A typical big data analysis goes like this: First, a data scientist finds some obscure data accumulating in a server. Next, he or she spends days or weeks slicing and dicing the numbers, eventually stumbling upon some unusual insights. Then, a meeting is organized to present the findings to business managers, after which, the scientist feels disgruntled or even disrespected while the managers wish they could take the time back.

When these meetings fail, the main points of contention usually include unclear purpose; analyses that are too narrowly focused; and over-confidence in the science, which turns off non-technical managers. If you’re facing this situation, you should read the FiveThirtyEight article on mining the baby names dataset. When you’re done, send the article to your analytics team.

What FiveThirtyEight’s Nate Silver and Allison McCann did with the baby names dataset sets an example for all data analysts: They imbued it with a relevant business problem, attached complementary data, made a bold, but acceptable, assumption to patch a hole in the data, and elaborated their conclusion with a margin of error. Their article represents the best of data journalism. It surpasses most examples of big data analytics, as we know it.

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