Details
Self-Service Data Analytics and Governance for Managers
1. Aufl.
28,99 € |
|
Verlag: | Wiley |
Format: | EPUB |
Veröffentl.: | 12.05.2021 |
ISBN/EAN: | 9781119773306 |
Sprache: | englisch |
Anzahl Seiten: | 352 |
DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.
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, MBA, CPA, Six Sigma Black Belt,</b> 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. After receiving both his BS and MBA in Accounting from Indiana University, much of his career has been spent in finance functions as controller and as change manager for products such as FX spot, forwards, and options, securities lending, margin, and equity finance at global investment banks. In the recent past, his career has evolved from building scalable controls and delivering strategic technology change, to putting data analytics tooling into the hands of users to drive aggressive digital transformation.<br /><b><br />GREGORY KOGAN, CPA, </b>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 in Accounting from Rutgers Business School and a BS in Computer Science from Rutgers University.</p>
<b>Help your firm's end-users make sense of self-service data analytics tools</b><br /><br />In <i>Self-Service Data Analytics and Governance for Managers</i>, distinguished accountants and authors Nathan E. Myers and Gregory Kogan provide readers with a concise and insightful treatment of the importance of dedicated process governance standards for the use of self-service data analytics tools. The book invites Chief Financial Officers, managers, and auditors to proactively structure and implement an analytics governance framework to protect process stability, as data analytics outputs are increasingly relied upon throughout the organization.<br /><br />With a focus throughout the book on the necessity for managers to structure the potential chaos that results from putting powerful and flexible application development capabilities directly into the hands of end users, <i>Self-Service Data Analytics and Governance for Managers</i> shows readers where and how to introduce and deploy prominant data analytics tools throughout their organization. Importantly, a fit-for-purpose foundational data analytics governance model is extended from the principles of mature control frameworks to promote process stability, risk management, and capture of ROI.<br /><br />Ideal for analytics managers and process owners, <i>Self-Service Data Analytics and Governance for Managers </i>will also earn a place in the libraries of executives and auditors who demand the ability to rely on data analyses performed with self-service data analytics tools within their organizations or to assess the control structures that protect the value created by digital portfolios.
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