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Introduction

      The breadth and scale of data are growing exponentially, and this growth of data is impacting the shape of organizations. Across industries, many companies have entire departments and functions devoted to processing vast numbers of data points into information, for delivery to internal and external stakeholders. Along with the growth of data, data analytics technology and tooling are advancing at a breakneck rate to process it, to identify and understand relationships and trends, and even to make predictions on future outcomes, before displaying them neatly in low-latency dashboard views for ultimate consumption by managers, executives, clients, counterparties, and regulators.

      Data analytics is coming to the fore as an exciting strategic and tactical enabler of higher-order analysis and value creation through insight generation and automation of manual processes. Data analytics includes a number of analytics tools, technologies, and buzzwords readers will have heard thrown about more and more over the last 5 to 10 years: 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 disciplines enabling descriptive techniques to understand past events and their drivers and to gain insight through the extraction of data trends. These technologies can allow us to forge more structured and intelligent processing steps, and at their sexiest, they can enable predictions, trigger recommendations or prescribed actions, and prompt informed decision-making.

More data analytics and automation tools are available to perform 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 the chief goals of digital transformation are to understand their customers better or to improve products or services, quite often there are more practical motivations for digital transformation, particularly 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 performed outside core systems, and to optimize human capital resources by reducing the proportion of low value-added processing tasks in their workday.

      Many large firms employ dozens, hundreds, or even thousands of employees, who spend their days enriching, processing, transforming, and perhaps to a limited degree, even analyzing data in Microsoft Excel. They may work in a variety of functional silos in the organization, whether as product controllers, entity controllers, or accountants within the CFO 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 in any other part of the organization. Spreadsheet processing continues to dominate in accounting, finance, and operations functions, but the 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 delivers 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 by day.

When we think of AI, data science, and related disciplines, for many of us, the most leading-edge advanced analytics capabilities come to mind – 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. While these represent striking and momentous opportunities to employ data analytics to great effect, we submit that these higher-order applications are the exception rather than the rule. The bread and butter use cases that will offer predictable and quantifiable benefits for rapid adoption of data analytics are the opportunities that exist in spreadsheet-driven environments to structure processes in self-service data analytics tooling. Data analytics is not always a theoretical predictive rocket science employing code-based technology and advanced algorithms to solve complex problems. It is the body of solutions that organizational leaders must cultivate and have at hand to forge ahead in their digital journey, at whatever pace is consistent with digital transformation goals and objectives. The development of practical data analytics tools has led to billions of dollars of investment 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. In many organizations, the cost of employees is the most significant expense on the income statement. Managers are motivated to structure their spreadsheet-based processes in a more mature and robust way. By reducing the manual processing performed in Excel, managers can stabilize and lock down spreadsheet-driven processes into more repeatable, structured, and time-efficient processing steps. By minimizing both process variance and time spent performing routinized processing steps, spreadsheet-based jobs of the past will evolve to remove the most manual and least value-added steps in the processing chain. While this book cannot but introduce and acknowledge many more advanced data analytics capabilities and technologies to provide the backdrop, the focus of this book is largely around one subset of the emerging tool suite, self-service data analytics.

Self-service data analytics 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. First, self-service tools are typically built in off-the-shelf vendor products with which individual operators, not technologists, can interact and configure directly, due to their ease of use. Process owners, with 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 inputs and the processing steps they routinely perform 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). Benefits cases based on control and efficiency are self-evident to those performing processing, and the decisions to invest time in structuring spreadsheet processes into automated workflows are directly in the hands of operators and their managers.

      The benefits of self-service data analytics tools include increased process stability, reduced dependency on the often over-subscribed technology function, improved time-to-market – and the instant relief as capacity is recaptured through process automation. Removing the technology function from

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