What makes a good data scientist?
Ken Rudin, Director of Analytics at Facebook, spoke at HP’s Big Data Conference in Boston today. He attacked the four myths of big data:
- That you need Hadoop to attack big data
- That big data gives you better answers
- That data science is a science
- That the reason for big data is to get “actionable insights” from the data.
Of course, there is a kernel of truth in all of these, but there are many tools that are useful in big data, and the answers you get from it are only as good as the questions you ask. Perhaps the most important point he made is that data science is both a science and an art. Those of us who have been in some part of the information industry for longer than we care to admit agree with him. You certainly need the tools, and being a whiz in the “how” of finding and analyzing information is important. That’s the science.
But it’s only half the battle. Knowing how to ask a good question is an art. Good askers of questions must be good listeners. They are steeped in the background of the organization. They absorb the underlying reasons for why information is needed, and how it will be used. Information analysis is a way station toward an action. It’s part of the process of gathering evidence to support a decision. If you just gather information for the sake of having it, it may be interesting, but it’s not useful.
What Rudin said is that our approach to why we gather information is evolving. It has moved from “Tell me our status” to “Tell me why it’s happening” to today’s, “What should I do about it?” But, he says, that’s not enough. Because you also have to decide to act on that recommendation in order to change a process, change a metric, change a policy or change behavior. People who can ask the right questions, balance the science and the art, and act on the conclusions will redefine the role of the data scientist or the analyst in the organization. And change the organization in the process.