Big Data’s Big Challenge: Misleading Data as a False Guide

Data can foster better decision making and greater business success. And now, with data’s expanding role and prominence, Big Data is seen as a valuable frontier where there is great potential for improving business performance. But, data–even Big Data–must be used properly in order to bring value to the business.

Fortunately, data often can be interpreted properly and, thus, serve as resource for better running the business. But, not always. Sometimes, data can be misinterpreted (see my previous writing on this). That’s why it’s so important to pay attention to what the data really means.

If the data means something very different from what you think it means, it may not improve the business. In fact, it might even hurt. Misinterpreted data can instill a false sense that you are making the right decision–after all it’s based on data, isn’t it? Yet, if the data is misused, problems can ensue.

The challenges involved with using data are discussed in the last two issues of Harvard Business Review, both May 2015 and June 2015. A May 2015 Harvard Business Review article is especially on target in pointing out the problem of mistakenly thinking you’re on track when basing decisions on data. The article is “Outsmart Your Own Biases” by Jack B. Soll, Katherine L. Milkman, and John W. Payne, and it warns that basing decisions on data does not necessarily eliminate bias. The article says, “Some see information-rich big data as a possible remedy, but data is subject to the same kinds of bias.” The article goes on to make a crucial point about how troublesome misinterpreted data can be. It says, “And, once misleading insights are data approved, they are even harder to challenge.”

This is so true. As Big Data gains popularity, more and more businesses will strive to make decisions based on data. They’ll want decisions to be based upon facts, upon data analysis, but not on mere opinions. Such a data oriented approach is thought to lead to the best decisions. And, often it does. But, sometimes it doesn’t. Still, people may think the decision is better because it is based upon data. Yet, how good those decisions actually are depends on how effectively the data is interpreted and used.

Early in my corporate career, I saw first-hand what data can do, as well as its limitations. Back then, my role was that era’s version of what data scientists do today. From my work back then, as well as from my years researching business success and failure patterns, I see how important it is to understand what the data really means. That’s why my research into business success and failure strives to not only find patterns, but also to explain them. This is important because, although data can be powerful, blindly following data without paying enough attention to its meaning can breed problems.

So, I encourage you to think about what your data really means. Ask more questions. Dig deeper. Understand how the data you use fits in with other information, even if that other information entails human judgment. In other words, do what you can to make sure the data gives you truly valuable results. Sometimes, unknowns will remain. So, you may have to do your best despite imperfect information, even today’s era of data aplenty. But, striving to understand what the data really means can often improve matters and is generally worthwhile.

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