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CONFIDENTIAL Advanced Analytics Business Intelligence with Data Mining 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 Methodology Statistical Analyst – Business Modeling Collected Sample Data Store Predictive Metrics & Segments DBA business interpretation Marts Warehouse •Optimize data marts 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 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 Classification with Scenario Segment and Classify combinations of stores, regions, divisions, customers or products Benchmark against last month! Path of success Model with 4Thought Avoids over-fitting Works well with Noisy Co-linear Not much or sparse data Factor Analysis What-if 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 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 Deploy with DecisionStream DecisionStream uses predictive function from 4Thought as UDF for derivation Deploy data marts, cubes, and metadata Analyze, Visualize, Scorecard Plan Determine Business Goals and apply NoticeCast Agents KPI Business Pack Exception highlighting with reports Forecast with 4Thought Access forecasted results with ETL 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 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 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.