The Practical Guide to Managing Data Science at Scale

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. While data scientists may have the sexiest job of the 21st century, data science managers probably have the most  important and least understood job of the 21st century. Our friends over at Domino Data Lab, Inc. have written a new whitepaper “The Practical Guide to Managing Data Science at Scale” that aims to demystify and elevate the current state of data science management. They identify consistent struggles around stakeholder alignment, the pace of model delivery, and the measurement of impact. The root cause of these  challenges can be traced to a set of particular cultural issues, gaps in process and organizational structure, and inadequate technology.

Based on 4+ years of working with leaders in data science, like Allstate, Monsanto, and Moody’s, Domino has observed that the best solution is a holistic approach to the entire project lifecycle from ideation to delivery and monitoring. Organizations that are able to develop a disciplined practice of iterative business value delivery and self-measurement, while utilizing data science platform technology to support a hub-and-spoke organizational structure, can scale data science to a core capability, and accelerate the delivery of a robust portfolio of models.

Data science success at scale is not as easy as bringing in a single “silver bullet” technology. It requires maturity and vision across many dimensions: hiring people, implementing processes, and acquiring technology to support those people and processes. While this can seem overwhelming, Domino has seen that organizations do best with a “crawl, walk, run” approach to build momentum towards the ultimate vision.

What’s Inside

There are seven chapters and 25 pages of insights based on 4+ years of working with leaders in data science such as Allstate, Monsanto, and Moody’s Analytics:

Chapters

1. Introduction: Where we are today and where we came from
2. Goals: What are the measures of a high-performing data science organization?
3. Challenges: The symptoms leading to the dark art myth of data science
4. Diagnosis: The true root-causes behind the dark art myth
5. Project Recommendations: Managing a data science project to a business outcome
6. System Recommendations: Scaling a good data science project to a business discipline
7. Conclusion: Recommended steps to get started

Download the whitepaper HERE.

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