IBM Technology Day 2013 BigData Salle Rome
 

IBM Technology Day 2013 BigData Salle Rome

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    IBM Technology Day 2013 BigData Salle Rome IBM Technology Day 2013 BigData Salle Rome Presentation Transcript

    • © 2013 IBM CorporationIBM Big Data PlatformTurning big data into smarter decisionsCorinne Fabre - Vincent Jeansoulin
    • © 2013 IBM Corporation© 2013 IBM Corporation6,000,000 users on Twitterpushing out 300,000tweets per day500,000,000 users on Twitterpushing out 400,000,000tweets per day83x1333xTwittergenerates over12 TB of tweetdata everysingle day07/06/2013 IBM Confidential2
    • © 2013 IBM Corporation© 2013 IBM Corporation“At the World EconomicForum last month in Davos,Switzerland, Big Data was amarquee topic. A report by theforum, “Big Data, Big Impact,”declared data a new class ofeconomic asset, likecurrency or gold.“Companies are beinginundated with data—frominformation on customer-buyinghabits to supply-chain efficiency.But many managers struggle tomake sense of the numbers.”“Increasingly, businesses areapplying analytics to socialmedia such as Facebook andTwitter, as well as to productreview websites, to try to“understand where customers are,what makes them tick and whatthey want”, says Deepak Advani,who heads IBM’s predictiveanalytics group.”“Big Data has arrived at SetonHealth Care Family, fortunatelyaccompanied by ananalytics tool that will helpdeal with the complexity ofmore than two millionpatient contacts a year ”“Data is the new oil.”Clive HumbyThe Oscar Senti-meter — a tooldeveloped by the L.A. Times, IBMand the USC AnnenbergInnovation Lab — analyzesopinions about the AcademyAwards race shared in millionsof public messages on Twitter.”“Data is the new Oil.”In its raw form, oil has little value. Once processed & refined, it helps power the world.“ now Watson is being put towork digesting millions ofpages of research,incorporating the best clinicalpractices and monitoring theoutcomes to assist physicians intreating cancer patients.”
    • © 2013 IBM Corporation© 2013 IBM CorporationImagine the Possibilities of Harnessing your Data ResourcesRetailer reduces time torun queries by 80% tooptimize inventoryStock Exchange cutsqueries from 26 hours to2 minutes on 2 PBGovernment cuts acousticanalysis from hours to 70MillisecondsUtility avoids powerfailures by analyzing 10PB of data in minutesTelco analyses streamingnetwork data to reducehardware costs by 90%Hospital analyzes streamingvitals to intervene 24hours earlierBig data challenges exist in every business today
    • © 2013 IBM Corporation© 2013 IBM CorporationThe characteristics of big dataCollectivelyAnalyzing thebroadening VarietyResponding to theincreasing VelocityCost efficientlyprocessing thegrowing VolumeEstablishing theVeracity of bigdata sources30BillionRFIDsensors andcounting1 in 3 business leaders don’t trustthe information they use to makedecisions50x 35ZB202080% of theworlds data isunstructured2010
    • © 2013 IBM Corporation© 2013 IBM CorporationBig data is a hot topic because technology makes itpossible to analyze ALL available dataCost effectively manage and analyze all available data,in its native form – unstructured, structured, streamingERPCRM RFIDWebsiteNetwork SwitchesSocial MediaBilling
    • © 2013 IBM Corporation© 2013 IBM CorporationIn order to realize new opportunities, you need to thinkbeyond traditional sources of dataTransactional &Application DataMachine Data Social Data• Volume• Structured• Throughput• Velocity• Semi-structured• Ingestion• Variety• Highly unstructured• VeracityEnterpriseContent• Variety• Highly unstructured• Volume
    • © 2013 IBM Corporation© 2013 IBM Corporation8HadoopStreamingDataNewSourcesUnstructuredExploratoryIterativeStructuredRepeatableLinearDataWarehouseTraditionalSourcesTraditional ApproachStructured, analytical, logicalNew ApproachCreative, holistic thought, intuitionEnterpriseWideIntegrationWeb logs, URLsSocial dataText Data: emails, chatsRFID, sensor dataNetwork dataInternal App DataTransaction DataERP dataMainframe DataOLTP System DataAnalysis expanding from enterprise data to big data, creatingnew cost-effective opportunities for competitive advantage
    • © 2013 IBM Corporation© 2013 IBM CorporationBig Data ExplorationFind, visualize, understandall big data to improvebusiness knowledgeEnhanced 360o Viewof the CustomerAchieve a true unified view,incorporating internal andexternal sourcesOperations AnalysisAnalyze a variety of machinedata for improved business resultsData Warehouse AugmentationIntegrate big data and data warehousecapabilities to increase operational efficiencySecurity/IntelligenceExtensionLower risk, detect fraudand monitor cyber securityin real-timeThe 5 High Value Big Data Use Cases
    • © 2013 IBM Corporation© 2013 IBM CorporationBig Data Exploration: NeedsStruggling to manageand extract value fromthe growing 3 V’s ofdata in the enterprise;need to unifyinformation acrossfederated sourcesInability to relate “raw” datacollected from system logs,sensors, clickstreams, etc.,with customer and line-of-business data managed inenterprise systemsRisk of exposing unsecurepersonally identifiableinformation (PII) and/orprivileged data due to lackof information awarenessFind, visualize, understand all big datato improve decision making
    • © 2013 IBM Corporation© 2013 IBM CorporationWhat’s Possible – Integrated Service Solutions11
    • © 2013 IBM Corporation© 2013 IBM CorporationEnhanced 360º View of the Customer: NeedsNeed a deeperunderstanding ofcustomer sentimentfrom both internal andexternal sourcesExtend existing customer views (MDM, CRM,etc.) by incorporating additional internal andexternal information sourcesDesire to increasecustomer loyalty andsatisfaction byunderstanding whatmeaningful actionsare neededChallenged getting theright information to theright people to providecustomers what theyneed to solve problems,cross-sell & up-sell
    • © 2013 IBM Corporation© 2013 IBM CorporationEnhanced 360º View of the Customer: IllustratedMasterDataManagementUnified View of Party’s InformationCRMJ RobertsonPittsburgh, PA 1521335 West 15thName:Address:Address:ERPJanet RobertsonPittsburgh, PA 1521335 West 15thSt.Name:Address:Address:LegacyJan RobertsonPittsburgh, PA 1521336 West 15thSt.Name:Address:Address:SOURCE SYSTEMSJanet35 West 15th StPittsburghRobertsonPA / 15213F481/4/64First:Last:Address:City:State/Zip:Gender:Age:DOB:360° View ofParty IdentityBigInsights Streams WarehouseUnified View of Party’s Information
    • © 2013 IBM Corporation© 2013 IBM CorporationSecurity/Intelligence Extension: NeedsEnhancedIntelligence &Surveillance InsightReal-time CyberAttack Prediction &MitigationAnalyze network traffic to:• Discover new threats early• Detect known complex threats• Take action in real-timeAnalyze Telco & social data to:• Gather criminal evidence• Prevent criminal activities• Proactively apprehend criminalsCrime prediction &protectionSecurity/Intelligence Extension enhancestraditional security solutions by analyzing alltypes and sources of under-leveraged dataAnalyze data-in-motion & at rest to:• Find associations• Uncover patterns and facts• Maintain currency of information
    • © 2013 IBM Corporation© 2013 IBM Corporation15TerraEchos Turns to IBMBig Data for Low LatencySurveillance Data Analysis• Deployed security surveillance system to detect,classify, locate, and track potential threats athighly sensitive national lab• Stream computing collects and analyzes acousticdata from fiber-optic sensor arrays• Analyzed acoustic data fed into TerraEchosintelligence platform for threat detection,classification, prediction & communicationResults• Enables Terraechos solution to analyze andclassify streaming acoustic data in real-time• Provides lab & security staff with holistic view ofpotential threats & non-issues• Enables a faster and more intelligent response toany threat15Capabilities UtilizedStream ComputingIdentifies and classifiespotential security threatsmiles awayPublic & Safety
    • © 2013 IBM Corporation© 2013 IBM CorporationOperations Analysis: Needs• Gain real-time visibility into operations,customer experience, transactions andbehavior• Proactively plan to increase operationalefficiencyAnalyze a variety of machinedata for improved business resultsBecause of the complexity and rapid growth ofmachine data, many companies make decisionson a small fraction of the information available tothemThe ability to analyze machine data and combineit with enterprise data for a full view can enableorganizations to:• Identify and investigate anomalies• Monitor end-to-end infrastructure toproactively avoid service degradation oroutages
    • © 2013 IBM Corporation© 2013 IBM Corporation17KTH Swedish Royal Instituteof Technology ReducingTraffic Congestion• Deployed real-time Smarter Traffic system topredict and improve traffic flow.• Analyzes streaming real-time data gathered fromcameras at entry/exit to city, GPS data from taxisand trucks, and weather information.• Predicts best time and method to travel such aswhen to leave to catch a flight at the airportResults• Enables ability to analyze and predict trafficfaster and more accurately than ever before• Provides new insight into mechanisms that affecta complex traffic system• Smarter, more efficient, and moreenvironmentally friendly traffic17Capabilities UtilizedStream ComputingPublic & Transporation
    • © 2013 IBM Corporation© 2013 IBM CorporationIntegrate big data and data warehousecapabilities to increase operational efficiencyData Warehouse Augmentation: NeedsNeed to leverage variety of data Extend warehouse infrastructure• Optimized storage, maintenance and licensingcosts by migrating rarely used data to Hadoop• Reduced storage costs through smartprocessing of streaming data• Improved warehouse performance bydetermining what data to feed into it• Structured, unstructured, and streamingdata sources required for deep analysis• Low latency requirements(hours—not weeks or months)• Required query access to data
    • © 2013 IBM Corporation© 2013 IBM Corporation19Vestas optimizes capitalinvestments based on 2.5Petabytes of informationResultsModel the weather to optimize placementof turbines, maximizing power generationand longevity.Reduce time required to identify placementof turbine from weeks to hours.Incorporate 2.5 PB of structured and semi-structured information flows. Data volumeexpected to grow to 6 PB.19Capabilities UtilizedHadoop SystemReduced turbine placementidentification from weeks tohoursUtility
    • © 2013 IBM Corporation© 2013 IBM CorporationAn example of the big data platform in practiceIngestLanding and Analytics Sandbox ZoneIndexes,facetsHive/HBaseCol StoresDocumentsIn Varietyof FormatsAnalyticsMapReduceRepository, WorkbenchIngestion and Real-time Analytic ZoneDataSinksFilter, TransformIngestCorrelate, ClassifyExtract, AnnotateWarehousing ZoneEnterpriseWarehouseData MartsQueryEnginesCubesDescriptive,PredictiveModelsModelsWidgetsDiscovery,VisualizerSearchAnalytics andReporting ZoneMetadata and Governance Zone20Connectors
    • © 2013 IBM Corporation© 2013 IBM CorporationThe IBM Big Data PlatformProcess any type of data– Structured, unstructured, in-motion, at-restBuilt-for-purpose engines– Designed to handle differentrequirementsAnalyze data in motionManage and govern data in theecosystemEnterprise data integrationGrow and evolve on currentinfrastructure21SolutionsIBM Big Data PlatformAnalytics and Decision ManagementBig Data InfrastructureAcceleratorsInformation Integration & GovernanceHadoopSystemStreamComputingDataWarehouseSystemsManagementApplicationDevelopmentVisualization& Discovery
    • © 2013 IBM Corporation© 2013 IBM CorporationThe IBM Big Data PlatformBI /ReportingBI /ReportingExploration /VisualizationFunctionalAppIndustryAppPredictiveAnalyticsContentAnalyticsAnalytic ApplicationsBig Data PlatformSystemsManagementApplicationDevelopmentVisualization& DiscoveryAcceleratorsInformation Integration & GovernanceHadoopSystemStreamComputingDataWarehouse2 – Analyze Raw RataInfoSphere BigInsights5 – Analyze StreamingDataInfoSphere Streams1 – Unlock Big DataIBM DataExplorer3 – Simplify yourwarehouseNetezza/PureData4 – Reduce costs withHadoopInfoSphere BigInsights226 – Analyse your contentIBM CCI/SMAIBM Content Anayltique7 – ReportIBM Cognos8 – PredictIBM SPSS
    • © 2013 IBM Corporation© 2013 IBM CorporationGet Started on Your Big Data Journey TodayGet Educated• IBM Big Data: ibm.com/bigdata• IBMBigDataHub.com• BigDataUniversity.com• IBV study on big data• Books / analyst papersSchedule a Big Data Workshop• Free of charge• Best practices• Industry use cases• Business uses• Business value assessment
    • © 2013 IBM Corporation© 2013 IBM CorporationDisclaimer• No part of this document may be reproduced or transmitted in any form without written permission from IBM Corporation.• Product data has been reviewed for accuracy as of the date of initial publication. Product data is subject to change without notice.• This information could include technical inaccuracies or typographical errors. IBM may make improvements and/or changes in the• product(s) and/or program(s) at any time without notice. Any statements regarding IBMs future direction and intent are subject to• change or withdrawal without notice, and represent goals and objectives only.• The performance data contained herein was obtained in a controlled, isolated environment. Actual results that may be obtained in• other operating environments may vary significantly. While IBM has reviewed each item for accuracy in a specific situation, there is no• guarantee that the same or similar results will be obtained elsewhere. Customer experiences described herein are based upon• information and opinions provided by the customer. The same results may not be obtained by every user.• Reference in this document to IBM products, programs, or services does not imply that IBM intends to make such products, programs• or services available in all countries in which IBM operates or does business. Any reference to an IBM Program Product in this• document is not intended to state or imply that only that program product may be used. Any functionally equivalent program, that does• not infringe IBMs intellectual property rights, may be used instead. It is the users responsibility to evaluate and verify the operation on• any non-IBM product, program or service.• THE INFORMATION PROVIDED IN THIS DOCUMENT IS DISTRIBUTED "AS IS" WITHOUT ANY WARRANTY, EITHER EXPRESS OR• IMPLIED. IBM EXPRESSLY DISCLAIMS ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE OR• INFRINGEMENT. IBM shall have no responsibility to update this information. IBM products are warranted according to the terms and• conditions of the agreements (e.g. IBM Customer Agreement, Statement of Limited Warranty, International Program License Agreement,• etc.) under which they are provided. IBM is not responsible for the performance or interoperability of any non-IBM products discussed• herein.• Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other• publicly available sources. IBM has not tested those products in connection with this publication and cannot confirm the accuracy of• performance, compatibility or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should• be addressed to the suppliers of those products.