Why Data Science Projects Fail

The Harsh Realities of Implementing AI and Analytics, without the Hype

Häftad, Engelska, 2024

AvDouglas Gray,Evan Shellshear

719 kr

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The field of artificial intelligence, data science, and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects, and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven.This book aims to fix this by countering the AI hype with a dose of realism. Written by two experts in the field, the authors firmly believe in the power of mathematics, computing, and analytics, but if false expectations are set and practitioners and leaders don’t fully understand everything that really goes into data science projects, then a stunning 80% (or more) of analytics projects will continue to fail, costing enterprises and society hundreds of billions of dollars, and leading to non-experts abandoning one of the most important data-driven decision-making capabilities altogether.For the first time, business leaders, practitioners, students, and interested laypeople will learn what really makes a data science project successful. By illustrating with many personal stories, the authors reveal the harsh realities of implementing AI and analytics.

Produktinformation

Utgivningsdatum2024-09-05
Mått156 x 234 x 16 mm
Vikt340 g
FormatHäftad
SpråkEngelska
SerieChapman & Hall/CRC Data Science Series
Antal sidor208
FörlagTaylor & Francis Ltd
ISBN9781032660301

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Artificiell intelligens inom Data och IT
Databaser inom Data och IT

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

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