Presentation on theme: "South Central SAS Users’ Group November 5, 2012 Philip Easterling"— Presentation transcript:
1 The Effective Use of Business Analytics OR, THE FUTURE OF ANALYTICS IS HERE South Central SAS Users’ GroupNovember 5, 2012Philip EasterlingSAS Institute
2 Discussion Topics Analytics versus “Business Intelligence” Challenges to the use of AnalyticsRole and Definition of Centers of ExcellenceHow to evaluate Analytics projects
3 The opportunities afforded by analytics have never been greater. THE ANALYTICS GAPMany organizations:Can’t always generate the information they need.Can’t generate insight fast enough to act upon it.Continue to incur huge costs due to uninformed decisions and misguided strategies.The opportunities afforded by analytics have never been greater.
4 WHAT IF YOU COULD …. . . predict the buying behavior and decision criteria of your prospects weeks before your competition?. . . gain first-mover advantage by introducing new products and services to micro-segments that haven't been identified by competitors?. . . evaluate the impact of your marketing campaigns hourly and make adjustments in real-time?. . . improve customer experience scores that grow products per customer, reduce attrition, and leverage the power of customer recommendations for new business?. . . predict likely failures of critical equipment and processes?
5 ANALYTICS – SAS DEFINITION “Analytics” is probably one of the most nebulous, over-used terms in business today.To some people, it can mean simple arithmetical calculations, such as those found in spreadsheets and reporting queries.“Analytics” at SAS refers to the applied mathematical capabilities of SAS software which offer data-driven insight for better decisions.There are different types of applied mathematics, so there is no “one size fits all” solution to the myriad business problems which require advanced analytics.Analytics in SAS encompass a range of techniques for collecting, analyzing, and interpreting data in order to reveal patterns, anomalies, key variables, and relationships ( the “known unknown” and the “unknown unknown”).
6 Common types of Applied Mathematics in Industry “Analytics”Common types of Applied Mathematics in IndustryMathematical topics which analytical business consultants are unlikely to useTypes of applied mathematics with which analytical business consultants are likely to conduct projectsTypes of statistical analysis and applications of statisticsBayesianClassicalDescriptiveInferentialPredictiveTime series modelingQuality control and process controlSurvival analysisOperations research and optimizationText analytics
7 Descriptions of some types of mathematics which are commonly applied to Business problems “analytics”TypeDescriptionSimulationRandomized repetitions of a network of discrete events in order to model real-world systems and phenomena (queues)OptimizationAlgorithm selects the best possible outcome, subject to satisfying system constraintsMatrix algebraCalculations involving matrices solve multidimensional problemsNumerical methodsUse computers to iteratively calculate approximate solutions to calculus problems (common in engineering)Fitting functions to dataAlso called “curve fitting” by engineersPredictive modelingFit statistical models to historical data in order to make predictions of future valuesSurvival analysisOriginally used by life scientists, but adopted by marketers and actuariesTime series modelingSpecial case when data are “auto-correlated,” such as time-dependent data (also called “Box Jenkins”)StatisticsClassicalDescriptive: calculates metrics to characterize the distribution of values of data (mean, standard deviation, range, etc.)Inferential: involves drawing conclusions about relationships among variables in the data (including measures of correlation between variables)Predictive: involves studying historical data in order to make accurate predictions of future outcomesBayesianUsed to augment classical analysis when there is prior knowledge about how the data was generated
10 present challenges that other technology initiatives do not Analytics Projectspresent challenges that other technology initiatives do notThe processes of building solutions and answering complex business questions are often less "linear," requiring much exploratory analysis and modeling in a somewhat iterative processEven "canned" applications and toolsets may require special skills to implement and to use effectivelyAdvanced skills in statistics, econometrics, operations research, and/or computer science, and the expertise on their application to business problems, are often a constraintData from outside the organization may be needed (e.g., business partner, macroeconomic, demographic, survey, weather, government, trade association, etc.)
11 THE ANALYTICS LIFECYCLE IDENTIFY /FORMULATEPROBLEMBUSINESSMANAGERBUSINESSANALYSTEVALUATE /MONITORRESULTSDATAPREPARATIONDomain ExpertMakes DecisionsEvaluates Processes and ROIData ExplorationData VisualizationReport CreationDEPLOYMODELDATAEXPLORATIONIT / SYSTEMS MANAGEMENTDATA MINER /STATISTICIANVALIDATEMODELTRANSFORM& SELECTModel ValidationModel DeploymentModel MonitoringData PreparationExploratory AnalysisDescriptive SegmentationPredictive ModelingBUILDMODEL
25 SAS Research and Publications Information for Overcoming ChallengesSAS Research and PublicationsOrganizational Assessment and AlignmentInitial Framework for Centers of ExcellenceBusiness Analytics for Decision Making
27 “Enterprise centers of excellence should support the effort …” Organizational DimensionsTHE Journey Toward the TargetChange from a “Project” to a ”Program” mentalityChange from a “Tactical” to a “Strategic” approachProvide business and IT with shared ownership of the enterprise information environment“Enterprise centers of excellence should support the effort …”
28 THE Journey Toward the Target Organizational DimensionsTHE Journey Toward the TargetAn executive vision and commitment to demand an information-driven organization and cultureFocus upon key organizational dimensionsHuman capitalInternal information processesTechnical infrastructureOrganizational cultureContinual alignment with business objectivesSupported by an enterprise analytics Center of Excellence
31 Centers of excellence Characteristics Sponsored by the executive teamIncludes IT, business domain expertise, and analytic expertiseHas a well defined charter, responsibilities, and processesCollaborates with ALL appropriate stakeholdersStrives to make analytics a repeatable and accessible processEmphasizes the implementation of successful analytic applications