1. Big Data & Predictive Analytics Michael Stencl

2. Agenda  Big Data  Predictive Analytics  So what?

3. Big data definition Source: Big data analytics By Philip Russom TDWI best practices report, 4th Quarter 2011

4. Big data definition Source: Big data analytics By Philip Russom TDWI best practices report, 4th Quarter 2011 What’s the approximate total data volume that your organization manages only for analytics, both today and in three years?

5. Source: Big data analytics By Philip Russom TDWI best practices report, 4th Quarter 2011 What’s the approximate total data volume that your organization manages only for analytics, both today and in three years?

6. Source: Big data analytics By Philip Russom TDWI best practices report, 4th Quarter 2011

7. Paul Bachteal, SAS  Technology Strategies for Big Data Analytics

14. What is behind  Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends.  What the Predictive analytics is Business intelligence technology Produces a predictive model which has, in turn, been trained over your data Learning from the experience of your organization Uses data that has the company reported

15. What is good for  Behavioral Segmentation, Loyalty analysis and Revenue prediction are just the beginning  Bring to customer highly automated advanced analytics workflow Applications Churn Rate Product Portfolio Mix Bad Debt Reduction Fraud Detection Customer Life Cycle Cost of Sales and Marketing Methods Regression Classification Anomaly Detection Clustering Association Feature Selection and Extraction

16. Conference James Taylor Decision Management Solution Azhar Iqbal Wells Fargo Securities Eric Siegel, Ph.D. Predictive Analytics World Erick Brethenoux IBM Business Analytics Kelley Blue Book Kevin B. Pratt ZZAlpha LTD. Piyanka Jain Aryng Inc. Paul Bachteal SAS

17. 5 Myths of PA  Is Predictive Analytics (PA) new??  Is it a crystal ball??  Is it perfect??  Press a button solution??  Does it always work?? Piyanka Jain, Aryng In

18. When was the first application of Predictive Analytics? A. 1960’s – Advent of computing power B. 1930’s – Wall street crash C. 19th century – Birth of science as profession D. Before 15th century

20. What Business people expect?

22. BADIR

23. Keep it simple

24. You think its complicated... Hmm Did you ever dropped food on a floor? Do you eat it???

26. Predictive Analytics in Cloud 5 areas of opportunity  Pre-packaged cloud based solutions  Cloud based predictive analytics for SaaS  Cloud based Predictive analytics for on premise  Predictive modeling with data in the cloud  Elastic compute power James Taylor, Decision Management Solutions

27. Why? Predictive Analytics  Automatically discover patterns in data  Predict trends or likely future behavior  Identify population segments Cloud Computing Computing resources delivered as a serviceMulti-tenancy and shared resourcesUsage pricing and location transparency

28. Commonly Used Methods  other possible methods  Clustering (K-means), PCA  Factor Analysis, Time Series, Survival Analytics  Neural Networks, Genetic Regression and Algorithm Logistic Regression Decision Tree Linear Regression

29. Where to compute it?  2 groups of software  Framework based SW  R  Matlab  Solution based SW  Weka  RapidMiner  KNIME  Mahout

30. Thank You!