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Data Warehousing An Overview Outline • • • • What is Data Warehousing? (Definition) Why does anyone need it? (Applications) How is the data organized? (Star Schema) Implementation Issues. Data Warehouse Definitions • Dyche’: Used for decision making- duplicates existing data - Combination of hardware, specialized software and data extracted from other corporate systems. • Inmon: Subject-oriented, integrated, nonvolatile and time-variant collection of data in support of management decisions. Why Warehouse? • Provide single view of customers across enterprise • Improve turnaround time for common reports • Monitor customer behavior • Predict future purchases • Improved responsiveness Business issues. Coca Cola & IBM • IBM helping Coca Cola with warehouse. • Deal with Global companies like McDonalds – support for negotiating global contracts. Financial Services Example – Credit Life Cycle Product Planning Customer Acquisition Collections Customer Management Customer Acquisition Product Planning Support for Marketing • Market Segmentation Plus Forecasts with: • Response Models • Risk / Bankruptcy Models • Profitability Models Customer Acquisition Customer Management Who gets a credit increase? Which of delinquent customers is likely to default? What do you do (call, send letter, do nothing?) Decision Support: Forecast Customer Behavior (Behavior Models) Customer Management Customer Acquisition Collections/Recovery What is the likelihood of recovering money from an account sent to collections? Collections Decision Support: Collections models Customer Management Other Questions • How can we reduce attrition? • How can we activate inactive accounts? • How well are my current strategies performing? • How do we detect Fraud? Where is the data? • • • • Transaction Systems Marketing Database Credit Reports Customer Service How is it Organized? • • • • Separate from transactional data Contains Historical data Generally aggregated to some extent Optimized for flexible querying of large volumes of data Star Schema • Fact Table plus several dimensional tables • Un-normalized • Less flexible than normalized tables • Faster retrieval than normalized tables for large volumes of data Implementation • • • • Start with the Business Issues Project Planning/Human Resources Database design / data sources Application Development Business Analysis • What is the problem? • Who owns the problem? • Will data help solve it? When can data be used to Predict? High Low Chaotic Markets (fashion driven) Real-Time Markets (Stock Market) Linear Markets (Local authority - # of trash cans) Statistical Markets (retail) Low High Randomness Source: www.butlergroup.com Also read article in Wired Magazine on Data Mining and Terrorism