Business Intelligence Fad, Oxymoron, or Something Useful

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Business Intelligence Fad, Oxymoron, or
Something Useful?

• Peter Westfall
• With some help from his students in the Directed
  studies in Business Intelligence class

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Outline of Presentation

• Definitions of BI
• Categorizations of BI
• Uses of BI
• Some Data Mining Applications

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Business Intelligence Definition

• Simply Transforming Business Data into Action
• What Data?
• Text, numeric, sound, pictures, video.
• - IDG News Service, Wednesday, October 29, 2003
  Stored Data Doubles in Three Years Information
  overload hits new levels, UC Berkeley researchers
  find.
• More data than we can handle with our personnel
  -GM visit
• Firehose of data -researchers at Los Alamos,
  Counter-terrorism

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Types of BI

• Basic
• Making relevant business data available to all
• Common version of the truth
• Empowerment
• More advanced
• Business Problem -gt BI solution (structured)
• Mine data bases for opportunities (unstructured)

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BI Problems (Structured)

• Detecting Credit card fraud
• Setting Loan parameters
• Market segmentation/Mass customization
• Deciding Marketing mix
• Customer Churn
• Reducing employee turnover
• Improving Quality/Efficiency

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Data

• Common to structured and Unstructured BI Data
  identification/acquisition
• BI Process
• Problem -gt Data/Information -gt Extract,
  Transform, Load -gt Analysis -gt Action

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Unstructured BI

• Explore data bases
• Analysis at the speed of thought
• Identify anomalies, actionable clusters
• Utilization of resources (stored knowledge) to
  maximum effectiveness

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The BI Team
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CAABI

• Emergent Center for Advanced Analytics and
  Business Intelligence, Dept. of ISQS, Rawls
  College of Business.
• Looking to offer support to companies in
  developing BI capabilities.
• Lots of technical expertise.
• Free! (for now). Looking to involve students,
  develop domain competencies.

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Data Mining For Advanced BI

• Clustering (eg, customer segmentation)
• Affinity (eg, what items do people buy together)
• Exception analysis (eg, credit card fraud,
  terrorism)
• Predictive Modeling (eg, deciding loans,
  predicting employee turnover, predicting likely
  customers)

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Requirements of DM Tools

• Simple (even an MBA can use it)
• Actionable results
• Flexible, open-ended (Analysis at the speed of
  thought)
• Scale-Up Can handle massive data sets
• Drill-Down Ability to investigate sub-units

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A Data Mining Application

• Identify variations in quality of wafer
  manufacturing process
• Data set courtesy Intel, Inc.

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