Data Warehouse and Business Intelligence Dr. Minder Chen Minder.Chen@CSUCI.EDU Spring 2010

1. Data Warehouse and Business Intelligence Dr. Minder Chen Minder.Chen@CSUCI.EDU Spring 2010

2. BI “The key in business is to know something that nobody else knows.” -- Aristotle Onassis PHOTO: HULTON-DEUTSCH COLL 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. “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. Business Intelligence Increasing potential to support business decisions (MIS) Manager/executive Making Decisions Business Analyst Data Presentation Visualization Techniques Data Analyst Data Mining Information Discovery Data Exploration OLAP, MDA, Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts DBA Data Sources (Paper, Files, Information Providers, Database Systems, OLTP)

4. 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.

5. The Data Warehouse Process Data Marts and cubes Source Systems Clients Data Warehouse Query Tools Reporting Analysis Data Mining 1 2 3 4 Design the Populate Create Query Data Warehouse Data Warehouse OLAP Cubes Data

6. Performance Dashboards for Information Delivery

7. OLTP Normalized Design Ware- house Ordering Process Chain Retailer Store Retailer Payments Retailer Returns Product POS Process Brand Account GL Retail Cust Retail Promo Clerk Cash Register

8. OLTP Versus OLAP OLTP Questions When did that order ship? How many units are in inventory? Does this customer haveunpaid bills? Are any of customer X’s line items on backorder? OLAP 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?

9. OLTP vs. OLAP Source: http://www.rainmakerworks.com/pdfdocs/OLTP_vs_OLAP.pdf#search=%22OLTP%20vs.%20OLAP%22

10. 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

11. 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

12. Requirements

13. Identifying Measures and Dimensions Performance Measures for KPI Performance Drivers Measures Dimensions • The attribute varies • continuously: • Balance • Unit Sold • Cost • Sales • The attribute is perceived as • a constant or discrete value: • Description • Location • Color • Size

14. A Dimensional Model for a Grocery Store Sales

15. Product Dimension • SKU: Stock Keeping Unit • Hierarchy: • Department  Category  Subcategory  Brand  Product

16. 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.

17. 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

18. Facts Table Measurements of business events. Dimensions Measures The Fact Table contains keys and units of measure

19. Hierarchy

20. Operations in Multidimensional Data Model • Aggregation (roll-up) • dimension reduction: e.g., total sales by city • summarization over aggregate hierarchy: e.g., total sales by city and year  total sales by region and by year • Selection (slice) defines a subcube • e.g., sales where city = Palo Alto and date = 1/15/96 • Navigation to detailed data (drill-down) • e.g., (sales - expense) by city, top 3% of cities by average income • Visualization Operations (e.g., Pivot)

21. A Visual Operation: Pivot (Rotate) NY LA SF Month Juice Cola Milk Cream 10 47 Region 30 12 Product 3/1 3/2 3/3 3/4 Date

22. Date Dimension of the Retail Sales Model

23. Store Dimension • It is not uncommon to represent multiple hierarchies in a dimension table. Ideally, the attribute names and values should be unique across the multiple hierarchies.

24. ETL ETL = Extract, Transform, Load • Moving data from production systems to DW • Checking data integrity • Assigning surrogate key values • Collecting data from disparate systems • Reorganizing data

25. Pivot Table in Excel

26. OLAP and Data Mining Address Different Types of Questions While reporting and OLAP are informative about past facts, only data mining can help you predict the future of your business. Source: http://www.dmreview.com/editorial/dmreview/print_action.cfm?articleId=2367

27. Use of Data Mining • Customer profiling • Market segmentation • Buying pattern affinities • Database marketing • Credit scoring and risk analysis

28. Associates Which items are purchased in a retail store at the same time?

29. Sequential Patterns What is the likelihood that a customer will buy a product next month, if he buys a related item today?

30. Classifications Determine customers’ buying patterns and then find other customers with similar attributes that may be targeted for a marketing campaign.

31. Modeling Use factors, such as location, number of bedrooms, and square footage, to Determine the market value of a property