Details

Self-Service Data Analytics and Governance for Managers


Self-Service Data Analytics and Governance for Managers


1. Aufl.

von: Nathan E. Myers, Gregory Kogan

28,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 28.04.2021
ISBN/EAN: 9781119773313
Sprache: englisch
Anzahl Seiten: 352

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Beschreibungen

<p><b>Project governance, investment governance, and risk governance precepts are woven together in <i>Self-Service Data Analytics and Governance for Managers</i>, equipping managers to structure the inevitable chaos that can result as end-users take matters into their own hands</b></p> <p>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.</p> <p>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.</p> <p>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.</p>
<p>Preface ix</p> <p>Acknowledgments xiii</p> <p>About the Authors xv</p> <p>Introduction 1</p> <p>CHAPTER 1 Setting the Stage 9</p> <p>CHAPTER 2 Emerging AI and Data Analytics Tooling and Disciplines 25</p> <p>CHAPTER 3 Why Governance Is Essential and the Self-Service Data Analytics Governance Gap 51</p> <p>CHAPTER 4 Self-Service Data Analytics Project Governance 89</p> <p>CHAPTER 5 Self-Service Data Analytics Risk Governance 139</p> <p>CHAPTER 6 Self-Service Data Analytics Capabilities in Action with Alteryx 179</p> <p>CHAPTER 7 Process Discovery: Identify Opportunities, Evaluate Feasibility, and Prioritize 221</p> <p>CHAPTER 8 Opportunity Capture and Heatmaps 269</p> <p>Glossary 307</p> <p>Index 317</p>
<p><b>Nathan E. Myers</b>, MBA, CPA, Six Sigma Black Belt, has over 20 years in public accounting and investment banking experience, at flagship organizations including Ernst & Young, Morgan Stanley, UBS Investment Bank, Credit Suisse, and JP Morgan. He received both his bachelor’s degree in Accounting and an MBA in Accounting from the Indiana University, Kelley School of Business. Much of his career has been spent in finance functions as a controller and as a change manager for products such as FX spot, forwards, and options, securities lending, margin, and equity finance at global investment banks, building scalable controls and delivering strategic technology change. In the recent past, his career has evolved from owning a portfolio of large-scale technology change, to putting data analytics tooling into the hands of users to drive aggressive digital transformation. Nathan has worked to build rigid data quality standards to drive rich and accurate data sets as inputs to processing, has managed robotics process automation (RPA) automation portfolios, and has scaled end-user data analytics across organizations to capture control and efficiency benefits. He has seen first-hand the requirement to build and maintain close governance over these toolsets, as they rapidly proliferate across banks and large organizations. He resides on Long Island, New York, with his wife, son, and daughter.</p> <p><b> </b></p> <p><b>Gregory Kogan</b>, CPA is a professor of practice in accounting at Long Island University focusing on teaching undergraduate and graduate courses in accounting and finance. He has experience as an auditor at Ernst & Young and as a controller at Tiger Management. He received his MBA from Rutgers Business School in Accounting and a Bachelors in Science in Computer Science from Rutgers University. He is currently pursuing his Doctorate in Business Administration at the University of Scranton with the research focus of data analytics in accounting.</p> <p>While in public accounting Gregory worked on major clients in the asset management industry gaining exposure to auditing hedge funds and private equity funds. At Tiger Management he led the day-to-day accounting and finance operations of a long / short equity start up hedge fund as the controller of the fund. While at Long Island University he spearheaded the launching of an MBA program that delivers graduate business education to a leading US banking institution. At Long Island University he has been leading the effort of integrating data analytics into the accounting curriculum. Gregory resides in Manalapan, New Jersey with his wife, daughter, and son.</p>

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