Big Data & Predictive Analytics Michael Stencl. Agenda  Big Data  Predictive Analytics  So what?

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<ul><li>Slide 1</li></ul> <p>Big Data &amp; Predictive Analytics Michael Stencl Slide 2 Agenda Big Data Predictive Analytics So what? Slide 3 Big data definition Source: Big data analytics By Philip Russom TDWI best practices report, 4th Quarter 2011 Slide 4 Big data definition Source: Big data analytics By Philip Russom TDWI best practices report, 4th Quarter 2011 Whats the approximate total data volume that your organization manages only for analytics, both today and in three years? Slide 5 Source: Big data analytics By Philip Russom TDWI best practices report, 4th Quarter 2011 Whats the approximate total data volume that your organization manages only for analytics, both today and in three years? Slide 6 Source: Big data analytics By Philip Russom TDWI best practices report, 4th Quarter 2011 Slide 7 Paul Bachteal, SAS Technology Strategies for Big Data Analytics Slide 8 Slide 9 Slide 10 Slide 11 Slide 12 Slide 13 Slide 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 Slide 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 Slide 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 Slide 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 Slide 18 When was the first application of Predictive Analytics? A. 1960s Advent of computing power B. 1930s Wall street crash C. 19th century Birth of science as profession D. Before 15th century Slide 19 Slide 20 What Business people expect? Slide 21 Slide 22 BADIR Slide 23 Keep it simple Slide 24 You think its complicated... Hmm Did you ever dropped food on a floor? Do you eat it??? Slide 25 Slide 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 Slide 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 Slide 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 Slide 29 Where to compute it? 2 groups of software Framework based SW R Matlab Solution based SW Weka RapidMiner KNIME Mahout Slide 30 Thank You! </p>