$64.49
Author: Myers Nathan E.
Edition: 1
Number Of Pages: 352
Details: From the Inside Flap
The phenomena of data democratization and the widespread adoption of data analytics tools have resulted in the decentralization of application development, as users are equipped to both perform their own analyses on data sets and to automate manual spreadsheet processing steps in pursuit of control and efficiency gains. Many users, process owners, and managers are unfamiliar with the basic and crucial information governance techniques needed to maintain process control in this new environment.
Self-Service Data Analytics and Governance for Managers delivers an introduction to prominent data analytics tools and capabilities and demonstrates how these tools can be effectively deployed using real-world scenarios. The book provides finance, accounting, and operations managers with chapters on building tool familiarity, process discovery methodologies, matching tools with common use cases, and managing tool deployment in a way that bolsters control and stability of digital outputs. Throughout, the focus remains on establishing robust process governance standards as self-service digital tooling makes its way through an organization, equipping managers to structure the chaos that can result as development tools are placed into the hands of end users.
Perfect for process owners and operations managers, business intelligence and analytics managers, auditors, and Chief Financial Officers, Self-Service Data Analytics and Governance for Managers makes a compelling case for the necessity of laying the foundation for analytics governance early in the digital transformation journey. Further, the authors present an analytics governance framework that readers can adopt and adapt to protect their organizations, as increased reliance is placed on self-service data tools.
Product 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.
From the Back Cover
Help your firm’s end-users make sense of self-service data analytics tools
In Self-Service Data Analytics and Governance for Managers, distinguished accountants and authors Nathan E. Myers and Gregory Kogan provide readers with
Release Date: 02-06-2021
Package Dimensions: 33x234x570
