Why Data Science Projects Fail

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. Douglas A. 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.

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