Gartner Research

Six Pitfalls to Avoid When Planning Data Science and Machine Learning Projects

Published: 30 November 2017

ID: G00325649

Analyst(s): Nigel Shen, Alexander Linden , Carlie Idoine , Jim Hare

Summary

Organizational and process pitfalls in data science and machine learning projects could inadvertently derail success and destroy organizationwide confidence in these techniques. Data and analytics leaders should use this checklist to avoid six planning pitfalls for those projects.

Table Of Contents
  • Key Challenges

Introduction

Analysis

  • Evaluate How DSML and AI Fit Into Your Organization
    • Pitfall 1. Misjudging the Business Value
    • Pitfall 2. Rushing to Kick Off Without Defined Plans and Processes
    • Pitfall 3. Lack of Security and Privacy Awareness
  • Build Use Cases by Linking Business Understanding and Quality Data to Specific Business Benefits
    • Pitfall 4. Data Scientists Lacking Significant Credibility With the Business
    • Pitfall 5. Benchmarking With Poorly Defined Metrics
    • Pitfall 6. Underestimating the Importance of Data Management

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