The Practical Guide to Managing Data Science at Scale
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Lessons from the field on managing data science projects and portfolios
The ability to manage, scale, and accelerate an entire data science discipline increasingly separates successful organizations from those falling victim to hype and disillusionment.
Data science managers have the most important and least understood job of the 21st century.
This paper demystifies and elevates the current state of data science management. It identifies best practices to address common struggles around stakeholder alignment, the pace of model delivery, and the measurement of impact.
There are seven chapters and 25 pages of insights based on 4+ years of working with leaders in data science such as Allstate, Bayer, and Moody’s Analytics:
- Introduction: Where we are today and where we came from
- Goals: What are the measures of a high-performing data science organization?
- Challenges: The symptoms leading to the dark art myth of data science
- Diagnosis: The true root-causes behind the dark art myth
- Project Recommendations: Managing a data science project to a business outcome
- System Recommendations: Scaling a good data science project to a business discipline
- Conclusion: Recommended steps to get started
Bonus: The Data Science Lifecycle
This guide includes a printable flowchart of The Data Science Lifecycle.