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Data Warehousing: A Strategic View

Marshall School Of Business. Data Warehousing: A Strategic View. Developed by: Dr Eddie Ip Modified by: Dr Arif Ansari. Introduction : What is a DW?. Overview. Functions of DW Definition & characteristics of DW Why DW and why it is possible now Business perspective Technical perspective

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Data Warehousing: A Strategic View

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  1. Marshall School Of Business Data Warehousing: A Strategic View Developed by: Dr Eddie Ip Modified by: Dr Arif Ansari

  2. Introduction : What is a DW?

  3. Overview • Functions of DW • Definition & characteristics of DW • Why DW and why it is possible now • Business perspective • Technical perspective • Examples of analytical functions of DW • Evolution of DW

  4. What is a DW? • Function: Creates a physical separation between the collection of daily transaction data and a copy of it. Use copy for analytical purposes • Avoid engineering conflict • Challenges • E.g., Scattered data create political & technical challenges

  5. Definition of DW • “A collection of integrated, subject-orienteddatabases designed to supply the information required for decision-making.” - W. Inmon (1992)

  6. Integrated • Data from many “silos” operational systems, or OLTP’s(= On-Line-Transaction-Processing) to form an integrated view of the customer

  7. OLTP • Handle a business’s daily activities & commerce • “Bread and butter” activities

  8. OLTP • ATM, airline reservation, catalog order, supermarket (bar-code data)

  9. OLTP • Can think of it as “A data storage with blinking data items” (regular and frequent updating activities)

  10. OLTP • Based on well-defined business & technical requirement • Large volumes of simple transactions • Rigid specs • Maintained by IT professionals • Process oriented

  11. OLTP • Store in relational database tables or hierarchical files • Supports many users • Frequently updated

  12. OLTP • Does not support • analysis • ad hoc query & reporting • multiple platform • evolution

  13. OLTP

  14. Subject Oriented • Subject oriented (DW) vs process oriented (OLTP) • Process: transaction

  15. Subject-oriented • Subject = entity of business interest • Examples of subject • customers • sales • profits

  16. Databases • DW requires a large repository (DB) with proven technology for extracting relevant information • DW = internal DB + external DB + metadata DB

  17. Decision Making • Dimensional view of data • Start from a high level (summary data) & drill down to more detail to answer specific questions • Function similar to Executive Information System (EIS)

  18. Other characteristics of DW • DW takes a snap-shot of operation & stores it away • Allows trend/ pattern analysis

  19. Other characteristics of DW • DW stores atomic & lightly summarized data • Summarized = aggregates • Trade-off consideration • performance • cost

  20. Other characteristics of DW • Business users vs IT users • User interface • Security • Read-only

  21. Strategic importance of DW

  22. Basis for analytics • Form the basis for developing analytical capacity (Davenport et. al 2001) • Allows data -> knowledge -> action • Three-layer model • Context (data, infrastructure, strategy, skills) • Transformation (analysis and decision making) • Outcome • Data is the raw material for analytics

  23. Single view of customer • DW: A single, integrated view of customer • Data integrated from across, and even outside organization >> corporate memory

  24. Rising tide • Multiple benefits with a single action • Empower everyone to make decision at an appropriate own level (Rising Tide) • Improve customer intimacy • Support partners in supply chain • Support process control • Facilitate various levels of analysis & action • Enhance performance of customer contact points (e.g. call center, website)

  25. Raise corporate IQ • Raise corporate intelligence quotient = tide level at which employees operate • High IQ=> high flexibility, high maneuverability • Especially important for industries with high info intensity • Swift 2001 (reader)

  26. “The ultimate goal is simple: Give the battlefield commander access to all the information needed to win the war. And give it to him when he wants it, where he wants he and how he wants it.”-- Gen. Colin L. Powell, “Information Warriors,” BYTE, 1992

  27. Background to proliferation of DW • Shift in business model • Enterprise-centric to customer-centric • E.g., At Marriott Hotel: give customer what they want, when they want it, in the way they want it. • Old days: the public wants what the public gets* • Mass marketing: “You buy what I can produce.”

  28. Customer-Centric Business • What bring about the shift of c.g. ? • Internet • Improvement in production & supply chain mgt • Other disruptive and new technologies (lead to high productivity & even overcapacity) • Globalization • Deregulation • Consolidation • Repositioning of companies

  29. Customer-Centric Business • Everyone is after everyone else’s customers

  30. Customer-Centric Business • Optimize all aspects of your business to improve acquisition, retention and profitability. • Find the right Customer • Offer the right product • At the right Time • Using the right Channel

  31. Customer-centric Business • Enabling technologies: DW, client-server, MPP, wireless, internet (email & interactive sites), DM, personalization • DW as IS strategy is driven by business strategy

  32. Data-Knowledge-Result Model • Data-to-Knowledge-to-action-to-results (Reader : Davenport et. al.) • Information value chain analysis • Process view : value added when data are managed and used

  33. Data-Knowledge-Result Model A collection of raw data has no value in itself • Data warehousing : collect & consolidate raw data • Analytical processing : analyze data • Decision making: act • Improve internal operational excellence • Improve external partner, supplier,customer relationship management

  34. DW creates value thro’ action • DW creates value only when it is driven by business requirements, not IT • Turn analyses into marketplace advantage

  35. Examples of action • DM to understand customer purchasing behavior -> marketing campaign (Walmart, reading) • Web stream analysis -> personalization of Webpages (Amazon.com) • Ad hoc queries -> support for marketers (Sears, Swift, p.364)

  36. Examples of action • DW financial data -> portfolio & risk management (BoA, Swift, p.340) • DW credit data -> risk assessment, credit decision (Capital One, reader) • Customer preference data -> channel management

  37. Examples of action • DW inventory data -> revenue analysis (e.g. Walmart, reading) • DW customer profile -> cross selling opportunities • DW customer history -> relationship marketing (Peppers & Rogers, 1998)

  38. Summary • DW is physically separated from OLTP • DW provides the basis (memory, analytics) to achieve enterprise intelligence • Business & technical requirements (e.g., user interface, subject orientation) • Background : shift to customer-centered • DW’s key role in Data>Knowledge>Action>Result model • Business-driven DW is the key to ROI

  39. DW : summary

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