Here is an excerpt from an article written by Thomas C. Redman for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.

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You can be a good data scientist by sitting at your computer. After all, the job description involves poring through huge quantities of often disparate data to find insights that may prove helpful in every aspect of a business, including marketing, logistics, and human resources. It also includes cleaning data, dealing with gaps, and sifting through incomplete poor definitions.

But great data scientists know they must do more. They recognize that there are nuances and quality issues in the data that they can’t understand while sitting at their desks. They recognize that the world is filled with “soft data,” relevant sights, sounds, smells, tastes, and textures that are yet to be digitized — and hence are unavailable to those working at their computers. (Think of things like the electricity in the air at a political rally and the fear in the eyes of an executive faced with an unexpected threat.) They know they must understand the larger context, the real problems and opportunities, how decision makers decide, and how their predictions will be used.

Great data scientists know the only way to acquire this smorgasbord of information is to go get it. So they spend time on the road with truckers, probe decision makers, wander the factory floor, pretend to be a customer, ask experts in other disciplines for help, and so forth. They delve deeply into processes of data creation and the complexities of measurement equipment. They ask old hands how their recommendations will be used, what the likely results are, and what can go wrong.

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Here is a direct link to the complete article.

Thomas C. Redman, Ph.D, “the Data Doc,” helps companies, including many of the Fortune 100, improve data quality. His most recent book Getting In Front on Data: The Who Does What (Technics Publications, 2016) has just been published.