I just finished reviewing the third annual research report on analytics from the MIT Sloan Management Review, “From Value to Vision: Reimagining the Possible with Data Analytics”, the latest version a collaboration with SAS Institute. I’ve blogged on the previous two reports, finding the studies generally well done.

Befitting the status of MIT and SAS, “From Value to Vision” seems a strong study as well. With over 2,500 survey respondents representing executives, managers and analysts from companies covering 121 countries, 30 industries and revenues ranging from $250M to $20B, the analysis far eclipses the norm for BI and data science industry research. In addition, detailed interviews were conducted with academic and subject area experts to embellish survey findings.

The percentage of respondents noting a competitive advantage from analytics has progressed from 37 to 58 to 67 in the three years of the survey. Another important change from the earlier reports: The introduction of the term “Data Analytics” in place of “Analytics” alone to connote the combination of big data and analytics. According to eBay’s Neel Sundaresan, “Everybody in the organization ... has to be data-driven. Now, not everybody has to look at data, but everybody has to understand data at some level.”

For this year’s survey, an observational “case-control” design was used. In epidemiology, case-control studies involve retrospective investigation to determine factors associated with having a condition or disease. Risk exposures and other factors are contrasted between the disease and “control” groups to formulate or confirm hypotheses. Case-control is generally the design deployed in feel-good business books such as “In Search of Excellence” by Tom Peters, contrasting “successful” and unsuccessful companies.

For “From Value to Vision,” the “disease” or condition of interest is degree of organizational commitment to analytics, determined by summing the strength of responses to survey items on creating a competitive advantage with analytics and using analytics to innovate. With “From Value to Vision,” that calculation reveals three groups: Analytic Innovators – 11%, Analytic Practitioners – 60% and Analytically Challenged – 29%. Much of the remainder of the report contrasts differences between the three groups on other “risk exposure” survey questions.

Not surprisingly, Analytic Innovators “get it” about data and analytics within the organization. AI’s “tend to view data as a core asset; they challenge the status quo; they believe in the possible; and they are open to new ways of thinking.” Compared to the other groups, AI’s are more committed to real-time, data-driven decision-making, are more efficient in their usage of data, and are more likely to drive enterprise strategy from analytics. They like Mom and apple pie too!

Tellingly, Analytics Innovators report changes in organizational dynamics precipitated by data analytics, challenging the management experience and intuition mode of decision-making. At Neiman Marcus, analytics, once the purview of only mid-level managers, are now regularly consumed by senior executives and the board, with a concomitant change in the project-funding process: “How we choose to spend our marketing dollars has shifted by substantial dollar figures, based on analytics.” According to former Wells Fargo VP Pascal Hoffman, “When you look at the decision making process, it has become more quantitative and more scientific than it used to be a few years back. Some people want to challenge the status quo and look harder at the evidence on how decisions are made, and what decisions are made ... And because they have the evidence and they can build a case with hard science and data, they get paid attention to.”

The Analytically Challenged, in contrast, pale in comparison to Innovators on several critical factors. ACs are data deficient, both in quantity and quality; ACs don’t effectively use data analytics strategically; the intra-organizational AC collaboration with data is lacking, making for information silos; and, perhaps most importantly, ACs have no compelling driver to advancing with analytics. “My organization doesn’t get it at this point; there are some pockets of the company where teams are pushing for increased analytics, but the C-suite doesn’t yet see the value.”

If imitation is indeed flattery, both the Analytically Challenged and Analytics Practitioners should strive to emulate Innovators. As a foundation, they should obsess on the understanding that data analytics can enhance company performance. And they should relentlessly seek top-down support from senior management to build a data-driven culture where evidence reigns supreme.