TechUpdate
TechUpdate is published quarterly and is available exclusively at www.tdwi.org.
By: Michael L. Gonzales
HandsOn-BI, LLC
Quarter 1, 2006
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Table of Contents
Introduction.............................................................................................................. 3
Machine Intelligence Quotient (MIQ) ....................................................................... 4
MIQ Survey ........................................................................................................................ 5
HeadsUp ............................................................................................................................. 6
MIQ Survey Results ................................................................................................ 8
Cognos ................................................................................................................................ 8
Business Objects XI r2 ....................................................................................................... 9
MicroStrategy Version 8 .................................................................................................... 9
Hyperion System 9 ............................................................................................................. 9
TIBCO .............................................................................................................................. 10
Appendix A – Decision Making.............................................................................. 11
Figures and Tables
Figure 1 – MIQ Survey Results ............................................................................... 6
Table 1 – MIQ Survey Detail ................................................................................... 8
Figure 2 – Decision Process.................................................................................. 11
Figure 3 – Control level given based on MIQ ........................................................ 12
2
Introduction
Today’s business climate makes greater demands on the decision-making
processes of organizations than ever before. Financial investment planning,
currency trading, nuclear reactor operations—these are just three examples of
the remarkably complex disciplines that pervade the modern world of work. To
remain competitive in such a climate requires smarter decisions, taken ever
more quickly. This dictates widening the scope and scale of the data domain,
the analytic landscape, and the technological infrastructure.
The relationship between human and machine intelligence must therefore be
the focus of BI architects going forward. Machine intelligence has proven itself
central to our ability to manage more complexity and the Machine Intelligence
Quotient (MIQ) will serve as the watermark for measuring the capability of
your BI environment.
The Chief BI Architect in an organization must lobby for and establish an
intelligent service layer in the BI infrastructure, using MIQ to discern those
modern technologies that support this new service layer. Machine intelligence,
tools and techniques, implemented as a multi-agent network, will provide the
decision-support platform promised by BI teams and envisioned by company
executives.
This edition of the TechUpdate focuses on assessing the MIQ of traditional
technologies marketed as BI solutions, as well as one that is not considered
part of the BI space. We established a means to survey the technologies
mentioned such that we could assign a MIQ survey score to each. You may be
surprised by the results.
3
Machine Intelligence Quotient (MIQ)
Recently, the AI and intelligent systems of the late 1990s have experienced a
resurgence, as modern “smart systems” have exhibited abilities to adapt and
learn--performing “rational” decision-making with little or no human
intervention.
Professor Lofti Zadeh, director of the Berkeley Initiative in Soft Computing, is
credited with coining the term “MIQ” to describe the measure of intelligence of
man-made systems with machine intelligence. Zadeh says that systems
exhibiting high levels of MIQ are mostly hybrid intelligent systems, combining
hard and soft techniques often implemented as a network of agents.
Financial investment planning might use neural networks to watch for patterns
in the stock market, or genetic algorithms applied to predict interest rates, or
fuzzy logic (to approximate reasoning) to determine client risk-tolerance with
regard to financial investments. Each of these—neural networks, genetic
algorithms, and fuzzy logic—are considered soft computing techniques. There
are, of course, hard computing techniques that can be applied to complex
problems such as business rules engines and expert systems. Those systems
with the highest MIQ combine hard and soft techniques with traditional
reporting and process control to achieve the promise of actionable insight.
There is considerable debate surrounding machine intelligence—ranging from
the original Turing Test1 to an array of sophisticated indexing, numeric or
linguistic frameworks, all of which attempt to quantify machine intelligence2.
MIQ is a measure, a means to assess, to quantify the intelligence of an
autonomous system. In this Tech Update, we will focus on using a simple
inventory of techniques and technologies associated with machine intelligence
in order to achieve two objectives:
-
Illustrate the various components important to machine intelligence.
Provide a means for readers to quickly, quantifiably estimate the level of
MIQ in their own environments.
1
Turing, Alan, “The Imitation Game,” Computing Machinery and Intelligence, Vol. 59, No. 236, pp. 433460.
2
Park, Hee-Jun, “Measuring the Machine Intelligence Quotient (MIQ) of Human-Machine Cooperative
Systems,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 31, No. 2, March 2001.
4
MIQ Survey
From the perspective of a BI architect, measuring the MIQ of the BI
environment starts with a simple of inventory of the technologies and
techniques exploited to support better decision-making. Table 1 contains four
classes that help quantify the level of machine intelligence in your BI system.
Within each class are a few examples that serve to indicate the application of
the techniques or the existence of the necessary infrastructure and autonomy
of its agents.
Table 1 – MIQ Survey
Class
Hard
Intelligence
Soft
Intelligence
Infrastructure
Autonomy
Component Examples:
Tangible, Measurable Evidence of
MIQ
Expert System
Business Rule Engine
Fuzzy Logic
Neural Network
Genetic Algorithms
Message Broker
Web-based Service
Self-learning
Self-reconfigurable
Reasoning
Product Name &
Support (Yes/No)
Readers should quickly be able to discern the level of MIQ in their
organizations by examining the components of the survey. If you are not
exploiting any of them, then chances are your environment is more of a
passive repository, reporting obvious operational data to users. It may
efficiently do so, but it can hardly be described as providing business
intelligence. On the other hand, if you are implementing three or four of the
components, then your MIQ is quite high, especially if you are implementing
some component(s) from each of the classes listed. A high MIQ does not
guarantee that your BI environment is providing actionable insight to all user
communities, but it does suggest that you have the necessary infrastructure
and foresight to do so. For more information, refer to “What’s Your BI
Environment IQ?.”3
3
Gonzales, Michael L., “What’s your BI environment IQ?,” DM Review, July 2005.
5
HeadsUp
Many products that are typically advertised as BI tools are really not active participants in
business intelligence, other than as conduits for publishing and viewing results. Often, such
products are just reporting front-ends. Their value is found in publishing BI insight, not
actually gleaning that insight. Much of the real BI effort is done in some other tool(s)
and/or environments, such as relational databases4.
In this study, we compared technologies that are widely accepted as BI tools with a product
suite not considered part of the BI space. We examined Cognos, Business Objects,
MicroStrategy, and Hyperion as representatives of typical BI tools, whereas TIBCO is our
non-traditional BI product suite. The results of our study are shown in Figure 1.
Figure 1 – MIQ Survey Results
MIQ Survey Results
20
15
10
Series1
5
0
Cognos
BOBJ
MS
Hyperion
TIBCO
Product Suite
As you can see, given the components of the MIQ survey, it is the non-traditional
technology, TIBCO, that provides more BI functionality than any of those products often
considered as BI solutions. The results of this study are critical for BI architects to consider.
4
Refer to the HandsOn-TechUpdate, Quarter 1, 2005, www.TDWI.org.
6
Business intelligence is never manufactured from a single piece of data or a particular
event. Instead, BI is a composite image made from several disparate data pieces and events
that, when taken as a whole, create intelligence. Think of an image displayed on a computer
screen. The image manifests itself only by the synchronized effort of hundreds of pixels,
each representing a single piece of data. Although the pixel is critical to create the overall
image, it is the image itself that provides value.
The most difficult challenge in delivering BI is to create, reliably and on-demand, an
amalgamation and correlation of data and events in order to produce a BI image that fulfills
a specific business need. This difficulty comes mainly from the fact that traditional
technology and techniques used in BI efforts are focused on a particular task, such as ETL,
OLAP, or Query and Reporting. These warehouse-centric technologies are simply not
designed to reach across the enterprise and -- in near-real-time -- monitor, collect, and
analyze an array of data and events, creating the BI necessary for better decision-making.
However, just because warehouse-centric technology cannot achieve these tasks does not
mean that no technology exists to do so. The results of this study demonstrate that point.
7
MIQ Survey Results
A panel of expert practitioners were used to assemble the responses below.
Each expert provided the research for each of the four product suites
evaluated, including Cognos v8, Business Objects XI r2, MicroStrategy v8,
Hyperion System 9, and TIBCO.
A score is applied in order to accommodate for partial implementation of
feature sets. The scale is from zero to five. Zero represents no presence of
features, whereas five is the full application of the features typically associated
with the component being evaluated. Values such as two, three, and four are
applied by the expert reviewer based on their best judgment.
Table 1 – MIQ Survey Detail
Class
Hard
Intelligence
Component
Expert System
Business Rule Engine
Cognos
0
0
BOBJ
0
0
MS
0
0
Hyperion
0
2
TIBCO
0
4
Soft
Intelligence
Fuzzy Logic
Neural Network
Genetic Algorithms
0
0
0
0
0
0
0
0
0
0
5
0
0
0
3
Infrastructure
Message Broker
Web-based Service5
0
-
2
-
0
-
2
-
5
-
Autonomy
Self-learning
Self-reconfigurable
Reasoning
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
2
0
9
16
Totals
Cognos
Cognos epitomizes the typical BI solution promoted today in that the product
suite provides a great means to publish BI content, but does little to contribute
to business intelligence itself.
5
Since there is substantial debate on what constitutes a web-based service, this study elected to leave it out
for all products suites.
8
Business Objects XI r2
There are a couple of features in the new release of BOBJ that at least lend
themselves to the notion of business intelligence. For example, Intelligent
Questions is a new module that helps users define requests for information.
While it may be more than building SQL code, it hardly achieves a relevant
level of MIQ. This author witnessed the application of Intelligent Question
during a custom workshop and was less than impressed.
Moreover, BOBJ includes a message service; however, it is dependent on a set
schedule established by the user. Again, while BOBJ is providing some features
that move toward higher levels of MIQ, they still fall short.
MicroStrategy Version 8
MicroStrategy provides an Analytical Engine Calculation Plan (AE) for SQL
View. Essentially, a SQL query is executed, the resultant set is processed by
the AE before the report is published to the users. The AE can perform
mathematical and logical calculations, including such functions and Thresholds,
Subtotals, Derived Metrics, and Consolidations among others.
Not unlike Business Objects, while the features may move toward higher MIQ,
they still fall short of the features required for real BI.
Hyperion System 9
A product resembling the functions of a Business Rule Engine is provided in
Hyperion System 9, specifically Hyperion Business Modeling (HBM). The
product provides a procedural modeling environment for creating and
executing business process models that include conditional rules. While HBM
does not compete with a full-featured business rule engine, it does provide
reasonable functionality.
Hyperion Essbase now comes with several data mining algorithms to mine
against multidimensional data. The algorithms include Simple Regression,
Multi-linear regression, Clustering, Neural Networks (NN), Decision Trees,
Association Rules, and Naïve Bayes.
9
Hyperion Application Link is good for applying complex rules for data
transformation with connectors for more than 60 data sources, for example,
Websphere MQ, Peoplesoft, SAP, XML, and others. The product supports the
Hyperion products for both the business rule engine as well as a message
broker.
TIBCO
There are several products within the TIBCO family that add to the MIQ of the
entire product suite, they include Staffware Rules Manager, BusinessEvents,
BusinessWorks, Enterprise Messaging Service, Rendezvous, and iProcess.
What is important to note is that TIBCO is often associated with the network
software management market niche as opposed to BI. But, in fact, this product
provides more true BI functionality than any product typically considered by BI
teams, including those of this study.
10
Appendix A – Decision-Making
Building systems to support better decision-making requires answers to the
following two questions:
What is the decision making process? You must understand the DM
process before you can support it. There exists a mature body of research
called Decision Support Science (DSS) that can assist.
How can the BI environment make the DSS process better? Once the BI
architect has a grasp of DSS, it is important that they answer two subordinate
questions:
What are the most dominant decision-making process patterns used in my
organization?
What technologies and techniques can I implement in my BI environment to
support those DM patterns?
Figure 2 – Decision Process
5. Evaluate
Options
Goals
4. Predict
Consequences
Target
Options
3. Identification
6. Choice of Task
Information
Task
2. Observation
7. Planning
Procedure
Alert
1. Activation
8. Execution
Event
To understand the BI architect role in DSS, we must first understand what is
involved in the DM process. Although there are many models that attempt to
formalize this process, this paper chose Rasmussen’s Decision Ladder since it is
11
closely associated with the growing body of research attempting to model the
human-machine interaction necessary for today’s complex, real-world systems.
Rasmussen defines eight steps6 to the decision-making process as illustrated in
Figure 2. Assuming that these eight steps accurately summarize the decision
process, then BI architects must ask how much of this process can be
addressed by the BI infrastructure we implement for our organizations.
Figure 3 – Control level given based on MIQ
Predict
Evaluate
Predict
Evaluate
Identify
Choice of
Task
Identify
Choice of
Task
Observe
Planning
Observe
Planning
Activation
Execute
Activation
Execute
Machine
Control
Human
Control
Machine
Control
Human
Control
For example, the diagram on the right in Figure 3 places the majority of the
decision process to human intervention. The machine activity is limited to the
more traditional role of activating the process once an event is detected,
interpreting and predicting the impact of the event, and finally executing the
plan chosen by human intervention. This balance may be effective in some
cases, but it simply does not scale. When we increase the amount of data
volume to consume, add real time analysis, and a fairly complex problem to
resolve, leaving most of the analytic processing to a human exposes your
organization to very high risk. The diagram on the left in Figure 3, however,
requires more machine participation in the decision-making cycle. This
configuration affords greater scalability.
6
Rasmussen, J., Information Processing and Human-Machine Interaction – An Approach to Cognitive
Engineering, 1986.
12