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16 The Real Work of Data Science

value, how they change, the politics surrounding seemingly mundane issues like “data sharing,”
how the data comes to be fouled up, what happens when the data is wrong, and so forth. This is
a great way to understand many of the problems and opportunities facing a company today.

Build Your Network
In Chapter 2, we noted that great data scientists have enormous networks. Establishing a
network of sufficient breadth and depth takes time, patience, and initiative. Obviously
some people are better at it than others. On the other hand, a data scientist, isolated from
the organization, is almost certainly ineffective. We emphasize here the human element of
data science – human beings are responsible for the annual report, SWOT, balanced scorecard,
and KPIs. You should get to know them. And you should validate what you see through the
data lens by talking to various people in different positions.
One final point: the best way to build a great network is to be part of other people’s
networks. Be generous in helping others understand data science.

Implications

If you are going to help people make better decisions, you need to understand them and the
context in which they make decisions. This means immersing yourself in the business. One
way a data scientist can do this is to be embedded with them. Hahn (2003) coined the term
“embedded statistician” when he worked at General Electric. Of course, not all data scientists
are embedded (more on the best organizational spots for data scientists in Chapter 15), but
they should act as though they are. Thus, the real work of data scientists involves learning all
they can about “the business,” how it really operates, the values of both the organization and
people working there, and who makes the really important decisions.