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Business Intelligence
BI Deployment Case Study
Bruce JonesDirector of Information SystemsDC Lottery and Charitable Games Control Board
2009 NASPL IT Subcommittee MeetingColorado Springs, COSeptember 14-17, 2009
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
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
Goals and Objectives I
Information Technology• Provide strategic support to the Lottery• Support lottery with analytics• Drive success with innovated solutions
Goals and Objectives II
Enterprise and Business Units• Accurate answers• Valuable insights• On-time information• Actionable conclusions
Goals and Objectives III
• Leverage information to achieve improved business performance
• Democratization of information• Evidence-based decision making
Key People
• Executive Sponsorship• Cross Departmental – Center of
Excellence (COE)– Sales and Marketing– Finance and Information Technology– Optimal Solutions Technology
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
Data Warehouse Design I
A single logical repository for Lottery transactional and operational data.
Gaming System sales and liability data
Retailer key attributes
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
Data Integration
• Extract, Load and Transform• Data Quality– Operational vs. Analytical– Common, non-ambiguous, definitions– Single Point-of-Truth
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
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
Performance Dashboards I
Rich visual display of historical sales information by each product with a performance metrics
Performance Dashboards II
Compact, concise display of weekly sales information with bar chart (sales amounts) overlaid with line graph (percent goal attained)
Intranet Integration
Bringing Data to the Desktop with a Digital Dashboard Speedometer Integrated into the Lottery’s SharePoint Portal Homepage
Speedometer DC-4 Detail
Details available – the numbers behind the speedometer representation. Shows DC-4 product 15.74% off sales goal for the week.
Speedometer Lucky Numbers
Lucky Numbers product details – Shows the product 18.32% off sales goal for week.
Speedometer Powerball
Powerball product details – Shows Powerball 35.03% ahead of the sales goal for week.
Next Move
• Additional and Faster Data Integration• New Capabilities– Simulation– Forecasting– Optimization
• Improved User Adoption– Evidence-based decision making