Learning how to make a real difference with big data in Latin America – Lee Ullmann

Lee Ullmann, Director of the MIT Sloan Latin America Office Office of International Programs

Lee Ullmann, Director of the MIT Sloan Latin America Office
Office of International Programs

Big data is a popular buzz word these days. Companies are told they should harness the vast amount of data produced globally and it will lead to greater profitability and productivity. By using big data, they can reap benefits like producing better products and customization options. That’s all well and good, but it’s contingent on managers understanding how to use and analyze the data. How many can really do that across all industries?

A McKinsey Quarterly report in 2015 found that very few legacy companies have achieved “big impact” through big data. In the study, participants were asked what degree of revenue or cost improvement they had seen through use of big data. The answer was less than 1 percent for the majority of the respondents.

A big problem with big data is that, although everyone talks about it, most people don’t really know what to do to ensure that investing in it is a win-win proposition. To shed light on this issue, MIT Sloan is bringing its deep expertise to a May 26 conference in Bogotá, Colombia called, “Big Data: Shaping the Future of Latin America.” The presenters include faculty from across the MIT campus as well as the Department of National Planning in Colombia. With examples from their own research, they will share new and innovative ways to use big data to achieve specific goals.

Read More »

Run field experiments to make sense of your big data — Duncan Simester

MIT Sloan Prof. Duncan Simester

MIT Sloan Prof. Duncan Simester

From Harvard Business Review

Making marketing decisions based on an analysis of Big Data can be risky if not done properly, because data seldom reveal the causal links between correlated events. Take the case of one large retailer we studied. The company noticed that customers who purchased perishables also tended to purchase large-screen TVs. Based on this observation, the company made a significant investment in marketing activities directed at increasing purchases of perishables, in the hope that this would trigger more TV purchases. But while they sold more perishables, they didn’t manage to shift any more TVs, and the profits from selling extra perishables weren’t enough to cover the marketing investment.

Read More »

How big data can be used to improve early detection of cognitive disease — Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

From The Health Care Blog

The aging of populations worldwide is leading to many healthcare challenges, such as an increase in dementia patients. One recent estimate suggests that 13.9% of people above age 70 currently suffer from some form of dementia like Alzheimer’s or dementia associated with Parkinson’s disease. The Alzheimer’s Association predicts that by 2050, 135 million people globally will suffer from Alzheimer’s disease.

While these are daunting numbers, some forms of cognitive diseases can be slowed if caught early enough. The key is early detection. In a recent study, my colleague and I found that machine learning can offer significantly better tools for early detection than what is traditionally used by physicians.

One of the more common traditional methods for screening and diagnosing cognitive decline is called the Clock Drawing Test. Used for over 50 years, this well-accepted tool asks subjects to draw a clock on a blank sheet of paper showing a specified time. Then they are asked to copy a pre-drawn clock showing that time. This paper and pencil test is quick and easy to administer, noninvasive, and inexpensive. However, the results are based on the subjective judgment of clinicians who score the tests. For instance, doctors must determine whether the clock circle has “only minor distortion” and whether the hour hand is “clearly shorter” than the minute hand.

In our study, we created an improved version of this test using big data and machine learning. For the past seven years, a group of neuropsychologists have had patients use a digital pen to draw the clocks instead of a pencil, accumulating more than 3,400 tests in that time. The pen functions as an ordinary ballpoint, but it also records its position on the page with considerable spatial and temporal accuracy.  We applied machine learning algorithms to this body of data, constructing a data-driven diagnostic tool. So rather than having doctors subjectively analyze the pencil-drawn clocks, the data from the digital pen drawings goes into the machine learning algorithm’s model which provides the result of the test.

Read the full post at The Health Care Blog.

Cynthia Rudin is an Associate Professor of Statistics at the MIT Sloan School of Management in Operations Research and Statistics.

To Manage a Successful Sports Team, Focus on Data — Ben Shields

MIT Sloan Lecturer Ben Shields

From Xconomy

The mantra of youth sports where “everyone gets a trophy” is permeating professional leagues. These days every team can claim some semblance of winning. In the bygone era of the NFL, two teams made the playoffs and that consisted of one game, the Super Bowl. Today six teams from each conference advance, and there is talk of adding more. In MLB, it used to be that the league leaders won the pennant and then went to the World Series; now, five teams in each league make the playoffs. In the NBA and the NHL, meanwhile, more than half of all teams make the post-season.

As the definition of post-season success broadens and winning becomes a commodity, a team’s performance isn’t enough to stand out in the $750 billion sports industry. And at a time where traditional revenue streams are under pressure and the competition for money, media, and sponsors remains stiff, sports organizations have to be more innovative.

So, what should they be doing to drive revenue? How can they use technology to attract and interact with fans? And, in the Age of Big Data, what’s the best use of analytics to increase ticket sales? These are some of the questions on the table at the 2015 MIT Sloan Sports Analytics Conference.

Read More »

Using big data to manage medical expectations — Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

From The Health Care Blog 

For all the advances in both medicine and technology, patients still face a bewildering array of advice and information when trying to weigh the possible consequences of certain medical treatments. But a hands-on, data-driven tool I have developed with some colleagues can now help patients obtain personalized predictions for their recovery from surgery. This tool can help patients better manage their expectations about their speed of recovery and long-term effects of the procedure.

People need to be able to fully understand the possible effects of a medical procedure in a realistic and clear way. Seeking to develop a model for recovery curves, we developed a Bayesian modeling approach to recovery curve prediction in order to forecast sexual function levels after prostatectomy, based on the experiences of 300 UCLA clinic patients both before radical prostatectomy surgery and during the four years immediately following surgery. The resulting interactive tool is designed to be used before the patient has a prostatectomy in order to help the patient manage expectations. A central predicted recovery curve shows the patient’s average sexual function over time after the surgery. The tool also displays a range of lighter-colored curves illustrating the broader range of possible outcomes.

Read More »