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Chapman and Hall/Crc Data Science Ser.: Why Data Science Projects Fail : The Harsh Realities of Implementing AI and Analytics, Without the Hype by Douglas Gray and Evan Shellshear (2024, Trade Paperback)
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About this product
Product Identifiers
PublisherCRC Press LLC
ISBN-101032660309
ISBN-139781032660301
eBay Product ID (ePID)14069428601
Product Key Features
Number of Pages208 Pages
Publication NameWhy Data Science Projects Fail : The Harsh Realities of Implementing AI and Analytics, Without the Hype
LanguageEnglish
SubjectEnterprise Applications / Business Intelligence Tools, Intelligence (Ai) & Semantics, Computer Science, General
Publication Year2024
TypeTextbook
AuthorDouglas Gray, Evan Shellshear
Subject AreaMathematics, Computers
SeriesChapman and Hall/Crc Data Science Ser.
FormatTrade Paperback
Dimensions
Item Weight14.5 Oz
Item Length9.2 in
Item Width6.1 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2024-015369
Dewey Edition23
IllustratedYes
Dewey Decimal001.42
Table Of ContentABOUT THE AUTHORS FOREWORD INTRODUCTION The Sepsis Scourge An Epic Challenge A Focus on Failures: The Purpose Behind Our Literary Venture The Epic Battle Beyond the Clickbait: When Headlines Just Scratch the Surface Data-driven Projects are Complex Begin Your Journey to Outsmart Failure Critical Thinking: How Not to Fail Introduction Bibliography ANALYTICALLY IMMATURE ORGANIZATIONS The AI Hype Mapping the Terrain: Prior Insights What Happened to Best Practices? What Counts as an ADSAI Failure? Our Thesis Facing Challenges Critical Thinking: How Not to Fail Chapter 1 Bibliography STRATEGY RetailCo's Strategic Nightmare The Difficult and Critical Role of Strategy7Failing to Build Organizational Need Not Understanding the Real Business Problem The Problem with Selecting Good Business Problems Mike's Story: AI in the Outback Putting the Cart (Technology) Before the Horse (Business) The Solution: Put Economics Back in the Driver's Seat Resolving Mike's AI Investment Challenge Solving a Problem That is Not a Business Priority WayBlazer: Companies Will Not Always Pay for the Fancier Mousetrap Challenges in Aligning Vision, Strategy, and Measuring Success Lack of Leadership Buy-in Critical Thinking: How Not to Fail Chapter 2 Bibliography PROCESS Data Quality and Reliability Issues Let the Data Hunt Begin (Un)reasonable Expectations Houston, We Have a Communication Problem Presenting the Message Breaking Down Silos Starting Small and Simple Project Management for ADSAI Asking the Right Questions Critical Thinking: How Not to Fail Chapter 3 Bibliography PEOPLE Lacking the Right Resources The New Digital Divide Analytics (or AI) Translators Where Do You Find Analytics Translators? Strengthening ADSAI Curricula Analytically-driven Leadership Change Management Justification for Change Critical Thinking: How Not to Fail Chapter 4 Bibliography TECHNOLOGY Model Mishaps Misapplying the (Right or Wrong) Model Keep it Simple: Overemphasizing the Model, Technique, or Technology From Sandbox Model to Production System Tools Make Mistakes The Final Hurdle: Proper Data and Tool Infrastructure Critical Thinking: How Not to Fail Chapter 5 Bibliography ANALYTICALLY MATURE ORGANIZATIONS (More) Real-life Failures Outside Influences Humility Small Stumbles, Solid Outcome The Journey to Perfection Critical Thinking: How Not to Fail Chapter 6 Bibliography CONCLUSION Continuing the Success Strategy Process People Technology Summary Final Words Critical Thinking: How Not to Fail Conclusion Bibliography
SynopsisThe 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., 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.
LC Classification NumberQA76.9.Q36S54 2024
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