Why Data Science Projects Fail

Douglas Gray is a practitioner, leader, and educator with over 30 years of experience leading award-winning teams at industry luminaries in Analytics, including INFORMS Prize-winning American Airlines and Walmart. His teams have delivered advanced game-changing solutions in the airline operations, healthcare, and omnichannel retail supply chain domains which deliver hundreds of millions of dollars in business value and economic impact annually. He teaches Analytics and AI Strategy at Southern Methodist University (SMU) in the Executive MBA, Executive Education, and MS Data Science programs, and has published over a dozen articles on Analytics best practices and applications.Dr Evan Shellshear is an expert in artificial intelligence with a Ph.D. in Game Theory from the Nobel Prize winning University of Bielefeld in Germany. He has almost two decades of international experience in the development and design of AI tools for a variety of industries having worked with the world's top companies on all aspects of advanced analytical solutions from optimisation to machine learning in applications from HR to oil and gas, and robotics to supply chain. He is also the author of the Amazon best seller, Innovation Tools. Evan is currently based in Brisbane, Australia and is the CEO of a global AI digital platform.

• ABOUT THE AUTHORSFOREWORDINTRODUCTIONThe Sepsis ScourgeAn Epic ChallengeA Focus on Failures: The Purpose Behind Our Literary VentureThe Epic BattleBeyond the Clickbait: When Headlines Just Scratch the SurfaceData-driven Projects are ComplexBegin Your Journey to Outsmart FailureCritical Thinking: How Not to FailIntroduction BibliographyANALYTICALLY IMMATURE ORGANIZATIONSThe AI HypeMapping the Terrain: Prior InsightsWhat Happened to Best Practices?What Counts as an ADSAI Failure?Our ThesisFacing ChallengesCritical Thinking: How Not to FailChapter 1 BibliographySTRATEGYRetailCo’s Strategic NightmareThe Difficult and Critical Role of Strategy7Failing to Build Organizational NeedNot Understanding the Real Business ProblemThe Problem with Selecting Good Business ProblemsMike’s Story: AI in the OutbackPutting the Cart (Technology) Before the Horse (Business)The Solution: Put Economics Back in the Driver’s SeatResolving Mike’s AI Investment ChallengeSolving a Problem That is Not a Business PriorityWayBlazer: Companies Will Not Always Pay for the Fancier MousetrapChallenges in Aligning Vision, Strategy, and Measuring SuccessLack of Leadership Buy-inCritical Thinking: How Not to FailChapter 2 BibliographyPROCESSData Quality and Reliability IssuesLet the Data Hunt Begin(Un)reasonable ExpectationsHouston, We Have a Communication ProblemPresenting the MessageBreaking Down SilosStarting Small and SimpleProject Management for ADSAIAsking the Right QuestionsCritical Thinking: How Not to FailChapter 3 BibliographyPEOPLELacking the Right ResourcesThe New Digital DivideAnalytics (or AI) TranslatorsWhere Do You Find Analytics Translators?Strengthening ADSAI CurriculaAnalytically-driven LeadershipChange ManagementJustification for ChangeCritical Thinking: How Not to FailChapter 4 BibliographyTECHNOLOGYModel MishapsMisapplying the (Right or Wrong) ModelKeep it Simple: Overemphasizing the Model, Technique, or TechnologyFrom Sandbox Model to Production SystemTools Make MistakesThe Final Hurdle: Proper Data and Tool InfrastructureCritical Thinking: How Not to FailChapter 5 BibliographyANALYTICALLY MATURE ORGANIZATIONS(More) Real-life FailuresOutside InfluencesHumilitySmall Stumbles, Solid OutcomeThe Journey to PerfectionCritical Thinking: How Not to FailChapter 6 BibliographyCONCLUSIONContinuing the SuccessStrategyProcessPeopleTechnologySummaryFinal WordsCritical Thinking: How Not to FailConclusion Bibliography