000			03145nam a22001937a 4500
999			_c4145 _d4145
005			20221130120606.0
008			221130b ||||| |||| 00| 0 eng d
020			_a9781119773290
082			_a657.0285 _bMYE
100			_aMyers, Nathan E. _99344
245			_aSelf-service data analytics and governance for managers
260			_bJohn Wiley & Sons, Inc. _aNew Jersey _c2021
300			_axvi, 336 p.
365			_aUSD _b44.95
504			_aTABLE 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
520			_aDESCRIPTION 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.
650			_aAccounting--Data processing _95641
942			_2ddc _cBK