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LEVERAGING YOUR ANALYTIC CAPACITY TO DRIVE VALUE FROM YOUR DATA ASSETS - Marc Smith
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LEVERAGING YOUR ANALYTIC CAPACITY TO DRIVE VALUE FROM YOUR DATA ASSETS - Marc Smith

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  • 1. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.LEVERAGING YOUR ANALYTIC CAPACITY TODRIVE VALUE FROM YOUR DATA ASSETSMARC SMITH, SAS PRINCIPAL, INFORMATION MANAGEMENT
  • 2. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.Passport CanadaRead the Full Storyhave to be careful how we spendus to have these tools and develop adiscipline to use that information. For ushaving the right information, at the rightabout improving the service we provideHubert LaferriereDirector, Strategic ManagementBusiness IssuePassport Canada needed to better forecast its revenuesand demand to appropriately allocate budget andresources, while improving service delivery andcustomer satisfaction.SolutionSAS® Forecast ServerSAS® Data Integration StudioSAS® Activity Based-ManagementResults/BenefitsPassport Canada has improved its forecast accuracy towithin 5%. Analysts have reduced time spent capturingand cleaning data by 10%. Passports turnarounds arenow completed in 10 business days.
  • 3. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.Manitoba Centre for Health PolicyRead the Full Storyand letting SAS Enterprise Minercome up with decision trees to findout whats important in theidentification of chronic diseasessuch as diabetes, asthma andheart disease."Charles BurchillManager of Program and Analysis SystemBusinessIssueMaintains a comprehensive population-based data repository foruse by research community, which supports the development ofhealth policies, programs and services for Manitobans.To meet new provincial requirements around auditing and accesscontrol, while its data was growing at an unprecedented rate.SolutionSAS® Scalable Performance Data ServerSAS® Enterprise MinerResults/BenefitsResearchers are now able to build queries in hours instead ofdays, helping to provide insights into disease trends and serviceuse.
  • 4. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.LA COUNTYBusiness IssueUnderstand cross-agency service utilization.Measure the cost of serving the indigent population.Reduce duplication of services without compromising privacy.SolutionSAS® AnalyticsSAS® Data IntegrationDataFlux de-identification toolResults/BenefitsDiscover and correct Service duplication to reduce costs.Identification of general relief recipients who are eligible forapplied.Statistical evidence that placing homeless individuals intoapartments is cost-effective.Predict costs for new programs. Read the Full Storyand difficult budgetary issues.evidence-based research tohelp elected officialsunderstand the costs andManuel MorenoDirector of Research, Chief ExecutiveOffice
  • 5. 5Copyright © 2012 SAS Institute Inc. All rights reserved. 5Company Confidential - For Internal Use OnlyCopyright © 2012, SAS Institute Inc. All rights reserved.Finding treasures in unstructured datalike social media or survey toolsthat could uncover insightsabout citizen sentimentMine transaction databasesfor data of migration patternsthat represent a shift incomposition..Leveraging historical datato drive better insight intotrends for the futureAnalyze massiveamounts of data inorder to accuratelyidentify areas likely toproduce the mostsustainable outcomeFORECASTINGDATA MININGTEXT ANALYTICSOPTIMIZATIONSTATISTICSADVANCED ANALYTICS FOR BIG DATAINFORMATIONMANAGEMENT
  • 6. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.STRATEGICIMPERATIVEDRIVING THE NEED FOR ANALYTICSDriving better outcomes through evidence-baseddecisions based on sound research and analysisImproving quality of service and sustainable fundingEfficiency and fact based performance management -collect data and use it to evaluate whether objectivesare being met and how efficientlyGaining public trust and providing transparencythrough governance, risk and compliancedevelopment andthe public service ingeneral should be moreevidence-based. Thisrequires setting clearobjectives based onsound research andevidencePublic Services, 2012
  • 7. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.EXTERNALVIEWPOINTCHALLENGES INANALYTICS ADOPTIONSource:The CurrentState of Business Analytics:Where Do We Go From Here?Prepared byBloombergBusinessweek Research Services,2011
  • 8. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.THE SHIFT ANALYTICAL CULTUREFacts, evidence, analysis as the primaryway of decidingfactsDataEnterpriseLeadershipTargetsAnalysts
  • 9. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
  • 10. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.DATA THE RAW MATERIALThe prerequisite for everything analyticalClean, consistent, accurate, common, integrated,accessibleNeeds to be centralized, linked and governed- measuring somethingnew and important
  • 11. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.DATA QUALITY MULTIPLE VERSIONS OF SAME DXSite A Site B Site CHYPERTENSION ESSENTIAL HYPERTENSION* (401) ESSENTIAL, BENIGN HYPERTENSIONESSENTIAL HYPERTENSION* (401) ESSENTIAL HYPERTENSION* (401.) HYPERTENSION (ESSENTIAL)ESSENTIAL HYPERTENSION (401) HYPERTENSION NOS (401.9) HYPERTENSION UNCOMPLICATEDHYPERTENSION (401) HYPERTENSION (401)ESSENTIAL HYPERTENSION (401)HYPERTENSIONHYPERTENSION (401.9)
  • 12. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.DATA QUALITY MULTIPLE VERSIONS OF SAME MEDSite A Site B Site CAPO-HYDRO 25MG TABLET METFORMIN HCL 500MG ORAL TABLET INFLUENZAHYDROCHLOROTHIAZIDE 25MG ORALTABLET METFORMIN HCL 500MG TA FLU VACCINEHYDROCHLOROTHIAZIDE TAB 25MG APO-METFORMIN 500MG TABLET FLUVIRALAPO-HYDRO 25 MG TABLET APO-METFORMIN - TAB 500MG FLU SHOTAPO HYDRO TAB 25MG VAXIGRIP
  • 13. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.DATA QUALITY POOR CAPTUREOF RISK FACTORSSite A Site B Site CNON-SMOKER TOBACCO NON-SMOKER NON SMOKERT TOBACCO NEVER SMOKEREX-SMOKER TOBACCO EX SMOKER QUIT > 1 YEARSMOKER: QUITTING TOBACCO NON-SMOKER QUIT < 1 YEARSMOKER: NO PLAN TO QUIT TOBACCO SMOKERSMOKER: ACTIVELY QUITING NEVER SMOKEDTOBACCO USE (305.1) TOBACCO NON SMOKERSMOKER: ACTIVELY QUITTINGSMOKINGNON SMOKERNICOTINE ADDICTIONNONSMOKEREX SMOKER
  • 14. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.VOLUMEVARIETYVELOCITYVALUETODAY THE FUTUREDATASIZETHE CHALLENGE
  • 15. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.SAS DATAGOVERNANCE FRAMEWORKCorporate  Drivers  Business  Framework  Process  &  Policy  Data  Management  Data  Governance  Execution  Process  P  R  O  G  R  A  M    O  V  E  R  S  I  G  H  T  Data  Governance  Charter  Guiding  Principles  Decision-­‐  making  Bodies  Decision  Rights  Strategic  Priorities:  Public  Trust,  Quality  of  Service,  Policy  Outcomes,  Open  Government    Business  Drivers:  Data  Quality  Improvement;  Operational  Efficiencies,  Program  Integrity  Data  Stewardship  Roles  &  Tasks  Mechanisms:    Stewardship  Dashboards,  Workflow  Automation,  Data  Profiling  Tools  People:    Council,  Stakeholders,  Meeting  Agendas  Process:    Metrics  Definition,  Workflow,  Council  By-­‐Laws  Data  Requirement  Data  Architecture  Data  Administration  Metadata  Management  Data  Quality  Security  &  Access  Rights  
  • 16. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
  • 17. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.Domain ExpertMakes DecisionsEvaluates Processes and ROIBUSINESSMANAGERModel ValidationModel DeploymentModel MonitoringData PreparationIT SYSTEMS /MANAGEMENTData ExplorationData VisualizationReport CreationBUSINESSANALYSTExploratory AnalysisDescriptive SegmentationPredictive ModelingDATAMINER /STATISTICIANIDENTIFY /FORMULATEPROBLEMDATAPREPARATIONDATAEXPLORATIONTRANSFORM& SELECTBUILDMODELVALIDATEMODELDEPLOYMODELEVALUATE /MONITORRESULTSANALYTICS LIFECYCLE
  • 18. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.ANALYTICAL CENTER OFEXCELLENCE (ACE) CHARTERTo promote the use of analytics and tosupport the end-to-end analyticalrequirements of the enterprise.
  • 19. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.** Used withpermission fromAlberta HealthServices
  • 20. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.ResearchCoordination with external organizations / academic centers todrive research: Establish single point of coordination related to data andresources that supports the research agenda with academic institutions and otherexternal organizations.SeniorHealthPrimaryCare/CDMPublicHealthClinicalSupportServicesResearchCardiologyCritical CareCancerMentalHealth &AddictionBone andJointRespiratoryEmergencyCareSurgeryCoreConsolidated core functions to drive strategicanalytics: the goal is to establish a single source oftruth, scale and the development of best practices toanswer the key strategic questions for top executives.  Major Clinical Program AreasDistributed clinical resources: Rebalanceresources to have a net increase of embedded analyticswithin the major clinical program areas and strategicprograms.Embedded AnalyticsCoordinatedStrategic AnalyticsDIMRPopulationHealthobservatoryZonesActivityBasedFundingHRCaseCostingStrategic Hub and Spoke Model Hybrid** Used withpermission fromAlberta HealthServices
  • 21. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.SAS® HIGHPERFORMANCEANALYTICS-la?Server NServer 2Server 1SAS In-MemoryAnalyticsSAS High PerformanceDeploymentMPIMPIproc  hplogistic  data=MPPLib.MyTabl e;;        class  A  B  C  D  ;;        model  y  =  a  b  c  b*d  x1-­x100;;        output  out=MPPlib.logout  pred=p;;  run;;  MultipleThreadsMultipleThreadsMultipleThreadsHDFS StorageHDFS StorageHDFS Storage
  • 22. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.REQUIRES THE RIGHT ARCHITECTUREHighPerformanceAnalyticsANALYTICALREPORTINGOPERATIONALSYSTEMSBUILT FOR PURPOSEANALYTICALDATA STORESFOUNDATIONAL ENTERPRISE& ANALYTICAL DATA WAREHOUSEDATASERVICESANALYTICSSERVICESEDWADW
  • 23. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.SAS® HIGH-­PERFORMANCEANALYTICSKEY COMPONENTS
  • 24. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.INFORMATION MANAGEMENTHOW DO WE DO IT?
  • 25. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.ANALYTICSHOW DO WE DO IT?
  • 26. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.BUSINESS INTELLIGENCEHOW DO WE DO IT?
  • 27. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.HIGH-PERFORMANCE ANALYTICSHOW DO WE DO IT?BUSINESS SOLUTIONS INFORMATION MANAGEMENT ANALYTICS BUSINESS INTELLIGENCE
  • 28. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.ALL IN A SINGLE, SEAMLESS FRAMEWORKHOW DO WE DO IT?