Martin Wildberger Presentation


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Martin Wildberger Presentation

  1. 1. Martin WildbergerIBM Big Data and Integration Portfolio OverviewBringing Big Data to the Enterprise © 2011 IBM Corporation
  2. 2. Martin Wildberger Vice President, Information Management Development IBM Software Group2 © 2011 IBM Corporation
  3. 3. A Big Data Platform Addresses Big Data Use Cases … Multi-channel customer sentiment and experience a analysis Big Data Platform 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 feeds3 © 2011 IBM Corporation
  4. 4. …But Can’t Do It Alone What does Big Data mean for your Information Architecture? Data Warehouse Big Data Platform Big Data will be a permanent part of your information architecture It cannot be a silo – it must be fully integrated Enterprise in order to leverage its Integration value It must be easy to deploy and integrate Traditional Sources New Sources4 © 2011 IBM Corporation
  5. 5. IBM’s Big Data Platform Vision Bringing Big Data to the Enterprise Data IBM Big Data Solutions Client and Partner Solutions Warehouse InfoSphere Warehouse Warehouse Appliances Big Data User Environments Netezza Developers End Users Administrators Master Data Mgmt InfoSphere MDM INTEGRATIONAGENTS Database Big Data Enterprise Engines DB2 Content Analytics ECM Information Server Business Analytics Streaming Analytics Internet Scale Analytics Cognos & SPSS Marketing Open Source Foundational Components Unica Hadoop HBase Pig Lucene Jaql Data Growth Management InfoSphere Optim5 © 2011 IBM Corporation
  6. 6. One Example - The 360°Multi-Channel Customer Sentime nt Analysis Business Processes Master Data Campaign Cognos Consumer Management Management Insight Events and Alerts Big Data Platform Web Traffic and Social Media Insight Website Logs Social Media Internet Scale Analytics Information Data Integration Warehouse Call Detail Call Behavior and Reports Experience Insight (CDRs) Streaming Analytics6 © 2011 IBM Corporation
  7. 7. IBM’s Big Data Platform Addresses the Key Requirements 1. Platform for V3 – Variety, Velocity, Volume Variety - manage data & content “As Is” Handle any velocity - low-latency streams and large volume batch Volume - huge volumes of at-rest or streaming data Big Data Platform 2. Analytics for V3 Analyze Sources in their native format - text, data, rich content Analyze all of the data - not just a subset Dynamic analytics - automatic adjustments and actions 3. Ease of Use for Developers and Users Developer UIs, common languages & automatic optimization End-user UIs & visualization 4. Enterprise Class Failure tolerance, Security and Privacy Scale Economically 5. Extensive Integration Capabilities Integrate wide variety of sources Leverage enterprise integration technologies7 © 2011 IBM Corporation
  8. 8. 1. Platform for V3 – Addresses All 3 V’s Analyze telemetry, fuel consumption, schedule and Variety weather patterns to optimize Big Data Platform shipping logistics. Analyze 100k records/ Velocity second to address customer satisfaction in real time Optimize capital investments Volume based on 6 Petabytes of information8 © 2011 IBM Corporation
  9. 9. 2. Analytics for V3 – Built-for-Purpose, Built-for-Variety Leading analytics from IBM Research Built-for-purpose to analyze data in its native format Text Statistics Image & Video Mining Acoustic Predictive Financial Geospatial Times Series Mathematical IBM Differentiator – significant research investment in analytics; designed for use with Big Data.9 © 2011 IBM Corporation
  10. 10. 3. Ease of Use for Developers and Users End-user Visualization Development Environment Data exploration, crawling, and Familiar coding and tooling analytics environment, testing, and optimization10 © 2011 IBM Corporation
  11. 11. 4. Enterprise Class High availability architecture Failure to support hardware or Tolerance application failure. Big Data Platform Runs on scalable hardware Scale with the ability to Economically dynamically add additional nodes. Security protection for Security & granular data access Privacy control.11 © 2011 IBM Corporation
  12. 12. 5. Enterprise Integration Data Warehouse Big Data Platform Trusted Information & Governance – Companies need to govern what comes in, and the insights that come out Enterprise Integration Data Management – Insights from Big Data must be incorporated into the warehouse Traditional Sources New Sources12 © 2011 IBM Corporation
  13. 13. Building with the Open Source Community Big Data Platform Leveraging …and Open Source Giving Innovation … Back …Contributing… jaql PIG ZooKeeper13 © 2011 IBM Corporation
  14. 14. Announcing: InfoSphere BigInsights v 1.1 Platform for V3 Hadoop foundation Large-scale indexing Platform BigInsights Enterprise Analytics for V3 Edition Integrated text analytics Licensed Enterprise Class DB2/RDBMS and Data Warehouse Integration Usability Provisioning and Advanced Security Development Studio BigInsights Job and workflow management Admin console (incl. HDFS Basic Edition explorer) Free download with Large Scale Indexing Enterprise Class Apache 24 x 7 Web Text Analytics Tooling Hadoop support Provisioning, storage, and advanced security Integration Capabilities Integrated install Hadoop POC Pilot Enterprise Connectivity with DB2, Up-and-running Deployment InfoSphere Warehouse and Deployment Sizes IBM Smart Analytics System.14 © 2011 IBM Corporation
  15. 15. Internet-Scale Analytics in Action Financial Services Utilities Improved risk decisions Weather impact analysis on Customer sentiment analysis power generation AML Smart meter data analysis Transportation IT Weather and traffic Transition log analysis impact on logistics and for multiple fuel consumption transactional systems Call Centers E Commerce Voice-to-text mining for Analyze internet behavior customer behavior and buying patterns understanding Digital asset piracy Telecommunications Operations and failure Multi-channel Integration Integrated customer behavior analysis from device, sensor, modeling and GPS inputs15 © 2011 IBM Corporation
  16. 16. Announcing: InfoSphere Streams v 2.0 A Platform for V3 Runtime optimizations delivering performance improvements. Improved Java™ support allows shared Java Virtual Machines for better resource utilization and improved extensibility Analytics & Usability New toolkits that delivers more operators and functions out of the InfoSphere Streams box Analytics for text, data mining, statistics, among others Enterprise Class Improved monitoring capabilities and deployment flexibility to enhance availability and simplify administration Integration Capabilities Connectivity is expanded to support Netezza TwinFin, Microsoft SQLServer, and MySQL, in addition to DB2, Informix®, solidDB®, and Oracle databases.16 © 2011 IBM Corporation
  17. 17. Streaming Analytics in Action Stock Market Impact of weather on securities prices Natural Systems Analyze market data at ultra-low latencies Wildfire management Water management Law Enforcement, Defense & Cyber Security Real-time multimodal surveillance Transportation Situational awareness Intelligent traffic Cyber security detection management Fraud Prevention Detecting multi-party fraud Real time fraud prevention Manufacturing Process control for microchip fabrication e-Science Space weather prediction Detection of transient events Health & Life Sciences Synchrotron atomic research Neonatal ICU monitoring Epidemic early warning Other system Telephony Smart Grid Remote healthcare CDR processing Text analysis monitoring Social analysis Who’s talking to whom? Churn prediction ERP for commodities Geomapping FPGA acceleration17 © 2011 IBM Corporation
  18. 18. IBM clients have embraced the Big Data opportunity and are stretching beyond the traditional frontiers of Business Intelligence Derive a 360 degree view of Enable real-time customer Process and correlate large customer behavior across all analysis that processes volumes of physiological channels and Identify billions of records per day. data streams in conjunction opportunities for more with persistent data, such targeted marketing activities. Support IT and business as lab test results to requirements for uncover hidden patterns sophisticated analytics in in test results that would real-time, with a focus on otherwise be very difficult to churn prevention. identify. Integration: Integration: Integration: POS data sourced from Improve analytics Use data store to define rules existing data warehouse. performance of warehouse by for streaming data analytics. offloading record processing. Iteratively refine rules.18 © 2011 IBM Corporation
  19. 19. Leading Organizations are Partnering with IBM for Big Data Big Data Platform IBM’s Big Data Platform Broadest platform to bring Big Data to the Enterprise A Platform for V3 – Analyzing the Variety, Velocity and Volume of structured and unstructured data Leveraging the Broader IBM InfoSphere Information Integration and Governance portfolio InfoSphere Warehouse, Netezza appliances and IBM Smart Analytics System Cognos Consumer Insight – Big Data social media analytics solution ECM – content management and analytics Tivoli – integrated service management Smarter Computing – efficient and innovative IT infrastructure GBS – Business Analytics and Optimization services19 © 2011 IBM Corporation