C O N F I D E N T I A L

Advanced Analytics

Business Intelligence with Data Mining

Data MiningData Mining

What’s important Association/Binning

Clustering

Classification

Segmentation

What to expect What-if

Estimation Curve Fitting Fill in Sparse Matrix

Prediction Probability Quantitative

MethodologyMethodology

Collected Sample

Statistical Analyst – Business Modeling

Warehouse

Marts

business interpretation

•Optimize data marts

Data

StoreDBA

Predictive Metrics & Segments

Methodology - EDMDAPAMethodology - 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

ExtractExtract

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 ScenarioClassification with Scenario

Segment and Classify combinations of stores, regions, divisions, customers or products

Benchmark against last month!

Path of successPath of success

Model with 4ThoughtModel 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. #1Filling 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. #2Filling 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 DecisionStreamDeploy with DecisionStream

DecisionStream uses predictive function from 4Thought as UDF for derivation

Deploy data marts, cubes, and metadata

Analyze, Visualize, ScorecardAnalyze, Visualize, Scorecard

PlanPlan

Determine Business Goals and apply

NoticeCast Agents

KPI Business Pack

Exception highlighting with reports

Forecast with 4Thought Access forecasted results with

ETL

Keys to MiningKeys 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 MiningProblems 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 SolutionThe 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.