Gartner Research

Four Real-World Case Studies: Implement Augmented DSML to Enable Expert and Citizen Data Scientists

Published: 19 August 2019

Summary

Augmented data science and machine learning not only gives citizen data scientists access to DSML capabilities, it also makes experts more efficient and productive. Data and analytics leaders should study these case studies to understand the business impact of augmented DSML.

Included in Full Research

  • Key Challenges
  • Incorporate Governance to Manage and Guide Your Augmented Data Science Approach, With Significant Focus on Data Access and Data Quality (GWC — Data)
    • Business Problem
    • Approach
    • Benefit
    • Lessons Learned
    • Recommendations
  • Recruit and Enable Citizen Data Scientists to Increase Accessibility and Grow Use of Augmented DSML Incrementally and Agilely (Merrow — People)
    • Business Problem
    • Approach
    • Benefit
    • Lessons Learned
    • Recommendations
  • Leverage Augmented Data Science to Extend, but Not Replace, More Traditional DSML Approaches (AES — Process)
    • Business Problem
    • Approach
    • Benefit
    • Lessons Learned
    • Recommendations
  • Extend and Integrate With the Current Technology Stack Where/When Possible While Driving to Operationalization and Collaboration (G5 — Technology)
    • Business Problem
    • Approach
    • Benefit
    • Lessons Learned
    • Recommendations

Analysts:

Carlie Idoine,  Jim Hare

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