Section 2.1 Thinking Like a Domain Expert
Data Science is different from other areas such as mathematics or statistics. Data Science is an applied activity and data scientists serve the needs and solve the problems of data users. Before you can solve a problem, you need to identify it and this process is not always as obvious as it might seem. In this chapter, we discuss the identification of data problems.
Apple farmers live in constant fear, first for their blossoms and later for their fruit. A late spring frost can kill the blossoms. Hail or extreme wind in the summer can damage the fruit. More generally, farming is an activity that is first and foremost in the physical world, with complex natural processes and forces, like weather, that are beyond the control of humankind.
In this highly physical world of unpredictable natural forces, is there any role for data science? On the surface there does not seem to be. But how can we know for sure? Having a nose for identifying data problems requires openness, curiosity, creativity, and a willingness to ask a lot of questions. In fact, if you took away from the first chapter the impression that a data scientist sits in front of a computer all day and works a crazy program like R, that is a mistake. Every data scientist must (eventually) become immersed in the problem domain where she is working. The data scientist may never actually become a farmer, but if you are going to identify a data problem that a farmer has, you have to learn to think like a farmer, to some degree.
To get this domain knowledge you can read or watch videos, but the best way is to ask "subject matter experts" (in this case farmers) about what they do. The whole process of asking questions deserves its own treatment, but for now there are three things to think about when asking questions. First, you want the subject matter experts, or SMEs, as they are sometimes called, to tell stories of what they do. Then you want to ask them about anomalies: the unusual things that happen for better or for worse. Finally, you want to ask about risks and uncertainty: what are the situations where it is hard to tell what will happen next and what happens next could have a profound effect on whether the situation ends badly or well. Each of these three areas of questioning reflects an approach to identifying data problems that may turn up something good that could be accomplished with data, information, and the right decision at the right time.
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