Business intelligence uses applications and technologies to analyze data and help users make better business decisions. Online transaction processing (OLTP) is used for daily operations like processing, while online analytical processing (OLAP) is used for data analysis and decision making. Data warehouses integrate data from different sources to provide a centralized system for analysis and reporting. Dimensional modeling approaches like star schemas and snowflake schemas organize data to support OLAP.

1. What is Business Intelligence? Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. Ekta Pardhi

5. Online Analytical Processing (OLAP ) It enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. Time Product Region SQL 2000 Oracle Access SQL 2005 1KEY OLAP

11. Graphically Data Warehouse Subject Oriented Credit Risk Interest Rate Risk Forex Risk ALM Subject Area OLTP Systems Integrated Credit Risk Code: XXXX Interest Rate Risk Code: CXXYY Forex Risk Code: XX/XX.XX Common Code for various source codes ALM Subject Area Non Volatile OLTP U S E R S Data Warehouse U S E R S Read Write Read Time variant Dec 98 1995 1996 1997 1998 OLTP OLAP

14. OLTP vs. OLAP

15. Examples of OLAP applications in various functional areas

19. Dimension

21. Attribute

23. Fact Table

25. Measure

28. Aggregation Hierarchies

30. T i m e p r o d c u s t p r o m o f a c t date, custno, prodno, cityname, sales

31. Region Scheme Time East West North South 1997 1998 1999 2000 Housing Car Edu. Industry Cell 200 Measure Dimension Components of a Data Cube (3D)

32. 1KEY OLAP console

36. Components of a star schema Excellent for ad-hoc queries, but bad for online transaction processing

37. Star schema example

38. Star Schema – Example 1 OrderNo SalespersonID CustomerNo ProdNo DateKey CityName Quantity TotalPrice Fact Table CityName State Country City DateKey Date Month Year Date ProdNo ProdName ProdDescr Category CategoryDescr UnitPrice QOH Product OrderNo OrderDate Order SalespersonID SalespersonName City Quota Salesperson CustomerNo CustomerName CustomerAddress City Customer

39. Snowflake – Example 1

40. Star Schema – Example 2 Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures time_key day day_of_the_week month quarter year time location_key street city province_or_street country location item_key item_name brand type supplier_type item branch_key branch_name branch_type branch

41. Snowflake Schema –Example Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures time_key day day_of_the_week month quarter year time location_key street city_key location item_key item_name brand type supplier_key item branch_key branch_name branch_type branch supplier_key supplier_type supplier city_key city province_or_street country city

43. Some variants

46. Enterprise Data Warehouse vs. Data Mart

47. DW and Data Mart: some other issues Characteristics Data Mart Enterprise DW Time to build Months Years Complexity to build Low to medium High Shared Shared in Dept. Shared across Update Frequently Less Frequently Type of user Subject focused Corporate and top executives