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IBM CEC Big Data 2011 06-11 final

COMMON Europe Congress 2012 - Vienna

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IBM CEC Big Data 2011 06-11 final

  1. 1. Smarter Computing - Big Data11 June 2012Dipl.Ing.Wolfgang NimführInformation Agenda Executive ConsultantBig Data Tiger TeamIBM Software Group © 2012 IBM Corporation
  2. 2. Legal Disclaimer © IBM Corporation 2012. All Rights Reserved. The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM’s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM’s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results. Information regarding potential future products is intended to outline our general product direction and it should not b e relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal ob ligation to deliver any material, code or functionality. Information about potential future products may not b e incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.2 © 2012 IBM Corporation
  3. 3. Welcome to the Instrumented Interconnected World! INSTRUMENTED INTERCONNECTED INTELLIGENT Build a Smarter Planet 3 © 2012 IBM Corporation
  4. 4. Why Big DataSearches for "big data" on Gartners website “most enterprise data warehouse (EDW) and BIhave increased 981% between March 2011 - teams currently lack a clear understanding of bigOctober 2011 data technologies… They are increasingly asking the question, "How can we use big data to deliver new insights?" Gartner 2012 “Big Data: The next frontier for innovation, competition and productivity” McKinsey Global Institute 2012 will be the year of big data BBC Nov 30 2011 Big Data will be the CIO Issue of 2012 IDC Prediction 2012 report Big Data - We are at a huge inflection point and this opportunity comes only once. We are declaring that IBM is the #1 leader in providing a Big Data platform. Alyse Passarelli, WW VP IM Sales Jan 10th 20124 © 2012 IBM Corporation
  5. 5. The Information Explosion in Data and Real World Events 44x as much Data and Content 2020 35 zettabytes Business leaders frequently Over Coming Decade 1 in3 make decisions based on information they don’t trust, or don’t ha ve 2009 800,000 petabytes 1 in2 Business leaders say they don’t have access to the information they need to do their jobs 80% of CIOs cited “Business Of world’s data is unstructured 83% intelligence and analytics” as part of their visionary plans to enhance competitiveness of CEOs need to do a better job 60% capturing and understanding information rapidly in order to make swift business decisions Organizations Need Deeper Insights5 5 © 2012 IBM Corporation
  6. 6. The resulting explosion of information creates a need for anew kind of intelligence The percentage of available data an enterprise can analyze is decreasing proportionately to the available to it Quite simply, this means as enterprises, we are getting Missing Insights and “more naive” about our business over time Analytics We don’t know what we could already know…. Data AVAILABLE to an organization The Blind Spot Data an organization can PROCESS 6 © 2012 IBM Corporation
  7. 7. ChallengeStudy a Large Volume and Variety of Data to Find New Insights Multi-channel customer sentiment and experience a analysis Support medical diagnostics Detect life-threatening conditions Predict weather patterns to plan optimal wind turbine usage, and optimize capital expenditure on asset placement Make risk decisions and frauds detection based on real-time transactional data Identify criminals and threats from disparate video, audio, and data feeds 7 © 2012 IBM Corporation
  8. 8. Leveraging Big Data AnalyticsHow do you address the challenges presented by empowered marketparticipants generating mountains of data? Can you capture data Can you do it in real- Can you turn that data generated by these time? into insights to predict interactions? customer / competitive / Sourc e: market behavior? 1 – Barrera, Clod and Wojtowecz. “Cloud Leads Five Storage Trends for 2011.” CIO. J anuar y 27, 2011 . 2 – http://www.i nternetworlds tats .com/stats.htm. 3 – http://www.abires ess/3584-More+than+Seven+Trillion+SMS+Messages+Will+Be+Sent+in+2011 8 © 2012 IBM Corporation
  9. 9. How does Big Data Analytics impact business?Deploying these competencies extensively correlates to long-termfinancial performance Listen and Anticipate consistently deployed across the enterprise correlate to higher compound annual growth rates (5-year CAGR, 2005-2010) Source: Outperforming in a Data Rich, H yper Connected W orld, an IB M Center for Applied In sights r esear ch r eport. Copyright © IBM 2012 9 © 2012 IBM Corporation
  10. 10. Leveraging Big Data Analytics can improve Experience … Client Mgr Data Scientist Dashboards Call Center …Information Management Capabilities Natural Language External Data Internal Data • Web Logs • Relationship / risk • Event triggers • Twitter feeds data • Customer Profitability • Facebook chats • Product analysis • YouTube Video profitability data • Complaint Data • Blogs/Posting Big Data • Email • Voice to Te xt Data • Appraisal data Analytics correspondents • Transactional data • Company website • Policy & Procedure • Credit bureau data Hub logs data 10 © 2012 IBM Corporation
  11. 11. On 16 Feb 2011 the IBM Watson system won Jeopardy! Can we design a computing system that rivals a human’s ability to answer questions posed in natural language, interpreting meaning and context andretrieving, analyzing and understanding vast amounts of information in real-time? 11 © 2012 IBM Corporation
  12. 12. IBM Watson‘s project started 2007• Project started in 2007, lead David Ferrucci• Initial goal: create a system able to process natural language & extract knowledge faster than any other computer or human• Jeopardy! was chosen because it’s a huge “IBM is not in the entertainment challenge for a computer to find the questions business. But we are in the business of to such “human” answers under time pressure technology and pushing frontiers.” David Shepler, IBM Research Program Manager• Watson was NOT online!• Watson weighs the probability of his answer being right – doesn’t ring the buzzer if he’s not confident enough• Which questions Watson got wrong almost as interesting as which he got right! 12 © 2012 IBM Corporation
  13. 13. Different Types of Evidence: Keyword Evidence In May 1898 Portugal celebrated In May, Gary arrived in the 400th anniversary of this India after he celebrated his explorer’s arrival in India. anniversary in Portugal. arrived in celebrated Keyword Matching Keyword Matching celebrated In May Keyword Matching Keyword Matching In May 1898Evidence 400th Keyword Matching anniversarysuggests “Gary” anniversary Keyword Matchingis the answerBUT the system Portugal Keyword Matching Keyword Matching in Portugalmust learn thatkeyword arrival inmatching maybe weak relative India Keyword Matching Keyword Matching Indiato other types ofevidence explorer Gary 13 © 2012 IBM Corporation
  14. 14. Different Types of Evidence: Deeper Evidence In May 1898 Portugal celebrated On 27th May 1498, Vasco da Gama On 27th May 1498, Vasco da Gama On 27th May 1498, Vasco da Gama the 400th anniversary of this On landedin Kappad Beach Vasco da landed in of May Beach the in th Kappad 1498, landed 27Kappad Beach explorer’s arrival in India. Gama landed in Kappad Beach Search Far and Wide Explore many hypotheses celebrated Find Judge Evidence landed in Portugal Many inference algorithms Temporal May 1898 400th anniversary 27th May 1498 Reasoning Date Math arrival StatisticalStronger in Paraphrasing Para-evidence can phras es GeoSpatialbe much India Reasoning Kappad Beachharder to find Geo-KBand score. explorer Vasco da Gama 14 The evidence is still not 100% certain. © 2012 IBM Corporation
  15. 15. DeepQA:Massively Parallel Probabilistic Evidence-Based ArchitectureQuestion 1000’s of 100,000’s scores from many simultaneous 100s Possible Pieces of Evidence 100s sources Text Analysis Algorithms Answers Multiple InterpretationsQuestion & Final Confidence Question Hypothesis Hypothesis and Topic Synthesis Merging & Decomposition Generation Evidence Scoring Analysis Ranking Hypothesis Hypothesis and Evidence Generation Scoring Answer & Confidence ... 15 © 2012 IBM Corporation
  16. 16. Maximum Benefit Requires Combining Deep andReactive Analytics Hypotheses Predictions Real time Optimization 100,000 updates/sec, 5 ms/decision Exa Round-trip automation Deep Deep 10 PB f or Deep AnalyticsAnalytics Peta History Predictive Analytics 100,000 records/sec, 6B/day 10 ms/decision 6 PB f or Deep Analytics Feedback Data Scale Tera nio In Smart Traffic ra t te 250K GPS probes/sec g Reality Actions g ra Inte 630K segments/sec tio n Giga 2 ms/decision, 4K vehicles DeepQA Fast Traditional Data 100s GB for Deep Analytics Mega Warehouse and 3 sec/decision 1 PB training corpus Business Integration Intelligence Observations Kilo Reactive yr mo wk day hr min sec … ms µs Analytics Occasional Frequent Real-time 16 Decision Frequency © 2012 IBM Corporation
  17. 17. Traditional Approach vs Big Data Approach 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 Ma ximum asset utilization 17 © 2012 IBM Corporation
  18. 18. Big Data use cases across all industries Financial Services Utilities Fraud detection Weather impact analysis on Risk management power generation 360° View of the Customer Transmission monitoring Smart grid management Transportation IT Weather and traffic Transition log analysis impact on logistics and for multiple fuel consumption transactional systems Cybersecurity Health & Life Sciences Epidemic early warning Retail system 360° View of the Customer ICU monitoring Click-stream analysis Remote healthcare monitoring Real-time promotions Telecommunications Law Enforcement CDR processing Real-time multimodal surveillance Churn prediction Situational awareness Geomapping / marketing Cyber security detection Network monitoring 18 © 2012 IBM Corporation
  19. 19. Monetizing Relationships - not just Transactions Calling Network Merged Network company Telco Amy Bearn 32, Married, mother of 3, How v aluable is Amy to my mobile phone network? How likely is she to Accountant switch carriers? How many other Telco Score: 91 customers will f ollow CPG Score: 76 Fashion Score: 88 Retail Telco How v aluable is Amy to my retail sales? Who does she influence? Social Network Public What do they spend? Database 19 © 2012 IBM Corporation
  20. 20. °Sample: Big Data 360°Lead GenerationPersonal Attributes Personal Attributes• Identifiers: name, address, age, gender, • Identifiers: name, address, age, gender,occupation… occupation… Timely Insights Timely Insights• Interests: sports, pets, cuisine… • Intent to buy various products • Interests: sports, pets, cuisine… • Intent to buy various products• Life Cycle Status: marital, parental • Current Location • Life Cycle Status: marital, parental • Current Location Social Media based • Sentiment on products, services, campaigns • Sentiment on products, services, campaigns 360-degree • Incidents damaging reputation • Incidents damaging reputation Consumer Profiles • Customer satisfaction/attrition • Customer satisfaction/attritionLife Events Life Events• Life-changing events: relocation, having a • Life-changing events: relocation, having ababy, getting married, getting divorced, buying baby, getting married, getting divorced, buyinga house… a house… Products Interests Products Interests • Personal preferences of products • Personal preferences of products • Product Purchase history • Product Purchase historyRelationships Relationships • Suggestions on products & services • Suggestions on products & services• Personal relationships: family, friends and • Personal relationships: family, friends androommates… roommates…• Business relationships: co-workers and • Business relationships: co-workers andwork/interest network… work/interest network…Monetizable intent to buy products Life Events I need a new digital camera for my food pictures, any College: Off to Stanford for my MBA! Bbye chicago! I need a new digital camera for my food pictures, any College: Off to Stanford for my MBA! Bbye chicago! recommendations around 300? recommendations around 300? Looks like well be moving to New Orleans sooner than I thought. What should I buy?? A mini laptop with Windows 7 OR a Apple Looks like well be moving to New Orleans sooner than I thought. What should I buy?? A mini laptop with Windows 7 OR a Apple MacBook!??! MacBook!??! Intent to buy a house Location announcements Im thinking about buying a home in Buckingham Estates per a Im thinking about buying a home in Buckingham Estates per a Im at Starbucks Parque Tezontle recommendation. Anyone have advice on that area? #atx #austinrealestate 20 at Starbucks Parque Tezontle Im recommendation. Anyone have advice on that area? #atx #austinrealestate © 2012 IBM Corporation #austin #austin
  21. 21. °Sample: Big Data 360°Lead Generation Real-time product Real-time product intents enriched with intents enriched with consumer attributes consumer attributes Entries contain promotional messages, Entries contain promotional messages, wishful thinking, questions, etc wishful thinking, questions, etc Integration across Social Media sites Integration across Social Media sitesMicro-segmentation of Micro-segmentation of product intents by product intents by Real-time tracking by occupation Real-time tracking by occupation micro-segmentation micro-segmentation For many of the attributes we need to extract, For many of the attributes we need to extract, cleanse, normalize and categorize cleanse, normalize and categorize Micro-segmentation of Micro-segmentation of consumers by hobbies consumers by hobbies 21 © 2012 IBM Corporation
  22. 22. Sample: Institutional Risk ApplicationComprehensive view of publicly traded companies and relatedpeople based on regulatory filings Extract Integrate22 © 2012 IBM Corporation
  23. 23. Requirements for a Big Data Solution Platform Analyze a Variety of Information Novel analytics on a broad set of mixed information that could not be analyzed before Multiple relational & non-relational data types and schemas Analyze Information in Motion Streaming data analysis Large volume data bursts & ad-hoc analysis Analyze Extreme Volumes of Information Cost-efficiently process and analyze petabytes of information Manage & analyze high volumes of structured, relational data Discover & Experiment Ad-hoc analytics, data discovery & experimentation Manage & Plan Enforce data structure, integrity and control to ensure consistency for repeatable queries 23 © 2012 IBM Corporation
  24. 24. IBM Big Data Platform for Ingest, Data and Analytics Analytic Applications BI / Exploration / Functional Industry Predictive Content Reporting Visualization App App Analytics Analytics New analytic applications drive the requirements for a big data platform IBM Big Data Platform • Integrate and manage the full variety, velocity and volume of data Visualization Application Systems & Discovery Development Management • Apply advanced analytics to information in its native form • Visualize all available data for ad- Accelerators hoc analysis • Development environment for Hadoop Stream Data building new analytic applications System Computing Warehouse • Workload optimization and scheduling • Security and Governance Information Integration & Governance 24 © 2012 IBM Corporation
  25. 25. Big Data Hadoop Capabilities Big Data Challenges IBM Big Data Solutions • Very high volumes (TBs to PBs) NoSQL Data unstructured data IBM BigInsights Hadoop-based processing for • Exploration and discovery analytics on variety and • Text, Entity and Social Media volumes of data Analytics • Real time processing IBM Streams Streaming • Detect failure patterns • High volume, low latency Low latency analytics for processing streaming data • Scoring and decision analytics 25 © 2012 IBM Corporation
  26. 26. High Level Conceptual View *) Real Time Scoring and Response Streaming Sens ors Streaming Structured or Unstructured • Smart Grid Analytics Analytics and • Distribution Grid Reporting Monitoring Unstructured IBM • Root Cause Failure Streams Analysis • Demand ResponseRegulations Effectiveness Exploration/Discovery Queryable Archive Improv ed Analytics Web/social Social Structured Unstructured • Sentiment analysis • Call Centre analysis IBM • Log analysis BigInsights Analytics and • Outage Information Reporting • Micro customer Improv ed Analytics segmentation Structured • Offering ManagementData Asset LandscapeGeneration Transmission Distribution Smart Meters Foundational • Meter Data Management • Customer Portals Trading Supplier Orders Customer • Smart Meter Analytics • Demand Forecasting • Generation Scheduling Operational Legacy • Customer Segmentation Systems IBM • Campaign Management Applcations Marketing Maintenance Employee GIS power i • Outage Management • Estimate Load Shedding • Time of Use Tariffs • Maintenance Scheduling 26 *) Example for Industry Energy & Utility © 2012 IBM Corporation
  27. 27. IBM InfoSphere BigInsightsAnalytical platform for Big Data at-rest Based on open source & IBM Analytic Applications technologies BI / Exploration / Functional Industry Predictiv e Content Reporting Visualization App App Analytics Analytics Distinguishing characteristics • Built-in analytics enhances business IBM Big Data Platform knowledge Visualization Application Systems • Enterprise software integration & Discovery Development Management complements and extends existing capabilities Accelerators • Production-ready platform with tooling for analysts, developers, and administrators Hadoop Stream Data speeds time-to-value and simplifies System Computing Warehouse development/maintenance IBM advantage • Combination of software, hardware, services and advanced research Information Integration & Governance 27 © 2012 IBM Corporation
  28. 28. IBM InfoSphere BigInsightsEmbrace and Extend Hadoop Analytics BigSheets Text Analytics ML Analytics *) Interface Management Console Application (browser based) Pig Hive Jaql Avro IBM LZO Compression Zookeeper MapReduce AdaptiveMR FLEX BigIndex Developing Tooling (Eclipse Plug-Ins) Oozie Lucene Rest API Storage HBase (for Applications) HDFS GPFS-SNC *) Data Streams Netezza BoardReader R IBM Sources/ Open Source Data Stage DB2 CSV/XML/JSON SPSS Connectors Flume JDBC Web Crawler *) future release 28 © 2012 IBM Corporation
  29. 29. BigSheetsA visual tool for data manipulation and prototyping • Ad-hoc analytics for LOB user • Analyze a variety of data - unstructured and structured • Spreadsheet metaphor for exploring/ visualizing data • Browser-based 29 © 2012 IBM Corporation
  30. 30. Text AnalyticsTurns disparate words into measurable insights Physically Identify positive or Reporting/Monitoring assemble data, Part-of-speech negative sentiment, Iterative social commentary, standardize identification, standard NLP-based classification using combination w /structured form ats, address and custom ized analytics, define autom ated and data, clustering, auto-identify extraction dictionaries, variables, m acros m anual techniques. associated concepts, language, process proper noun and rules. Concept derivation & correlated concepts, auto- punctuation and identification, concept inclusion, semantic classification of non-gramm atical categorization, networks and co- documents, sites, posts. characters, synonyms, exclusions, occurrence rules standardize m ulti-terms, regular spelling. expressions, fuzzy- m atching Pre-configured text annotators ready for distributed processing on Big Data Support for native languages including double-byte 30 © 2012 IBM Corporation
  31. 31. Text AnalyticsHighly accurate analysis of textual content Unstructured text (document, email, etc) How it works Football World Cup 2010, one team • Parses text and detects meaning with distinguished themselves well, losing to annotators the eventual champions 1-0 in the Final. Early in the second half, Netherlands’ • Understands the context in which the striker, Arjen Robben, had a breakaway, text is analyzed but the keeper for Spain, Iker Casillas • Hundreds of pre-built annotators for made the save. Winger Andres Iniesta names, addresses, phone numbers, scored for Spain for the win. along others Accuracy • Highly accurate in deriving meaning from complex text Classification and Insight Performance • AQL language optimized for MapReduce 31 © 2012 IBM Corporation
  32. 32. ML AnalyticsStatistical and Predictive Analysis Framework for machine learning (ML) implementations on Big Data • Large, sparse data sets, e.g. 5B non-zero values • Runs on large BigInsights clusters with 1000s of nodes Productivity • Build and enhance predictive models directly on Big Data • High-level language – Declarative Machine Learning Language (DML) • E.g. 1500 lines of Java code boils down to 15 lines of DML code • Parallel SPSS data mining algorithms implementable in DML Optimization • Compile algorithms into optimized parallel code 4500 • For different clusters and different data characteristics 4000 3500 • E.g. 1 hr. execution (hand-coded) down to 10 mins E xecution Time (sec) 3000 2500 2000 1500 1000 500 0 0 500 1000 1500 2000 # non zeros (million) Java Map-Reduce Sy stemML Single node R 32 © 2012 IBM Corporation
  33. 33. Workload OptimizationOptimized performance for big data analytic workloadsAdaptive MapReduce Hadoop System Scheduler Algorithm to optimize execution time of Identifies small and large jobs from prior multiple small jobs experience Performance gains of 30% reduce Sequences work to reduce overhead overhead of task startupTask Map Adaptive Map Reduce (break task into small parts) (optimization — (many results to a order small units of work) single result set) 33 © 2012 IBM Corporation
  34. 34. Public wind data is available on 284km x 284 km grids (2.5o LAT/LONG) More data means more accurate and richer models (adding hundreds of variables) - Vestas wind library at 2.5 PB: to grow to over 6 PB in the near-term - Granularity 27km x 27km grids: driving to 9x9, 3x3 to 10m x 10m simulations Reduced turbine placement identification from weeks to hours Perspective: The Vestas Wind library 34 34 © 2012 IBM Corporation34
  35. 35. InfoSphere StreamsAnalytical platform for Big Data in-motion Analytic Applications BI / Exploration / Functional Industry Predictiv e Content Reporting Visualization App App Analytics Analytics Built to analyze data in motion • Multiple concurrent input streams IBM Big Data Platform • Massive scalability Visualization Application Systems & Discovery Development Management Process and analyze a variety of Accelerators data • Structured, unstructured content, video, Hadoop Stream Data audio System Computing Warehouse • Advanced analytic operators Information Integration & Governance 35 © 2012 IBM Corporation
  36. 36. Stream ComputingAnalyze Data in Motion Traditional Computing Stream Computing Historical fact finding Current fact finding Find and analyze information stored on disk Analyze data in motion – before it is stored Batch paradigm, pull model Low latency paradigm, push model Query-driven: submits queries to static data Data driven – bring the data to the query Data Base 36 © 2012 IBM Corporation
  37. 37. Streams approach illustrated tuple 37 © 2012 IBM Corporation
  38. 38. IBM InfoSphere StreamsMassively Scalable Stream Analytics Linear Scalability Deployments Clustered deployments – unlimited Source Analytic Sync scalability Adapters Operators Adapters Automated Deployment Automatically optimize operator deployment across clusters Streams Studio IDE Performance Optimization Automated and Optimized JVM Sharing – minimize memory use Deployment Fuse operators on Streaming Data Streams Runtime Sources same cluster Telco client – 25 Million Visualization messages per second Analytics on Streaming Data Analytic accelerators for a variety of data types Optimized for real-time performance 38 © 2012 IBM Corporation
  39. 39. Cisco turns to IBM big data for intelligent infrastructure management • Optimize building energy consumption with centralized monitoring • Automate preventive and corrective maintenance Capabilities Utilized: • Streaming Analytics • Hadoop System • Business Intelligence Applications: • Log Analytics • Energy Bill Forecasting • Energy consumption optimization • Detection of anomalous usage • Presence-aware energy mgt. • Policy enforcement39 © 2012 IBM Corporation
  40. 40. University of Ontario Institute of Technology Use case – Neonatal infant monitoring – Predict infection in ICU 24 hours in advance Solutions – 120 children monitored :120K msg/sec, billion msg/day – Trials expanding to include hospitals in US and China Event Pre- Analysis processer Framework Sensor Stream-based Distributed Interoperable Solutions Network Health care Infrastructure (Applications) 40 © 2012 IBM Corporation
  41. 41. Without a Big Data Platform You Code… Over 100 sample applications and toolkits with industry focused toolkits with 300+ functions and operators Event Custom SQL Handling and Scripts Multithreading Check Application Pointing M anagement Accelerators Streams provides development, deployment, HA and Tool kits runtime, and infrastructure services Performance Debug Connectors Optimization Security “TerraEchos developers can deliver applications 45% faster due to the agility of Streams Processing Language…” – Alex Philip, CEO and President, TerraEchos 41 © 2012 IBM Corporation
  42. 42. IBM is Committed to Innovation 2012 IBM Resarch Selected SW Acquisitions Almaden Austin Melbourne Sao Paulo Beijing Haif a Delhi Ireland Y amato Watson Zurich • •$16B+ in acquisitions since 2005 $16B+ in acquisitions since 2005 • •10,000+ technical professionals 10,000+ technical professionals • •~8000 dedicated consultants ~8000 dedicated consultants • •27,000+ business partner 27,000+ business partner certifications certifications • •88 Analytics SolutionsCenters Analytics Solutions Centers • •100 analytics-based research assets; 100 analytics-based research assets; almost 300 researchers almost 300 researchers “Watson is going to revolutionize many, many industries and it will fundamentally change the way we interact with computers & machines.” John Kelly, SVP & Head of IBM Research2005 * TeaLeaf, Varicent Vivismo pending acquisition close 42 © 2012 IBM Corporation
  43. 43. Making Learning Easy and FunAsk for a Big Data Discovery 43 © 2012 IBM Corporation
  44. 44. Questions & Answers Dipl.Ing. IBM Austria Wolfgang Nimführ Obere Donaustrass e 95 A1020 Vienna Information Agenda Executive Consultant Tel +43-664-618-5389 Big Data Tiger Team IBM Software Group Europe44 © 2012 IBM Corporation