Why data science needs great communicators

One of the biggest problems facing data science (and many other technical industries) today is communication. This is true on both an individual level, but at a much bigger organizational and cultural level. On the one hand, we can all be better communicators, but at the same time organizations and businesses can do a lot more to facilitate knowledge and information sharing.

At an individual level, it’s important to recognize that some people find communication very difficult. Obviously it’s a cliché that many of these people find themselves in technical industries, and while we shouldn’t get stuck on stereotypes, there is certainly an element of truth in it. The reasons why this might be the case is incredibly complex, but it may be true that part of the problem is how technology has been viewed within institutions and other organizations. This is the sort of attitude that says “those smart people just have bad social skills. We should let them do what they're good at and leave them alone.” There are lots of problems with this and it isn’t doing anyone any favors, from the people that struggle with communication to the organizations who encourage this attitude.

Statistics and communicating insights

Let’s take a field like statistics. There is a notion that you do not need to be good at communicating to be good at statistics; it is often viewed as a primarily numerical and technical skill. However, when you think about what statistics really is, it becomes clear that that is nonsensical. The primary purpose of the field is to tease out information and insights from noisy data and then communicate those insights. If you don’t do that you’re not doing statistics. Some forms of communication are inherent to statistical research; graphs and charts communicate the meaning of data and most statisticians or data scientists have a well worn skill of chart making. But there’s more than just charts – great visualizations, great presentations can all be the work of talented statisticians.

 Of course, there are some data-related roles where communication is less important. If you’re working on data processing and storage, for example, being a great communicator may not be quite as valuable. But consider this: if you can’t properly discuss and present why you’re doing what you’re doing to the people that you work with and the people that matter in your organization you’re immediately putting up a barrier to success.

The data explosion makes communication even more important

There is an even bigger reason data science needs great communicators and it has nothing to do with individual success. We have entered what I like to call the Data Century. Computing power and tools using computers, like the Internet, hit a sweet spot somewhere around the new millennium and the data and analysis now available to the world is unprecedented. Who knows what kind of answers this mass of data holds?

Data scientists are at the frontier of the greatest human exploration since the settling of the New World. This exploration is faced inward, as we try to understand how and why human beings do various things by examining the ever growing piles of data. If data scientists cannot relay their findings, we all miss out on this wonderful time of exploration and discovery. People need data scientists to tell them about the whole new world of data that we are just entering. It would be a real shame if the data scientists didn’t know how.

Erik Kappelman wears many hats including blogger, developer, data consultant, economist, and transportation planner. He lives in Helena, Montana and works for the Department of Transportation as a transportation demand modeler.