Nathan E. Myers - Self-Service Data Analytics and Governance for Managers

1. Chapter 1
2. Chapter 3
3. Chapter 4
4. Chapter 5
5. Chapter 6
6. Chapter 7
7. Chapter 8

NATHAN E. MYERS

GREGORY KOGAN

Copyright 2021 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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ISBN 9781119773290 (hardback)
ISBN 9781119773313 (ePDF)
ISBN 9781119773306 (epub)

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My wife, Grazia, my son, Joseph, and our new daughter, Nicole Emma, who arrived just in time to keep me company during this project.
Nathan E. Myers

To my parents, Asya and Yaakov, my wife Carolyn and children Lucia and Matthias. Thank you for all your love and support.
Gregory Kogan

The breadth and scale of data are growing exponentially, and the growth of data is impacting the shape of organizations. According to the On Risk 2020 report by the IIA, Organizations face significant disruption driven by the accelerating pace of technology and the growing ease of mass data collection. Executives are placing increased reliance on the abundance of data in their decision-making process. A number of factors are driving the steep growth of data: data and metadata created and captured from the Internet of Things (IoT), increasingly interconnected supply chains, proprietary company information generated in unified ERPs, and the increasing number of third-party cloud data sources available for subscription and consumption. Organizations have available far more data than ever before.

The rise of data analytics is transforming business functions in an accelerated pace. This transformation is driven by managements' strategic goal of evolving their organizations to being data-driven, interconnected, and innovative in their approach to unlocking insights from the rapidly increasing volume of data. While data analytics is an extremely broad category of technologies and disciplines, self-service analytics has recently emerged as a significant driver of accelerated digital transformation. At the heart of digital transformation in accounting, finance, and operations functions is the move from manual spreadsheet-based data processing to structured, robust, and efficient automated processing using self-service analytics tools. Business professionals who spend their days assembling and enriching information from disparate sources in spreadsheets, performing routine formulaic processing steps, comparisons, and aggregations, now have data analytics tools at their disposal to rapidly automate the least value-added portions of their roles.

Even though automation techniques through core IT systems have existed for decades, the defining features of the new wave of self-service analytics automation is that it is small. Small automation is relatively inexpensive from a capital investment perspective compared to multi-million-dollar enterprise IT investments. Self-service data analytics offers flexible, no-code, low-cost processing capabilities that are directly accessible to process owners outside of IT. Without the need to involve IT or to await the technology investment cycle, self-service data analytics solutions can be developed and implemented in only hours or days directly by those overseeing the process. Such capabilities allow for dramatic acceleration of routine processes and reduce the likelihood that manual processing errors are introduced. Perhaps most importantly, they transform the role of business professionals who can reclaim time to focus on higher-order data analysis, the implementation of improved controls, or on other emerging business priorities. These benefits taken together amount to a quantum leap forward for organizations intent on truly harnessing data capabilities and optimally deploying human capital.

While self-service data analytics deployments can improve process control and efficiency in the finance function, they also markedly impact the internal control environment. In the past, the control environment which safeguarded the enterprise from IT general and application risks was centralized around the core technology stack. As processing capabilities are now placed directly into the hands of end-users to import and analyze data, structure processing steps, and export outputs, the risk environment has been dramatically shifted. Robust internal control governance built around systems over several decades has been side-stepped by increased data analytics processing outside of systems, necessitating a commensurate shift in governance. We will systematically inventory the body of existing regulatory guidance, legacy IT governance precepts, and data governance and model governance frameworks to identify foundational principles which must be extended to forge a data analytics governance framework. Building upon existing governance literature, we will outline the first comprehensive, fit-for-purpose, and actionable data analytics governance framework for managing the governance triad: project governance, risk governance, and investment governance.