Meshing Big Data With Reason

There’s little doubt that the use of big data and analytics is having a dramatic impact in helping data scientists arrive at fact-based hypotheses.

Data-driven decision-making is “the practice of basing decisions on the analysis of data rather than purely on intuition,” according to an article by Tom Fawcett and Foster J. Provost, professor of information systems at New York University’s Leonard N. School of Business.

But just as senior executives continue to balance data-driven decision-making with gut instinct, it’s also important for data scientists to avoid over relying on data—it can at times be out-of-date or skewed . To some degree, judgment and reason must be applied to the decisions that are ultimately made.

Data Won’t Show You Everything

For instance, let’s say a team of data scientists for a consumer packaged goods company is analyzing data based on tests for a new laundry detergent that the company is developing. The detergent relies on a mix of oxygen- and enzyme-based formulas that are aimed at handling different types of stains (for example, coffee or grass).

Although initial tests are encouraging, the data doesn’t reveal some of the potential side effects of combining the two formulas, including allergic reactions in consumers with sensitive skin.

In this scenario, the data scientists would have benefitted from examining the data-driven results from the product tests. But they also would have benefitted from considering the risks associated with combining the chemical agents, including the potential side effects that ultimately affect a significant percentage of the test group.

Analytics Isn’t All-or-Nothing

Part of the challenge in many organizations is that the use of analytics is treated as an all-or-nothing proposition, requiring data or algorithmic perfection before actions can be taken, according to an article authored by Deloitte Consulting LLP’s James Guszcza and John Lucker.

Other organizations spend years deliberating before taking the first step toward embracing analytical methods, the authors note. And some organizations go from one extreme to the other.

“Of course, the preferred point is somewhere between these extremes: In many business settings, analytics is best viewed as an iterative process of continued improvements and data-driven refinements of core business operations,” according to the article.

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