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5

Get Out There

Lawrence Peter (Yogi) Berra once observed, “You can learn a lot just by watching.” It is
especially poignant for data scientists who must “get out there” and learn all they can about
everything surrounding the data they use.
There are nuances and quality issues in the data you simply cannot understand sitting at
your desk. Further, the world is filled with “soft data,” relevant sights, sounds, smells, tastes,
and textures that are yet to be digitized – and may never be. Things like the electricity in the
air at a political rally, the smell on a cancer patient’s breath, and the fear in the eyes of an exec-
utive faced with an unexpected threat. Just as data scientists must understand the larger con-
text, the real problems, and the opportunities, as discussed in the previous two chapters, so too
they must understand how the data they analyze was collected, in great detail.
Of course, the importance of these efforts is nothing new, as Joiner (1985, 1994), Hahn
(2007), Hahn and Doganaksoy (2011), Kenett (2015), and Kenett and Thyregod (2006) attest.

Understand Context and Soft Data
This section is about contextual background and soft data. Great data scientists know that 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,
visit call centers, 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, the likely results, and what can go wrong.
We have already explored how failing to get out there contributed to the poor showing
of pollsters in the 2016 presidential election in the United States (Chapter 2).
In the 1980s, one of us (Kenett) was director of statistical methods at Tadiran, a large tele-
communications corporation. He was appointed to this job after his CEO attended a Deming
seminar. In these seminars, Deming recommended that organizations seeking to gain compet-
itive advantage, improve quality, and increase productivity create such a position (more on this
in Chapter 15). In that position, Kenett was able to drive process improvement initiatives and
applications of statistical methods such as designed experiments and statistical process control.
Tadiran became an industry leader with innovative products and high‐efficiency processes.

The Real Work of Data Science: Turning Data into Information, Better Decisions, and Stronger Organizations,
First Edition. Ron S. Kenett and Thomas C. Redman.
© 2019 Ron S. Kenett and Thomas C. Redman. Published 2019 by John Wiley & Sons Ltd.
Companion website: www.wiley.com/go/kenett-redman/datascience