MIT Sloan Management Review Article on The Rise of Connector Roles in Data Science

For all of the current focus on using data, analytics, and AI to improve organizational decisions and operations, too many data science projects fail. Even for those that succeed, progress is often slow and expensive. Why? Organizational gaps between teams are wreaking havoc with the ability to develop, apply, and scale data science projects. A new type of role is needed to bridge these gaps.

Let’s not minimize the many aspects of data science that can trip a company up — from defining the right business problem to developing a solution and then supporting it properly. However, our diagnosis of the situation, researched in part with our colleagues Roger Hoerl and Diego Kuonen, shows that many companies struggle less with the solution and more with the organizational issues.

About the Author

Thomas C. Redman is president of Data Quality Solutions and the author of People and Data: Uniting to Transform Your Organization (Kogan Page, 2023). Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting professor at Oxford’s Saïd Business School, and a fellow of the MIT Initiative on the Digital Economy. He is coauthor of Working With AI: Real Stories of Human-Machine Collaboration (MIT Press, 2022).

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