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2 years ago

Tips for Building a Data Science Capability

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iv | Introduction

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

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