Tips for Building a Data Science Capability

INTRODUCTION
Many organizations believe in the power and potential of data science
but are challenged in establishing a sustainable data science capability.
How do organizations embed data science across their enterprise so
that it can deliver the next level of organizational performance and return
on investment?
Building a data science capability in any organization isn’t easy—there’s
a lot to learn, with roadblocks and pitfalls at every turn. But it can be
done—and done right. This booklet will show you how. We’ve filled it with
the most valuable best practices and lessons we’ve learned—both in our
pioneering work with clients, and in building our own, 500-member data
science team, one of the world’s largest.
In these pages, you’ll discover:
+ + Why your data science investment may not
have delivered on its promise so far. It could
be that you haven’t developed an analyticsdriven
culture.
+ + How to keep from getting bogged down.
Some organizations have difficulty prioritizing
their data science projects. Others face skepticism
from leadership. Here’s how to overcome
the most common roadblocks.
+ + How to get buy-in for data science throughout
the entire organization. We show you how to
overcome resistance to everything from using
analytics to sharing information.
+ + Where to place your data science teams in your
organization. Should they be centralized?
Dispersed? Permanently embedded in individual
business units? Each option has its advantages
and risks.
+ + How to stand up the position of Chief Data
Officer (CDO). We tell you why a CDO needs
to be one part “enforcer” and two parts
“data evangelist.”
+ + How you can leverage our Data Science Talent
Management Model. We’ll help you answer three
key questions: Who do you need? Where do you
need them? How do you keep them?
+ + Why design thinking-when applied to data
science-can unlock organizational value.
How the designer’s mindset can ground and
amplify analytic insights.
Introduction | v