Robert LeBlanc - Why Big Data? Why Now?


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Robert LeBlanc - Why Big Data? Why Now?

  1. 1. Robert LeBlancWhy Big Data? Why Now? © 2011 IBM Corporation
  2. 2. Robert LeBlanc Senior Vice President, Middleware Software IBM Software Group2 © 2011 IBM Corporation
  3. 3. Information is at the Center … And Organizations of a New Wave of Opportunity… Need Deeper Insights 44x 2020 35 zettabytes Business leaders frequently as much Data and Content Over Coming Decade 1 in 3 make decisions based on information they don’t trust, or don’t have 1 in 2 Business leaders say they don’t have access to the information they need to do their jobs of CIOs cited “Business 83% intelligence and analytics” as 2009 800,000 petabytes 80% Of world’s data part of their visionary plans to enhance competitiveness is unstructured of CEOs need to do a better job 60% capturing and understanding information rapidly in order to make swift business decisions3 3 © 2011 IBM Corporation
  4. 4. The Challenge: Bring Together a Large Volume and Variety of Data to Find New Insights Multi-channel customer sentiment and experience a analysis Detect life-threatening conditions at hospitals in time to intervene Predict weather patterns to plan optimal wind turbine usage, and optimize capital expenditure on asset placement Make risk decisions based on real-time transactional data Identify criminals and threats from disparate video, audio, and data feeds4 © 2011 IBM Corporation
  5. 5. The Big Data Opportunity Extracting insight from an immense volume, variety and velocity of data, in context, beyond what was previously possible. Variety: Manage the complexity of multiple relational and non- relational data types and schemas Velocity: Streaming data and large volume data movement Volume: Scale from terabytes to zettabytes 55 © 2011 IBM Corporation
  6. 6. The Traditional Approach: Business Requirements Drive Solution Design Business Defines Requirements – What Questions Should we Ask? New requirements IT Designs a Solution require redesign with a set structure and rebuild and functionality Business executes queries to answer questions over and over6 © 2011 IBM Corporation
  7. 7. The Traditional Approach: Business Requirements Drive Solution Design Business Defines Requirements – What Questions Should we Ask? New requirements IT Designs a Solution require redesign with a set structure and rebuild and functionality Business executes queries to answer questions over and over Well-Suited To: Stretched By: • High value, structured data • Highly variable data and content • Repeated operations and processes (e.g. • Iterative, exploratory analysis (e.g. scientific transactions, reports, BI, etc.) research, behavioral modeling, etc.) • Relatively stable sources • Volatile sources • Well-understood requirements • Ill-defined questions and changing requirements7 © 2011 IBM Corporation
  8. 8. The Big Data Approach: Information Sources Drive Creative Discovery Business and IT Identify Information Sources Available New insights drive IT Delivers a Platform integration to that enables creative traditional exploration of all technology available data and content Business determines what questions to ask by exploring the data and relationships8 © 2011 IBM Corporation
  9. 9. Big Data Shouldn’t Be a Silo Must be an integrated part of your enterprise information architecture Data Warehouse Big Data Platform Enterprise Integration Traditional Sources New Sources9 © 2011 IBM Corporation
  10. 10. Merging the Traditional and Big Data Approaches Traditional Approach Big Data Approach Structured & Repeatable Analysis Iterative & Exploratory Analysis IT Business Users Delivers a platform to Determine what enable creative question to ask discovery IT Business Structures the Explores what data to answer questions could be that question asked Monthly sales reports Brand sentiment Profitability analysis Product strategy Customer surveys Maximum asset utilization10 © 2011 IBM Corporation
  11. 11. The Solution – IBM’s Big Data Platform Bring together any data source, at any velocity, to generate insight Analyzing a variety of data at enormous volumes Insights on streaming data Large volume structured data analysis IBM Big Data Platform • Variety • Velocity • Volume11 © 2011 IBM Corporation
  12. 12. Optimize capital investments based on 6 Petabytes of information Model the weather to optimize placement of turbines, maximizing power generation and longevity Build models to cover forecasting and real-time operation of power generation units Incorporate 6 PB of structured and semi-structured information flows12 12 © 2011 IBM Corporation
  13. 13. A Platform Approach Address Enterprise Client Needs Enterprise Client Needs Big Data Platform Delivers Enable creativity and agility Focus on outcomes Platform for V3 Reduce and manage Analytics for V3 complexity Ease of Use for Developers/Users Enterprise Class Lower development and integration costs Extensive Integration13 © 2011 IBM Corporation
  14. 14. IBM WatsonIBM Watson is a breakthrough in analytic innovation, but it is only successful because of the quality of the information from which it is working.14 © 2011 IBM Corporation
  15. 15. Big Data and Watson Big Data technology is used to build Watson can consume insights from Watson’s knowledge base Big Data for advanced analysis Watson uses the Apache Hadoop open framework to distribute the workload for loading information into memory. CRM Data POS Data Social Media Approx. 200M pages of text (To compete on Jeopardy!) Distilled Insight - Spending habits - Social relationships - Buying trends InfoSphere BigInsights Watson’s Memory Advanced search and analysis15 © 2011 IBM Corporation
  16. 16. Imagine the Possibilities …in a World with No Limits Information Radical Extreme from Everywhere Flexibility Scalability • Data & content • Virtualization at • “Big data” analytics • Apps, web & sensors every level • Real-time stream • At rest & in motion • Automated administration processing • Integrated & federated • Easy-to-use analytics • Efficient parallelism • Workload-optimized16 16 © 2011 IBM Corporation