Page 14 - The Real Work Of Data Science Turning Data Into Information, Better Decisions, And Stronger Organizations by Ron S. Kenett, Thomas C. Redman (z-lib.org)_Neat

xviii Preface

We believe that data scientists have huge roles to play in tipping the scales toward the good in
the questions above. This will require incredible commitment, determination, and follow‐
through. We encourage data scientists, statisticians, and those who manage them to take up the
cause, as we have. We want to do all we can to fully equip them.

Data Scientists and Chief Analytics Officers
In writing the book, we adopted four “personas” as readers. First is Sally, a 31‐year‐old data
scientist who works in a midsize department or company. Sally’s job involves producing
management reports, although she does have some time for teasing insights from ever‐increasing
volumes of untrustworthy data. Her title could be any of “data scientist,” “statistician,” “analyst,”
“machine learning specialist,” and others. We are well aware that some people see differences
between these titles. But (with one exception, below) those distinctions are meaningless for us.
Whether you are trained as a statistician, computer scientist, physicist, or engineer, your job is
to turn “data into information and better decisions,” as part of our title demands.
Our second reader persona is Divesh, the 50‐year‐old who has the top analytics job within
his department, business unit, or company. His title may be “chief analytics officer,” “head of
data science,” or something similar. Divesh may have no formal training in data science, but
he is a seasoned manager. While Divesh’s day job is to manage data science across his
department, within his sphere, he also bears special responsibility for the “building stronger
organizations” portion of our title.
Brian, a solid industrial statistician, aged 46 and employed as an internal consultant, is our third
persona. Brian is simultaneously bemused and threatened by data science, and he sits on the side-
lines way too much. We think Brian has much to offer and encourage him to join the effort.
A fourth persona has an outsized impact on data science and this book. It is Elizabeth, who
heads up some department, division, even an entire company. Liz hated statistics in college – it
was a required course, poorly taught, and not connected to the rest of her studies. She has seen
more and more power in data and data science over the last several years and is just beginning
to explore what it means for her department. Liz is both excited about the possibilities and
fearful that her efforts will fail miserably.
More than anything, Liz’s success, or failure, will dictate the future of data science. She can
ignore it (and there are plenty of good reasons to do so) or become an increasingly demanding
customer. If she fully embraces data and data science, she can transform her department.
Introduction to the Book

Sally, Divesh, and Brian have different needs but share a common theme. Their business is to
turn numbers into information and insights. To be useful, their analyses need to guide decisions
that carry a positive impact in the workplace. In other words, they need to help Liz succeed.
We packaged our experience in 18 short chapters directly relevant to our four main per-
sonas. We do not deal with technical issues but instead focus on the make or break ingredients
in data‐driven transformation.
The chapters cover the different steps data scientists take in organizations. We discuss their
role as individuals and through their organizational positions. We present lots of models that
have helped us, we discuss the integration of hard and soft data in analytic work, and we stress
the importance of impact (as opposed to technical excellence). The book also provides a context
and opens curtains to landscapes that are not usually explored by most experts in data analysis.