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Big data cloud cloud circle keynote_final laura colvine 8th november 2012
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Big data cloud cloud circle keynote_final laura colvine 8th november 2012


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  • 1. Understanding the real impact when#Big Data meets #CloudReal world experiences, observations and findingsLaura ColvineCloud Strategy LeaderIBM United Kingdom and Ireland @LauraColvine ©2012 IBM Corporation
  • 2. The ‘art of the possible.’| ©2012 IBM Corporation
  • 3. Big Data? - hype or reality?| ©2012 IBM Corporation
  • 4. Big data characteristics in today’s digitised marketplace Characteristics of big data4 | ©2012 IBM Corporation
  • 5. The need for Cloud based Big Data and insight isaccelerating 85% of Fortune 500 organisations will be unable to exploit big data for competitive advantage through 2015 Source: Gartner: predictions for 201260% potential 50% of Global 1000 companies will store customer-increase in retail operatingmargins with big data sensitive data in cloud by year-Source: McKinsey Global Institute: Big data, The end 2016.Next Frontier for innovation, competition and Source: Gartner: predictions for 2012productivity, May 2011 | ©2012 IBM Corporation
  • 6. ‘Three out of four organisations have big data activitiesunderway’…. and patterns of adoption are forming Big data activities Converging Data Architectures Rebalancing data architecture portfolio, blending compute and storage requirements Context-based services Where you are and what you are doing will drive the next wave of digital services. Consumable data services The ability to share data will make it more valuable--but only if it is managed differently. Data for Insight and Impact Visualisation and Discovery: Discover, understand, search, and navigate federated Respondents were asked to describe the state of sources of big data while leaving that data in big data activities within their organisation. place. Total respondents n = 1061 Totals do not equal 100% due to rounding6 | ©2012 IBM Corporation
  • 7. Key Finding 1: Customer Outcomes and Optimisation aredriving big data initiatives across industry groups. Healthcare / Consumer Goods Financial Services Life Sciences Customer- centric outcomes Operational optimisation Risk / financial Manufacturing Public Sector management Telecommunications New business model Employee collaboration7 | ©2012 IBM Corporation
  • 8. Convergence of physical, human and business processdata for better outcomes Enterprises Human/ People Serving Aspirations of Domain Intergenerational Consumer Consumers Physical Domain Operational Domain | ©2012 IBM Corporation
  • 9. How are forward thinking organisations using big data and cloud?| ©2012 IBM Corporation
  • 10. Organisations are Putting Big Data and Big Insightsto work Creating scalable, efficient, and trusted information, systems Optimising complex decision making, spot trends and anomalies, predict outcomes Using resilient architectures either on premise or in the cloud. | ©2012 IBM Corporation
  • 11. To Unlock the potential organisations master threecompetencies to drive sustainable advantage Align Anticipate Act Organise, collaborate see, predict and shape with confidence to optimise and connect the people, business outcomes service outcome data and processes| ©2012 IBM Corporation
  • 12. Collaborating & Connecting People, Align Organisations, Data and ProcessConnecting Healthcare in the Cloud Ecosystems & Multi-Agency Stadium in the Clouds | ©2012 IBM Corporation
  • 13. Using Data and Cloud to Provide Anticipate Actionable Insight Growing from 2.5 PB to 6 PB of data 97% Reduction in wind forecast response time – from weeks to hours. Vestas 71 71| ©2012 IBM Corporation
  • 14. Act on Insights to improve service Act outcome and customer satisfaction 50%30%Reduction in surgery- savings in data management costs . 30% improvement inrelated hospitalisations transaction processingIdentify Genetic Patterns: efficiencyEnterprise Content Management Banco de Crédito del PeruData Warehousing, ApplicationInfrastructure, Cloud and ITOptimisationRizzoli Orthopaedic 1200% Increase in speed of collecting traffic data. Bucheon City, South Korea | ©2012 IBM Corporation
  • 15. “Are we there yet?”.... | ©2012 IBM Corporation
  • 16. Convergance will increase as cloud stretches above ITcommoditisation into business optimisation Next Generation Cloud Cloud-Scale Data Challenges Easy to Use Tools for Big Insights Cloud based Social & Collaboration tools Server and Storage Optimisation Cloud Workload Analysis Data Center Lifecycle Cost Analysis Tool Security Analytic services IBM Big Data/ Cloud Overview Cloud Smarter Smarter Social Security Big Data & SustainabilityComputing Computing Commerce Business Analytics| ©2012 IBM Corporation
  • 17. There are Snakes and Ladders in the Big Data andCloud Discussion Security Cost Compliance Business Innovation Complexity Simplicity & Speed Workload Optimisation Scalability Legacy & Transition Collaboration Skills & Culture Customer Experience /Outcome| ©2012 IBM Corporation
  • 18. Key Finding 2: Big data is dependent upon a scalableand extensible foundation Big data infrastructure • Multitenant Data Platforms • Solid information foundation • Scalable and extensible • Data in the Cloud • Platforms for Data Analysis • Platforms for Update intensive workloads • Data Platforms for Large Applications Respondents with active big data efforts were asked which • Data Mash Ups platform components were either currently • Open Research Challenges in pilot or installed within their organization.18 | ©2012 IBM Corporation
  • 19. Engaging the Unengaged, Reducing risk and driving revenue In S ec te u lli rit ge y Enterprise ncEmbedded & Edge e O Automated of Enterprise…… pt Predictive Analytics im is ed Multi-Agency or Prr P Ecosystems… Real-time, offi o Responsive, Aware ic i ci Single Issue or en enSingle Business ttFunction at this Ba Manual level s ………Structured icData, or Unstructured text data Reactive Proactive | ©2012 IBM Corporation
  • 20. From Siloed to Connected bridging the line of business and IT perspectives Information Silos Typical Enterpri se Functional SilosExample Enterprise Life Cycle Analytic Applications BI / Exploration / Functional Industry Predictive Content BI / Initiation Phase Strategy/Policy Reporting Visualization App App Analytics Analytics Reporting Plan Definition silo silo silo silo silo silo Big Data Platform Visualization Application Systems Design & Discovery Development Management Realization Phase silo silo silo silo silo silo Contract Definition silo silo silo silo silo siloRealization/Build/Warranty Analytics Accelerators Operational silo silo silo silo silo silo Operation Phase Hadoop Stream Data silo silo silo silo silo silo System Computing WarehouseMaintenance/Modifications Disposal Efficiency loss/cost estimated due loss or lack Efficiency loss/cost Efficiency loss/cost estimated due loss or lack estimated per life cycle of real-time information phase/step due loss or lack integration between PA of real-time information of information between (process automation) & OA integration between the Information Integration & Governance phases/steps (office automation) different enterprise silos | ©2012 IBM Corporation
  • 21. Deployment Complexity combines with the conflictingneeds of multiple stakeholders, each with specificrequirements Algorithm Composition and Invention Data Evaluation and Fusion Testing and Execution Optimization Streaming data Data mining Text data & statistics Optimization Multi-dimensional & simulation Semantic Time series analysis Fuzzy Geo spatial matching Video Solutions & image Network algorithms Relational New algorithms Social network ✔ Business Rules Engine Data & Analytic Services Data Models Filtering and Composition and Data Acquisition Core Analytics Deployment Extraction Validation Packaging Data & Analytic Runtimes Information Sources| ©2012 IBM Corporation Workload Optimization
  • 22. Workloads have specific characteristic that impactScalability, Optimisation and Resiliency design. L a r g e n u m b e r o f d a t a f e e d s, A n d / o r lo t s o f d a t a , a n d / o r L o w / V e r y F a st u n st r u ct u r e d d a t a Telecom Latency/Biz Decision Throughput netw ork Deep Q&A Automated trading security (DPI) Capital market Trade surveillance desk Industrial process Sensor based monitoring control w ater mgt Call center monitoring Risk analytics Asset tracking (quality) Seismic Real-time game platform Data Complexity monitoring Risk management in Processing energy trading Card fraud Intelligent Traffic Geospatial Real-timedetection & tracking Online hotel Systems Cross-sales Battlespace Inventory prevention booking Telco QoS & SLA Shop floor command & Reservoir monitoring monitoring control Optimization Weather ModelingModeling Liquidity Call center monitoring Fraud Early w arning system Astrophysic al data management (cross sale)Manufacturing detection & for energy trading mining system process control prevention Clickstream analysis CAD/CAE Salesforce L o w n u m b e r o f d a t a f e e d s, Retail inventory Massive Social EDA A n d / o r fe w e r d a t a , a n d / o r enablement optimization Media Analysis Health Climate Telecom monitoring Lease Prediction billing management system Nuclear Energy Baggage Health records Hig h / S lo w S t r u ct u r e d d a t a Simulation handling screening D e gre e of A naly t ic C omple xit y Simple Middle Road Complex (Alerts) (Forecasting) (Simulation and Optimizat ion) | ©2012 IBM Corporation
  • 23. Cloud Systems of the future will be more data centric,composable and scalable… but different data oranalytics workloads demand different systemcharacteristics Predictive Analytics Text Analytics Optimisation Modeling, Simulation Hadoop Workloads Sensitivity Analysis Future SystemCores SCM Cores SCM Cores SCM Cores SCM General Purpose + + Integrated Network Integrated Processing Integrated Storage Network Storage Network Storage Network Storage Network Storage Balanced, reliable, power efficient systems, with integrated software that scales seamlessly Integrated analytics, modeling and simulation capabilities to address generation, management and analysis of Big Data for Business Advantage ... there is NOT a one size fits all | ©2012 IBM Corporation
  • 24. Determining your highly valuable data from commoditydata will affect how you span on-premise and off premisedata workloads In-House Private cloud Hosted cloud Public cloud for for for for For Organisations High Value Data Commodity Data Data mashups, with deep Big Data Sets that are sets with High Social Data, skills Enterprise Unique Volume, High Capacity Beware the economics of data in cloud| ©2012 IBM Corporation
  • 25. The Emergence of the Data Scientist Source: DJ Patil building the data and analytics groups at Facebook and LinkedIn. “Netflix, the movie-lender awarded $1m in 2009 to a team that improved the accuracy of its recommendation algorithm” | ©2012 IBM Corporation
  • 26. In Conclusion What have we learnt from our Big Datacloud journey?Winners in the era of cloud and big data will be those who collaborate to unlockdata assets to drive innovation, make real-time decisions, and gain actionableinsights to be more competitive.Plan an Align ApplyInformation Your OutcomeAgenda Information Analyticsto align with your strategy and to govern the creation and to measure, anticipate andpriorities use of an integrated set of shape business outcomes accurate and relevant information | ©2012 IBM Corporation
  • 27. Thank You| ©2012 IBM Corporation