With more access to useful data, companies are increasingly using sophisticated analytical methods. That means there’s often a gap between an organization’s capacity to produce analytical results and its ability to apply them effectively to business issues.

In an increasingly data-driven business environment, many executives must make critical decisions based on analyses that use data and statistical methods that they do not fully understand. How can executives with limited analytics expertise become adept consumers of analytics under such conditions? This question has become an important management issue as senior executives increasingly recognize the importance of analytics to creating business value.

XL Group plc, a global insurance and reinsurance company based in Dublin, Ireland, is a case in point. Like others in the insurance industry, XL has long relied heavily on data analysis to understand and price its products. Actuarial science itself is rooted in using historical data to understand future risk and uncertainty. Across the insurance industry, companies have access to better data and more sophisticated methods of analysis than they did in the past; analyses of only a few years ago are no longer adequate to keep modern insurers competitive. In response, XL produces increasingly complex analytics, and demand for analytical insights progressively permeates the organization. According to Kimberly Holmes, senior vice president of strategic analytics at the XL Group, “An increasing number of managers must take action based on analytical results. But unlike the earlier adopters who embraced analytical approaches, these more recent adopters are not as well versed in the concepts, tools, systems and techniques of contemporary analytics. They are not comfortable making decisions based on analytical approaches that they do not fully understand. Yet they must still make these decisions.”

XL is far from alone. Our research — based on a survey of 2,719 managers in organizations from around the world — finds that the foremost barriers to creating business value from analytics are not data management or complex modeling skills. (See “About the Research.”) Instead, the number one barrier by far in this year’s survey was translating analytics into business actions — in other words, making business decisions based on the results, not producing the results themselves. One survey respondent described his organization’s top analytical challenge as “developing middle management skills at interpretation.”


1. For example, in “Using Simulated Experience to Make Sense of Big Data,” Hogarth and Soyer point out limitations in description and illustration techniques commonly used to communicate analytical results. See R.M. Hogarth and E. Soyer, “Using Simulated Experience to Make Sense of Big Data,” MIT Sloan Management Review 56, no. 2 (winter 2015): 49-54.

2. T.C. Redman, “Improve Data Quality for Competitive Advantage,” Sloan Management Review 36, no. 2 (winter 1995): 99-107.

3. T.H. Davenport, “Keep Up With Your Quants,” Harvard Business Review 91, no. 7/8 (July-August 2013):120-123.

4. Ibid.

5. For more details about these three categories, see D. Kiron, P.K. Prentice and R.B. Ferguson, “The Analytics Mandate,” May 2014, https://sloanreview.mit.edu/projects/analytics-mandate.

6. Ibid.