MIT Sloan Management Review Article on How AI Is Improving Data Management

Data management is crucial for creating an environment where data can be useful across the entire organization. Effective data management minimizes the problems that stem from bad data, such as added friction, poor predictions, and even simple inaccessibility, ideally before they occur. Managing data, though, is a labor-intensive activity: It involves cleaning, extracting, integrating, cataloging, labeling, and organizing data, and defining and performing the many data-related tasks that often lead to frustration among both data scientists and employees without “data” in their titles.

About the Author

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). Thomas C. Redman (@thedatadoc1) is president of New Jersey-based consultancy Data Quality Solutions and coauthor of The Real Work of Data Science: Turning Data Into Information, Better Decisions, and Stronger Organizations (Wiley, 2019).

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