Datawarehousing and Business Intelligence


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Datawarehousing and Business Intelligence

  1. 1. Data Warehousing & Business Intelligence Introduction What do you think of when you hear the words Data Warehousing ? Prithwis Mukerjee, Ph.D.
  2. 2. Conceptual DW Definition <ul><li>Data warehousing is a program dedicated to the delivery of information which advances decision making, improves business practices, and empowers workers. </li></ul>© Prithwis Mukerjee
  3. 3. The Knowledge Management Framework © Prithwis Mukerjee Data Structure Technology Infrastructure Management Business Applications Information Technology Process People
  4. 4. How it all fits in .. © Prithwis Mukerjee Database CRM : Customer Relationship Management Transactional Systems ERP : Enterprise Resource Planning SCM : Supply Chain Management Data Warehouse
  5. 5. Typical Business Uses of the Data Warehouse © Prithwis Mukerjee Target Advertising campaigns Strategic Initiatives Business Processes Functions Profitability Analysis Market Basket Analysis Product Pricing Cross-selling and upgrade selling Just-in-Time Inventory Category Management Human Resources Management Determine Customer Lifetime Value Predict Customer Behavior Management Reporting Customer Acquisition and Retention
  6. 6. Benefits of the Data Warehouse Program © Prithwis Mukerjee Improves the way we do business and the bottom line Revenue Stimulation & Revenue Protection Cost Reduction and Cost Avoidance Productivity Improvement Profitability Enhancement Performance Analysis DecisionMaking Market Response Competitive advantage
  7. 7. Non-integrated Decision Support Architecture © Prithwis Mukerjee DSSs,Report writers, Excel, databases, etc. Data Feeds Budgeting Analysis Sales Forecasting Inventory System Order System Procurement System Accounting System
  8. 8. Basic Data Warehouse Architecture © Prithwis Mukerjee Enterprise DW/ODS Subject oriented Data Warehouses or Data Marts One Stop Data Shopping Fewer Data Feeds Inventory System Order System Procurement System Accounting System
  9. 9. Performance Measures : Definition & Examples <ul><li>Carefully selected set of measures derived from strategies, goals and objectives that represents a tool to communicating strategic direction to the organization for motivating change. </li></ul><ul><li>These form the basis to plan, budget, structure the organization and to control results. </li></ul>© Prithwis Mukerjee Innovation & Learning Measures Customer Measures Financial Measures Internal Process Measures <ul><li>% Sales of New Products </li></ul><ul><li>Customers Acquired </li></ul><ul><li>Customer Satisfaction </li></ul><ul><li>Market Share </li></ul><ul><li>ROI and ROA </li></ul><ul><li>Revenue Growth </li></ul><ul><li>Product Time to Market </li></ul><ul><li>Unit Manufacturing Cost </li></ul><ul><li>Days Supply to inventory </li></ul><ul><li>New Product Introduction </li></ul><ul><li>Management Skills </li></ul><ul><li>Employee Turnover </li></ul>
  10. 10. Differences between OLTP and DW <ul><li>Data Access, Manipulation and Use </li></ul><ul><li>Data Organisation and Integration </li></ul><ul><li>Time Handling </li></ul><ul><li>Usage </li></ul><ul><li>Data Structures and Schemas </li></ul>© Prithwis Mukerjee Explanations ..
  11. 11. Data access, manipulation and use <ul><li>Data Entry </li></ul><ul><li>Transaction Oriented </li></ul><ul><li>Consistent use patterns </li></ul><ul><li>Data retrievals are lookups of single records </li></ul><ul><li>Users deal with one record at a time </li></ul><ul><li>Performance is critical </li></ul><ul><li>Reporting is generally table lists </li></ul><ul><li>Data Query </li></ul><ul><li>Bulk data oriented </li></ul><ul><li>Spiked, uneven use patterns </li></ul><ul><li>Queries are unpredictable, they change continuously </li></ul><ul><li>Data retrievals are summary and sorts of millions of records </li></ul><ul><li>Performance is relaxed (sec/min) </li></ul><ul><li>Reporting is primary activity (on line, presented in small chunks) </li></ul>© Prithwis Mukerjee OLTP DW Differences between OLTP and DW
  12. 12. Data Organisation And integration <ul><li>Organized around applications </li></ul><ul><li>Unintegrated data </li></ul><ul><li>Different key structures </li></ul><ul><li>Different naming conventions </li></ul><ul><li>Different file formats </li></ul><ul><li>Organized around subject areas </li></ul><ul><li>Integrated data </li></ul><ul><li>Standardized key structures </li></ul><ul><li>Standardized naming conventions </li></ul><ul><li>Standardized file formats </li></ul>© Prithwis Mukerjee OLTP DW Differences between OLTP and DW
  13. 13. Time Handling <ul><li>No time series analysis </li></ul><ul><li>Data relationships constantly change </li></ul><ul><li>Changes are instantaneous </li></ul><ul><li>Limited history, 60-90 days </li></ul><ul><li>Twinkling Database … . </li></ul><ul><li>Time series analysis </li></ul><ul><li>Data is static over time </li></ul><ul><li>Series of data snapshots </li></ul><ul><li>Snapshots create historical database, often greater than two years </li></ul><ul><li>Quiet database </li></ul>© Prithwis Mukerjee OLTP DW Differences between OLTP and DW
  14. 14. Usage <ul><li>Place an order for a product </li></ul><ul><li>Look up price for a product </li></ul><ul><li>Apply discount </li></ul><ul><li>Assign shipper </li></ul><ul><li>Trigger inventory pick-list </li></ul><ul><li>Verify shipment of product </li></ul><ul><li>Create invoice for the product </li></ul><ul><li>Apply credit to sales representative </li></ul><ul><li>Essential to RUN the company </li></ul><ul><li>What type of customers are ordering this product? </li></ul><ul><li>Who are my top 10% accounts? By name, by revenue, by profitability, by region? </li></ul><ul><li>How are these different by customer segments? By sales rep? By store? </li></ul><ul><li>Which shippers have the best on time delivery records ? </li></ul><ul><li>How does this vary by shipment size? By season of year? </li></ul><ul><li>Essential to WATCH the company </li></ul>© Prithwis Mukerjee OLTP DW Differences between OLTP and DW
  15. 15. Data Structures & Schemas <ul><li>Drives out all data redundancy </li></ul><ul><ul><li>Improves performance </li></ul></ul><ul><li>Divides data into many discrete entities </li></ul><ul><li>Tables are symmetrical </li></ul><ul><ul><li>Can ’ t tell most important, largest, which hold measures, which are static descriptors </li></ul></ul><ul><li>Lots of connection paths between tables </li></ul><ul><ul><li>prefers to use tables individually or in pairs </li></ul></ul><ul><li>Too complex for users to understand </li></ul><ul><li>Data redundancy is encouraged </li></ul><ul><ul><li>Improves table browsing </li></ul></ul><ul><li>Subject area oriented. Groups data into categories of business measure and characteristics </li></ul><ul><li>Tables are symmetrical </li></ul><ul><ul><li>Large dominant tables </li></ul></ul><ul><li>Clearly defined connection paths for table joins </li></ul><ul><li>Simple for users to understand and navigate </li></ul>© Prithwis Mukerjee OLTP DW Differences between OLTP and DW
  16. 16. Basic Datawarehousing Topics <ul><li>The Four Building Blocks </li></ul><ul><li>DW Definition </li></ul><ul><li>DW Usage and Benefits </li></ul><ul><li>DW Vs. the non-integrated DSS environment </li></ul><ul><li>Performance Measures </li></ul><ul><li>Dimensional Modeling </li></ul><ul><li>Technical Infrastructure </li></ul><ul><li>Knowledge Mgmt. Architecture </li></ul><ul><li>IT and Business Perspectives </li></ul><ul><li>DW Methodology </li></ul>© Prithwis Mukerjee
  17. 17. Dimensional Data Modeling <ul><li>Dimensional Data Modeling techniques organize the content of the data warehouse. It structures the data according to the way users ask business questions. </li></ul>© Prithwis Mukerjee
  18. 18. The Technical Infrastructure <ul><li>A technical infrastructure provides the physical framework to support data acquisition, storage, access, and data management. It involves development and integration of hardware and software components. </li></ul>© Prithwis Mukerjee
  19. 19. Knowledge Management Architecture © Prithwis Mukerjee Metadata Source Data Purchasing Systems General Ledger Other Internal Systems External Data Sources Data Resource Management And Quality Assurance . Invoicing Systems Data Extraction Integration and Cleansing Processes Extract ODS Purchasing Marketing and Sales Corporate information Product Line Location Translate Attribute Calculate Derive Summarize Synchronize Segmented Data Subsets Summarized Data Data Warehouse Applications Custom Developed Applications Data Mining Statistical Packages Query Access Tools Data Marts Transform
  20. 20. The Business and The IT Perspective © Prithwis Mukerjee Business What will it do? What value will it bring? How is it built? How does it work? Information Technology Data Warehouse
  21. 21. The Business Perspective of the Data Warehouse <ul><li>It takes forever to get the information I need to do my job </li></ul><ul><li>When I do get it, it ’ s wrong </li></ul><ul><li>We have mountains of data, but I can ’ t figure out what ’ s important </li></ul><ul><li>It takes so long to get the data that I don ’ t have any time left over to analyze it </li></ul><ul><li>I want it to be easy. Just let me point and click my way to an answer </li></ul><ul><li>I want to see my data in every possible combination </li></ul><ul><li>Data is scattered everywhere across our organization. Where do I look ? </li></ul><ul><li>I want a historical view of the business </li></ul><ul><li>I want to predict the future </li></ul>© Prithwis Mukerjee Focuses on needs and usage
  22. 22. The IT Perspective of the Data Warehouse <ul><li>Organizes and stores data by subject area rather than application </li></ul><ul><li>Extracts and integrates data from multiple source systems into a single database </li></ul><ul><li>Provides data cleansing, summarization, and calculation </li></ul><ul><li>User does not create, update, or delete data </li></ul><ul><li>Provides snapshots of data over periods of time </li></ul><ul><li>Supports analytical processing, not transactional processing </li></ul><ul><li>Builds a technology infrastructure to support data acquisition, data storage, data access, and metadata capture </li></ul>© Prithwis Mukerjee Focuses on database, technology, organizational features
  23. 23. DW Methodology <ul><li>The methodology provides a detailed roadmap to organize and perform the tasks required in building the data warehouse </li></ul>© Prithwis Mukerjee
  24. 24. Data Warehouse System Development Life Cycle © Prithwis Mukerjee CONSTRUC- TION IMPLEMEN- TATION DESIGN ANALYSIS PLANNING MANAGING Business Architecture Data Architecture Technology Architecture Management Infrastructure
  25. 25. © Prithwis Mukerjee stop