1. C O N F I D E N T I A L Advanced Analytics Business Intelligence with Data Mining

2. Data Mining  What’s important  Association/Binning  Clustering  Classification  Segmentation  What to expect  What-if  Estimation  Curve Fitting  Fill in Sparse Matrix  Prediction  Probability  Quantitative

3. Methodology Collected Sample Statistical Analyst – Business Modeling Warehouse Marts business interpretation Optimize data marts Data Store DBA Predictive Metrics & Segments

4. Methodology - EDMDAPA  Extract  Integrate disparate data systems  Build holistic business view  Group and organize large sets of categorize  Discretize/Classify  Grouping and Segmentation  Simplify large flat dimensions  Model  Create predictive estimation functions  Deploy  Build/score data marts, cubes with predictive probability and quantitative metrics and simplified dimensional categories  Analyze, Visualize, Scorecard  Identify KPI's, Identify business problems  Plan  Predict(Forecast)/Test(What-If)  Apply performance rules on KPI’s  Act  Campaigns, personalization, optimization

5. Extract  DecisionStream unites information from disparate data sources for sampling the enterprise  80% of the work involved in analytics is collecting, cleansing, and preparing data

6. Classification with Scenario  Segment and Classify combinations of stores, regions, divisions, customers or products  Benchmark against last month! Path of success

7. Model with 4Thought  Avoids over-fitting  Works well with  Noisy  Co-linear  Not much or sparse data  Factor Analysis  What-if

8. Filling in the sparse matrix – e.g. #1  Revenue estimation:  Dimensional intersect:  Red shoes, southwest, women, springtime:  $50,000  Black shoes, northeast, men, summer:  $38,000  Black shoes, southwest, women, summer:  $43,000  Black shoes, northeast, men, springtime:  ????  Once a model is build against historical data, the resultant function can productively fill in the question marks

9. Filling in the sparse matrix – e.g. #2  Insurance cost estimation:  Dimensional intersect:  Age 38, southwest, female, non-smoker, married:  $1,800  Age 24, northeast, male, smoker, single:  $2,300  Age 32, southwest, female, smoker, single:  $3,000  Age 28, southwest, men, non-smoker, married:  ????  Once a model is build against historical data, the resultant function can productively fill in the question marks

10. Deploy with DecisionStream  DecisionStream uses predictive function from 4Thought as UDF for derivation  Deploy data marts, cubes, and metadata

11. Analyze, Visualize, Scorecard

12. Plan  Determine Business Goals and apply  NoticeCast Agents  KPI Business Pack  Exception highlighting with reports  Forecast with 4Thought  Access forecasted results with ETL

13. Keys to Mining  Usefulness  Can the information discovered be considered knowledge?  Certainty  How viable is the discovered knowledge  Expressiveness  Can the discovered knowledge be represented in a meaningful way

14. Problems for Mining  Missing data  Inconsistent categories  Too much data  Difficult to focus  Not enough data  Nothing meaningful  Too many patterns  Hard to discern knowledge from garbage  Complexity of discoveries  Knowledge is too complex to be used  Unavailable data

15. The Cognos BI Solution  Integrating touch-points leads to a 360-degree view of your business.  Many scored metrics are loaded via predictive models.  Segmentation is useful for simplifying large flat dimensions.