book

Advanced Analytics Methodologies: Driving Business Value with Analytics

by Michele Chambers, Thomas W. Dinsmore

September 2014

Beginner to intermediate

336 pages

7h 41m

English

Pearson

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Deliver Business Value and ImpactFocus on the Last MileLeverage KaizenAccelerate Learning and ExecutionDifferentiate Your AnalyticsEmbed AnalyticsEstablish Modern Analytics ArchitectureBuild on Human FactorsCapitalize on ConsumerizationSummary

OverviewBusiness AreaDataApproachPrecisionAlgorithmsEmbeddingSpeedSummary
OverviewCase StudiesSummary
OverviewStep 1: Identify Key Business ObjectivesStep 2: Define Your Value ChainStep 3: Brainstorm Analytic Solution OpportunitiesStep 4: Describe Analytic Solution OpportunitiesStep 5: Create Decision ModelStep 6: Evaluate Analytic Solution OpportunitiesStep 7: Establish Analytics RoadmapStep 8: Evolve Your Analytics RoadmapSummary
OverviewStrategic AnalyticsManagerial AnalyticsOperational AnalyticsScientific AnalyticsCustomer-Facing AnalyticsSummary
OverviewPredictionExplanationForecastingDiscoverySimulationOptimizationSummary
Overview: The Modern Analytics ApproachDefine Business NeedsBuild the Analysis Data SetBuild the Predictive ModelDeploy the Predictive ModelSummary
OverviewStatistics and Machine LearningThe Impact of Big DataSupervised and Unsupervised LearningLinear Models and Linear RegressionGeneralized Linear ModelsGeneralized Additive ModelsLogistic RegressionEnhanced RegressionSurvival AnalysisDecision Tree LearningBayesian MethodsNeural Networks and Deep LearningSupport Vector MachinesEnsemble LearningAutomated LearningSummary
OverviewUser PersonasAnalytic Programming LanguagesBusiness User ToolsSummary
OverviewDistributed AnalyticsPredictive Analytics ArchitectureModern SQL PlatformsSummary
OverviewCultureData Scientist RoleSummary
OverviewCentralized versus Decentralized Analytics TeamCenter of ExcellenceChief Data Officer versus Chief Analytics OfficerLab TeamAnalytic Program OfficeSummary
Unsupervised Feed-Forward ArchitecturesKohonen’s Self-Organizing MapRelated Neural Network ArchitecturesExamples and Related Neural Network ModelsReferences

Overview

Advanced Analytics Methodologies is today's definitive guide to analytics implementation for MBA and university-level business students and sophisticated practitioners. Its expanded, cutting-edge coverage helps readers systematically "jump the gap" between their organization's current analytical capabilities and where they need to be.

Step by step, Michele Chambers and Thomas Dinsmore help readers customize a complete roadmap for implementing analytics that supports unique corporate strategies, aligns with specific corporate cultures, and serves unique customer and stakeholder communities. Drawing on work with dozens of leading enterprises, Michele Chambers and Thomas Dinsmore provide advanced applications and examples not available elsewhere, describe high-value applications from many industries, and help you systematically identify and deliver on your company's best opportunities. They show how to:

Go beyond the Analytics Maturity Model: power your unique business strategy with an equally focused analytics strategy

Link key business objectives with core characteristics of your organization, value chain, and stakeholders

Take advantage of game changing opportunities before competitors do

Effectively integrate the managerial and operational aspects of analytics

Measure performance with dashboards, scorecards, visualization, simulation, and more

Prioritize and score prospective analytics projects

Identify "Quick Wins" you can implement while you're planning for the long-term

Build an effective Analytic Program Office to make your roadmap persistent

Update and revise your roadmap for new needs and technologies

This advanced text will serve the needs of students and faculty studying cutting-edge analytics techniques, as well as experienced analytics leaders and professionals including Chief Analytics Officers; Chief Data Officers; Chief Scientists; Chief Marketing Officers; Chief Risk Officers; Chief Strategy Officers; VPs of Analytics or Big Data; data scientists; business strategists; and many line-of-business executives.