors
No action; the underlying fact is not supported in the target data
warehouse.
Compound metrics
20
Revenue by Sales
Representative
This object can be moved because the metrics defining it were
already moved to the destination project.
Name the updated object to Revenue by Sales Person.
21
Average Product
Revenue by Order
This object can be moved because the metrics defining it were
already moved to the destination project.
22
% Opportunities
Converted
No action; the Opportunities metric does not exist in the destination
project.
23
% Wins vs.
Competitor
No action; one of the metrics is not supported because the Primary
Competitor attribute does not exist in the target data warehouse.
24
% Lost vs. Competitor No action; one of the metrics is not supported because the Primary
Competitor attribute does not exist in the target data warehouse.
25
% Quota Achieved
This object can be moved because the metrics defining it were
already moved to the destination project.
26
% Quota Achieved
(Previous Quarter)
This object can be moved because the metrics defining it were
already moved to the destination project.
27
% Revenue Change
vs. Previous Quarter
This object can be moved because the metrics defining it were
already moved to the destination project.
28
% Contribution to
Total Revenues
No action; one of the metrics is not supported in the target data
warehouse.
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Order
Packaged Object
Action
29
% Conversion vs.
Opportunity Size
No action; one of the metrics is not supported in the target data
warehouse.
30
% Conversion vs.
No action; one of the metrics is not supported in the target data
Weighted Opportunity warehouse.
Size
31
% Contribution to
Product Group
Revenue
This object can be moved because the metrics defining it were
already moved to the destination project.
32
% Contribution to
Quarter Sales District
Revenue
This object can be moved because the metrics defining it were
already moved to the destination project.
5
Update the name to % Contribution to Quarter Sales Office Revenue.
Note: Metrics used for Lead and Pipeline Analysis are not supported; no action.
5 Reports: The following table shows the example actions taken to move
SFAM reports to the destination project.
Order Packaged Object
Action
Product Sales Analysis reports
1
Product Performance
Summary by Quarter
This report can be moved because all components of this report
(attributes, metrics, and prompts) exist in the destination project.
2
Products with Increasing
Sales by Sales District
This report can be moved because all components of this report
(attributes, metrics, and prompts) exist in the destination project.
Update the name to Products with Increasing Sales by Sales Office.
3
Products with Decreasing This report can be moved because all components of this report
Sales by Sales District
(attributes, metrics, and prompts) exist in the destination project.
Update the name to Products with Decreasing Sales by Sales Office.
4
Products with Increasing
Quarterly Sales
This report can be moved because all components of this report
(attributes, metrics, and prompts) exist in the destination project.
5
Products with Decreasing This report can be moved because all components of this report
Quarterly Sales
(attributes, metrics, and prompts) exist in the destination project.
6
Top N Products Sold Quarter Revenue Trend
This report can be moved because all components of this report
(attributes, metrics, and prompts) exist in the destination project.
7
Top N Products Sold by
Sales District
The report in the master project includes a prompt that does not exist
in the destination project. Remove the Sales Region attribute and
prompt from this report, then move the report and change the name
to Top N Products Sold by Sales Office.
Note: This can be done because the report still has business value
after replacing Sales Region by Sales District (Sales Office in the
destination project).
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Order Packaged Object
Action
8
Quarter Product
Contribution to Sales
District Revenues
The report in the master project includes a prompt that does not exist
in the destination project. Remove the prompt from the master
project report, then move the report to the destination project.
9
Quarterly Orders Trend
by Product Group
This report can be moved because all components of this report
(attributes, metrics, and prompts) exist in the destination project.
10
Product Revenue Trend
This report can be moved because all components of this report
(attributes, metrics, and prompts) exist in the destination project.
11
Revenue Distribution by This report can be moved because all components of this report
Product Group & Quarter (attributes, metrics, and prompts) exist in the destination project.
12
Quarterly Closure Rate
based on Product
No action; Opportunity-related objects are not supported in the target
schema.
Sales Performance Analysis reports
13
Summary Sales
Performance by Sales
Region & Quarter
The report in the master project includes some objects that do not
exist in the destination project. Update the definition in the master
project by replacing Sales Region with Sales District, and remove
metrics related to Opportunity analysis. Then move the report and
change the name to Summary Sales Performance by Sales District &
Quarter.
Note: After testing the report, the metrics Target Quota and Quota
Achieved do not make sense in this scenario, because Sales Person
is not present. This is because the relationship between Sales
Person and Sales Office does not exist in the target schema (as
compared with packaged components where there was a hierarchy
relationship between Sales District and Sales Representative).
Remove the metrics.
14
Revenue Distribution by
Sales Region
The report in the master project includes some objects that do not
exist in the destination project. Update the definition in the master
project by replacing Sales Region with Sales District. Then move the
report and change the name.
15
Quarterly Revenue Trend
by Sales Region
The report in the master project includes some objects that do not
exist in the destination project. Update the definition in the master
project by replacing Sales Region with Sales District. Then move the
report and change the name.
16
Quarterly Revenue &
Closure Trend
No action; Opportunity-related objects are not supported in the target
schema.
17
Quarterly Revenue &
Closure Trend by Sales
Region
The report in the master project includes some objects that do not
exist in the destination project. Update the definition in the master
project by replacing Sales Region with Sales District, and removing
all Opportunity-related metrics. Then move the report and change the
name.
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Order Packaged Object
Action
18
All components required for the report exist in the destination project;
however, because the relationship between Sales Person and Sales
Office has been removed, the report meaning is changed.
Quarterly Quota
Performance by Sales
District
Replace Sales District with Sales Representative in the master
project, then move the report to the destination project and change
the name to Quarterly Quota Performance by Sales Person.
19
Quota Achievement by
Quarter & Sales
Organization
The report in the master project includes some objects that do not
exist in the destination project. Update the definition in the master
project by removing Sales Region. Then move the report and change
its name.
Note: After testing this report, the metrics Target Quota and Quota
Achieved do not make sense in this report because they are not
calculated at the Sales Office level. Remove Sales Office.
20
Competitive Activity by
Quarter
No action; Opportunity-related objects are not supported in the target
schema.
21
Quarterly Competitive
Win Rate Trend
No action; Opportunity-related objects are not supported in the target
schema.
22
Top 10 Companies by
Deal Size - Quarter
The report in the master project includes some objects that do not
exist in the destination project. Update the definition in the master
project by replacing the Deal Size metric with Revenue, and replace
the Deals metric and the Opportunity metric with Orders. (The Rank
filter was updated previously.) Then move the report and change its
name to Top 10 Customer Groups by Deal Size.
23
Top 10 Deals by Quarter
The report in the master project includes some objects that do not
exist in the destination project. Update the definition in the master
project by replacing the Deal Size metric with Revenue, and remove
all Opportunity attributes. (The Rank filter was updated previously.)
Then move the report and change its name to Top 10 Orders by
Quarter.
24
Top 3 Deals by Quarter &
Primary Competitor
No action; Opportunity-related objects are not supported in the target
schema.
25
Top 3 Deals by Quarter &
Sales Region
The report in the master project includes some objects that do not
exist in the destination project. Update the definition in the master
project by replacing the Deal Size metric with Revenue, remove all
Opportunity attributes, and replace the attribute Sales Region with
Sales District. (The Rank filter was updated previously.) Then move
the report and change its name to Top 3 Deals by Quarter and Sales
District.
26
Top 10 Lost
Opportunities by Quarter
No action; Opportunity-related objects are not supported in the target
schema.
27
Top 3 Lost Opportunities
by Quarter & Primary
Competitor
No action; Opportunity-related objects are not supported in the target
schema.
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Order Packaged Object
Action
28
No action; Opportunity-related objects are not supported in the target
schema.
Top 3 Lost Opportunities
by Quarter & Sales
Region
Note: Lead and Pipeline Analysis reports are not supported; no action.
6 Execute all reports to test the application objects and ensure that the
results of your work in this procedure are correct.
Map the module without Object Manager
If you decide not to use MicroStrategy Object Manager to assist you in the
mapping part of the porting process, you will:
•
Use MicroStrategy Architect and Developer to update all application
objects to use only those schema objects supported in the target schema
•
Map schema object links to use the new schema
In this mapping method, all changes are completed in the original project,
using Architect and Developer.
If you decide to use Object Manager, see Map the module with Object
Manager, page 74.
Update application objects without Object Manager
This first procedure updates the definition of the application objects to use
only those schema objects that are supported, based on your gap analysis
completed earlier in this chapter. For gap analysis details, see Example:
Identifying application objects, page 63.
 Be aware of the following:
90 Map the module
•
You must update application objects in a certain order, based on
which application objects are used as building blocks for other
application objects. For example, metrics must be removed from
all reports before they can be deleted from the project. An object
can be removed from the MicroStrategy metadata only if no
dependents exist in the project.
•
Document in detail all actions taken during this procedure.
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An example follows this procedure.
To update application objects without Object Manager
1 Move reports based on the results from the gap analysis:
•
If all components of the application object are supported, no change is
required.
•
If all underlying objects are not supported, remove underlying objects
from the definition.
•
If an application object is not supported, delete it from the project.
2 Move metrics based on the results from the gap analysis:
•
If all components of the application object are supported, no change is
required.
•
If all underlying objects are not supported, remove underlying objects
from the definition.
•
If an application object is not supported, delete it from the project.
3 Move base formulas based on the results from the gap analysis:
•
If all components of the application object are supported, no change is
required.
•
If all underlying objects are not supported, remove underlying objects
from the definition.
•
If an application object is not supported, delete it from the project.
4 Move filters based on the results from the gap analysis:
•
If all components of the application object are supported, no change is
required.
•
If all underlying objects are not supported, remove underlying objects
from the definition.
•
If an application object is not supported, delete it from the project.
5 Move prompts based on the results from the gap analysis:
•
If all components of the application object are supported, no change is
required.
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•
If all underlying objects are not supported, remove underlying objects
from the definition.
•
If an application object is not supported, delete it from the project.
6 Execute all reports to test the application objects and ensure that the
results of your work in this procedure are correct. Then follow the final
mapping procedure, Update application objects without Object
Manager, page 90.
Example: Updating application objects without Object
Manager
This example summarizes the actions taken to implement application objects
from the MicroStrategy Sales Force Analysis Module (SFAM) against the
example data warehouse. For details on the gap analysis, see Example:
Identifying application objects, page 63.
1 Reports: The following table shows the example actions taken to update
the reports.
Order Packaged Object
Action
Product Sales Analysis reports
1
Product Performance
Summary by Quarter
No action; all components of this report are supported with the new
target schema; once the underlying objects (attributes, metrics, and
prompts) are updated, this report will work with the new schema.
2
Products with Increasing
Sales by Sales District
No action; all components of this report are supported with the new
target schema; once the underlying objects (attributes, metrics, and
prompts) are updated, this report will work with the new schema.
Update the name to Products with Increasing Sales by Sales Office.
3
Products with Decreasing
Sales by Sales District
No action; all components of this report are supported with the new
target schema; once the underlying objects (attributes, metrics, and
prompts) are updated, this report will work with the new schema.
Update the name to Products with Decreasing Sales by Sales
Office.
4
Products with Increasing
Quarterly Sales
No action; all components of this report are supported with the new
target schema; once the underlying objects (attributes, metrics, and
prompts) are updated, this report will work with the new schema.
5
Products with Decreasing
Quarterly Sales
No action; all components of this report are supported with the new
target schema; once the underlying objects (attributes, metrics, and
prompts) are updated, this report will work with the new schema.
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Order Packaged Object
Action
6
Top N Products Sold Quarter Revenue Trend
No action; all components of this report are supported with the new
target schema; once the underlying objects (attributes, metrics, and
prompts) are updated, this report will work with the new schema.
7
Top N Products Sold by
Sales District
Remove the Sales Region attribute and prompt from this report; the
remaining components of the report are supported in the new target
schema.
Change the name to Top N Products Sold by Sales Office, to
synchronize the report with the new attribute name for Sales
District.
8
Quarter Product
Contribution to Sales
District Revenues
Remove the Sales Region prompt from the report; the remaining
components of the report are supported in the new target schema.
9
Quarterly Orders Trend by
Product Group
No action; all components of this report are supported with the new
target schema; once the underlying objects (attributes, metrics, and
prompts) are updated, this report will work with the new schema.
10
Product Revenue Trend
No action; all components of this report are supported with the new
target schema; once the underlying objects (attributes, metrics, and
prompts) are updated, this report will work with the new schema.
11
Revenue Distribution by
Product Group & Quarter
No action; all components of this report are supported with the new
target schema; once the underlying objects (attributes, metrics, and
prompts) are updated, this report will work with the new schema.
12
Quarterly Closure Rate
based on Product
Delete the report from the project; it is not supported in the target
schema.
Change the name to use Sales Office.
Sales Performance Analysis reports
13
Summary Sales
Performance by Sales
Region & Quarter
Replace Sales Region with Sales District, and remove metrics
related to Opportunity analysis and Target Quota. The other
components of the report are supported in the new target
schema.Then change the name to use Sales Office.
14
Revenue Distribution by
Sales Region
Replace Sales Region with Sales District. The other components of
the report are supported in the new target schema. Then change
the name to use Sales Office.
15
Quarterly Revenue Trend
by Sales Region
Replace Sales Region with Sales District. The other components of
the report are supported in the new target schema. Then change
the name to use Sales Office.
16
Quarterly Revenue &
Closure Trend
Delete the report from the project; it is not supported in the target
schema.
17
Quarterly Revenue &
Closure Trend by Sales
Region
Replace Sales Region with Sales District and remove all
Opportunity metrics. The other components of the report are
supported in the new target schema. Then change the name to use
Sales Office.
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Order Packaged Object
Action
18
Quarterly Quota
Performance by Sales
District
Replace Sales District with Sales Representative. The other
components of the report are supported in the new target schema.
Then change the name to use Sales Person.
19
Quota Achievement by
Quarter & Sales
Organization
Remove Sales Region and all quota metrics in the report. The other
components of the report are supported in the new target schema.
20
Competitive Activity by
Quarter
Delete the report from the project; it is not supported in the target
schema.
21
Quarterly Competitive Win
Rate Trend
Delete the report from the project; it is not supported in the target
schema.
22
Top 10 Companies by
Deal Size - Quarter
Remove all Opportunity metrics and replace the Deal Size metric
with Revenue. The other components of the report are supported in
the new target schema, but the filter requires changes to adapt it to
the new schema.
Change the report name to use Customer Group instead of
Companies.
23
Top 10 Deals by Quarter
Remove all Opportunity metrics and substitute Deal Size metric
with Revenue. The other components of the report are supported in
the new target schema, but the filter requires changes to adapt it to
the new schema.
Change the report name to use Orders instead of Deals.
24
Top 3 Deals by Quarter &
Primary Competitor
Delete the report from the project; it is not supported in the target
schema.
25
Top 3 Deals by Quarter &
Sales Region
Remove all Opportunity attributes and substitute Deal Size metric
with Revenue, and Sales Region with Sales District. The other
components of the report are supported in the new target schema,
but the filter requires changes to adapt it to the new schema.
Change the report name to use Orders instead of Deals, and Sales
Office instead of Sales Region.
26
Top 10 Lost Opportunities
by Quarter
Delete the report from the project; it is not supported in the target
schema.
27
Top 3 Lost Opportunities
by Quarter & Primary
Competitor
Delete the report from the project; it is not supported in the target
schema.
28
Top 3 Lost Opportunities
Delete the report from the project; it is not supported in the target
by Quarter & Sales Region schema.
Lead Analysis & Pipeline Analysis reports
29
All Lead Analysis reports
Delete the report from the project; it is not supported in the target
schema.
30
All Pipeline Analysis
reports
Delete the report from the project; it is not supported in the target
schema.
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2 Metrics: The following table shows the example actions taken to update
the metrics.
Order Packaged Object
Action
Compound metrics
1
Revenue by Sales
Representative
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
Change the name to Revenue by Sales Person.
2
Average Product Revenue
by Order
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
3
% Opportunities Converted
Delete from the project; not supported with the new target schema.
4
% Wins vs. Competitor
Delete from the project; not supported with the new target schema.
5
% Lost vs. Competitor
Delete from the project; not supported with the new target schema.
6
% Quota Achieved
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
7
% Quota Achieved
(Previous Quarter)
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
8
% Revenue Change vs.
Previous Quarter
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
9
% Contribution to Total
Revenues
Delete from the project; not supported with the new target schema.
10
% Conversion vs.
Opportunity Size
Delete from the project; not supported with the new target schema.
11
% Conversion vs. Weighted Delete from the project; not supported with the new target schema.
Opportunity Size
12
% Contribution to Product
Group Revenue
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
13
% Contribution to Quarter
Sales District Revenue
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
Change the name to % Contribution to Quarter Sales Office
Revenue.
Simple metrics with conditionality
14
Deals
Delete from the project; not supported with the new target schema.
15
Deal Size
Delete from the project; not supported with the new target schema.
Because this is used in several filters, it cannot be deleted until the
filters are updated.
16
Lost Deal Size
Delete from the project; not supported with the new target schema.
Because this is used in several filters, it cannot be deleted until the
filters are updated.
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Order Packaged Object
Action
17
Opportunities with a
Competitor
Delete from the project; not supported with the new target schema.
18
Lost Opportunities vs.
Competitor
Delete from the project; not supported with the new target schema.
19
Wins vs. Competitors
Delete from the project; not supported with the new target schema.
Simple metrics with dimensionality
20
Revenue (Region Filtered)
Delete from the project; not supported with the new target schema.
21
Revenue (Product Group)
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
22
Revenue (Sales District,
Quarter)
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
Change the name to Revenue (Sales Office, Quarter).
Simple metrics with transformation
23
Revenue (Previous
Quarter)
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
24
Target Quota (Previous
Quarter)
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
Simple metrics
25
Revenue
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
26
Target Quota
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
27
Orders
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
28
Orders containing Product
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
29
Opportunity Size
Delete from the project; not supported with the new target schema.
30
Weighted Opportunity Size
Delete from the project; not supported with the new target schema.
31
Opportunities
Delete from the project; not supported with the new target schema.
32
Number of Sales
Representatives
No action; all components are supported and will work with the new
target schema once the underlying objects are updated.
Change the name to Number of Sales Persons.
Note: Metrics used for Lead Analysis and Pipeline Analysis are not supported; delete them.
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3 Base formulas: The following table shows the example actions taken to
update the base formulas.
Order
Packaged Object
Action
1
Sum of Lead Size
Delete from the project; not supported with the new target schema.
2
Sum of Opportunity Size
Delete from the project; not supported with the new target schema.
3
Sum of Weighted
Opportunity Size
Delete from the project; not supported with the new target schema.
4
Sum of Sales
Representative Quota
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
5
Sum or Order Amount
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
6
Count of Leads
Delete from the project; not supported with the new target schema.
7
Count of Opportunities
Delete from the project; not supported with the new target schema.
8
Count of Orders
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
9
Count of Sales
Representatives
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
4 Filters: The following table shows the example actions taken to update
the filters.
Order
Packaged Object
Action
1
Rank of Top 10 Companies by
Deal Size
Change the filter to use Revenue instead of Deal Size for the
ranking.
Change the filter name to use Revenue.
Note: This filter must be updated before the Deal Size metric
is removed from the project.
2
Rank of Top Deals by Deal Size
Change the filter to use Revenue instead of Deal Size for the
ranking.
Change the filter name to use Revenue.
Note: This filter must be updated before the Deal Size metric
is removed from the project.
3
Rank of Top 10 Lost
Opportunities by Lost Deal Size
Delete from the project; not supported with the new target
schema.
4
Rank of Top 3 Deals by Deal
Size, break by Primary
Competitor
Delete from the project; not supported with the new target
schema.
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Order
Packaged Object
Action
5
Rank of Top 3 Deals by Deal
Size, break by Sales Region
Change the filter to use Revenue instead of Deal Size for the
ranking.
Change the filter name to use Revenue.
Note: This filter must be updated before the Deal Size metric
is removed from the project.
6
Rank of Top Lost Opportunities
by Lost Deal Size, break by
Sales Region
Delete from the project; not supported with the new target
schema.
7
Rank Top N Products by
Revenue
No action; all components are supported and will work with
the new target schema once the underlying objects are
updated.
8
All filters in Folder “Filter for
Conditional Metrics”
Delete from the project; not supported with the new target
schema.
5 Prompts: The following table shows the example actions taken to update
the prompts.
Order
Packaged Object
Action
1
Account List
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
Change the name to Customer List.
2
Company List
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
Change the name to Customer Group Account.
3
Lead Characteristics
Delete from the project; not supported with the new target
schema.
4
Lead Source - Multiple
Selections
Delete from the project; not supported with the new target
schema.
5
Product Group - Multiple
Selections
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
6
Quarter - Multiple Selections
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
7
Quarter - One Selection Only
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
8
Sales District - Multiple
Selections
No action; all components are supported and will work with the
new target schema once the underlying objects are
updated.Change the name to Sales Office.
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Order
Packaged Object
Action
9
Sales District - One Selection
Only
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
Change the name to Sales Office.
10
Sales Region - Multiple
Selections
Delete from the project; not supported with the new target
schema.
11
Sales Region - One Selection
Only
Delete from the project; not supported with the new target
schema.
12
Time - Year to Month
Hierarchy
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
13
Year - One Selection Only
No action; all components are supported and will work with the
new target schema once the underlying objects are updated.
6 Execute all reports to test the application objects and ensure that the
results of your work in this procedure are correct.
Map schema objects
This second procedure maps schema object links to the target schema, based
on results from your gap analysis at the beginning of this chapter. For details
on the gap analysis, see Perform a gap analysis, page 48.
all actions taken during this mapping process to allow you
 Document
to backtrack if necessary.
An example follows this procedure.
To map schema objects
1 Map hierarchies based on the results from your gap analysis:
•
If a hierarchy is supported in the target schema, proceed with
mapping. Actions can include changing the name, removing or adding
attributes, and so on.
•
If a hierarchy is not supported, remove it from the project.
can only be removed from the MicroStrategy metadata if no
 Objects
dependents exist in the project. The order for removing objects
throughout this procedure is determined based on that principle.
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2 Map transformations based on the results from your gap analysis:
•
If a transformation is supported in the target schema, proceed with
mapping. Actions can include changing the name, updating the
definition to the target schema tables and columns, and so on.
•
If a transformation is not supported, remove it from the project.
3 Map facts based on the results from your gap analysis:
•
If a fact is supported in the target schema, proceed with mapping.
Actions can include changing the fact name, updating facts
expressions to point to the target schema tables and columns,
removing or adding fact expressions, and so on.
•
If a fact is not supported, remove it from the project.
4 Map attributes based on the results from your gap analysis:
•
Start with the lowest attributes in each hierarchy and proceed
sequentially with all parents.
•
If an attribute is supported in the target schema, proceed with
mapping. Actions can include changing the attribute name; updating
forms to point to the target schema tables and columns; adding or
removing forms; updating, removing or adding relationships; and so
on.
•
If an attribute is not supported, remove it from the project.
5 Finally, after all schema objects have been mapped, remove all tables that
are not used. The project schema must be updated to reflect all of the
changes.
Example: Mapping schema objects
This example summarizes the actions taken to map packaged schema objects
from the MicroStrategy Sales Force Analysis Module (SFAM) to the example
target data warehouse.
1 To update the connection to the target schema, define the database
connection to the target data warehouse.
example, some tables from the default physical schema are
 Inused,thissuch
as F_SALES_REP_QTA. Tables must be created before
this step is taken; the Erwin file containing the default physical
schema can be used to generate scripts for these tables.
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2 Update the warehouse catalog, selecting the relevant tables based on the
gap analysis between physical schemas. For more details, see Example:
Mapping physical schemas, page 52.
3 Map all schema objects in the destination project:
•
Hierarchies: The following table summarizes the example actions
taken to map SFAM hierarchies.
Order
Packaged
Object
Action
1
Time - Year to
Month
No changes are required.
2
Product
No changes are required.
3
Time
No changes are required.
4
Sales
Organization
Delete Sales District and Sales Region from
this hierarchy.
•
Transformations: No changes are required for transformations
because all base attributes and tables are the same.
•
Facts: The following table summarizes the example actions taken to
map the SFAM facts to the example target data warehouse.
Order
Packaged Object
Action
1
Order Amount
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
2
Sales Representative
Quota
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
3
Index for Orders Counts
Proceed with changes based on the gap analysis; see
Example: Mapping the logical data model, page 56 for details.
4
Opportunity Size
Delete the fact from the MicroStrategy project.
5
Weighted Opportunity
Size
Delete the fact from the MicroStrategy project.
6
Lead Size
Delete the fact from the MicroStrategy project.
7
Index for Lead Counts
Delete the fact from the MicroStrategy project.
8
Index for Opportunity
Counts
Delete the fact from the MicroStrategy project.
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Attributes: The following table summarizes the example actions taken
to map SFAM attributes to the example target data warehouse.
of the folders require name changes based on new attribute
 Some
names. For example, change the name of the Account folder to
Customer.
Order
Packaged
Object
1
Account
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
2
Company
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
3
Industry
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
4
Lead
Delete the attribute from the MicroStrategy project.
5
Lead Source
Delete the attribute from the MicroStrategy project.
6
Lead Status
Delete the attribute from the MicroStrategy project.
7
Lead Type
Delete the attribute from the MicroStrategy project.
8
Opportunity
Delete the attribute from the MicroStrategy project.
9
Current
Opportunity
Status
Delete the attribute from the MicroStrategy project.
10
Opportunity
Open Date
Delete the attribute from the MicroStrategy project.
11
Opportunity
Close Date
Delete the attribute from the MicroStrategy project.
12
Primary
Competitor
Delete the attribute from the MicroStrategy project.
13
Opportunity
Status
Delete the attribute from the MicroStrategy project.
14
Order
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
15
Discount
Indicator
Delete the attribute from the MicroStrategy project.
16
Product
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
17
Product Group
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
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Packaged
Object
Action
18
Sales
Representative
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
19
Sales District
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
20
Sales Region
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
21
Date
Delete the attribute from the MicroStrategy project.
22
Month
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
23
Quarter
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
24
Year
Proceed with changes based on the gap analysis; see Example:
Mapping the logical data model, page 56 for details.
Order
5
4 Finally, after all schema objects have been mapped, remove all tables that
are not used. The project schema must be updated to reflect all of the
changes.
Customize and extend the module
When you customize or extend any of the existing Analytics Modules, you are
adding data elements that are specific to your company.
For typical customization scenarios, ideas and suggestions, and specific
information on how to customize and extend an Analysis Module, see
Customize and extend the Analytics Modules, page 34.
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6.
CREATING PORTABLE
ANALYTICAL APPLICATIONS
Introduction
This chapter presents the MicroStrategy best practices for designing and
building enterprise-class analytical applications using the portability
paradigm. Portability and the MicroStrategy portability methodology are
explained fully in Portability methodology, page 43.
MicroStrategy has developed the content in this chapter based on experience
developing the MicroStrategy Analytics Modules.
This chapter describes how to build analytical applications using the
portability paradigm and helps you understand how the MicroStrategy
Analytics Modules were designed and developed. For specific examples and
more details, see each Analysis Module’s individual reference guide and their
related metadata components in your MicroStrategy software.
Architecture of portable analytical applications
The portability paradigm revolves around the concept of metadata-only
applications. The MicroStrategy metadata repository allows the logical data
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model to be defined independently from the physical schema. This means
that the business representation of the data is separate from the data’s
storage structure. For a given logical data model, MicroStrategy supports a
large number of physical schemas.
The diagram below reflects the independence of the Analytics Modules, with
their analytics library and logical data model, from the physical schema.
Each major element’s role in the architecture is also described.
•
Logical Data Model: This is the cornerstone of the architecture. It is the
representation of the analytical domain in the MicroStrategy metadata.
The logical data model is defined purely in terms of MicroStrategy
schema objects such as facts (measures) and attributes (analysis
concepts). The logical data model definition should not depend on any set
physical data structures.
•
Analytics Library: The reports, or analytics, for a given analytical domain
are built on top of the logical data model. The reports consist of purely
MicroStrategy application objects such as metrics, reports, filters, and so
on. Reports are organized within a given domain into several analysis
areas. Definitions of the analytic objects should not depend on any set
physical data structures but should be based entirely on the logical data
model objects.
•
Physical Schema: (Optional) The physical schema that comes with each
Analysis Module describes the data warehouse that links to the logical
data model, and ultimately holds the data. It is used to build and test the
logical data model, as well as to store data for evaluation purposes.
Although the Analytics Modules each come with a default physical
schema, you can use the Modules’ portability feature to substitute an
existing physical schema. The physical schemas that come with the
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Modules are an optional part of the final components delivered as an
analytical application.
Analytics Modules versions
Each MicroStrategy Analysis Module includes two MicroStrategy projects, an
Evaluation project and a Production project, as described in the table below.
You can use this concept for your own analytical applications.
MicroStrategy Project
Description
Evaluation version of a
given Analysis Module
• This project allows you to evaluate the Analytics Modules reports without
setting up a data warehouse.
• The project metadata repository and warehouse are built using Microsoft
Access 2000.
• The project is included in the evaluation CD of the MicroStrategy
products. Installation automatically configures the project in a direct
connection (in 2-tier mode).
Note: The Access database presents some limitations for supporting count
distinct and outer join, because some of the reports in the evaluation
version were specifically modified to support the Access data warehouse.
Therefore, this project cannot be used directly against any warehouse
other than Access 2000.
Production version of a
given Analysis Module
• This project contains a production-ready metadata repository, and logical
data model objects are mapped to a default physical schema.
• The project metadata repository is built using Access 2000.
• The project is configured for an Oracle database, but can be used to
access any other major relational database such as DB2 or SQL Server.
See the MicroStrategy readme file for details on supported databases.
Best practices for building portable
applications
This section outlines the best practices for designing and developing
analytical applications using the portability paradigm. It presents
development project guidelines with procedures and a development plan.
The development process starts with selecting an analytical domain (the
business analysis area) and defining the basic analysis requirements. Then
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developers can use the best practices outlined in this section to design and
build the analytical application.
Analytical
Domain
Development
Process
Package
Business
Documentation
Metadata
Content
Using Portability
Rules &
Recommendations
Design and development premises
Developing a portable analytical application is not very different from
developing a typical business intelligence application. However, the
portability paradigm and architecture introduce a number of specific design
and development premises:
•
The application must model a well-known and standard business
problem area. The MicroStrategy development process ensures that most
data elements in the application have a high probability of existing in the
target data warehouse.
•
The application must be divided into several modular analysis areas, each
of them aligned with sub-areas of the application business model. The
MicroStrategy development process limits the impact on an analysis area
if certain data elements do not exist in the data warehouse.
•
The logical data model must be defined to support typical analysis types.
The MicroStrategy development process minimizes changes required to
customize the application to each company’s requirements.
•
The logical data model must be tested against existing physical schemas
(data warehouses) in the domain area, using different database
platforms. The MicroStrategy development process ensures the
application’s independence from a specific physical schema and database
platform.
•
The development process must accomplish the dual goal of developing
the application using MicroStrategy technology and developing the
required business and technical documentation. The MicroStrategy
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development process builds documentation to support implementation
and later customization and extension of the packaged components.
Documentation is a key part of the analytical application package.
Development process overview
Based on the premises listed above and the application deliverables,
MicroStrategy uses the following general process to build the analytics
modules:
1 Define the analytical domain.
2 Design and develop the logical data model, ensuring that it meets the
analytical and portability requirements.
3 Define the report library.
4 Develop and test reports, including the packaged application
components.
Throughout the development process, each phase includes creating
important technical or business documentation. The documentation is used
both for specifications for the next step of the development process and as
the basis of the final business and technical documentation delivered with
the application.
The development process is broken down into four main phases. Each phase
is, in turn, broken down into several procedures. Follow each phase and its
procedures in the order that they appear below.
Phase I: Define the analytical domain
In this first phase, you follow the procedures as they appear below, to:
•
Select the analytical domain (business analysis area)
•
Define the analytical domain by identifying the analysis requirements
that specify the application’s logical data model
•
Review the domain specifications that result from your work in the
previous two procedures
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To complete this phase successfully, make sure you have access to the
following:
Knowledge of the analytical domain area.
Knowledge of the criteria necessary to build portable applications.
Experience defining and building analytical applications.
Skills
Experience with overall application architecture and design, including the logical
data model, physical schema, and report specifications.
This role can be filled by experts in the domain, such as consultants with
experience implementing this type of application.
Experience with defining the business requirements.
Select the analytical domain
In this first Phase I procedure, you select an analytical domain and check to
ensure it meets the criteria required for portable applications.
procedure should be performed by a person responsible for
 This
defining the business requirements.
To select the analytical domain
1 Apply the criteria listed in this step to determine an appropriate
analytical domain. Based on the requirements for portability, not all
analytical domains can be used to build this type of analytical application.
Ensure you select your analytical domain with the following criteria in
mind:
Criteria 1: The analytical domain must represent one of the following:
•
A standard business process, such as sales as defined by
Miller-Heiman
•
A business problem area, such as customer retention
•
A functional area, such as marketing or service
•
A standard data source type, such as Web logs or SAP ERP
By restricting the application to a standard domain, you will ensure that
the solution applies to a wide number of existing data warehouses and
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that it takes advantage of standard processes, best practices, and
standard data that apply to the domain area.
are developing packaged analytical applications, complete
 Ifanyyoupreliminary
market research necessary to decide on a given
analytical domain.
Criteria 2: The analytical domain’s business concepts must be standard.
For example, sales analysis is defined by standard, generic concepts such
as lead, opportunity, revenue, cost, order, product, and sales
organization.
To identify a data element as standard, review different sources for the
analysis area. These sources can include data warehouses, operational
systems, logs, and so on. If the data element (for example, Product) is
always present in most or all data sources, you can consider Product as a
standard data element. However, if you identify a data element, such as
Product Family, that is not always present in data sources, do not
consider it a standard data element and do not include it in your
application.
This criteria determines the scope of the application, since standard
business concepts define the analytical domain. It also ensures that those
data elements will exist in the majority of the data warehouses for that
domain.
2 Taking the criteria above into consideration, select the analytical domain
that your application will address.
3 Evaluate your chosen analytical domain to assess the feasibility of
building the application. Consider the following characteristics:
•
Size of the analysis area, measured as the number of standard data
elements, process complexity, potential number of different report
types, and so on. As the analysis domain becomes bigger,
development becomes more complicated and the portability paradigm
becomes more difficult to accomplish.
•
In-house domain expertise. This is the knowledge required to define
and build the application. This knowledge must exist internally.
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Stability of the business rules associated with the domain area, due to
industry/regulatory changes. For example, in a financial analysis
application, the accounting rules can change frequently.
are developing packaged analytical applications, you should
 Ifalsoyouevaluate
the following:
•
The domain’s applicability across multiple verticals/industries.
Be sure the application can be reused without major changes in
existing data warehouses, independent of the industry.
•
Applicability across a sizeable geographic market. For example,
a general ledger application supports American requirements,
but not those required in European countries.
4 Document the information you gather from the steps above. Your
documentation should include:
•
A description of the analysis domain
•
Key business processes and problem areas
•
Typical data sources and data elements for the domain
This information will define the general scope of the analytical
application and will identify the initial analysis requirements.
Define the analytical domain specifications
In this second Phase I procedure, you define the business analysis
specifications to build the portable application.
procedure should be performed by a person responsible for
 This
defining the business requirements.
To define the analytical domain specifications
1 Define and document the data elements that make up the analytical
domain. Begin by documenting a business process, including:
•
An explanation of the business process and main entities; for
example, in a sales analysis, the different stages in the sales cycle,
such as lead, qualification, opportunity, deal, order, and revenue
•
Business process diagrams
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Analytical needs, including typical usage scenarios and user roles
2 Document a list of business concepts, which describes all key concepts
such as:
•
Business attributes (business-oriented concepts)
•
Facts
•
Metrics (measures)
•
The relationships between the previous items; for example, how each
fact relates to the different business attributes and how they are
calculated
3 Define and document the analytical domain’s reporting areas. These are
the logical analysis areas into which the domain can be broken. Focus on
defining independent analysis areas to limit the impact of non-existent
data elements in a future target data warehouse. Be sure this
documentation includes:
•
A description of each reporting area within the analytical domain (the
analytical scope)
•
For each reporting area, an explanation of the key analysis challenges,
a list of the relevant attributes and facts, user analysis requirements,
and how this reporting area fits with other analysis areas
•
A list and description of the key reports and types of questions they
answer for each reporting area, with a focus on defining which and
how concepts are analyzed together
4 Document a glossary of terms, including detailed descriptions and
examples.
5 Combine all documented content from the first procedure above, with the
content from this procedure to create your Analytical Domain
Specifications document. See any of the MicroStrategy Analytics Modules
reference guides for examples of these different types of content.
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Review the analytical domain specifications
In this third Phase I procedure, you discuss the analytical domain with
external experts and the development team. You also identify the necessary
quality assurance criteria for the application.
procedure should be performed by a person responsible for
 This
overall application architecture and design, working with other
management, development, and testing team members, as well as
domain experts, as appropriate.
To review the analytical domain specifications and define quality assurance
criteria
1 Using the Analytical Domain Specifications document from the previous
procedure, review the domain with external domain experts and your
development team, or the project team in charge of developing the
analytical application. Be sure discussions include:
•
Identifying potential issues and challenges that can appear when you
define the logical data model that supports the application
•
Identifying the criteria necessary to ensure logical data model quality
best strategy to ensure logical data model quality is to build
 The
the key reports that identify the typical analysis types and
relationships between business concepts. Success reproducing the
key reports assures the robustness of the logical data model.
2 Update your Analytical Domain Specifications document to reflect the
reviews above and any revisions necessary due to feedback, corrections,
and potential issues.
3 Create a document of the list of key reports. This document will
eventually summarize the criteria that assure the logical data model
meets the analytical domain requirements. It will be finalized in a
subsequent procedure, when the testing plan for the logical data model is
defined.
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Phase II: Develop the logical data model
In this second phase, you follow the procedures as they appear below, to:
•
Create the logical data model
•
Create the physical schema
•
Define an assurance/testing plan
•
Create a MicroStrategy project and execute testing
To complete this phase successfully, make sure you have access to the
following:
Knowledge of the analytical domain area, typical logical data models used to model the
domain, and typical physical schemas used.
Knowledge of the criteria necessary to build portable applications.
Extensive experience building analytical applications, as well as solid logical data and
physical modeling skills.
Working experience creating analytical solutions using MicroStrategy technology,
including creating reports, metrics, filters, and other MicroStrategy objects.
Skills
Experience with overall application architecture and design, including the logical data
model, physical schema, and report specifications.
This role can be filled by experts in the domain, such as consultants with experience
implementing this type of application, or expert users in the analysis domain.
Experience with defining business requirements.
Experience with application development using MicroStrategy technology.
Experience performing testing tasks.
rules that appear in each of the Phase II procedures below are a
 The
collection of general design strategies. Additional rules and
recommendations apply when defining and building logical data
model objects and reports for portability using MicroStrategy tools
and technology. For more details see Portability rules and
recommendations, page 131.
Create the logical data model
In this first Phase II procedure, you define the logical data model on which
the application will be built, by following the logical data model design rules
listed within the procedure. This is a key procedure in the design and
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development process, since all further work depends on a robust logical data
model that supports all the requirements of the analytical domain.
design rules listed in this procedure are general design strategies.
 The
When you design a logical data model for portability and are using
MicroStrategy technology, additional design rules apply. For detailed
rules for each object, see Portability rules and recommendations at
the end of this chapter.
procedure should be performed by a person responsible for
 This
overall application architecture and design, working with developers
and domain experts.
To create the logical data model
1 Using the Analytical Domain Specifications document you created in
Phase I: Define the analytical domain, page 109, identify the data
elements that define the core of the analytical domain and that are
required to provide the core analysis. This step helps you adhere to the
following rule:
Rule 1: Use only objects (data elements) that are standard across the
domain.
•
Logical data model elements must be based on viable source system
data. For example, sales analysis must include the data elements that
usually exist in a standard source system, such as Account, Item, and
Order.
•
The logical data model must incorporate only the basic facts captured
by a standard organization. For example, avoid adding a fact such as
Profit because it could be a precalculated value or dynamically
calculated as “Revenue less Cost.”
•
Avoid elements that are industry- or country-specific, as well as those
that may require customization. For example, some levels of the
Product hierarchy can be different for each industry and/or client.
2 Using the Analytical Domain Specifications document, identify and
document the key analysis types for the analytical domain. This step
helps you adhere to the following rule:
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Rule 2: The logical data model must be designed to support generic types
of analysis, not fixed reports.
•
For example, you might define a customer analysis logical data model
to support certain analyses such as customer count by geographic
and/or demographic characteristics.
•
A logical data model based on fixed reports is more rigid and harder to
customize further.
3 Using the examples and tips listed in this step, identify and document
30-40 data elements (attributes and facts) to be included in the model.
This step helps you adhere to the following rule:
Rule 3: Limit the logical data model to a manageable number of data
elements and relationships.
•
Limiting the number of data elements and relationships helps keep
the model simple.
•
By limiting the number to 30-40, you maximize the probability that
the majority of the data elements are present in the target data
warehouse.
•
Use simple (one-to-many) parent-child relationships. This ensures
the model is less dependent on a specific physical schema.
4 Identify and document how different areas of the logical data model
relate to each other. This includes identifying fact-attribute relationships
and whether different facts exist at the same level of analysis. This step
helps you adhere to the following rule:
Rule 4: Use a modular approach.
•
A modular approach ensures the different analysis areas depend on
limited portions of the logical data model. If some data elements are
missing from the target data warehouse, modularity limits the impact
to only the areas using those data elements.
are developing packaged analytical applications, you should
 Ifalsoyouidentify
areas that can be customized and extended by
customers. This allows you to provide guidance to clients when
they customize and extend the application.
5 Identify and document any elements that depend on specific physical
structures. This step helps you adhere to the following rule:
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Rule 5: The logical data model should not depend on a fixed physical data
structure.
•
For example, avoid many-to-many relationships because they require
a specific physical schema.
•
This rule implies that the logical data model is supported by different
types of physical schemas. This will be discussed further in the next
procedure, Create the physical schema, page 118.
6 Create a graphical view of your logical data model.
7 Combine all your documentation from this procedure. Make sure it
includes technical and business content explaining each of the model
objects with a description, relationships, data examples, and loading
strategies.
any of the MicroStrategy Analytics Modules reference guides
 See
for examples of this documentation.
8 Review your work products from this procedure with appropriate team
members, and incorporate any feedback.
9 If applicable, incorporate any changes or additions to the Analytical
Domain Specifications document you created in Phase I: Define the
analytical domain, page 109 that may be required due to additions or
corrections that occurred during this procedure. Be sure to track the
changes in a revision table or similar method.
Create the physical schema
In this second Phase II procedure, you define a physical schema to support
the analytical application development. This physical schema will be used to
develop all MicroStrategy objects, such as attributes, facts, metrics, reports,
and so on. The physical schema will be populated with test data for
development, and with demonstration (demo) data to create a demo or
evaluation version of the analytical application.
Most of the physical schema rules that appear in this procedure exist to
facilitate a gap analysis between the default physical schema (the one you are
creating with this procedure) and any future target physical schema (a data
warehouse to which you will port the logical data model later). These rules
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also facilitate the mapping process that occurs during the porting process.
For more details on porting, see Portability methodology, page 43.
design rules listed in this procedure are general design strategies.
 The
When you design a physical schema for portability and are using
MicroStrategy technology, additional design rules apply. For detailed
rules on designing a physical schema, see Portability rules and
recommendations, page 131.
procedure should be performed by a person responsible for
 This
overall application architecture and design, working with developers
and testers.
To create the physical schema
1 Using the logical data model and related documentation you created in
the procedure above, design the physical schema using a star or
snowflake pattern. This step helps you adhere to the following rule:
Rule 1: The physical schema must be designed to be simple and efficient.
•
Star and snowflake schemas are more common database schemas for
analysis.
•
While different physical schemas can support the same logical data
model, using the simplest one requires the fewest tables, joins, and
columns.
•
Minimize the number of tables, especially lookup and relationship
tables. For example, the attributes Product and Product Group have a
one-to-many relationship. This can be implemented with two lookup
tables and a relationship table, or with a single lookup table that
includes all the attribute columns. Using a single lookup table
minimizes the number of tables, and the logical data model remains
flexible enough to use an existing data warehouse that has tree tables.
•
Avoid consolidated models and recursive hierarchies. They require a
specific physical schema.
•
Avoid using any views in the schema design. Views can be dependent
on the database engine. If they are required, use them when the target
schema is mapped to the application during the porting process; see
Map the module, page 73 for more details.
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2 Establish a naming convention to facilitate a gap analysis. For more
information on a gap analysis and porting, see Port an Analysis Module,
page 48. This step helps you adhere to the following rule:
Rule 2: Define a naming convention for all schema objects.
•
Use prefixes to indicate specific table types. For example, use F_ to
denote fact tables, L_ for lookup tables, A_ for aggregate tables, and
so on.
•
Schema object names should be self-explanatory. For example, the
CUST_ID column identifies the ID assigned to a customer. Similarly,
F_CUST_TXN_HIST provides historical tracking of customer
transactions.
•
Limit object names to 18 characters since some DB2 databases have a
limit of 18 characters.
•
Use suffixes to identify column types. For example, use _ID and
_DESC for identifier and descriptor fields, respectively.
•
Ensure that a data element has the same column name across all
tables. For example, a customer numeric identifier (CUST_ID) can
exist in several tables. In all the tables, the column name must be
CUST_ID.
3 To support portability across database engines, check to ensure your
physical model avoids specific database parameters. This step helps you
adhere to the following rule:
Rule 3: The physical model should be defined to avoid any specific
database platform parameters.
•
Use standard data types across database platforms. For example, use
VARCHAR2(30).
•
Use the same data type for all columns representing the same data
element. For example, the CUST_ID column should have the same
data type in all tables where it exists.
•
Use the same data type for all data elements containing similar data.
For example, define all description columns as VARCHAR2(50).
4 Document the following items to facilitate mapping the application to a
target physical schema:
•
Naming conventions, standard data types, and so on
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•
Any deviation from the rules listed in this procedure, including an
explanation of why the deviation occurred
•
Loading procedures for each of the physical schema tables
5 Make sure your physical schema adheres to the final two rules:
Rule 4: The physical schema will not include any query performance
optimization.
Optimization is addressed during implementation, to account for specific
client requirements and needs of the physical schema.
Rule 5: Document primary keys based on the logical data model
relationships, although they are not required during the definition of the
physical schema.
The logical data model, including attributes, relationships, and facts,
determines which keys define the primary key for each physical schema
table.
6 Review the physical schema with the appropriate individuals to ensure
quality. Incorporate any feedback into the model where appropriate.
can also modify and update the logical data model to fit with
 You
these design rules or to address issues not previously considered.
Be sure to track any changes made.
7 Using the information you have documented in this procedure, create:
•
A physical schema in Erwin format
•
A data dictionary and loading requirements to populate data
structures
•
A mapping between the logical data model and the physical schema
Define an assurance/testing plan
In this third Phase II procedure, you determine the quality criteria that will
ensure the logical data model supports the requirements of the analytical
domain, and that the application is portable.
procedure should be performed by a person responsible for
 This
overall application architecture and design, working with developers
and testers.
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To define an assurance/testing plan
1 Using the Analytical Domain Specifications document created above, as
well as the logical data model and physical schema also created above,
identify the key reports that define the analytical domain.
2 Based on the key reports, create data scenarios for testing purposes. Be
sure to include expected results.
3 Create a Test Plan document to ensure the logical data model can support
different physical schema variations. This can include developing a few
variations of your default physical schema.
4 Once your Test Plan is created, review it with appropriate individuals.
Incorporate any feedback into the test plan where appropriate. In your
review, be sure the Test Plan includes:
•
Report specifications and expected results based on your test data set
•
Data scenarios and a plan to generate the data to populate the default
physical schema
•
A plan to map the MicroStrategy project to several physical schemas
Create a project and execute testing
In this last Phase II procedure, you develop and test a MicroStrategy project
to ensure the logical data model meets requirements.
 This procedure should be performed by developers and testers.
To create a project and execute testing
1 Using the logical data model and physical schema documentation you
created, as well as the Test Plan you created in the procedure above with
its list of key reports and test data, create the MicroStrategy schema
objects that represent the logical data model.
Portability rules and recommendations, page 131, for
 See
important rules on building MicroStrategy objects for portability.
to your MicroStrategy documentation for basic information
 Refer
on creating a MicroStrategy project and objects.
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2 Link the newly created schema objects to your default physical schema.
3 Create the reports you have identified and test them to ensure the data
results are correct, the generated SQL is optimal and uses the appropriate
tables, and so on.
4 Map the MicroStrategy project to different physical schemas to ensure
that it supports other physical schema variations. See Porting the
Analytics Modules, page 41, for details on mapping a logical data model
to a different physical schema.
5 Review the results of your testing from the steps above, and identify and
complete any fixes necessary to the MicroStrategy project.
to update the Analytical Domain Specifications document,
 Bethesure
logical data model and physical schema documentation, the
Test Plan, and any other related documentation. Track any
changes made to each individual document.
Once you have completed your review and made any updates, you have
confirmed the quality of your MicroStrategy project, its logical data model,
and its related set of reports. You will reuse this project in the next two
phases to build the final report library.
Phase III: Define the report library
In this third phase, you follow the procedures as they appear below to define
the reports library. The procedures are:
•
Define reports specifications
•
Review reports specifications and defining testing criteria
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To complete this phase successfully, make sure you have access to the
following:
Knowledge of the analytical domain area and the best analytical practices in that
area.
Working experience creating reports, metrics, filters, and other MicroStrategy
objects.
Experience with overall application architecture and design, including the logical
data model, physical schema, and report specifications.
Skills
This role can be filled by experts in the domain, such as consultants with
experience implementing this type of application, or expert users in the analysis
domain.
Experience with defining business requirements.
Experience with application development using MicroStrategy technology.
Experience performing testing tasks.
Define the reports library
In this first Phase III procedure, you define a library with 30 to 50 reports
that address the key analytical requirements for your analytical domain.
design rules listed in this procedure are general design strategies.
 The
When you design a report library for portability using MicroStrategy
technology, additional design rules apply. See Portability rules and
recommendations, page 131 for detailed rules for designing reports.
procedure should be completed by a person responsible for
 This
overall application architecture and design and a person responsible
for gathering business requirements. It can include input from
developers.
To define the reports library
1 Research the analytical domain and identify the reports that need to be
developed. This step should adhere to the following rule:
Rule 1: The report library should be defined within the boundaries of the
given logical data model.
only objects and relationships that exist in the model. Do not
 Use
modify a tested model to support new report requirements.
Changes can impact the integrity and robustness of the model. If
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new reports require changes, move back to Phase II: Develop the
logical data model, page 115 and proceed again from that point.
2 Define a set of 30 to 50 reports that focus on the best analytical practices
for your analytical domain. This step helps you adhere to the following
rule:
Rule 2: The application should provide 30 to 50 reports.
This set of reports will include:
•
Key reports defined in the procedures completed above
•
Additional reports that use MicroStrategy advanced analytical
capabilities such as ranks, contribution, transformations, and so on;
see the MicroStrategy Advanced Reporting Guide for details on
advanced reporting features
sure the reports do not rely on any physical schema
 Make
structure. The design rules listed in this procedure are general
design strategies. When you design reports for portability and are
using MicroStrategy technology, additional design rules apply. See
Portability rules and recommendations, page 131 for detailed
rules for designing a report.
This step provides a core group of reports to address key business
questions in the analytical domain. They can also be used as templates to
create additional reports later.
3 Arrange your reports into groups that reflect business problem areas.
This step helps you adhere to the following rule:
Rule 3: The analytical library should be organized using analysis areas.
•
Make sure the analysis areas are aligned with specific problem areas,
so that each group of reports consists of a unique report collection.
For example, customer analysis is organized into analysis areas such
as customer acquisition, customer retention, and customer attrition.
•
Make sure each analysis area uses a different part of the logical data
model, so reports do not overlap. This limits the impact on the porting
process later, if data elements are missing from the target data
warehouse.
4 For each report identified in the steps above, write a definition that
includes:
•
Report display (grid/graph)
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•
Report formatting
•
Report filter conditions
•
Any other report definitions that are appropriate
any of the MicroStrategy Analytics Modules reference guides
 See
for examples of the types of report definitions to be documented.
5 Identify all the objects required to build each report, such as metrics and
filters.
6 Using the information gathered in this procedure, as well as the
Analytical Domain Specifications document, the logical data model and
physical schema documentation, and the MicroStrategy project created in
the procedures above, create a Report Specifications document. The
Report Specifications document should contain the following
information for each report:
•
A description of each report’s business value, key performance
indicators (KPIs), and usage scenarios
•
The layout and formatting for each report
•
The filter conditions applied to each report
•
Typical drilling scenarios for each report, where applicable
•
The definition of all objects required to build the reports, such as
metrics, custom groups, filters, attributes, and so on
any of the MicroStrategy Analytics Modules reference guides
 See
for examples of this documentation.
Review report specifications and define testing criteria
In this last Phase III procedure, you review the report specifications you
created in the procedure above, and you define the report testing criteria.
procedure should be completed by developers and testers. It can
 This
include input from management, a person responsible for overall
application architecture and design, and/or other experts in the
analytical domain area.
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To review reports specifications and define testing criteria
1 Using the Reports Specifications you created in the previous procedure,
review each report with experts in the analytical domain.
2 Define criteria to test each report. Document your criteria in the Reports
Specifications document, and be sure to track the update to the
document.
Portability rules and recommendations, page 131 for special
 See
object design rules for portability. These rules can also inform your
report testing scenarios.
3 Define and document a real scenario for data generation to address
testing and demonstration (demo) needs. Your documentation should
include:
•
A data generation plan
•
Your data scenarios for testing and demo needs
Phase IV: Develop and test the reports
In this fourth (and final) phase, you follow the procedures as they appear
below to create and test the reports for your analytical application. The
procedures are:
•
Develop the reports
•
Test the reports
•
Package the application
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To complete this phase successfully, make sure you have access to the
following:
Knowledge of the analytical domain, logical data model, physical schema,
reports library, and report specifications.
Working experience creating reports, metrics, filters, and other MicroStrategy
objects.
Experience building and documenting analytical applications.
Working experience developing technical documentation.
Skills
Experience with overall application architecture and design, including the logical
data model, physical schema, and report specifications.
This role can be filled by experts in the domain, such as consultants with
experience implementing this type of application, or expert users in the analysis
domain.
Experience with application development using MicroStrategy technology.
Experience performing testing tasks.
Develop the reports
In this first Phase IV procedure, you create the report library for your
analytical application, including any supporting objects such as metrics and
filters.
you develop reports for portability and are using MicroStrategy
 When
technology, additional design rules apply. See Portability rules and
recommendations at the end of this chapter for detailed rules for
reports.
procedure should be completed by developers, working with a
 This
person responsible for overall application architecture and design.
To develop the reports
1 Make sure you have the Reports Specifications document as well as the
logical data model and physical schema documentation available. Make
sure you also have access to the MicroStrategy project as created in Phase
II: Develop the logical data model, page 115.
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2 Create all objects required for the reports. Objects to be created include
such supporting objects as metrics and filters.
sure to add a detailed description to all objects created with
 BeMicroStrategy
tools.
3 Once the objects are created, design the reports to include the final
formatting. Your completed reports form the report library for your
MicroStrategy project.
sure to update the Reports Specifications document if any
 Bemodifications
were required during report development.
Test the reports
In this second Phase IV procedure, you test all reports to ensure they meet
your documented Reports Specifications.
 This procedure should be performed by developers and testers.
To test the reports
1 Make sure you have access to your MicroStrategy project, including your
default data warehouse with test/demo data loaded. Also be sure you
have your Report Specifications document and your Test Plan.
2 Execute your Test Plan.
3 Review report results, such as the following:
•
Report data set
•
Report display
•
Report formatting
•
SQL
4 Identify any issues and resolve them. Be sure to update any related
documentation that may be affected.
5 Take final screen shots of each report, and include the screen shots in
your Report Specifications document.
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Once this procedure is complete, you have produced the final version of your
MicroStrategy project. You have also produced updated versions of the
Report Specifications document to include screen shots of the final reports.
Package the application
In this last Phase IV procedure, you create the final package for your
analytical application, including the metadata components and
documentation.
procedure should be performed by developers, testers, and
 This
writers.
To package the application
1 Gather all documentation created during the development process
(Phases I-III plus the completed procedures in Phase IV). Be sure to
include all business and technical specifications.
2 Be sure you have access to the MicroStrategy project, including metadata,
default physical schema, and demonstration (demo) database.
3 Transform your existing documentation to a format appropriate for your
target audience (such as developers, system administrators, end users, or
another group). Depending on the audience, your documentation’s level
of detail can differ significantly. For example, a software company
developing a packaged application intended for an outside audience
might create more detailed documentation. A company producing an
application for internal use might need less detailed documentation in
certain areas.
4 Package the software components, and create installation routines if
required.
MicroStrategy Analytics Modules, page 2 for details about
 See
packaged components included with the MicroStrategy Analytics
Modules. The Analytics Modules can be used as templates for
documentation and packaging.
Your final package includes documentation, metadata-ware (MicroStrategy
metadata, a demo data warehouse, and so on), and a demo/evaluation
version of your analytical application.
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Portability rules and recommendations
This section presents development rules and recommendations for creating
enterprise-class portable analytical applications within the context of the
MicroStrategy platform, based on best practices used by MicroStrategy to
develop the Analytics Modules. It includes recommendations for creating
MicroStrategy objects.
The rules and recommendations are important to ensure that an analytical
application is portable. See Chapter 5, Porting the Analytics Modules for a
full explanation of portability. These rules help minimize the impact a
missing data element in the target data warehouse has on the logical data
model (the packaged components). Some of the rules and recommendations
are best practices guidelines and can be applied when you are developing any
analytical application. The rules also facilitate application customization and
extension.
This section is organized into two subsections:
•
Schema Objects: This subsection has rules for developing the schema
objects (attributes, facts, and hierarchies) in the logical data model.
•
Application Objects: This subsection has rules for developing the
application objects (reports, metrics, filters, and so on).
Rules and recommendations include examples from the MicroStrategy
Analytics Modules. See each module’s reference guide and metadata
components for details and definitions.
defining MicroStrategy objects, it is recommended that you
 When
always add a detailed description. This facilitates future changes and
additions, particularly if an object does not comply with the rules and
recommendations below. A detailed description can include such
information as an object’s type, a specific design rule and the reason
why it was not used, a general object definition, guidelines for
customization, and so on.
Schema objects
MicroStrategy schema objects are the representation of the logical data
model in MicroStrategy technology. Schema objects include such items as
attributes, facts, and hierarchies. They are defined with MicroStrategy
Architect.
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Attribute rules and recommendations
Attributes are the entities that represent the business dimensions (business
concepts), such as Customer or Product.
Attribute forms
Attributes are represented by at least one attribute form.
•
Designing the logical data model:

•
Defining the physical schema:

•
Assign two forms to each attribute, an identifier and descriptor. For
example, in the Sales Force Analysis Module (SFAM), a numeric
identifier and a product name represent the Product attribute.
Each form requires a column in the database. For example, an
attribute with two forms requires two columns.
Defining the attribute in MicroStrategy:





Define two forms, ID and DESC, using the standard form of numeric
for ID and text for DESC.
For date attributes, use one form, defined as ID with a form type of
Date.
Set the ID form as the key for the attribute. Set the DESC form as the
display default.
Add descriptions to all forms. Descriptions should indicate the
business meaning and the source.
Avoid compound keys and group forms. Only specific physical
schemas support compound keys.
Attribute form expression
Each attribute form is linked to the physical schema, defining a form
expression. A form expression can be column names from the physical
schema such as CUST_ID or a combination of names using a database
operator, such as [FIRST_NAME]+[LAST_NAME].
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•
Defining the physical model:


•
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Map each attribute form to a single column in the physical schema.
Avoid combining columns using database operators.
Use a homogeneous naming convention by applying the same column
name in all tables where it appears. This convention requires only one
form expression to be set up. If the target warehouse has different
column names, additional form expressions can be added. For
example, in the Sales Force Analysis Module (SFAM), the Order
attribute is represented in all tables by the ORDER_ID column.
Defining the form expression in MicroStrategy:



Use the guidelines in the bullet above for a single column and one
form expression per form, with the same column name in all tables.
If a more complex form is required, avoid database-specific operators
or functions.
Avoid constant values (also called derived attributes) in the form
expression. These are mapped to a specific table and require a similar
table to exist in the target data warehouse.
Attribute relationships
Attribute relationships define how different attributes are associated.
•
Designing the logical data model:


•
Use one-to-many or one-to-one relationships whenever possible,
since they are less dependent on specific table structures. The
Customer Analysis Module Reference Guide provides an example
where all relationships are one-to-many relationships.
Avoid many-to-many or many-to-one relationships, because such
relationships assume a certain physical schema and require a
relationship table.
Defining the physical schema:


Use lookup or relationship tables to establish attribute relationships.
Avoid using fact tables. For example, CAM and SFAM primarily use
lookup tables to establish relationships.
A many-to-many relationship cannot be defined in lookup tables and
requires an additional relationship table. For example, SFAM has a
many-to-many relationship between Opportunity and Product. It is
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defined using a relationship table with Opportunity ID and Product
ID columns.
•
Defining the attribute relationship in MicroStrategy:

Use lookup tables as the relationship table, based on the guidelines in
the bullet above.
Fact rules and recommendations
Facts are the entities that define the business measures, such as revenue,
cost, and price.
Fact expressions
Facts are represented by at least one fact expression in MicroStrategy. Fact
expressions link facts with the physical schema. They can be a single column
such as REVENUE, several columns combined using operations such as
profit=REVENUE-COST, or a constant value.
•
Designing the logical data model:

•
Defining the physical schema:


•
Define facts based on a single column.
Use a homogeneous naming convention by using the same column
name in all the tables where the fact exists. For example, the
Opportunity Size fact exists in two tables in SFAM. In both tables, the
column name is OPTY_SIZE.
Defining the fact expression in MicroStrategy:


•
Represent each fact by a single concept in the logical data model.
Use one fact expression per fact, mapped to a single column.
Irregularities in the target data warehouse, such as different column
names or multiple columns, can be addressed during the
implementation.
If several columns and operators are required, avoid database-specific
functions.
Defining fact expressions with a constant value (derived facts):

Fact expressions with a constant value (derived facts) are used to
define count metrics in a specific fact table, and so they assume a
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specific table name and table structure. Document any derived facts,
including table structure. This facilitates mapping to a target data
warehouse, since derived facts require a fact table with a similar
structure. The Sales and Distribution Analysis Module (SDAM)
provides an example of derived facts, where they are used to enable
counting different attributes from a specific table.
Fact levels
The fact level determines the attribute level at which the fact can be analyzed.
•
Designing the logical data model:

•
Defining the physical schema:

•
Define facts at the lowest attribute level that can be used for analysis.
For example, CAM is defined at the lowest levels used for analysis,
which are Customer, Date, Transaction ID, and Product.
Ensure all required attribute keys are in the fact table. These are
usually the lowest attribute in the hierarchy.
Defining the fact level in MicroStrategy:

Avoid fact level extensions whenever possible. All required relations
must exist at the fact table level.
Hierarchy rules and recommendations
Hierarchies are groups of attributes, organized in a way which defines their
relationships to each other. For example, the Time hierarchy consists of the
group of all time-related attributes such as Year, Quarter, Month, and Date.
MicroStrategy comes with one hierarchy, called System, which represents
the relationships defined at the attribute level. You can also define user
hierarchies to create custom groupings.
•
Defining hierarchies in MicroStrategy:


Use the System hierarchy.
Avoid user hierarchies. However, you can use them to provide prompt
selections for any level in a hierarchy. For example, in both SFAM and
CAM a Time hierarchy is defined to allow time selection in any time
period.
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Rules for other schema objects
You must consider some additional schema objects when building analytical
applications. They include transformations, tables, and mapping methods.
Transformations
•
Defining transformations in MicroStrategy:


If you need transformations, use simple base table transformations.
For example, SFAM includes typical Time transformations such as
Last Quarter. The Quarter lookup table includes an extra column for
the Last Quarter transformation.
Do not use expression-based transformations, especially if they
require database-specific functions.
Tables
•
Defining the physical schema tables in MicroStrategy:


Use the table size calculated by MicroStrategy Architect.
Use the default values generated by MicroStrategy Architect for the
keys.
Mapping methods
This parameter, which applies to attributes and facts, indicates how to
update physical mappings when the data warehouse catalog is updated.
•
Defining a mapping method:


Use the Automatic method because it automatically updates all
mappings when new tables are added.
If required, change the mappings manually in each attribute or fact.
Alternatively, disable automatic checking for all objects.
Application objects
MicroStrategy application objects are the representation of the reports and
business measures in MicroStrategy, such as metrics, filters, prompts,
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reports, and so on. These are defined based on schema objects (attributes
and facts) and are created using MicroStrategy Developer.
Consolidations and custom groups rules and
recommendations
These application objects are used to define special groupings of attributes.
Consolidations
Avoid using consolidations because their definition is based on filter
conditions. Consolidations assume that certain attribute values exist in the
target data warehouse. For more information, see Filters rules and
recommendations, page 140.
Custom groups
Custom groups can be based on filters (qualifying attributes or metrics) or on
banding metric values.
•
Avoid using custom groups based on filters. They assume that certain
attribute values exist in the target data warehouse.
•
Use custom groups based on metric bands. See the Customer Analysis
Module Reference Guide for examples of custom group definitions.
•
Document the definition, including the steps required to customize them
for the target data warehouse.
Base formulas and metrics rules and recommendations
Base formulas are used to define a metric formula without including other
parameters such as level, conditionality, transformation, VLDB properties,
and so on. Base formulas create an additional level of abstraction between
the physical schema and the metrics, that is, the actual calculations.
Base formulas
Use base formulas if several metrics share the same formula.
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If changes are required when implementing the analytical application
against a target data warehouse, applying the changes to the base formula
automatically updates all metrics using that base formula.
The Analytics Modules make extensive use of base formulas. The majority
of the metrics are defined based on a collection of key formulas, which are
located in one folder for easy access. For example, in SFAM, all Revenue
metrics are based on the “Sum of Order Amount” base formula, defined
as SUM(REVENUE). If revenue is calculated as UNITS multiplied by
PRICE in the target data warehouse, updating the base formula to
SUM(UNITS*PRICE) automatically updates all metric definitions that
rely on the “Sum of Order Amount” base formula. This makes the schema
change transparent to the metrics.
•
Do not use database-specific functions in the formula.
•
Minimize the number of facts or attributes (for counts) used.
•
Use simple functions or operators.
•
For some count metrics, special parameters such as FACT ID may be
required. The associated fact must be updated to point to the new
physical schema. If a table with a similar structure does not exist, then the
base formula cannot be implemented.
Metrics
Metrics define how facts are calculated when used in reports. The definition
can include a formula based on facts, attribute counts, or other metrics.
Simple metrics
Simple metrics include a calculation applied to facts or attributes. For
example, the simple metric for Sales is ($)=SUM(DOLLAR_SALES).
•
Creating the metric formula:




Use base formulas whenever possible. See Base formulas and metrics
rules and recommendations, page 137, for more details.
Use simple functions or operators. Do not use database-specific
functions.
Minimize the number of facts or attributes (for counts) used.
Avoid special operator parameters, although FACT ID and count
distinct may be required for some count metrics.
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Adding conditionality to metrics:


Avoid conditions because a qualification implies the existence of
specific values in the target data warehouse. For more information,
see Filters rules and recommendations, page 140.
If conditions are required, document the qualification and how to
customize it to the target data warehouse.
•
For example, a number of conditional metrics were added to SFAM, such
as Qualified Leads, which identifies only those leads that are transformed
into sales opportunities. All filters are located in the same folder. The
Sales Force Analysis Module Reference Guide provides strategies for
updating the qualification to the values that exist in the data warehouse.
•
Adding metric levels:



Use the default value of the report level.
Avoid defining a specific level, which implies the existence of the
attribute in the target data warehouse. For non-aggregatable metrics,
levels require a specific physical schema.
If a level is required, document the definition and how to customize it
to the target data warehouse.
For example, the Analytics Modules include a number of dimensional
metrics as examples of contribution analysis (percent to all
calculations). The modules also include a number of non-aggregatable
metrics.
•
Creating transformation metrics:

Add examples based on typical time transformations, such as Month
vs. Previous Month. For example, the Analytics Modules include some
transformation metrics as examples of time-based analysis.
Compound metrics
Compound metrics are defined based on other metrics and arithmetical
operators. These metrics do not include level, condition, or transformation.
•
Creating compound metrics:

Limit the number of metrics used in the definition. This minimizes the
impact if some metrics are missing from the target data warehouse.
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Other metric parameters
Other metric parameters include formatting, subtotals, VLDB properties,
join type, and so on.

Use outer join as the default for all metrics in both the formula and
metric join type. The join required could differ depending on the
report in which the metric is used. However, this setting can be
changed at the report level.
Most of the metrics in the Analytics Modules use outer joins by
default. This ensures that data for an attribute is always returned,
even if one metric does not return any values.



Use the default value for all VLDB properties. To address
irregularities, change the property at the report level and document
the change.
The metric format is based on the business purpose of the metric. For
example, a Revenue metric is formatted as currency, Lead Counts is
an integer, and so on.
Use defaults for the subtotals. If changes are required for a particular
report, make the changes at the report level and document them as
part of the definition.
Filters rules and recommendations
A filter specifies the conditions that apply to the data included in the report.
A qualification assumes the existence of certain values or a particular data
structure in the target data warehouse.



Minimize filter usage. If a filter is needed, document it and how to
change the definition.
Create a collection of reusable filters. Creating standard filters and
reusing them in metrics and reports minimizes the number of filters
that are needed. When a filter must be modified, the changes are
automatically applied to all objects using it. If filters are defined in
each report, changes must be made in each embedded filter. A
collection of filters has been defined for the Analytics Modules.
Use prompts to define a qualification when the report is executed,
rather than hard-coding it.
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Attribute qualification


Avoid qualifying on attributes, because this assumes the values exist
in the target data warehouse. For example, a filter is defined as
month=Oct-2001. However, the value may not exist or the client
may use a different format for the month ID, such as month=102001.
Do not use database-specific functions when defining the
qualification. If data functions are required for date type filters, use
the MicroStrategy date functions such as Current Date. These are
independent of the database engine.
Set qualification: metrics


Avoid metric qualifications, because they assume the values exist in
the target data warehouse.
Do not use database-specific functions when defining the
qualification.
Set qualification: relationship filters
Avoid using output level, including relate output level and filter qualification,
because it assumes a physical schema structure or table name.
Prompts rules and recommendations
Prompts can be used to define a run-time qualification or to select attributes
and metrics to be displayed in the report.




Use prompts instead of static filters, as discussed in Filters rules and
recommendations, page 140.
Create a collection of reusable prompts. All the reports in SFAM and
CAM include prompts, which are saved in a central location.
Use object prompts to define the report template dynamically or select
it from a list of filters. Document the attributes or metric used,
including an explanation of how to update the prompt if the objects
are missing from the target data warehouse.
Avoid value prompts. If a value prompt is required, do not define the
default values.
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
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Avoid level prompts, because they assume that the metric can be
calculated at a certain attribute level in the target data warehouse.
Reports rules and recommendations
The report object defines how information is calculated and displayed for
each analytic.
Filter definition


Use the rules from Filters rules and recommendations, page 140.
Use embedded filters and prompts instead of static filters. SFAM and
CAM use embedded filters that are stored in the Filters folder. All
reports include prompts to allow dynamic selection. In SFAM, some
reports have static filters to calculate the top ten deals, top three deals,
and so on. These are embedded filters as well.
Template definition: report layout


Limit the number of metrics and attributes in the report. This
minimizes changes if some data elements do not exist in the target
data warehouse.
Use object prompts to allow dynamic selection of metrics or attributes
when they could be different in the target data warehouse.
In CAM, many reports include object prompts to select customer
characteristics such as Age Range and Gender. Because these
attributes may not exist in the target data warehouse, attributes will
need to be added or changed. Consequently, the reports must change.
The advantage to object prompts is that the attributes can be added
without modifying the report definition.
Template definition: formatting



Use the defaults for sorting, attribute display, subtotal, and so on.
Avoid advanced sorting and subtotals. Users can set them up based on
their needs.
Minimize using graphs, because they are very dependent on the report
template and values. If the report changes, the graph may be
invalidated.
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Template definition: other parameters

Use the default VLDB properties.

Use the default values for the report data options.


Avoid report limits because they assume certain values exist in the
target data warehouse.
Avoid metric aliases.
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GLOSSARY
aggregate data Information or facts added together or "aggregated" to form
summaries of information considered as a whole. For
example, weekly revenues are aggregated to determine a
company's monthly profits.
aggregate table A fact table that stores data that has been aggregated along
one or more dimensions.
analytical application In MicroStrategy, a software application designed to provide
predefined reports and other analytics, based on a predefined
metadata repository, for various industries to gain insight
into their business data. The application is not fixed to a
specific physical schema, giving it the flexibility to be ported
to a company’s existing data warehouse.
analytics library The collection of reports and related objects in the
MicroStrategy metadata repository. Library objects include
reports, metrics, filters, and prompts. Library objects are
defined based on attributes and facts (objects in the logical
data model.)
application object MicroStrategy object used to provide analysis of and insight
into relevant data. Application objects are developed in
MicroStrategy Developer and they are the building blocks for
reports and documents. Application objects include these
object types: report, document, template, filter, metric,
custom group, consolidation, prompt.
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Glossary: aggregate data
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Installation and Porting Guide
attribute A data level defined by the system architect and associated
with one or more columns in a data warehouse lookup table.
Attributes include data classifications like Region, Order,
Customer, Age, Item, City, and Year. They provide a means
for aggregating and filtering data at a given level.
See also:
•
form
•
expression
•
child attribute
•
parent attribute
attribute drill path See drill path.
attribute form One of several columns associated with an attribute that are
different aspects of the same thing. For example, ID, Name,
Last Name, Long Description, and Abbreviation could be
forms of the attribute Customer. Every attribute supports its
own collection of forms.
banding A method of organizing values according to a set of
descriptive or meaningful data ranges called buckets. For
example, customers in the age ranges of 10–20, 21–30, and
31–40, where each set of ages is a band. Banding is also used
for display purposes, where every other row is a different
color and the two colors alternate.
Compare consolidation.
base table A fact table that stores data at the lowest level of
dimensionality.
business intelligence A system that facilitates the analysis of volumes of complex
(BI) system data by providing the ability to view data from multiple
perspectives.
146 Glossary: attribute
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Glossary
catalog A table that contains the names of all non-temporary tables in
a data warehouse.
category See dimension or hierarchy.
characteristic attribute An attribute that is a parent of a child attribute, but not part
of the "main" hierarchy associated with the child attribute.
For example, consider a hierarchy consisting of Year, Month,
Day. Day of Week is a parent of Day, and a characteristic
attribute.
child attribute The lower-level attribute in an attribute relationship.
See also:
parent attribute
relationship
column 1) A one-dimensional vertical array of values in a table.
2) The set of fields of a given name and datatype in all rows of
a given table.
3) MicroStrategy object in the schema layer that can
represent one or more physical table columns or no columns.
compound metric A metric that cannot have a level placed on the entire metric,
although it can be set separately on each of the components.
conditional metric A metric containing filter criteria in its definition.
consolidation An object that can be placed on a template and is made up of
an ordered collection of elements called consolidation
elements. Each element is a grouping of attribute elements
that accommodates inter-row arithmetic operations.
Compare custom group.
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Glossary: catalog
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custom group An object that can be placed on a template and is made up of
an ordered collection of elements called custom group
elements. Each element contains its own set of filtering
qualifications.
data modeling A method used to define and analyze data requirements
needed to support the business functions of an enterprise.
These data requirements are recorded as a conceptual data
model with associated data definitions. Data modeling
defines the relationships between data elements and data
structures.
data source name Provides connectivity to a database through an ODBC driver.
(DSN) A DSN generally contains host machine name or IP address,
instance name, database name, directory, database driver,
User ID, password, and other information. The exact
information included in the DSN varies by DBMS. Once you
create a DSN for a particular database, you can use it in an
application to call information from the database.
data warehouse 1) A database, typically very large, containing the historical
data of an enterprise. Used for decision support or business
intelligence, it organizes data and allows coordinated updates
and loads.
2) A copy of transaction data specifically structured for query,
reporting, and analysis.
database connection Stores all database-specific connection information such as
DSN, driver mode and SQL execution mode as well as
connection caching information.
database instance 1) Database server software running on a particular machine.
Though it is technically possible to have more than one
instance running on a machine, there is usually only one
instance per machine.
2) The MicroStrategy object that represents a logical
definition of a data warehouse. It stores all information
necessary for MicroStrategy to access the data warehouse for
a particular project.
148 Glossary: custom group
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Glossary
derived attribute An attribute calculated from a mathematical operation on
columns in a warehouse table. For example, Age can be
calculated from the expression [Current Date–Birth Date].
See also attribute.
dimension An element or factor making up a complete entity or variable
(a quantity that may assume any one of a set of values). For
example, product types, times and regions are three
dimensions pertaining to sales. Different product types are
sold over different time zones in different regions.
dimensionality See level.
drill path (attribute drill In MicroStrategy, a path that determines which attributes are
path) presented to an interface; typically a project defines drill
paths from parent attributes to their children.
entry level The lowest level set of attributes at which a fact is available
for analysis.
ETL (extraction, 1) The process used to populate a data warehouse from
transformation, and disparate existing database systems.
loading)
2) Third-party software used to facilitate such a process.
expression Formulas built from functions, attributes, facts, metrics, and
consolidations that can be used to define attribute forms, fact
calculations, metrics, or filters.
fact 1) A measurement value, often numeric and typically
aggregatable, stored in a data warehouse.
2) A schema object representing a column in a data
warehouse table and containing basic or aggregated
numbers—usually prices, or sales in dollars, or inventory
quantities in counts.
See also metric.
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fact table A database table containing numeric data that may be
aggregated along one or more dimensions. Fact tables may
contain atomic or summarized data.
Compare base table.
filter A MicroStrategy object that specifies a set of criteria used to
limit the data returned in a report. Specifically, it limits the
returned values of an attribute in the result set to a specified
range. It is normally implemented in the SQL WHERE
clause. Examples include: “1997”, “All weekdays in May”,
“Stores in the Northeast”.
folder A MicroStrategy object used for grouping and storing in a
single place a set of objects that are similar, such as filters,
templates, and reports.
form One of several columns that are different representations of
the same thing, as ID, Name, Long Description, Abbreviation.
hierarchy A set of attributes defining a meaningful path for element
browsing or drilling. The order of the attributes is
typically—though not always—defined such that a higher
attribute has a one-to-many relationship with its child
attributes.
hint A comment that passes instructions to a database optimizer
about choosing an execution plan for a given SQL statement.
In MicroStrategy, a hint can be defined in VLDB properties to
appear within a MicroStrategy-issued SQL statement.
HTML document 1) A compound report displaying multiple grids and graphs.
2) The MicroStrategy object that supports such a report.
join A SQL operation that combines data from multiple tables into
a single result table.
150 Glossary: fact table
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Glossary
level 1) In a data warehouse, facts are said to be stored at a
particular level defined by the attribute IDs present in the fact
table. For example, if a fact table has a Date column, an
Item_ID column, and a fact column, that fact is stored at the
Date/Item level.
2) With regard to metric calculation, the level is the level of
calculation for the metric. For example, a metric on a report
with Year and Store attributes would be calculated at the
Year/Store level.
logical data model A representation of the business logic of a given analytical
domain in the MicroStrategy metadata repository. The logical
data model shows the facts and attributes of that business
logic, and the hierarchies between them.
lookup table A database table used to uniquely identify attribute elements.
They typically consist of descriptions of dimensions. Lookup
tables are usually joined to fact tables to group the numeric
facts in the fact table by dimensional attributes in the lookup
tables.
many-to-many An attribute relationship in which multiple elements of a
relationship parent attribute can relate to multiple elements of a child
attribute, and vice versa.
many-to-one An attribute relationship in which (1) multiple elements of a
relationship parent attribute relate to only one element of a child
attribute, and (2) every element of the child attribute can
relate to multiple elements of the parent.
metadata (or metadata A repository whose data associates the tables and columns of
repository) a data warehouse with user-defined attributes and facts to
enable the mapping of the business view, terms, and needs to
the underlying database structure. Metadata can reside on
the same server as the data warehouse or on a different
database server. It can even be held in a different RDBMS.
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Glossary: level
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metric 1) A business calculation defined by an expression built with
functions, facts, attributes, or other metrics. For example:
sum(dollar_sales) or [Sales] - [Cost]
2) The MicroStrategy object that contains the metric
definition.
See also fact.
MicroStrategy A group of MicroStrategy projects with prepackaged
Analytics Modules metadata, including best practices reports, scorecards, and
dashboards, key performance indicators, attributes, business
metrics, filters, and custom groups; default physical and
logical data models to allow the modules to work with your
physical schemas and data model or with the module’s
packaged data warehouse schema; and reference guides for
each Analysis Module’s data model, the individual analysis
areas, metadata object definitions, data dictionary, and
individual report use scenarios.
MicroStrategy Core of the MicroStrategy architecture, MicroStrategy
Intelligence Server Intelligence Server manages and organizes users, projects,
and database connections; coordinates, prioritizes, and
executes all user requests; and allocates the resources
necessary to complete them. It tracks schedules, manages
security, and provides the ability to monitor and analyze the
daily activity of the entire decision support environment.
object Conceptually, an object is the highest grouping level of
information about one concept, used by the user to achieve
the goal of specified data analysis. More concretely, an object
is any item that can be selected and manipulated, including
folders, reports, facts, metrics, and so on.
ODBC (open database An open standard with which client computers can
connectivity) communicate with relational database servers. Client
machines make a connection to a particular logical database,
on a particular physical database server, using a particular
ODBC driver.
OLAP See online analytical processing.
152 Glossary: metric
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Glossary
one-to-many An attribute relationship in which every element of a parent
relationship attribute can relate to multiple elements of a child attribute,
while every element of the child attribute relates to only one
element of the parent. The one-to-many attribute
relationship is the most common in data models.
one-to-one relationship An attribute relationship in which every element of the
parent attribute relates to exactly one element of the child
attribute, and vice versa.
online analytical In general, a system with analytical processing that involves
processing activities such as manipulating transaction records to
calculate sales trends, growth patterns, percent to total
contributions, trend reporting, and profit analysis.
parent attribute The higher-level attribute in an attribute relationship with
one or more children.
See also:
child attribute
relationship
partition A relational database table broken down into smaller
component tables. This can be done at the database level or at
the application level. See the MicroStrategy System
Administration Guide for more information.
partition mapping The division of large logical tables into smaller physical tables
based on a definable data level, such as month or department.
Partitions minimize the number of tables and records within
a table that must be read to satisfy queries issued against the
warehouse. By distributing usage across multiple tables,
partitions improve the speed and efficiency of database
queries.
partition mapping table A warehouse table that contains information used to identify
the partitioned base tables as part of a logical whole. (A
partitioned base table is a warehouse table that contains one
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part of a larger set of data. Partition tables are usually divided
along logical lines, such as time or geography.) Also referred
to as a PMT.
physical warehouse A detailed graphic representation of your business data as it
schema is stored in the data warehouse. It organizes the logical data
model in a method that make sense from a database
perspective.
portability The ability of an analytical application to be integrated into
an existing data warehouse. To port a given Analysis Module,
you “map” the module to the physical schema of an existing
data warehouse.
primary key In a relational database, the set of columns required to
uniquely identify a record in a table.
production metadata The repository you create during the configuration portion of
the installation process, and which works with your data
warehouse and serves as your working metadata repository.
project 1) The highest-level intersection of a data warehouse,
metadata repository, and user community, containing
reports, filters, metrics, and functions.
2) An object containing the definition of a project, as defined
in (1). The project object is specified when requesting the
establishment of a session.
project source Defines a connection to the metadata database and is used by
various MicroStrategy products to access projects. A direct
project source is a direct (two-tier) connection to a metadata
repository. A server project source is a server (three-tier)
connection to a MicroStrategy Intelligence Server. One
project source can contain many projects, and the
administration tools found at the project source level are
used to monitor and administer all projects in the project
source.
154 Glossary: physical warehouse schema
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Glossary
prompt MicroStrategy object in the report definition that is
incomplete by design. The user is asked during the resolution
phase of report execution to provide an answer that
completes the information. A typical example with a filter is
choosing a specific attribute on which to qualify.
ranking A type of OLAP function that returns the rank of a value in a
group of values. Rows with equal values with respect to the
ordering are assigned the same rank.
relate table A table containing the ID columns of two or more attributes,
thus defining associations between them.
relationship An association specifying the nature of the connection
between one attribute (the parent) and one or more other
attributes (the children). For example, City is a child attribute
of State.
See also:
child attribute
parent attribute
report The central focus of any decision support investigation, a
report allows users to query for data, analyze that data and
then present it in a visually pleasing manner.
See also:
filter
template
schema 1) The set of tables in a data warehouse associated with a
logical data model. The attribute and fact columns in those
tables are considered part of the schema itself.
2) The layout or structure of a database system. In relational
databases, the schema defines the tables, the fields in each
table, and the relationships between fields and tables.
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schema object MicroStrategy object created, usually by a project designer,
that relates the information in the logical data model and
physical warehouse schema to the MicroStrategy
environment. These objects are developed in MicroStrategy
Architect, which can be accessed from MicroStrategy
Developer. Schema objects directly reflect the warehouse
structure and include attributes, facts, functions, hierarchies,
operators, partition mappings, tables, and transformations.
simple metric A type of metric that can stand alone or be used as a building
block for compound metrics. Simple metrics always contain
at least one aggregate function, such as sum or average,
applied to a fact, attribute, or another metric. The entire
metric can only contain one level.
SQL (Structured Query The standardized query language established in 1986 by the
Language) American National Standards Institute (ANSI) and used to
request information from tables in a relational database and
to manipulate the tables' structure and data.
table The primary physical component of a data warehouse,
logically consisting of columns of data of varying types.
transformation A schema object that encapsulates a business rule used to
compare results of different time periods. Transformations
are used in the definition of a metric to alter the behavior of
that metric.
156 Glossary: schema object
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INDEX
A
Access database 24, 107
aggregate 51
analytical application 4
advantages 5
architecture 105
best practices 107
designing 105
disadvantages 4
packaging 130
analytical domain
defining 109
designing a report and 125
reporting areas 113
rules 110
size 111
specifications 112
stability 111
analytics library defined on 106
in the architecture 106
porting and 48
Analytics Modules. See MicroStrategy Analytics Modules.
application object defined on 34, 62
comparing 63
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design rules 131, 136
identification example 63
identifying dependencies 62
moving
example 82
order of 79
to a new project 79
updating
example 92
order of 90
attribute defined on 3
design rules 132
determining support 55
mapping. See attribute mapping.
relationship 133
troubleshooting 60
attribute mapping
example 57, 77
to target schema 54
with Object Manager 76
without Object Manager 100
B
banding 137
base formula defined on 137
157
Index
design rules 137, 138
identifying dependencies 64
moving example 83
moving to a new project 81
updating example 95
base table 136
building an analytical application 105
C
catalog, data warehouse 75, 136
characteristic attribute 36
child attribute 60
column 36
compound metric
design rules 139
moving 81
conditional metric
design rules 139
moving 81
configuring a Microsoft Access
database 24
configuring MicroStrategy Analytics
Modules 21
data warehouse connection 25
metadata 23
prerequisites 23
connection type 22, 38
creating 24
consolidation defined on 137
design rules 137
custom group design rules 137
D
data modeling 49
data provider. See project source.
data source name (DSN) 24, 26
data warehouse
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catalog 75, 136
default 32
mapping target schema to logical data
model 54
mapping target warehouse to physical
schema 49
database connection 26, 76
database instance 26
derived attribute 133
derived fact 134
design and development premises 108
designing an analytical application 105
development process overview 109
dimensional metric moving 81
dimensionality 71, 81
direct project source 22
documentation
Analytics Modules 3
developing 108
DSN. See data source name (DSN).
E
Erwin file 121
ETL routine 32, 34
evaluation version 107
expression
mapping logical model to schema
and 55
missing 61
missing column and 54
F
fact defined on 134
design rules 134
determining support 56
mapping. See fact mapping.
troubleshooting 61
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fact level 135
fact mapping
example 59, 77
to target schema 54
with Object Manager 75
without Object Manager 100
fact table
identifying 51
mapping 50
filter defined on 140
design rules 140
identifying dependencies 68
moving
example 82
order of 80
to a new project 80
updating 91
example 97
folder, changing the name of 78
form expression 55, 132
G
gap analysis
designing software to facilitate 118
facilitating 46
overview 48
prerequisites 49
troubleshooting 60
H
hardware requirements 9
hierarchy defined on 135
comparing 51
design rules 135
determining support 56
mapping. See hierarchy mapping.
troubleshooting 61
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Index
hierarchy mapping
example 59, 77
to target schema 54
with Object Manager 75
without Object Manager 99
HTML document object 22
I
installation
file 14
MicroStrategy Architect 23
MicroStrategy Developer 23
prerequisites 9
procedure 10
setup type 14
system requirements 9
international support xxii
J
join type 140
L
logical data model
design quality 114
designing independence 118
designing modularity 117
developing 114
element 116
in the architecture 106
mapping example 56
mapping to target schema 54
mapping to the target schema 54
portability 42
testing 108
lookup table
comparing 50
159
Index
mapping 50
M
mapping
schema object example 76
schema object without Object
Manager 99
metadata defined on 3, 22, 25
building an analytical application
and 105
certified platform 23
configuring 23
metric defined on 138
alias 143
compound, moving 81
conditional
design rules 139
moving 81
design rules 138
dimensional, moving 81
formatting 140
identifying dependencies 64
join type 140
level 139
moving example 84
order of moving 80
simple, moving 81
transformation, moving 81
updating 91
updating example 92
Microsoft Access
configuring 24
in evaluation version 107
MicroStrategy Analytics Module
configuring 21
MicroStrategy Analytics
Modules defined on 2
as a template 105
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customization scenario 34
customizing 34, 103
documentation 3, 47
extending 34, 103
porting 41
using 31
version 107
MicroStrategy Architect 34
as a prerequisite 49
for mapping 73
installing 23
porting and 46
MicroStrategy Developer 22, 34
as a prerequisite 49
for mapping 73
identifying dependencies 56
installing 23
porting and 46
MicroStrategy Intelligence Server 23
MicroStrategy Object Manager 46
as a prerequisite 49
identifying dependencies 56, 80
in mapping 73
modularity 108
module ix
O
object
application 35
customizing 34
HTML document 22
portability and 45
public 35
schema 35
ODBC 22
OLAP 2
outer join 140
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Index
P
partition mapping 54
partitioning 38
performance optimization 38
permissions 38
physical schema
designing 118
Erwin file and 121
ETL routine and 32
in the architecture 106
mapping
example 51
solutions 53
to target warehouse 49
portability defined on 42
building an analytical application
and 105
design driver 43
introduction 42
methodology 43
rules and recommendations 130
porting
documentation 47
overview 48
prerequisites 46
prerequisites for using the Analytics
Modules xii
primary key 121
production preparation 37
project duplication 75
project source defined on 22
connecting 24
direct 22
server 22
prompt
design rules 141
identifying dependencies 67
moving example 82
© 2015 MicroStrategy, Inc.
moving to a new project 80
updating 91
updating example 97
R
ranking 97
reference guide 49
application object and 63
object definition 55
relate table 60
report
design rules 142
designing a library of 123
developing for library 128
identifying dependencies 68
limit 143
moving example 85
moving to a new project 81
testing 82, 92, 126, 129
updating 91
updating example 92
S
schema object defined on 34
design rules 131
mapping. See schema object mapping.
schema object mapping 73
example 76
partial 56
with Object Manager 74
without Object Manager 99
security 38
server project source 22
simple metric
design rules 138
moving 81
software requirements 10
161
Index
SQL
missing expression and 61
testing 123
SQL Server 107
subtotals 140
support
international xxii
support. See technical support.
Installation and Porting Guide
V
VLDB properties 26, 140, 143
T
table
design rules 136
removing after update 100
technical support xxiii
testing a report 126
testing plan
designing 121
executing 122
transformation
design rules 136, 139
determining support 56
mapping. See transformation mapping.
troubleshooting 62
transformation mapping
example 59, 77
with Object Manager 75
without Object Manager 99
transformation metric moving 81
troubleshooting
attribute 60
fact 61
gap analysis 60
U
uninstalling 18
162
© 2015 MicroStrategy, Inc.