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.
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.
Before companies can profit from big data, they often must deal with bad data. There may indeed be gold in the mountains of information that firms collect today, but there also are stores of contaminated or “noisy” data. In large organizations, especially financial institutions, data often suffer from mislabeling, omissions, and other inaccuracies. In firms that have undergone mergers or acquisitions, the problem is usually worse.
Contaminated data is a fact of life in statistics and econometrics. It is tempting to ignore or throw out bad data, or to assume that it can be “fixed” (or even identified) somehow. In general, this is not the case.