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Business Intelligence

Business Intelligence. BI Deployment Case Study. Bruce Jones Director of Information Systems DC Lottery and Charitable Games Control Board 2009 NASPL IT Subcommittee Meeting Colorado Springs, CO September 14-17, 2009. BI Defined by Hans Peter Luhn.

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Business Intelligence

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  1. Business Intelligence BI Deployment Case Study Bruce Jones Director of Information Systems DC Lottery and Charitable Games Control Board 2009 NASPL IT Subcommittee Meeting Colorado Springs, CO September 14-17, 2009

  2. BI Defined by Hans Peter Luhn business is a collection of activities carried on for whatever purpose, be it science, technology, commerce, industry, law, government, defense, et cetera. The communication facility serving the conduct of a business (in the broad sense) may be referred to as an intelligence system. The notion of intelligence is also defined here, in a more general sense, as "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.” Luhn, H. P. (1958). A Business Intelligence System, IBM Journal, October 1958, 314-319.Retrieved from http://www.research.ibm.com/journal/rd/024/ibmrd0204H.pdf

  3. Presentation Summary • What we did • How we did it • What we accomplished

  4. What we did • Modernized the Lottery’s information technology • Enterprise Architecture – what we had, where we wanted to go • Data Warehouse and Business Intelligence technologies deployed throughout the Lottery

  5. Goals and Objectives I Information Technology • Provide strategic support to the Lottery • Support lottery with analytics • Drive success with innovated solutions

  6. Goals and Objectives II Enterprise and Business Units • Accurate answers • Valuable insights • On-time information • Actionable conclusions

  7. Goals and Objectives III • Leverage information to achieve improved business performance • Democratization of information • Evidence-based decision making

  8. How we did it • The Team • People • Processes • The Target • Technology • Data

  9. Implementation Pathway

  10. Key People • Executive Sponsorship • Cross Departmental – Center of Excellence (COE) • Sales and Marketing • Finance and Information Technology • Optimal Solutions Technology

  11. DCLB Lottery Processes

  12. The Target • Oracle Data Warehouse • A single logical repository for transactional and operational data. • Gaming System sales and liability data • Business Objects BI Suite • A platform designed to let IT manage and securely deploy end-user tools and applications for reporting, query and analysis, a performance management

  13. Building Data Warehouse

  14. Data Warehouse Design I • A single logical repository for Lottery transactional and operational data. • Gaming System sales and liability data • Retailer key attributes

  15. Data Warehouse Design II • A dimensional model/star schema implemented • The dimensional database can be conceived of as a database cube of three or four dimensions where users can access a slice of the database along any of its dimensions • The main table within this architecture is called the fact table. The other dimension table are connected to the fact table through foreign keys. • Lottery Defined Dimension examples • Agent • Times • Location • Lottery Defined Fact Tables examples • Daily Sales • Claim Sales

  16. Star Schema Example • Daily Sales • Fact Table with dimensions - • Product • Agent • Fiscal Time

  17. Data Integration • Extract, Load and Transform • Data Quality • Operational vs. Analytical • Common, non-ambiguous, definitions • Single Point-of-Truth

  18. Data Warehouse Process

  19. BI Technologies • Fifty years of constant and accelerating change and innovation • Market consolidation and competition • Beyond spreadsheets, reporting and query software • OLAP (Online Analytical Processing) • Business Performance Management • Digital Dashboards

  20. What we accomplished

  21. The BI Enabled Lottery • Performance Reporting • Sales Forecasting and Analysis • Game Sale Summary and Product Profitability • Market Analysis • What-if Analysis • DC Lottery Product Mix Analysis

  22. Performance Dashboards I Rich visual display of historical sales information by each product with a performance metrics

  23. Performance Dashboards II Compact, concise display of weekly sales information with bar chart (sales amounts) overlaid with line graph (percent goal attained)

  24. Intranet Integration Bringing Data to the Desktop with a Digital Dashboard Speedometer Integrated into the Lottery’s SharePoint Portal Homepage

  25. Dashboard Speedometer Typical intuitive analysis scenario for evidence-based decision making

  26. Speedometer DC-4 Drill down by product for the week reveals major product sales goal not met.

  27. Speedometer DC-4 Detail Details available – the numbers behind the speedometer representation. Shows DC-4 product 15.74% off sales goal for the week.

  28. Speedometer Lucky Numbers Lucky Numbers product details – Shows the product 18.32% off sales goal for week.

  29. Speedometer Powerball Powerball product details – Shows Powerball 35.03% ahead of the sales goal for week.

  30. Next Move • Additional and Faster Data Integration • New Capabilities • Simulation • Forecasting • Optimization • Improved User Adoption • Evidence-based decision making

  31. Bringing it Together

  32. Final Thoughts

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