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Meeting a Business Need

Meeting a Business Need. Chapter 2. Overview. Planning Warehouse Storage. Meeting a Business Need. Defining DW Concepts & Terminology. Choosing a Computing Architecture. ETT (Building The Warehouse). Managing The Data Warehouse. Modeling The Data Warehouse. Planning

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Meeting a Business Need

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  1. Meeting a Business Need Chapter 2

  2. Overview Planning Warehouse Storage Meeting a Business Need Defining DW Concepts & Terminology Choosing a Computing Architecture ETT (Building The Warehouse) Managing The Data Warehouse Modeling The Data Warehouse Planning For a Successful Warehouse Analyzing User Query Needs Supporting End User Access Project Management (Methodology, Maintaining Metadata)

  3. Characteristics of OLTP Systems Characteristic OLAP Update Typical operation Level of analytical requirement Low Screens Unchanging Small Amount of data per transaction Detailed Data level Age of data Current Records Orientation

  4. Why OLTP Is Not Suitable for Complex Analysis OLAP Complex Analysis Information to support day-to-day service Historical information to analyze Data stored at transaction level Data needs to be integrated Database design: Denormalized, star schema Database design: Normalized

  5. Management Information Systems and Decision Support Ad hoc access • MIS systems provided business data • Reports were developed on request • Reports provided little analysis capability • Decision support tools gave personal ad hoc access to data Production platforms Operational reports Decision makers

  6. Analyzing Data from Operational Systems • Data structures are complex • Systems are designed for high performance and throughput • Data is not meaningfully represented • Data is dispersed • OLTP systems may be unsuitable for intensive queries Production platforms Operational reports

  7. Data Extract Processing • End user computing offloaded from the operational environment • User’s own data Operational systems Extracts Decision makers

  8. Management Issuess Decision makers Operational systems Extracts Extract explosion

  9. Productivity Issues • Duplicated effort • Multiple technologies • Obsolete reports • No metadata

  10. Data Quality Issues • No common time basis • Different calculation algorithms • Different levels of extraction • Different levels of granularity • Different data field names • Different data field meanings • Missing information • No data correction rules • No drill-down capability

  11. From Extract to Warehouse DSS • Controlled • Reliable • Quality information • Single source of data Data warehouse Decision makers Internal and external systems

  12. Advantages of Warehouse Processing Environment • No duplication of effort • No need for tools to support many technologies • No disparity in data, meaning, or representation • No time period conflict • No algorithm confusion • No drill-down restrictions

  13. Business Motivators • Know the business • Reinvent to face new challenges • Invest in products • Invest in customers • Retain customers • Invest in technology • Improve access to business information • Be profitable • Provide superior services and products

  14. Business Motivators • Provides supporting information systems • Get quality information - Reduce costs - Streamline the business - Improve margins

  15. Technological Advances • 64-bit architecture • Indexing techniques • Affordable, cost-effective • Open systems • Robust warehouse tools • Sophisticated end user tools • Parallelism - Hardware - Operating system - Query - Index - Applications • Large database

  16. Growth Motivators and Inhibitors • Successful implementations • Decreased risk • Robust extraction software • Improving price to performance ratios • Improved staff training • Year 2000 compliance • Skills shortage • Lack of integrated metadata • Data cleaning cost

  17. Typical Uses of Data Warehouse • Airline • Banking • Health Care • Investment • Insurance • Retail • Telecommunications • Manufacturing • Credit card suppliers • Clothing distributors

  18. Summary This lesson covered the following topics: • Describing why an online transaction processing(OLTP) systems is not suitable for complex analysis • Describing how extracting processing for decision support querying led to data warehouse solutions employed today • Explaining why businesses are driven to employ data warehouse technology • Identifying some of the industries that employ data warehouses

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