MIT Sloan Management Review

The surprising finding that 55% of big data analytics projects are abandoned comes from a recent survey of 300 IT professionals. The most significant challenge with analytics projects, according to the survey? Finding talent. Most (80%) of the respondents said that the top two reasons analytics projects fail is that managers lack the right expertise in house to “connect the dots” around data to form appropriate insights, and projects lack of business context around data.

Becoming an elite athlete — or coaching a team of this rarified breed — has as much to do with talent and skill as it does with experience and intuition (not to mention some serious hard work). And data is increasingly part of that mix at the highest echelon of sports: the Olympic Games. At Team GB analytics are used to both monitor the performance of athletes and to predict how well a team will perform. But what could the future hold? Evidence-based coaching — and training.

Based on a global executive survey with 2,500+ respondents and interviews with more than two dozen executives and academics, MIT Sloan Management Review and SAS Institute Inc. report on the distinctive characteristics and habits of companies that are very effective at using analytics to compete and innovate. This report offers an in-depth analysis of Analytical Innovators, the early leaders in the analytics revolution that is changing how many companies are managed.

What can companies do to help fill their data scientist gap? That was the topic at a recent conference hosted by the MIT Center for Digital Business.

Erik Brynjolfsson, director of the MIT Center for Digital Business, talks about the big data revolution with Tom Davenport, who helped get that revolution started with his seminal article “Competing on Analytics.” Their discussion illuminates what’s different about big data and how big data is changing management and the way businesses run.

Top data scientists often share three characteristics: they are creative, they are curious and they are competitive. Anthony Goldbloom, CEO of Kaggle, a company that hosts data prediction competitions, has figured out how to tap all three of these characteristics to help companies crowdsource their analytics problems.

“The kind of people we hire want to know that their work is not gathering dust on some shelf, but has a real impact on the company,” says Veronika Belokhvostova, head of Global Business Analytics at PayPal. And indeed, business analysts there are collaborating with business leaders to answer the “what should we do next?” question.

A data and analytics survey conducted by MIT Sloan Management Review in partnership with SAS Institute Inc. found a strong correlation between the value companies say they generate using analytics and the amount of data they use. The creators of the survey identified five levels of analytics sophistication, with those at Level 5 being most sophisticated and innovative. These analytical innovators in Level 5 had several defining traits. This article explores those traits.

These days, lots of people in business are talking about “big data.” But how do the potential insights from big data differ from what managers generate from traditional analytics?

The increasing ability for companies to get transaction-level, detail-level data — clickstream data versus summary data — presents huge opportunity, says Boston College’s Sam Ransbotham.

One of the stars at the annual MIT Sloan Sports Analytics Conference was Kirk Goldsberry, who has applied his knowledge of geography to the ecosystem, or “court space,” of the basketball court. The event drew 2,200 people to Boston earlier this month.

The capabilities of computers are now improving so quickly that concepts can move from the realm of science fiction into everyday life in just a few years, rather than a lifetime. Rapid advances in information technology — computer hardware, software and networks — are yielding applications that can do anything from answering game show questions to driving cars. But to gain true leverage from these ever-improving technologies, companies need new processes and business models.

In this second joint MIT Sloan Management Review and IBM Institute for Business Value study, we see a growing divide between those companies that, on one side, see the value of business analytics and are transforming themselves to take advantage of these newfound opportunities, and those, on the other, that have yet to embrace them.

Our new survey suggests that companies experienced in analytics use are increasingly gaining competitive advantage — but their approaches vary.

Financial Times journalist Tim Harford and author of “Adapt: Why Success Always Starts With Failure” argues that companies that have a God complex look for smart people (what he calls “little Gods”) to solve complex problems — when what they should really be doing is establishing systematic processes of trial and error.

Almost all executives want more and faster information, and almost all companies are racing to provide it. What many of them overlook, though, is that the real aim should be not faster information but faster decision making — and those aren’t the same things.

Regular readers of MIT Sloan Management Review will recognize the name Gary Loveman.

Loveman earned a Ph.D. in economics at MIT and went on to become CEO, president, and chairman of Caesars Entertainment, owner of Harrah's casinos and other resorts worldwide.

How the smartest organizations are embedding analytics to transform information into insight and then action. Findings and recommendations from the first annual New Intelligent Enterprise Global Executive study.

I was in a meeting in Dallas when I looked out the window to see dark clouds on the horizon. Armed with my laptop, I monitored the storms around Dallas on weather.com, checked delays at Dallas/Fort Worth International Airport at dfwairport.com, and clicked over to flightstats.c

ERRORS IN DATA CAN COST A COMPANY MILLIONS OF DOLLARS, ALIENATE CUSTOMERS, AND MAKE IMPLEMENTING NEW STRATEGIES DIFFICULT OR IMPOSSIBLE. The author describes a process AT&T uses to recognize poor data and improve their quality. He proposes a three-step method for identifying data-quality problems, treating data as an asset, and applying quality systems to the processes that create data.