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Converting Big Data into Big Knowledge

Converting Big Data into Big Knowledge. Information from Every Entity – Machine. 640 Terabytes 1 Billion Lines of Code EACH engine generates 10 TB/30 minutes 280 flights every day 11 on British Airways alone. 30 billion RFID tags (1.3B in 2005) BMW 7 series Intelligent Tractors.

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Converting Big Data into Big Knowledge

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  1. Converting Big Data into Big Knowledge

  2. Information from Every Entity – Machine 640 Terabytes 1 Billion Lines of Code EACH engine generates 10 TB/30 minutes 280 flights every day 11 on British Airways alone • 30 billion RFID tags (1.3B in 2005) • BMW 7 series • Intelligent Tractors 1 Terabyte of Trade information Captured every day by the New York Stock Exchange LHR  JFK: NonStop • 200 million smart meters by 2014 • Yearly Reading • 120M Readings a Month • 3.65B Readings a Day 350B Transactions/Year

  3. Information from Every Entity – Man 12+ Terabytesof tweet data every day • 4.6 billion Camera phones world wide • Geospatial tagging • Location based advertising • Location based monitoring 25+ Terabyteslog data every day

  4. Big Data enabled doctors from University of Ontario to apply neonatal infant monitoring to predict infection in ICU 24 hours in advance IBM Data Baby youtube.com Analyzing 1000 pieces of unique medical diagnostic information / second and stored in a dynamic model. Perspective: 20% drop in mortality of control group in trials 4

  5. Why Didn’t We Use All of the Big Data Before? “IBM had experts within data mining, Big Data, and Apache Hadoop and it was clear to use from the beginning we wanted to improve our business, not only today, but also prepare for the challenges we will face in three to five years, we had to go with IBM.” – Lars Christian Christensen VP Plant Siting & Forecasting

  6. IBM Big Data Platform is your Big Data Knowledge Factory Your Current Analytic Applications BI / Reporting Exploration / Visualization FunctionalApp IndustryApp Predictive Analytics Content Analytics Visualization & Discovery Application Development Systems Management Accelerators HadoopSystem Stream Computing Data Warehouse Information Integration & Governance Analytic Knowledge Accelerators • Text Analytics, Sentiment Analytics • Statistical & Predictive Analytics • Entity Integration Analytics IBM Big Data Platform Enterprise Robustness • Visualization & Application Tools • Cluster and Workload Management • Security & Governance Integration • With current sources • With new Big Data sources

  7. IBM Big Data Strategy: Move the Analytics Closer to the Data • New analytic applications drive the requirements for a big data platform • Integrate and manage the full variety, velocity and volume of data • Apply advanced analytics to information in its native form • Visualize all available data for ad-hoc analysis • Development environment for building new analytic applications • Workload optimization and scheduling • Security and Governance Analytic Applications BI / Reporting Exploration / Visualization FunctionalApp IndustryApp Predictive Analytics Content Analytics BI / Reporting IBM Big Data Platform Visualization & Discovery Application Development Systems Management Accelerators HadoopSystem Stream Computing Data Warehouse Information Integration & Governance

  8. www.bigdatauniversity.com

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