1. Decision Support Systems

2. Decision Support Trends The emerging class of applications focuses on –Personalized decision support –Modeling –Information retrieval –Data warehousing –What-if scenarios –Data visualization

3. Business Intelligence Business intelligence refers to applications and technologies that are used to gather, provide access to, and analyze data and information about company operations. Business intelligence systems can help companies have a more comprehensive knowledge of the factors affecting their business, such as on sales, production, internal operations, and they can help companies to make better business decisions.

4. Business Intelligence Applications

5. DSS Components Data management function –Data warehouse –Data mart Model management function –Analytical models: Statistical model, management science model User interface –Data visualization –Web-based “dashboards”

6. Data Warehouse A subject-oriented, integrated, time-variant, non-updatable collection of data used in support of management decision-making processes –Subject-oriented: e.g. customers, employees, locations, products, time periods, etc. Dimensions for data analysis –Integrated: Consistent naming conventions, formats, encoding structures; from multiple data sources –Time-variant: Can study trends and changes –Nonupdatable: Read-only, periodically refreshed Data Mart: –A data warehouse that is limited in scope

7. Need for Data Warehousing Integrated, company-wide view of high-quality information. Separation of operational and informational systems and data.

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

9. The ETL Process Capture/Extract Scrub or data cleansing Transform Load and Index ETL = Extract, transform, and load

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

11. Star schema example Fact table provides statistics for sales broken down by product, period and store dimensions Dimension tables contain descriptions about the subjects of the business

12. Star schema with sample data

13. Example: Order Processing System Customer Order Product Has 1 M M M CID Cname City OIDODate PID Pname Price Rating SalesPerson Qty

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

15. From SalesDB to MyDataWarehouse Extract data from SalesDB: –Create query to get the data –Download to MyDataWareHouse File/Import/Save as Table Data scrub/cleasing,and transform: –Transform City to Location –Transform Odate to Period Load data to FactTable

16. 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 Relational OLAP (ROLAP) –Traditional relational representation Multidimensional OLAP (MOLAP) –Cube structure 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

17. Slicing a data cube

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

19. Access Pivot Form Drill Down

20. Data Mining Knowledge discovery using a blend of statistical, Artificial Intelligence, and computer graphics techniques Goals: –Explain observed events or conditions –Explore data for new or unexpected relationships Techniques –Statistical regression –Decision tree induction –Clustering – discover subgroups –Affinity – discover things with strong mutual relationships –Sequence association – discover cycles of evens and behaviors –Rule discovery – search for patterns and correlations –Neural nets – predictive models

21. Typical Data Mining Applications Profiling populations –High-value customers, credit risks, credit card fraud Analysis of business trends Target marketing Campaign effectiveness Product affinity –Identifying products that are purchased concurrently Customer retention Up-selling –Identifying new products and services to sell to a customer based on critical events

22. Data Visualization Representing data in graphical/multimedia formats for analysis. Example: –http://www.corda.com/lpage/data_visualizatio n_tool.htmlhttp://www.corda.com/lpage/data_visualizatio n_tool.html Click examples –Map or demo

23. Geological Information System GIS GIS is a computer-based tool for mapping and analyzing things that exist and events that happen on earth. GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. Typical application: –Site selection

24. Data of GIS Geodatabase: –A geodatabase is a database that is in some way referenced to locations on the earth. Longitude, latitude Attribute data: –Attribute data generally defined as additional information, which can then be tied to spatial data.

25. Chart

26. Charting Decision Rules An Internet Service Provider charges customers based on hours used: –First 10 hours$15 –Each of the next 20 hours$2 per hour –Hours over 30 hours$1 per hour

27. Comparing Decision Rules Plan 2: –First 20 hours: $20 –Hours over 20$1.5 Plan 3: –$35 unlimited access.

28. Charting Functions Demand function: –P = 150 – 6*Q ^2 Supply function: –P = 10* Q ^2 + 2*Q Note: –Positive area –Value axis maximum/minimum value: Format Value Axis

29. Frequency Distribution FREQUENCY(data_array,bins_array) –Calculates how often values occur within a range of values, and then returns a vertical array of numbers. For example, use FREQUENCY to count the number of test scores that fall within ranges of scores. Because FREQUENCY returns an array, it must be entered as an array formula. Note The formula in the example must be entered as an array formula. After copying the example to a blank worksheet, select the range A12:A15, press F2, and then press CTRL+SHIFT+ENTER.

30. Example

31. Chart Linear Regression Line Example: The amount of additive x and the reduction in nitrogen oxides y are measured in some suitable units. Seven different levels of x are included in the experiment and some of these levels are repeated for more than one car. The data is given in the table. A glance at the data shows that y generally increase with x.

32. Excel Regression Functions Regression line: y = mx + b LINEST(known_y's,known_x's) –An array function that calculates m and b TREND(known_y's,known_x's,new_x's) –Returns values along a linear trend. FORECAST(x,known_y's,known_x's) –Calculates, or predicts, a future value by using existing values.

33. Chart Regression Line Calculate the data for the regression line: –LinEst or Trend Create a scatter chart to show the original data and the regression data. Change the regression data to a line: –Select the regression data –Format/Selected data series –Choose the line style

34. 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.

35. Creating a Scenario Tools/Scenarios –Add scenario Changing cells Resulting cells Demo: benefit.xls