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  • Produktbild: Why Data Science Projects Fail
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Why Data Science Projects Fail The Harsh Realities of Implementing AI and Analytics, without the Hype

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.09.2024

Abbildungen

13 SW-Abb., 13 SW-Zeichn., 2 Tabellen

Verlag

Taylor & Francis

Seitenzahl

224

Maße (L/B/H)

24/16.1/1.7 cm

Gewicht

505 g

Sprache

Englisch

ISBN

978-1-03-266133-9

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.09.2024

Abbildungen

13 SW-Abb., 13 SW-Zeichn., 2 Tabellen

Verlag

Taylor & Francis

Seitenzahl

224

Maße (L/B/H)

24/16.1/1.7 cm

Gewicht

505 g

Sprache

Englisch

ISBN

978-1-03-266133-9

EU-Ansprechpartner

Taylor & Francis Verlag GmbH
Kaufingerstraße 24
80331 München
DE
GPSR@taylorandfrancis.com

Herstelleradresse

Taylor & Francis Group
5 Howick Place
SW1P 1WG London
UK
GPSR@taylorandfrancis.com

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  • Produktbild: Why Data Science Projects Fail
  • Produktbild: Why Data Science Projects Fail
  • ABOUT 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