I don't know if it's simply happenstance, but I've recently had the good fortune of digesting some hot-off-the-press material on analytics in business from those two august Cambridge B-Schools, Harvard and MIT.
Earlier this year I signed up for the free online edition of the MIT Sloan Management Review, and am now an enthusiastic consumer of “The New Intelligent Enterprise: The intensifying ‘data deluge,’ and the new analytical approaches that help organizations exploit that data will fundamentally change how managers make decisions and innovate. The New Intelligent Enterprise explores the challenges of the data-driven world, and seeks to define the new organizational capabilities this environment will demand.”
I would encourage Information Management readers to take advantage of this thought-provoking material.
The latest SMR newsletter revolves on an NIE survey of “global executives about turning the data deluge and analytics into competitive advantage.” The resulting articles “10 Insights” and “10 Data Points” offer preliminary “observations and questions about analytics-driven management that have popped out of research and interviews so far, and which we’ll be exploring more deeply in the major reports ahead.”
Results from the interviews seem to corroborate NIE's concept of the “science of business” that combines the notion of “strategy as theory” with the experimental method and high-end analytics. With this thinking, business leaders and analysts behave much like scientists: they first formulate hypotheses about their business; they then use the hypotheses to predict results of new observations; and they finally perform experiments to validate the predictions.
What kind of background should analytics business leaders have according to NIE? “... peculiar requirements of the ideal analytics-driven managers. They have to combine expertise in statistics, experiment design and interpretation and analytics with fundamental business knowledge and acumen. These analysts need the ability to ask the right questions and pose the right hypotheses. They need to know how to get data to tell them the things that matter (and not the things that don’t) … this cultural mentality of using data more effectively, running experiments and responding to the environment and replicating it is something that is going to happen regardless of what additional advances we see in the underlying technology. A decade from now, I expect companies to be far more responsive, far more innovative, far more analytics-minded.”
The researchers categorize the companies they interview into three levels of sophistication based on analytical prowess: Starters, Intermediates and Sophisticates. “When we parsed the data, we found that Starters (new users of analytics) were entrenched in ‘survival mode,’ focused on cuttings costs and creating efficiencies as their main challenge. Intermediates (moderate users of analytics) were in ‘growth mode,’ emphasizing revenue growth. Sophisticates (advanced users of analytics) were in an ‘expansion mode’, ‘focused on growing revenues and expanding their customer base through acquisition or retention strategies, perhaps because their use of analytics had already helped them optimize their operations and general growth approaches.’”
In addition to analytics capabilities, the NIE researchers depict companies as either Top Performers or Lower Performers, ultimately cross-tabulating performance with analytics sophistication. Not surprisingly, Top Performers tend to be Analytics Sophisticates and vice-verse. In addition Sophisticates realize they're in a marathon, not a sprint, and must obsess with constant improvement. They see the backward-looking report and OLAP-driven BI of today giving way to proactive visualization, simulation and predictive analytics embedded within business processes 24 months out. Finally, Top Performers view their strategies as driven much more by analytics than instincts – and conjecture this to be a competitive advantage over their peers.
Not to be outdone, crosstown rival Harvard Business Review sponsored an online seminar, “Analytics and Bottom Line: How Organizations Build Success” featuring rock star analytics experts Tom Davenport and Jeanne Harris. If you haven't participated in an HBR webinar, I'd recommend one based on my admittedly limited sample size of three. Much less self-promoting than your typical BI version.
The point of departure for the broadcast was material from the authors' recent book, “Analytics at Work: Smarter Decisions, Better Results.” The analytics methodology outlined in “AAW,” DELTA – Data, Enterprise, Leadership, Targets and Analysts – drives much of the content and confirms many findings from the NIE research. An analytics-friendly culture of evidence-based decision-making fueled by a test-and-learn strategic orientation that includes a free pass for pushback differentiates analytics leaders from suspects. As noted by Barry Beracha, Chairman and CEO of Sara Lee, “In God we trust, all others bring data.”
For all of their conceptual work on the evolution of corporate analytics over the last ten years, perhaps the most important contributions of Davenport and Harris have to do simply with the volume of organizations they interview – organizations apparently more than willing to share insights on what they almost certainly feel is a strategic differentiator. Consumers of D&H research can be confident the authors offer extensive illustrations of “targets across industries” that include such diverse verticals as retail, financial services, manufacturing, health care, government, hospitality, online, transportation and agriculture. Their characterization of the different types of top-tier “analytics competitors” as “old hands” (e.g. Marriott), “turnarounds” (e.g. Harrah's) and “born analytical” (e.g. Google) particularly hits the mark.
The webinar gave a sneak peak at coming research to be published in HBR, including an October article on HR, “Competing on Talent Analytics,” and one in November detailing the complex connections between analytics and decisionmaking, “Making Better Decisions.” An easy prediction? Davenport and Harris will continue to be in the vanguard of academic research on business analytics for years to come.
Just as Harvard and MIT are natural B-School rivals, their analytics initiatives collaborate with technology competitors SAS and IBM, respectively, as well. This is certainly good news for analytics consumers, who can be assured both the best of strategic research from top academic minds and the finest nuts and bolts deployment experiences from the archives of corporate successes. A resume that includes education at MIT and Harvard with job experience at IBM and SAS looks impressive, indeed!