TY  - BOOK
AU  - Myers, Nathan E.
TI  - Self-service data analytics and governance for managers
SN  - 9781119773290
U1  - 657.0285 
PY  - 2021///
CY  - New Jersey
PB  - John Wiley & Sons, Inc.
KW  - Accounting--Data processing
N1  - TABLE OF CONTENTS
Preface ix

Acknowledgments xiii

About the Authors xv

Introduction 1

Chapter 1 Setting the Stage 9

Chapter 2 Emerging AI and Data Analytics Tooling and Disciplines 25

Chapter 3 Why Governance Is Essential and the Self-Service Data Analytics Governance Gap 51

Chapter 4 Self-Service Data Analytics Project Governance 89

Chapter 5 Self-Service Data Analytics Risk Governance 139

Chapter 6 Self-Service Data Analytics Capabilities in Action with Alteryx 179

Chapter 7 Process Discovery: Identify Opportunities, Evaluate Feasibility, and Prioritize 221

Chapter 8 Opportunity Capture and Heatmaps 269

Glossary 307

Index 317
N2  - DESCRIPTION
Project governance, investment governance, and risk governance precepts are woven together in Self-Service Data Analytics and Governance for Managers, equipping managers to structure the inevitable chaos that can result as end-users take matters into their own hands

Motivated by the promise of control and efficiency benefits, the widespread adoption of data analytics tools has created a new fast-moving environment of digital transformation in the finance, accounting, and operations world, where entire functions spend their days processing in spreadsheets. With the decentralization of application development as users perform their own analysis on data sets and automate spreadsheet processing without the involvement of IT, governance must be revisited to maintain process control in the new environment.

In this book, emergent technologies that have given rise to data analytics and which form the evolving backdrop for digital transformation are introduced and explained, and prominent data analytics tools and capabilities will be demonstrated based on real world scenarios. The authors will provide a much-needed process discovery methodology describing how to survey the processing landscape to identify opportunities to deploy these capabilities. Perhaps most importantly, the authors will digest the mature existing data governance, IT governance, and model governance frameworks, but demonstrate that they do not comprehensively cover the full suite of data analytics builds, leaving a considerable governance gap.

This book is meant to fill the gap and provide the reader with a fit-for-purpose and actionable governance framework to protect the value created by analytics deployment at scale. Project governance, investment governance, and risk governance precepts will be woven together to equip managers to structure the inevitable chaos that can result as end-users take matters into their own hands
ER  -