Adding Value to the Data Warehouse:
Utilizing OLAP Technology and
Analytical Applications
January 16, 2003
Mark Max, Managing Partner
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Mark Max Bio
email: [email protected]
• B.S. Accounting & M.S. Business –
University of Maryland
• University of Maryland, Instructor
• 20 years Consulting, Corporate, Software
Vendor Work Experience
• Started iStrategy Consulting in 1999
– Maryland based consulting firm specializing in Business
Intelligence and Data Warehousing
– Principals have been working in BI for 15+ years
– Experience in BI/DW for higher education
– Launching new DW/Analytical Application for Higher
Education in Q1 2003
Discussion Points
1. Information Delivery Challenges
2. Data Warehousing and Business
Intelligence Technology
3. Higher Education Analytical
Application Framework
4. Demonstration
5. Q&A
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Shift Towards Information Based
Management – High Visibility Areas
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Recruiting Effectiveness
Retention
Enrollment Funnel
Student Demographics
Course Planning
Resource Management
Outcomes Management
Compliance Reporting
Early Intervention
Key Performance Indicators
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Emerging Strategies in in Higher Education
Strategic Enrollment Management (SEM)
“Strategic Enrollment Management is a comprehensive process
designed to achieve and maintain the optimum recruitment,
retention, and graduation rates of students where ‘optimum’ is
defined within the academic context of the institution”.
Strategic Planning Engine (SPE)
“The heart of the Strategic Planning Engine links strategic
decision making with organizational key performance indicators
(KPI's).”
from Michael G. Dolence & Associates
These processes require information!
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Typical Reporting Challenges
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Financials
•
Housing/
Judicial
Human
Resources
•
Student
Admin
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Recruiting
Alumni
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•
No central repository of official
information – many non-integrated
systems and databases
Databases are structured for
transaction processing, audit trail and
operational needs; they are not
organized for ease of reporting!
Lack of standardized metrics and
information rules (e.g., how is
retention % calculated?)
Some information needs require data
from multiple systems (e.g., Cost per
Student)
Many informal databases and
spreadsheets used by individuals for
reporting, analysis, external reporting
No standardized tools for reporting and
analysis
Application Reporting Complexity
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Student Administration
application database structures
are very complex
Reporting requires queries for
database extracts – need to
know SQL language
Reporting results are subject to:
– a) users understanding of
database structure,
– b) “interpretation” of query
criteria, and
– c) proper SQL syntax.
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Its easy to get the wrong
answer!
No easy way to combine data
across multiple systems and
database.
Limited number of people who
know how to query databases
The Impact
• No ability for self service access to
information – users are totally
dependent upon others to produce
information
• Time consuming, manually
intensive process to produce reports
• Different people produce reports
with the same information but have
different results
– What is the real answer?
– How do you know the information is
correct?
• Have to repeat the same time
consuming process each time you
want a report
• No time available for analysis
because of the extensive time
required to produce information
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Data Warehousing and
Business Intelligence Architecture
Data Sources
Data Warehouse
Data/Application Servers
Departmental
Data Marts
Financials/HR
OLAP
Server
Data
Mart
Data
Mart
OLAP
OLAP
Data
Mart
Data
Mart
OLAP
OLAP
E
Business
Intelligence
Analytical
Applications
OLAP
Tools
E T L
T
Student
Enterprise Data
Warehouse
L
Relational
Query &
Reporting
Tools
Data Mining
Other
ETL – Extraction, Transformation and Load
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2002 Higher Education ERP Survey
• 39% of institutions surveyed have implemented
or are in the process of implementing a Data
Warehouse
• 37% of institutions surveyed plan to implement
a Data Warehouse within the next three years,
with almost 1/3 of the projects beginning in
2003
Source: The Promise and Performance of Enterprise Systems,
2002 ECARS Research Study by Dr. Robert Kvavik
(500 Institutions surveyed)
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Recipe for Failure
• Start by looking for application data to
source a DW
• Move as much transactional data as
possible into a “warehouse database”
• Purchase a relational reporting or query
tool
• Send users to training
-- This approach rarely works! --
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Big Difference between
Data vs. Information vs. Knowledge
• Data – raw facts that have been collected,
processed, stored, but not organized to convey
meaning.
• Information – a collection of data organized in a
manner to be meaningful to a recipient.
• Knowledge – information combined with
understanding, experience, accumulated
learning, and expertise relevant to a problem,
decision, or process.
Data Transformation, Derivation and
Aggregation are necessary, along with a
self service access capability!
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DW Casual User vs. Power User
• Different audiences with different:
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Information needs
Analytical capabilities
Technical aptitudes
Level of insight into application data
Time constraints
• 80% – 90% of information consumers
are casual users
Need to consider both in technology decisions
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Confusing BI Product Space
• 25 to 50 legitimate vendors; many overlapping products that
may appear similar but are fundamentally different
• Reporting vs. Analytics – there’s a big difference!
• Relational vs. OLAP Technology
– MOLAP vs. ROLAP vs. HOLAP
– Multidimensional Presentation vs. OLAP engine
• Products/Vendors: Front-end only vs. Back-end only vs. Both
• Open vs. Proprietary platforms
• Web vs. Client Server
– HTML vs. Rich web client (JAVA, Active-X)
• Open component architecture vs. self contained products
– Portal integration
Conclusions
 There’s no magic product that does it all!
 Understand your user base, information needs and
objectives before selecting BI technology
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Why OLAP Technology?
• Multi-dimensional presentation is the
natural orientation for business
information and analysis
– Intuitive and easy to use
– Hides user from underlying relational data
model
• OLAP Technology is very fast
– Most reports run within 1-3 seconds
– Speed advantage substantial in highly
aggregated reports such as multi-year
trends
– Without OLAP, the burden is on the
developer to build the aggregation
• Enables calculations that are impractical
using relational technology
– e.g., moving averages, prior period %
change
• Produces consistent information
– Pre-calculated results
– Not subject to unexpected SQL query
behavior
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Aggregation Management:
Relational Summary Tables Scenario
• Fact table with four dimensions
• Each dimension has four levels in its hierarchy (e.g.,
Time: Section, Course, Subject, All)
• How many summary fact tables are required to support
every combination of dimension level?
255
• If you don’t build 255, how many should you build and
which ones?
• What if you have a 20 dimensional Student Term Fact
Table?
• OLAP Technology makes aggregation management very
easy!
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Why an Analytical Application?
(vs. Reporting Tools)
• Casual Users – majority of information users (80 – 90 %)
are casual users who will have difficulty mastering a
reporting tool. An Analytical Application will be much easier
to use and be more highly utilized
• Hide Database Complexity – most reporting tools require
the user to understand the reporting database content and
relationships. An analytical application enables casual users
to get information without understanding the underlying
database and functionality of reporting tools
• Guided Analysis – an application framework provides the
opportunity to guide users through an analytical process
and better leverage the metrics and analytical capabilities
inherent in the solution
• Personalization – provide users with the ability to
personalize their content and interface
• Embed Customized Analytical Functionality – enables
customized application functionality to be integrated with
reporting (e.g., Student Peer Group Analysis)
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What the experts are saying!
“ ...most decision support software is gathering
dust on office bookshelves”
“Whether you build and/or buy, the key is to …
deliver a robust analytic application that delivers
the information and analysis that business users
need.”
Wayne Eckerson, Director of Education and Research for
The Data Warehousing Institute (TDWI)
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Analytical Application for Higher Education
• Information Scope
– Serve a broad audience: institutional research, management
reporting, compliance reporting, operational analysis
– Span complete student lifecycle: admissions, enrollment,
course activity, graduation
– Address key objectives: recruiting effectiveness, retention,
student achievement, course curriculum and schedule
• Provide self service access to information:
– Intuitive and easy to use (the basics are simple)
– Minimal training required
– Easy to deploy
• Functionality:
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Interactive standard reports and charts,
Guided Analysis,
Key Performance Indicators (KPIs),
Personalized Dashboard (KPIs and Charts)
Ad hoc analysis,
“Actionable” analytical tools (e.g., support early intervention
through student risk analysis, student peer group analysis)
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Higher Education Analytical Application
Information Delivery Engine
Analytical Application
Personal
Dashboard
Ad Hoc
Analysis
Personal
Reports
Key Perf
Indicators
Guided
Analysis
Analytical
Modules
Download
Extracts
Compliance
Reports
Standard
Reports
Admissions
Student
Term
Data Warehouse
Class
Faculty
Offering
Term
Student
Class Enr.
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Graduation
Information Consumers
Strategic
Planning
Compliance
Reporting
Institutional
Research
Academic
Affairs
Admissions
Office
Deans/
Assoc. Deans
Registrar’s
Office
Department
Chairs
Financial
Aid
Administrative
Departments
Demonstration
Background
Information
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Student Administration Information
Categories
1. Admissions
2. Student Demographics
3. Enrollment Trends
4. Retention
5. Class Offering and Utilization
6. Student Class Enrollment
7. Student Performance
8. Student Risk Analysis
9. Student Peer Group Analysis
10.Graduation
11.Faculty Information
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Student Administration
Dimensional Data Model
Dimensions
Admissions:
• Application Method
• Applicant Home State
• Prior Applicant Ind.
• Applicant Fin Aid Interest
• Applicant Housing Interest
• Recruiting Category
• Applicant Status
• Admit Category
• Cohort
Faculty Attributes:
• Faculty
• Faculty Ethnicity
• Faculty Gender
• Faculty Rank
• Tenure Status
Graduation:
• Graduated Indicator
• Degree
• Years to Graduate
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Fact Areas
Admissions
Student Term
Class
Offering
Student
Class
Enrollment
Faculty
Term
Graduation
Dimensions
Institutional:
• Term
• School/Major
• Academic Department
Student Term:
• Academic Level
• Academic Standing
• Student Term Status
• FT/PT Indicator
Class/Grade:
• Subject/Class
• Course Level
• Class Type
• Grade
• GPA Band
Student Attributes:
• Student
• Student Citizenship
• Student Ethnicity
• Student Gender
• Student Home State
User Interface Terminology
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Grid/Chart
Presentation Orientation: Rows, Columns, Pages
Dimension/Measures
Hierarchy
Drill Down
Page Selection
Rotate
Dimension Filtering
– Top/Bottom Ranking
– Exception based selection
• Drill to Detail
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Application Demonstration
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Technology Architecture
Windows 2000 Server
ProClarity Analytical Server
Microsoft IIS Web Server
Microsoft Analysis Services
Microsoft SQL Server
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Data Warehouse Architecture
DW Build Process
1. Bulk load data from
transaction system into
temporary staging tables
(most recent n terms)
2. Perform edit, data
derivation and relational
DW build transformations
3. Build aggregate OLAP
cubes
Microsoft SQL Server Data Warehouse
Microsoft Analysis Server (OLAP) Cubes
3
Data Transformation Services (DTS)
Relational Warehouse
Dimensions/Attributes Star Schema Fact Tables
Edit & Transformation
Student Admin
Application
2
Data Transformation Services (DTS)
Staging Tables
Operational
Databases
Flat Files
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Bulk Load Process
1
Data Transformation Services (DTS)
Keys to Success
• Set reasonable expectations
– It’s impossible to address every imaginable information need
– It’s better to successfully deliver 80% - 90% of the
requirements than to deliver nothing
– Continue to expand scope based on needs
• Target a quick success story
• Ensure that the casual users have an application interface
that is:
– Simple to use
– Fast
– Supports analytics as the user skills develop
• Design must incorporate transformation of data to a
dimensional data model
• Provide a good support infrastructure
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