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Data-driven DSS

Data-driven DSS. Chapter 7. Types of Data-driven DSS. Data warehouses Executive Information Systems Spatial DSS Online Analytical Processing. Source of data. Internal data. External data. Detail and Summary. Drill-down. Dimensions. Dimensions. Dimension. Slice and Dice. Viewing

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Data-driven DSS

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  1. Data-driven DSS Chapter 7

  2. Types of Data-driven DSS • Data warehouses • Executive Information Systems • Spatial DSS • Online Analytical Processing

  3. Source of data Internal data External data

  4. Detail and Summary Drill-down

  5. Dimensions Dimensions Dimension

  6. Slice and Dice Viewing Data by Customer Area code

  7. Slice and Dice Viewing Data by Vendor state

  8. Data-driven DSS • Permits users to drill-down from summary to detail • Permits users to drill-up from details to summary • Permits users to view data on different dimensions

  9. Data warehouse • Database • Designed for decision support • Batch updated • Large amounts of data • Can be subject oriented (Data Mart) • Uses consistent definitions and formats • Time-variant • Historical data • Not the same as OLAP

  10. Sources of data for Data Warehouse • From relational databases or Non-relational data sources • Integration of data from distributed and differently structured databases • Definitions and formats may not be consistent across sources • Physical Separation of data used in daily operations from data used for purposes of reporting, decision support, analysis and controlling.

  11. Data warehouse ETL = Extract, Transform and Load

  12. Multi-dimensional analysisor OLAP systems

  13. Multi-dimensional analysis • OLAP software for creating multi-dimensional representations • Original data comes from normalized two-dimensional tables • Example database • Tables: Vendors, Products, Outlets, Sales persons, sale records

  14. Example application • Vendors supply products • Products belong to product-lines • Products sold by specific sales persons Products sold at specific outlets • Outlets located in specific regions • Sales done in specific time period Can you spot the dimensions of sales revenue data?

  15. Dimensional view of Sales data Salesperson Outlets Regions Location Item Product Time Product type Date Week Quarter Year

  16. How would you “Drill down” and “Slice and Dice” Salesperson Outlets Regions Location Item Product Time Product type Date Week Quarter Year

  17. Executive Information Systems

  18. EIS • To support information needs of the executive • Data for specific issues and problems • Can do trend analysis, and drill down • Uses internal and external data • Dashboard applications

  19. Differences between data that is available and data that is required

  20. Normalized data structure to Integrated data structure Operating data • Two dimensional flat tables – suitable for quick update, insert and delete operations • Efficient usage of hard drive space • Derived and derivable data not stored Integrated data • Denormalized; for quick retrieval • Frequently used summary and derived data is created and stored

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