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The case studies gathered and presented here tell, in a sense, a single story. It’s the story of a “management revolution,” brought about by the widespread adoption of big data and analytics in both the public and private sectors.1 In these dispatches from the front lines of that revolution, we see four strikingly dissimilar organizations — a health care system, a bank, a major industrial company, and a municipal government — in the process of becoming data-driven. (The organizations are Intermountain Healthcare, Nedbank, GE, and the city of Amsterdam. Other MIT SMR published case studies cited in this report can be found at sloanreview.mit.edu/case-study/.) We see them struggle and, to a greater or lesser extent, succeed at using analytics to improve the quality and variety of their products and services, engage in new and deeper ways with patients, customers, and citizens, and transform the way they operate. And crucially, we see them use data and analytics not just to improve productivity and make their operations more efficient, but also to change their fundamental business models.

A leading character in this story is technology. Quite simply, there would be no data and analytics revolution without easily accessible, increasingly inexpensive computing power: the cloud, the Internet, and powerful, versatile software and algorithms. Yet technology is only part of the story.

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References

1. The term “management revolution” was applied to this data phenomenon by Andrew MacAfee and Erik Brynjolfsson in 2012. See A. MacAfee and E. Brynjolfsson, “Big Data: The Management Revolution,” Harvard Business Review 90, no. 10 (October 2012): 60-68.

2. W. Fleener, M. Fitzgerald, “General Mills Builds Up Big Data to Answer Big Questions,” MIT Sloan Management Review, May 29, 2015. www.sloanreview.mit.edu.

3. J. Ross, D. Kiron, and R.B. Ferguson, “Do You Need a Data Dictator?” MIT Sloan Management Review, August 28, 2012, www.sloanreview.mit.edu.

Comments

1 Comment On: Lessons from Becoming a Data-Driven Organization

  • Richard Ordowich | October 19, 2016

    “language of data is, like any other language, a social construct”. The problem is we have technologists who are “governing” a social construct. These technologists are not Data Literate and as a result we have the massive data quality problems and lack of semantic integrity of data.

    Data is typically designed for transcriptional use not for analytical uses. As a result, there is missing data, mis-classified data, data that is lacking any temporal attributes so it cannot be used in time series analytics.

    “Data will often underdetermine a course of action” I assume this was supposed to read :undermine” but if some instances data will determine a course of action.

    “data is subjective in nature; how data is created, weighted, and analyzed is the product of many individual decisions that can introduce personal or political biases. Data-based evidence is often weighed with experience, bolstered with faith, and buffeted by politics.”

    If this is the conclusion, how is data different from an opinion or gut feeling?

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