IBM Big Data in Government

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1 IBM Big in Government Turning big data into smarter decisions Deepak Mohapatra Sr. Consultant Government IBM Software Group

2 The Big Paradigm Shift 2

3 Big Creates A Challenge And an Opportunity What If You Could...(Four Key Paradigm Shifts) BIG DATA TRADITIONAL & ANALYTICS APPROACH All available information All Analyzed available information analyzed Small amount of carefully organized Large information amount of messy information Analyze small Analyze subsets of information all information Leverage more of the data being captured Carefully Analyze cleanse information as is, before any cleanse analysis as needed Reduce effort required to leverage data Hypothesis Exploration Question Analysis Repository Analysis Insight Answer Insight Correlation Insight 3 Start with Explore hypothesis all data and and test against identify selected correlations data leads the way and sometimes correlations are good enough Analyze Analyze data data after in it s motion been processed as it s and landed generated, in a warehouse in real-time or mart Leverage data as it is captured

4 Big Creates A Challenge And an Opportunity Harnessing Big For What What will has happened and what and why should you do Predict and decide the best action the realm embedded of the specialist in everything Intuitive analytics for everyone 4 Pre-programmed Learn to sense analysis and predict using on structured all types data of information Cognitive computing Scheduled Real-time as and when you need it

5 IBM Big Platform 5

6 Next Generation Reference Architecture Open Architecture/ Multiple Entry Points Internal bases Real-time Content Repositories External Federated Social Media Ingestion & Integration Landing (Hadoop) Warehouse & Marts, Visualization and Consumption Matching and Linking Information Governance, Security and Business Continuity 6 6

7 Next Generation Architecture s in Motion at Rest in Many Forms Streams Integration Stream Processing Integration Federation Quality Federation Real-time Video/Audio Text Mining Network/Sensor Entity Predictive Landing (Hadoop) Raw Structured Unstructured Text Mining Entity Machine Learning Warehouse & Marts Structured Discovery Deep Reflection Operational Predictive Matching and Link Analysis Identity Resolution Matching and Linking Network Analysis Stewardship Reference Predictive Computational Statistics Business Intelligence Exploration & Visualization Collaboration Social Networking Inspectors Investigators Researchers Administrators Others Information Governance, Security & Business Continuity 7 7

8 Next Generation Architecture s with IBM Products Mapped in Motion at Rest in Many Forms Streams Integration Stream Processing Integration Federation Quality Federation Information Server InfoSphere Streams Real-time Video/Audio Text Mining Network/Sensor Entity Predictive Landing (Hadoop) Raw Structured Unstructured Text Mining Entity Machine Learning InfoSphere BigInsights, SPSS Warehouse & Marts Structured Discovery Deep Reflection Operational Predictive Information Governance, Security & Business Continuity Optim, Guardium Pure for (Netezza) Matching and Link Analysis Identity Resolution Matching and Linking Network Analysis Stewardship Reference MDM, SPSS Predictive Computational Statistics Business Intelligence Exploration & Visualization SPSS Modeler SPSS Statistics Cognos Collaboration Social Networking Inspectors Investigators Researchers Administrators Others Explorer, i2 Analyst Notebook 8 8

9 THINK 9 9

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