1. Business Intelligence

2. On-Line Analytical Processing (OLAP) Tools The use of a set of graphical tools that provides users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques OLAP Operations – Cube slicing–come up with 2-D view of data – Drill-down–going from summary to more detailed views – Roll-up – the opposite direction of drill-down – Reaggregation – rearrange the order of dimensions

3. Slicing a data cube

4. Example of drill-down Summary report Drill-down with color added Starting with summary data, users can obtain details for particular cells

5. Excel’s Pivot Table Insert/Pivot Table or Pivot Chart – Drill down, rollup and reaggregation – Filter Pivot Chart – Filter – Drilldown, rollup, reaggregation Import queries from Access to perform analysis. – Sales related to: Customer’s location, Rating and Products

6. Data Visualization Representing data in graphical/multimedia formats for analysis. – Web-based “dashboards” http://www.dundas.com/ – Dashboard Samples

7. Scenario A scenario is an assumption about input variables. Excel’s Scenarios is a what-if-analysis tool. A scenario is a set of values that Microsoft Excel saves and can substitute automatically in your worksheet. You can use scenarios to forecast the outcome of a worksheet model. You can create and save different groups of values on a worksheet and then switch to any of these new scenarios to view different results. Data/What If analysis/Scenario

8. Creating a Scenario – Add scenario Changing cells – Scenario Summary Resulting cells Demo: benefit363.xls

9. Data Warehouse Data warehouse is a repository of an organization's electronically stored data. A data warehouse houses a standardized, consistent, clean and integrated form of data that: – sourced from various operational systems in use in the organization, – structured in a way to specifically address the reporting and analytic requirements.

10. Example: Transaction Database Customer Order Product Has 1 M M M CID Cname City OIDODate PID Pname Price Rating SalesPerson Qty

11. Analyze Sales Data Detailed Business Data Total sales: – by product: Qty*Price of each detail line Sum (Qty*Price) Detailed business data: qty*price Total quantity sold: – By product: Sum(Qty) Detailed business data: Qty

12. Dimensions for Data Analysis: Factors relevant to the business data Analyze sales by Product Analyze sales related to Customer: – Location: Sales by City – Customer type: Sales by Rating Analyze sales related to Time: – Quarterly, monthly, yearly Sales Analyze sales related to Employee: – Sales by SalesPerson

13. Data Warehouse Design - Star Schema - Dimension tables – contain descriptions about the subjects of the business such as customers, employees, locations, products, time periods, etc. Fact table – contain detailed business data with links to dimension tables.

14. Star Schema FactTable LocationCode PeriodCode Rating PID Qty Amount Location Dimension LocationCode State City CustomerRating Dimension Rating Description Product Dimension PID Pname Category Period Dimension PeriodCode Year Quarter Can group by State, City

15. Define Location Dimension Location: – In the transaction database: City – In the data warehouse we define Location to be State, City San Francisco -> California, San Francisco Los Angeles -> California, Los Angeles – Define Location Code: California, San Francisco -> L1 California, Los Angeles -> L2

16. Define Period Dimension Period: – In the transaction database: Odate – In the data warehouse we define Period to be: Year, Quarter Odate: 11/2/2003 -> 2003, 4 Odate: 2/28/2003 -> 2003, 1 – Define Period Code: 2003, 4 -> 20034 2003, 1 -> 20031

17. The ETL Process E T L One, company- wide warehouse Periodic extraction  data is not completely current in warehouse

18. The ETL Process Capture/Extract Transform – Scrub(data cleansing),derive – Example: City -> LocationCode, State, City OrderDate -> PeriodCode, Year, Quarter Load and Index ETL = Extract, transform, and load

19. Performing Analysis Analyze sales: – by Location – By Location and Customer Type – By Location and Period – By Period and Product Pivot Table: – Drill down, roll up, reaggregation