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Enterprise Decision Support - A Commercial Perspective

Enterprise Decision Support - A Commercial Perspective. Pete Catley Supervisor, Sales Reporting Systems, Ford Europe Thursday 18 November, 1999 SEM304 - Commercial Programming. Agenda:. Introduction Enterprise Decision Support Overview Case Study: Smarts.

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Enterprise Decision Support - A Commercial Perspective

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  1. Enterprise Decision Support- A Commercial Perspective Pete Catley Supervisor, Sales Reporting Systems, Ford Europe Thursday 18 November, 1999 SEM304 - Commercial Programming

  2. Agenda: • Introduction • Enterprise Decision Support Overview • Case Study: Smarts

  3. Why Do BusinessesHave ITDepartments?

  4. Only Small BusinessesCan SurviveWithout IT

  5. As Soon As You Startto Scale up As anEnterpriseYou Begin to Recognisethe Need for anIT Department

  6. IT Departments HaveDeveloped Over the Years

  7. Adding Machines Business Systems Technical Systems Transaction Processing Process Control Operational Database Applications CAD / CAM / CAE Reporting Applications Analytical Analytical Applications

  8. Why Do We Analyse?

  9. Beware:ParalysisByAnalysis

  10. Business Reality:Difficult to Turn“Data”Into“Information”

  11. Actionable Intelligence Some Things You Could Do To Address The Issues • High Value Add • Very Rapid Response Decision Support Key Things That Happened In Important Locations At Relevant Times • Dynamic Format • Focused Output • Low Effort To Interpret Reporting Everything That Happened Everywhere All The Time • Fixed Format • High Redundancy • High Effort To Interpret

  12. Imagine If Your CarHad NoInstrument Panel?

  13. Now Imagine You AreA Captain Of IndustryAt The Helm Of TheSS “Shareholder Value”

  14. So the Vision is:Wouldn’t It Be NiceIf We Had A SystemWhich Told UsWhere The Issues Were?

  15. Vision Enablers • Reporting By Exception • “Traffic Lighting” • OLAP “Slice & Dice” • Key Performance Indicators • E-mail Notification Of Exceptional Events • Use of “Push” Web Technology • “What If” Analysis & Modelling • Thematic Mapping

  16. What Do You NeedTo Do To GainActionable Intelligence?

  17. Capture Operational Systems Mainframe Data Sources Cleanse Staging Areas Simple Relational Tables Store Data Warehouse Normalised Relational Model Aggregate Data Marts De-Normalised “Star Schema” Interpret Meta-Data Layer Deliver OLAP Ad-Hoc Reports Executive Information

  18. That Looks Pretty StraightforwardLet’s Do It!

  19. Case Study: smarts SALES MANAGEMENT ANALYSIS & REPORTING TOOL SET

  20. First:Sell The Vision

  21. “How Much Is All ThisGoing To Cost Me?”

  22. “Ouch!”

  23. Build A Cast-IronBusiness Case

  24. Gain A PowerfulSponsor

  25. Who, In Their Right Mind,Would DevelopA Mainframe-basedSpreadsheetIn-House?

  26. Nobody -They’d Buy Excel!

  27. Pick AnArchitecture • MOLAP • High Performance • No. Dimensions • Data Volume • ROLAP • No. Dimensions • Data Volume • Performance • HOLAP • High Performance • No. Dimensions • Data Volume • Devt. Complexity • DOLAP • Cost • Devt. Complexity • Implementation Ease

  28. Front-End Architecture • Buy OLAP Tools • Web-Based Ad-Hoc Query • Reporting • Web Publishing • Personal Web Portal Concept

  29. Back-End Architecture • Re-Use Existing Data Extract • Build Staging Areas • Build Data Transformations • Build Data Warehouse • Build Data Marts

  30. But:Choose The Right Tools

  31. Pick “Best-In-Class”(But Teacher ChoosesWho’s In The Class!)

  32. Tool Selection • ATAD /FSIC • Shortlist • “Proof Of Concept” Workshops • Tool Selection Matrix • FSIC Architecture Review

  33. Pick A Good TeamandA Strong Project Manager

  34. Metrics • 7 Months To Gain Funding Approval • 6 Months To Deliver Phase 1 • Team of 9 • 120 Gb Database • Sun E4500, 10 x 337mhz CPUs, 4Gb • $1.4m per annum Cost Saving • Launched On Time (to the day!)

  35. But:Do The Users Like It?

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