1. Data Warehouse and Business Intelligence Dr. Minder Chen Minder.Chen@CSUCI.EDU Fall 2009

2. Data Warehouse - 2 © Minder Chen, 2004-2010 BI Business Intelligence (BI) is the process of gathering meaningful information to answer questions and identify significant trends or patterns, giving key stakeholders the ability to make better business decisions. “The key in business is to know something that nobody else knows.” -- Aristotle Onassis PHOTO: HULTON-DEUTSCH COLLHULTON-DEUTSCH COLL “To understand is to perceive patterns.” — Sir Isaiah Berlin "The manager asks how and when, the leader asks what and why. " — “ On Becoming a Leader” by Warren Bennis

3. Data Warehouse - 3 © Minder Chen, 2004-2010 BI Questions What happened? –What were our total sales this month? What’s happening? – Are our sales going up or down, trend analysis Why? –Why have sales gone down? What will happen? –Forecasting & “What If” Analysis What do I want to happen? –Planning & Targets Source: Bill Baker, Microsoft

4. Data Warehouse - 4 © Minder Chen, 2004-2010 Increasing potential to support business decisions (MIS) End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA, Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources (Paper, Files, Information Providers, Database Systems, OLTP) Business Intelligence

5. Data Warehouse - 5 © Minder Chen, 2004-2010 Inmon's Definition of Data Warehouse – Data View A warehouse is a –subject-oriented, –integrated, –time-variant and –non-volatile collection of data in support of management's decision making process. –Bill Inmon in 1990 Source: http://www.intranetjournal.com/features/datawarehousing.html

6. Data Warehouse - 6 © Minder Chen, 2004-2010 Inmon's Definition Explain Subject-oriented: They are organized around major subjects such as customer, supplier, product, and sales. Data warehouses focus on modeling and analysis to support planning and management decisions vs. operations and transaction processing. Integrated: Data warehouses involve an integration of sources such as relational databases, flat files, and on- line transaction records. Processes such as data cleansing and data scrubbing achieve data consistency in naming conventions, encoding structures, and attribute measures. Time-variant: Data contained in the warehouse provide information from an historical perspective. Nonvolatile: Data contained in the warehouse are physically separate from data present in the operational environment.

7. Data Warehouse - 7 © Minder Chen, 2004-2010 The Data Warehouse Process Data Marts and cubes DataWarehouse SourceSystems Clients Design the PopulateCreateQuery Data Warehouse Data WarehouseOLAP CubesData Design the PopulateCreateQuery Data Warehouse Data WarehouseOLAP CubesData 34 Query Tools ReportingAnalysis Data Mining 2 1 Extract, Transform, & Loading ETL

8. Data Warehouse - 8 © Minder Chen, 2004-2010 OLTP Normalized Design Ordering Process Ware- house POS Process Chain Retailer Retailer Returns Retailer Payments Store Product Brand GL Account Clerk Retail Cust Cash Register Retail Promo

9. Data Warehouse - 9 © Minder Chen, 2004-2010 OLTP Versus Business Intelligence: Who asks what? OLTP Questions When did that order ship? How many units are in inventory? Does this customer have unpaid bills? Are any of customer X’s line items on backorder? Analysis Questions What factors affect order processing time? How did each product line (or product) contribute to profit last quarter? Which products have the lowest Gross Margin? What is the value of items on backorder, and is it trending up or down over time?

10. Data Warehouse - 10 © Minder Chen, 2004-2010 OLTP vs. OLAP Source: http://www.rainmakerworks.com/pdfdocs/OLTP_vs_OLAP.pdf#search=%22OLTP%20vs.%20OLAP%22http://www.rainmakerworks.com/pdfdocs/OLTP_vs_OLAP.pdf#search=%22OLTP%20vs.%20OLAP%22

11. Data Warehouse - 11 © Minder Chen, 2004-2010 Dimensional Design Process Select the business process to model Declare the grain of the business process/data in the fact table Choose the dimensions that apply to each fact table row Identify the numeric facts that will populate each fact table row Business Requirements Data Realities

12. Data Warehouse - 12 © Minder Chen, 2004-2010 Select a business process to model Not business departments or business functions Cross-functional business processes Business events Examples: –Raw materials purchasing –Order fulfillment process –Shipments –Invoicing –Inventory –General ledger

13. Data Warehouse - 13 © Minder Chen, 2004-2010 Requirements

14. Data Warehouse - 14 © Minder Chen, 2004-2010 Identifying Measures and Dimensions The attribute varies continuously: Balance Unit Sold Cost Sales The attribute is perceived as a constant or discrete value: Description Location Color Size DimensionsMeasures Performance Measures for KPI Performance Drivers

15. Data Warehouse - 15 © Minder Chen, 2004-2010 A Dimensional Model for a Grocery Store Sales

16. Data Warehouse - 16 © Minder Chen, 2004-2010 Product Dimension SKU: Stock Keeping Unit Hierarchy: –Department  Category  Subcategory  Brand  Product

17. Data Warehouse - 17 © Minder Chen, 2004-2010 Creating Dimensional Model Identify fact tables Translate business measures into fact tables Analyze source system information for additional measures Identify base and derived measures Document additivity of measures Identify dimension tables Link fact tables to the dimension tables Create views for users

18. Data Warehouse - 18 © Minder Chen, 2004-2010 Transaction Level Order Item Fact Table Always has a date dimension

19. Data Warehouse - 19 © Minder Chen, 2004-2010 Inside a Dimension Table Dimension table key: Uniquely identify each row. Use surrogate key (integer). Table is wide: A table may have many attributes (columns). Textual attributes. Descriptive attributes in string format. No numerical values for calculation. Attributes not directly related: E.g., product color and product package size. No transitive dependency. Not normalized (star schemar). Drilling down and rolling up along a dimension. One or more hierarchy within a dimension. Fewer number of records.

20. Data Warehouse - 20 © Minder Chen, 2004-2010 Fact Tables Fact tables have the following characteristics: Contain numeric measures (metric) of the business May contain summarized (aggregated) data May contain date-stamped data Are typically additive Have key value that is typically a concatenated key composed of the primary keys of the dimensions Joined to dimension tables through foreign keys that reference primary keys in the dimension tables