1. Business Insight Services Where Does Location Intelligence Fit in An Enterprise Data Mining/BI Strategy? Tim Pletcher pletc1ta@cmich.edu

2. Business Insight Services 2005 © CMU Research Corporation 2 A Broad Definition of Business Intelligence CMU-RC uses the Data Warehousing Institute’s definition of Business Intelligence (BI) to gain insight from data for the purpose of taking action. This definition encompasses the broad suite of business analytics: predictive modeling, data or text mining, geographic information systems, statistical analysis, operations research, systems dynamics, simulation, and advanced data visualization.

3. Business Insight Services 2005 © CMU Research Corporation 3 Common Applications for BI

4. Business Insight Services 2005 © CMU Research Corporation 4 Value Creation Time  When Spatial  Where

5. Business Insight Services 2005 © CMU Research Corporation 5 Reporting

6. Business Insight Services 2005 © CMU Research Corporation 6 LI Inspired Data for Business Intelligence Aerial/Imagery Data Street and Cartographic Data Census Geography and Data Customer Data Competitor Data Store Location Data Census/Postal Geography Street Networks Demographics Spatial Segmentation Aerial Photos and Land Use Data GPS & RFID captured/fed updates –Consumer Expenditure Data –Retail transactions –Market Potential Data –Shipping volumes –Utility usage –Traffic Counts

7. Business Insight Services 2005 © CMU Research Corporation 7 Unique Spatial Techniques Market Area Boundaries Drive Times Desire Lines Market Penetration Site Selection Gravity Models ETL for spatial data (Soils volumes/zip to census) Spatial Queries –E.g. based on Demographic or Household Data Spatial Statistics Networks and Process Maps

8. Business Insight Services 2005 © CMU Research Corporation 8 Advanced Visualization

9. Business Insight Services 2005 © CMU Research Corporation 9 Location Intelligence is Evolving with BI Desktop Tools & Data Client/Server Systems Projects Departmental Enterprise Platform Web Services Networks

10. Business Insight Services 2005 © CMU Research Corporation 10 Web Browser Web Server Application Server Web Server Application Server Database Server Connectivity Layer HTTP/SOAP Presentation Layer JSP/Java Servlets/JSP Tag Libraries BSP/BSP Extensions Business Layer ABAP/J2EE Persistence Layer JDBC/Open SQL Integration Layer Java Connector.NET Connector GBC BC XML SOAP BAPI/RFC XI Other …….. Tier 3 Tier 2 Tier 1 SAP Web Application Server Embedded Solutions : e.g. SAP Integration

11. Business Insight Services 2005 © CMU Research Corporation 11 Multiple Solutions That Span the Enterprise Views Analysis Mission Critical Applications Products Updates & Transactions

12. Business Insight Services 2005 © CMU Research Corporation 12 Enterprise Technology Adoption Well Understood Not Well Understood Economies of Scale Emerging Technology EnterpriseBusiness Unit BI LI

13. Business Insight Services 2005 © CMU Research Corporation 13 Enter the BI Competency Center A BI Competency Center is a group chartered to advocate and bolster the adoption of BI in the enterprise. Some specific charters –Generate awareness for executives and line managers about the competitive advantage and ROI –Inter Silo-data sharing –Establish standards and methodologies –Raise the alarm about the need for data quality –Ensures that quality analytics and applied

14. Business Insight Services 2005 © CMU Research Corporation 14 Models/Homes for a BI Competency Center Possible Structures or Organization Homes –Project management offices –Six Sigma & Continuous Quality Improvement –Repurposed Operations Research Teams –Newly constructed teams at strategic level or in IT Key Team Characteristics –Understands the business drivers –Can work with a process and get results –Ability to apply technology, but recognizes it is not about technology –Quantitatively competent.. Including spatial analysis

15. Business Insight Services 2005 © CMU Research Corporation 15 One Example Scenario: A large company wanted to understand their risk related to warranty on a product. Previous attempts using traditional analysis continued to miss the mark each quarter (by many millions of $). There was a physical driver for the defect (moisture, soil permeability, temperature, etc.) There was a people driver for the claim rate (once it started there was a claim “fad”) Result: A robust forecast using neural networks to score the data and predict the amount of claims that would occur during the warranty period.

16. Business Insight Services 2005 © CMU Research Corporation 16 Model Results The company had three groups do modeling. All produced the bottom line result with fairly close estimates. Example: $ XXX,XXX,XXX of future warranty expenses can expected to occur during the remaining warranty period for the product. This result has a 98% confidence interval within $ YYY,YYY,YYY and $ ZZZ,ZZZ,ZZZ

17. Business Insight Services 2005 © CMU Research Corporation 17 Predictive Modeling

18. Business Insight Services 2005 © CMU Research Corporation 18 Combining LI and BI

19. Business Insight Services 2005 © CMU Research Corporation 19 Actual Claims History

20. Business Insight Services 2005 © CMU Research Corporation 20 Predictions

21. Business Insight Services 2005 © CMU Research Corporation 21 Results

22. Business Insight Services 2005 © CMU Research Corporation 22 Contact Information THANK YOU! Timothy A Pletcher Director of Applied Research Central Michigan University Research Corporation Phone: (989) 774-2424 tim.pletcher@cmich.edu