MIT Sloan Management Review Article on Why So Many Data Science Projects Fail to Deliver

More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning. Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.

To better understand the mistakes that companies make when implementing profitable data science projects, and discover how to avoid them, we conducted in-depth studies of the data science activities in three of India’s top 10 private-sector banks with well-established analytics departments. We identified five common mistakes, as exemplified by the following cases we encountered, and below we suggest corresponding solutions to address them.

About the Author

Mayur P. Joshi (@mayur_p_joshi) is an assistant professor in FinTech at Alliance Manchester Business School at the University of Manchester.

Ning Su (@ningsu) is an associate professor of general management, strategy, and information systems at Ivey Business School at Western University.

Robert D. Austin (@morl8tr) is a professor of information systems at Ivey Business School.

Anand K. Sundaram (@iyeranandkiyer) is head of retail analytics at IDFC First Bank.

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