Self-Service Data Analytics and Governance for Managers

Nathan E. Myers, Gregory Kogan

• Éditeur: Wiley
• Année: 2021
• ISBN:
  9781119773306
• Édition: 1
• Sujet: Negocios y empresa
• Sous-sujet: Gobierno empresarial

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.

CHAPTER 1
Setting the Stage

Impact

The breadth and scale of data are growing exponentially, and the growth of data is impacting the shape of organizations. In a number of industries, there are multiple departments spanning many functions who process vast numbers of data points into information for delivery to a number of internal and external stakeholders. Along with the growth of data, data analytics technology and tooling are advancing at a break-neck rate to process it, to understand trends, and even to make predictions on future outcomes, before displaying results neatly in low-latency dashboard views for ultimate consumption by managers, executives, clients, counterparties, and regulators. Increasingly, data analytics and automation tools are available to perform more of the routinized tasks in the data-to-information processing chain than ever before. Digital transformation features in the concerns of most Fortune 500 CEOs, and while companies report that their top goal regarding their digital transformation is to understand their customers better or to improve products or services, in practice, there are more practical motivations for digital transformation in finance, accounting, and operations functions. The goals are to build capacity and create efficiencies through automating routinized processes, improve process stability and control by structuring the work done outside core systems, and to optimize human capital resources by reducing low-value-added processing tasks.

It is useful to introduce typical environments where self-service data analytics tools can be adopted to great effect. There is a vast number of companies who employ dozens, hundreds, or even thousands of employees, who spend their days in Microsoft Excel. They may work in a variety of functional silos in the organization, whether they are product controllers, entity controllers, or accountants within the CFO's organization, whether they work in an operations function, or whether they work in a business management or business intelligence function rolling up to a COO or another part of the organization. Microsoft Excel continues to dominate the data processing world in the accounting, finance, and operations functions, but a thick and lengthy manual processing tail performed outside of systems highlights the shortfall of core technology platforms in meeting users' needs. Advancements in data analytics and automation tooling may finally represent viable alternatives, with the potential to supplant and dethrone Microsoft Excel as the default business processing tool, and perhaps finally relegate it to where it belongs – one of several quick and dirty tactical tools available for selection if and as required, but not the default go-to, where the majority of processing teams live, day over day.

In many organizations, the cost of employees is the most significant expense on the income statement. Intellectual capital is carefully cultivated and is very often the strongest asset from which we derive our competitive advantage and stand out from our peers. Why then is our precious bandwidth being wasted on administrative tasks or those tasks that do not push us to use our full potential? It is in the best interests of organizations to free up their people assets from the rigors of routine, mundane tasks which are not befitting of our intellectual capacity and/or skillset. How can managers safeguard the focus of their employees from the burdens of low value-added task overload? The answer may be that these tasks are ripe for automation.

We all know that systems (“big” automation) are integral to nearly every job across nearly every industry, much as mechanization was a game-changer for manufacturing during the industrial revolution. In our digital and information age, where vast data is captured, stored, transformed, and processed for use to inform business decisions, core technology systems offer coded processing and considerable mature technology governance, tied up with a bow and a ribbon. Systems are a central hub for structured development – offering data storage and processing operations, long lists of features and functionality, they offer user experience (UX) styles, and perhaps a rich reporting suite – but importantly, they quietly serve unnoticed as a centralized funnel around which to build internal controls and governance to ensure the accuracy of processing output, financial accounting, and management reporting. The fact that systems are widely subscribed to in an organization ties many operators to enveloping control frameworks built around them. Thanks to mature system governance, many systems are designed with embedded internal controls such as those that ensure appropriate user entitlements, automatic system reconciliations and check totals to ensure data integrity, and workflows may even provide for required supervisory approvals as required. Critically, many companies already enforce robust change governance around technology deliveries, including the production and retention of key project development artifacts such as business and functional requirements, evidence of test scripts and testing results, evidence of sign-off, and the completion of post-implementation reviews following a release.

Emergence of Data Analytics

However, a game-changing add-on is now available. Data analytics is coming to the fore and emerging as an exciting strategic and tactical enabler of higher-order analysis and value creation through insight generation. Data analytics includes a number of analytics tools, technologies, and buzzwords you have heard thrown about over the last decade (perhaps not at parties or on date night, but you have heard them nonetheless): robotic process automation (RPA), machine learning (ML), artificial intelligence (AI), text mining technologies like natural language processing (NLP), optical character recognition (OCR), and intelligent character recognition (ICR), along with neural networks, logistic and linear regression analysis, and many more. At the most basic level, these are tools meant to enable descriptive techniques to understand past events and drivers, to gain knowledge through the extraction of trends, to forge more intelligent processing steps, and to inform decisions. Analytics at its sexiest enables predictions to be made and even for actions to be recommended, in response to scenarios encountered.

The ability to detect anomalies in financial data, the ability to detect and flag high-risk patient test results for further review by medical practitioners, and any of a host of proven use cases for descriptive and predictive analytics – these represent huge opportunities to employ data analytics. In many cases, these are enabling models run outside of core technology systems. Data science and data analytics encompass these and many other solutions. It is not always a theoretical predictive rocket science employing complex code-based technology and advanced algorithms to solve complex problems. It is the body of solutions that organizational leaders need to cultivate and have at hand to forge forward in their digital journey, at whatever pace is appropriate to meet digital transformation goals and objectives. This pursuit has led to billions of dollars of investment in technology across many service industries, aimed at building core competencies, increasing competitive advantage and organizational efficiency, doing more with fewer employees, or reducing employee costs and footprint.

It is this last goal that the authors predict will prompt a surge in adoption of data analytics tooling in the next five years, across medium-to-large scale enterprises. Managers are looking to structure their spreadsheet-based processes in a more mature and robust way. By reducing the amount of unstructured processing performed manually in Excel, managers can stabilize and lock down spreadsheet-driven processes into more automated, repeatable, structured, and time-efficient processing steps. By minimizing time spent in performing routinized processing steps, and by minimizing process variance through emulating system processing, the spreadsheet-based jobs of the past will evolve to remove the least value-added steps in the processing chain. While this book cannot but acknowledge many of these data analytics capabilities and technologies, its focus will be around one subset of the wide body of data analytics disciplines – self-service data analytics.

See Exhibit 1-1, which illustrates how productivity can be built through the reduction of time spent for the performance of routinized processes. This can be achieved by enlisting self-service analytics capabilities to address many of the routine steps performed daily, which are highlighted in the list at the left of the diagram. Remember that it is realistic to assume that there will always be some measure of a manual processing tail that is expensive or even impossible to eliminate, but the idea is to move as far to the right along the continuum as possible. The end result is to recapture processing time spent on low value-added steps, to allow for a greater proportion of the day to be spent on value creation.

EXHIBIT 1-1 Building Daily Productivity

Self-Service Data Analytics

The self-service data analytics toolset is an important growing subset of the suite of data analytics tools that is emerging as a focal point of digital transformations across large companies. It is distinct from the other sets of tools in the analytics toolkit in important ways. Self-service tools are typically off-the-shelf vendor products with which individual operators, not technologists, can interact and configure directly, due to their ease of use. Process owners that have no prior technology background and that may have never seen a piece of code are well equipped to lay out a customized, automated process, armed only with their knowledge of the raw data and the processing steps they previously performed in spreadsheets. Intelligent source data parsing and drag-and-drop operations replace SQL and Visual Basic commands, enabling the most inexperienced, inexpert, if not maladroit and bungling of us to quickly roll up our sleeves, forge and test processing steps, and implement a processing workflow, all in an afternoon (“small” automation).

The benefits of self-service data analytics tools include a reduced dependency on core technology when individuals have little influence over the development queue, an improved time-to-market and reduced “wait” in the core technology stack backlog – and importantly the ability to realize the benefits of time-savings through rapid process automation. Removing technology from the critical path is an important end, in itself, and this goal has led to a raft of self-service and user-configurable tools spanning processing and reporting. The trend of data-democratization throughout the organization is one of the main drivers behind the growth of data analytics, as the operators sitting directly on top of business processes are best placed to unlock data value. Perhaps chiefly, work that was previously unstructured, risky, and manual-intensive is now in a tool, emulating a system-driven process. Laborious and time-consuming spreadsheet processing has been replaced with nearly instantaneous computer-driven processing, leading to time savings and efficiency. Of course, there are drawbacks to these tools as well. A significant portion of the pages ahead will be focused on assessing, managing, and mitigating the risks introduced by widespread proliferation of this tooling, through the prescription of a foundational governance framework.

More immediately in this chapter, we will discuss a day in the life of operators, highlight that much of the work performed by operators and analysts is not in fact analysis, but low-value-added data staging, enrichment, and processing activities. We will also look at the processing landscape from the perspective of managers, who are increasingly under pressure to cut costs, produce more, and overall to do more with fewer hands. Then we will take a top-down strategic view from the perspective of executives who are motivated to uncover opportunities to drive efficiency across functions and silos, who share an interest in minimizing unstructured spreadsheet work across the plant, and who may be more directly accountable to internal auditors, external auditors, and regulators. They may also influence the approach, the course, and the speed of the organization's digital transformation. We will discuss the levers they can pull to increase control and to drive efficiency, and the decisions they can make to adapt the organization to expectations that routine processes must be structured and accelerated, that the focus of people resources must extend beyond low value-added mundane processing steps, and that higher-order pursuits such as unlocking data value and the enhancement of decision-making are of prime importance in the new age. Last, we will introduce one of the key topics of this book, which is the need to fill a noted governance gap, as data analytics builds saturate our respective organizations.

There are any number of relevant and overlapping frameworks that cover portions of IT governance and even portions of data analytics governance in the finance and accounting environment. However, no single framework exists that is fit for the universe of self-service data analytics builds. We will draw from mature system governance, model governance, data governance, process governance, SOX 404, COSO IC (internal control framework for the financial reporting process), COSO ERM, and COBIT 2019 (ISACA) frameworks, and even the AICPA's Statements on Auditing Standards – to sketch a foundational governance model that your organization can implement and build upon as necessary. This must be done early and determinedly, so it is in place and can play a formative role in safeguarding your organization, as it embarks on its inevitable digital journey.

Let's look at the environment from the perspective of the employee.

Employee/Analyst/Operator Perspective

Generically, these operators are analysts, though very often, actual analysis is only a sliver of their day, compared to the time spent on the raw processing steps they are expected to perform, prior to generating output for evaluative analysis. Such processing steps likely include capturing information from a number of sources, enriching the data to assemble suitably rich datasets, before completing further processing steps and transformation steps to yield final outputs in the form of information and reports. It is really only at this point that the operator can embark on true analysis in earnest.

Such outputs are often validated against prior periods to attempt to identify any abnormalities or errors. There may be key ratios that are calculated, observed, and compared to get comfort that the output is correct. There may be other sanity checks and detective controls performed to ensure process effectiveness and the integrity of deliverables. We will refer to these broadly as analytical review procedures, and we will assume that these procedures are partially about quality control and error detection, but also partially about understanding the business better, so that value can be added as a true business partner. It is these latter analytical processes that lead to actualization – ensuring high-quality outputs, owning your numbers and outputs, and gaining insights into the business through analysis.

If an organization is large enough to be ...

Table des matières

• Cover
• Table of Contents
• Title Page
• Copyright
• Dedication
• Preface
• Acknowledgments
• About the Authors
• Introduction
• CHAPTER 1: Setting the Stage
• CHAPTER 2: Emerging AI and Data Analytics Tooling and Disciplines
• CHAPTER 3: Why Governance Is Essential and the Self-Service Data Analytics Governance Gap
• CHAPTER 4: Self-Service Data Analytics Project Governance
• CHAPTER 5: Self-Service Data AnalyticsRisk Governance
• CHAPTER 6: Self-Service Data Analytics Capabilities in Action with Alteryx
• CHAPTER 7: Process Discovery: Identify Opportunities, Evaluate Feasibility, and Prioritize
• CHAPTER 8: Opportunity Capture and Heatmaps
• Glossary
• Index
• End User License Agreement