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

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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.

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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. 433-

460. 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.

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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 Component Examples:

Tangible, Measurable Evidence of

MIQ

Product Name &

Support (Yes/No)

Expert System Hard

Intelligence Business Rule Engine

Fuzzy Logic

Neural Network

Soft

Intelligence

Genetic Algorithms

Message Broker Infrastructure

Web-based Service

Self-learning

Self-reconfigurable

Autonomy

Reasoning

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.

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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

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.

MIQ Survey Results

0

5

10

15

20

Cognos BOBJ MS Hyperion TIBCO

Product Suite

Series1

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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.

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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 Component Cognos BOBJ MS Hyperion TIBCO

Expert System 0 0 0 0 0 Hard

Intelligence Business Rule Engine 0 0 0 2 4

Fuzzy Logic 0 0 0 0 0

Neural Network 0 0 0 5 0

Soft

Intelligence

Genetic Algorithms 0 0 0 0 3

Message Broker 0 2 0 2 5 Infrastructure

Web-based Service5 - - - - -

Self-learning 0 0 0 0 0

Self-reconfigurable 0 0 0 0 4

Autonomy

Reasoning 0 0 0 0 0

Totals 0 2 0 9 16

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.

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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.

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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.

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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

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

Event

1. Activation

2. Observation

3. Identification

4. Predict

Consequences

5. Evaluate

Options

6. Choice of Task

7. Planning

8. Execution

Goals

Options

Target

Alert

Information

Procedure

Task

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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

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.

Activation Execute

Observe Planning

Identify

Predict

Choice of

Task

Evaluate

Machine

Control

Human

Control

Activation Execute

Observe Planning

Identify

Predict

Choice of

Task

Evaluate

Machine

Control

Human

Control