I must admit I haven’t kept as up to date as I should on all the terrific new analytics articles from the MIT Sloan Management Review’s New Intelligent Enterprise. So when I finally got around last week to reviewing the goodies from the last few months, it was more reading than I anticipated. It’s the kind of work I like though, catching up on great ideas from top but practical academics on the conduct of analytics in the business world.

My first task was to complete the SMR Global Analytics Survey. I’d recommend that all analytics/data science professionals invest the 20 minutes to fill out the online form. The GAS is a substantive questionnaire with a broad and significant world-wide response base. Reports from the past two years are available on the site, with the 2012 results to be published in the Fall. If, like me, you’re “skeptical” of vendor survey reports on DS/analytics that seem much less than scientific but just happen to show a big demand for their products, this one’s for you.

If you’re a tennis fan in addition to an analytics enthusiast, you’ll enjoy “Wimbledon, Big Data and Business Intuition.” WBDBI outlines several big data systems developed jointly by the All England Tennis Club and IBM “to provide detailed player and tournament insights, and historical comparisons.”

The Professional Golfers Association (PGA) has also committed to analytics. Alas, the sports world is not unlike business, where data-driven initiatives often face uphill battles to dislodge intuitive management. According to Golfweek writer Sean Martin “Golf is a very traditional game. It’s viewed as an art form and not a sport. Some players are starting to see the value [of data analytics] but it’s still very much, ‘this is what I believe, this is what intuition tells me.’”

Adds IBM’s Jeremy Shaw, “Using analytics in a business is a bit like what we’re doing at Wimbledon, combining huge volumes of historical and real-time data to gain immediate insights … Often it’s not the insights you would expect that add the most value … it’s those unexpected insights and ability to predict outcomes, which really add the most value.”

MIT professors Erik Brynjolfsson and Andrew McAfee’s opine in “Skills That Will Remain in Demand In a Computer-Rich World” that it’s now necessary for career information workers to adapt to a “fast-changing economy filled with ever-faster, ever-smarter computers.” The key is “to develop new ways of combining human skills with ever-more-powerful technology to create value.”

According to the authors, critical skill sets start with applied math, statistics and writing from academia. The ability to frame and solve open-ended problems, often acknowledged as differentiating characteristics with today’s data scientists, is also prime on their list. Tellingly, Brynjolfsson and McAfee site interpersonal and group dynamic skills such as negotiation, persuasion and nurturing as keys to the expanding social collaboration between computers and information workers.

Two of OpenBI’s current big data customers are driving intelligence through analytics on their large transaction data stores. Both, however, are examining the data at the aggregate transaction level only – summarizing from line item detail. One of the customers has no choice: as a transaction intermediary, they’re provided only aggregate information. The other is just getting starting with analytics and wishes to walk before they run.

Boston College Information Systems professor Sam Ransbotham proposes that analytics-driven companies get to their detail data sooner rather than later, arguing that detail is perhaps even more important than big. “There’s the opportunity to try to figure out the ways that items, say customers, differ. And not just how they demographically differ … but how does their behavior differ. By observing detailed transactional level data, we can actually find much more interesting things than we can by lumping them into demographic groups.”

Data available from ecommerce facilitates taking detail a few steps further.  First, the Web provides the means to “show us the kinds of things that people looked at but didn’t buy. That’s opening up a new opportunity to understand how people are going about the process.”

Second, there’s the experimental method to drive quick results. “If you have a random way of showing people different things on your website, then you can pretty quickly, with a very small number of observations, start to figure out what’s working and what isn’t. In real time, you can begin to refine your presentation.”

Ransbotham uses the experiment illustration to contrast big with detail. “You really don’t have to have that much data for an experiment like that. It’s not like you need to run it for six months. These are answers you can find out with not the huge volume, not the billions of records, but with the detailed level of the records.”

Whether you’re Democrat or Republican and plan to vote for Obama or Romney, if you’re an analytics type, you’ll find “Big Data and the U.S. Presidential Campaign” highly informative. I suspect that both the electoral and popular votes will be a lot closer than the President’s 27M to 1.9M lead in Facebook followers. “It’s all about the data this year and Obama has that,” says Andrew Rasiej, a technology strategist and publisher of TechPresident. “More and more accurate data means more insight, more money, more message distribution, and more votes.”

Republican Party digital strategist Patrick Ruffini’s “skeptical,” challenging both businesses and politicians to look beyond the data they own. “If I am doing ‘get out the vote’ calls and 65 percent of the people I speak with say they are going to vote for me, how do I know what the bias is, if the organization is systemically biased? Internally, our numbers look great, the data is great, but often the data is skewed. How do you solve for that skew? … I view the challenge as one of getting away from biased sources of information.” Amen.

Good stuff, this New Intelligent Enterprise. Take a look. I bet you won’t be disappointed.

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