Why So Many Data Science Projects Fail to Deliver

Organizations can gain more business value from advanced analytics by recognizing and overcoming five common obstacles.

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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.1 Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.2

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.

Mistake 1: The Hammer in Search of a Nail

Hiren, a recently hired data scientist in one of the banks we studied, is the kind of analytics wizard that organizations covet.3 He is especially taken with the k-nearest neighbors algorithm, which is useful for identifying and classifying clusters of data. “I have applied k-nearest neighbors to several simulated data sets during my studies,” he told us, “and I can’t wait to apply it to the real data soon.”

Hiren did exactly that a few months later, when he used the k-nearest neighbors algorithm to identify especially profitable industry segments within the bank’s portfolio of business checking accounts. His recommendation to the business checking accounts team: Target two of the portfolio’s 33 industry segments.

This conclusion underwhelmed the business team members. They already knew about these segments and were able to ascertain segment profitability with simple back-of-the-envelope calculations. Using the k-nearest neighbors algorithm for this task was like using a guided missile when a pellet gun would have sufficed.



1. R. Bean and T.H. Davenport, “Companies Are Failing in Their Efforts to Become Data-Driven,” Harvard Business Review, Feb. 5, 2019, https://hbr.org.

2. T.H. Davenport, N. Mittal, and I. Saif, “What Separates Analytical Leaders From Laggards?” MIT Sloan Management Review, Feb. 3, 2020, https://sloanreview.mit.edu.

3. The names of people and organizations are pseudonyms, in keeping with our agreements with the companies.

4. S. Ransbotham, “Deodorizing Your Data,” MIT Sloan Management Review, Aug. 24, 2015, https://sloanreview.mit.edu.

5. T. O’Toole, “What’s the Best Approach to Data Analytics?” Harvard Business Review, March 2, 2020, https://hbr.org.

6. S. Ransbotham, “Avoiding Analytical Myopia,” MIT Sloan Management Review, Jan. 25, 2016, https://sloanreview.mit.edu.

7. P. Puranam, M. Raveendran, and T. Knudsen, “Organization Design: The Epistemic Interdependence Perspective,” Academy of Management Review 37, no. 3 (July 2012): 419-440.

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Comments (2)
S Obi
As Gartner has predicted, analytics is now spread across all users in the organization and as Citizen Data Scientists come to the forefront, the biggest issue for organizational failure is that businesses do not recognize the need for culture change. The business must encourage and champion the use of data to make decisions and it must create new processes and workflow to allow business users to collaborate with data scientists and business analysts etc. It must also change rewards and recognition to ensure that users are encouraged and required to use data to make recommendations and to make fact-based decisions.
Shankar Hn
I come across so many involved in data science, analytics, one out of 4 persons, I end up asking them where is the data and they are lost. Focus should be more on problems situation, data model, clean data to work on meaningful solutions. Everyone is out selling the hammer and not many have either sufficient nails or jobs needing nails to fix, Data sciennce has its value, bot more important is problem definition. Where is that expertise coming from? Shouldn't that approach be the driver for data science rather than the other way round?l