systems are rendered irrelevant, to the extent that processing is done outside of them. This evolution has dramatically shifted the risk environment.
Effectively, the robust governance that was built around systems has been side-stepped, now that systems are no longer the critical path for an increasing number of data analytics–assisted processes. A risk mitigation framework is needed that would address the reallocation of control risk to the end-user in the decentralized processing environment. Since process owners, themselves, oversee many facets of their processing, it is not appropriate for IT alone to administer and maintain governance for data analytics programs. As a complement to systems governance, a framework must be developed to pull together the mature elements of system controls to marry them with the unique profiles, risks, and capabilities of emerging self-service data analytics tools. This framework must be threaded and interconnected throughout the firm, championed and sponsored by firm leadership, and must provide for robust project governance, investment governance, and risk governance.
In the following chapters, we will take a very practical approach to getting readers comfortable with data analytics technologies and practices: surveying operations to uncover use cases with significant benefits warranting prioritization and investment, matching tools to opportunities, and deriving an achievable digital roadmap. We will introduce you to emerging technology that is markedly transforming the processing environment. Later, we will demonstrate the use of one prominent data analytics tool, Alteryx, to bring you aboard the journey. Perhaps most importantly, we will get you thinking about how to prepare your organization by establishing key governance steps now, so that you maintain control as your organization adopts self-service data analytics and business intelligence tooling at scale. In months of research prior to writing this book, your authors failed to identify an existing comprehensive governance framework that is suited entirely for self-service data analytics. Accordingly, in this book, we will draw from more mature and established frameworks (data governance, system governance, and model governance) to build a foundational governance model that can grow with your footprint, as your organization embarks on its inevitable digital journey.
CHAPTER 2 Emerging AI and Data Analytics Tooling and Disciplines
Companies pursue digital transformation as part of an overall overhaul of business models, as legacy models no longer apply in the current technology-driven environment. As notable technology companies have disrupted entire industries over the past two decades, service companies have pursued digital transformation to remain relevant, develop a competitive edge, and to integrate successful technology innovations into their overall strategy. Many companies regard data as one of their most valuable assets, and digital transformation initiatives are often centered around creating systems and processes to unlock this value. Some of the top reasons that organizations undertake the effort is to unlock data value, to better understand their customers, and to improve products and services. Within finance, accounting, and operations functions, digital transformation initiatives have been more focused on value creation through process control, efficiency savings, and improved reporting.
The low-hanging fruit in finance, accounting, and operations functions is to automate unstructured manual spreadsheet processes using self-service analytics tooling and robotic. process automation (RPA) to increase control and to capture process efficiencies. Enhanced processing and reporting can be achieved by ingesting high-quality data into self-service analytics tools for enrichment and automated processing, before outputs are communicated visually with low-latency, interactive dashboards. Cost savings can be realized by freeing up human capital from the manual and repetitive performance of routinized processes. Further, manual processes performed in spreadsheets are error-prone, and resulting errors can cascade into other dependent processes and continue to proliferate undetected. The reduced dependence on spreadsheets for manual processing can reduce the possibility of introducing errors and can thereby improve the expected quality of processing outputs. These two goals – control and efficiency – are the key motivations for organizations to adopt automation tooling and will be the predominant focus of this book. However, there are additional motivational factors as well.
Increased job satisfaction and full resource actualization can occur when employees focus on tasks that enhance value for the organization, rather than on mundane, redundant, and low value-added processing steps and activities. By reducing the proportion of time that individuals spend on the manual tail performed outside of systems (data staging, cleansing, enriching, reformatting, and processing), relative to the time spent performing value-added analysis to generate actionable business insights, employees can more readily create value. Organizations benefit through increased process stability and productivity, while employees benefit from increased focus, increased engagement, and true process ownership. In some cases, advanced analytics can be applied to use machine learning and artificial intelligence models for decision-making. New applications of advanced analytics emerge daily and are limited only by the collective imagination, but in large spreadsheet processing plants, “small” automation efforts aimed at improving control and realizing efficiencies and cost savings one process at a time will come to the fore. Self-service data analytics tooling can enable these efforts and will largely be the focus of this book.
Introduction to Data Analytics Tooling
A decade or more into the data analytics era, many readers will have heard reference to a number of data analytics disciplines and technologies. Some of them have highly technical and sexy sounding names, while others may sound vaguely familiar, if lackluster. In the following pages, we will provide a very high level introduction to a handful of key data analytics technologies, with which managers should be familiar. They all form the living backdrop against which specific analytics applications have emerged. Only a small subset of these are considered to be self-service data analytics tools, but all of these have been successfully demonstrated to add value, when applied appropriately to use cases with adequately rich benefits at scale. In the following sections, we will introduce some of the data analytics disciplines and technologies that have matured and risen to prominence. Note that these abbreviated introductions only scratch the surface of the suite of emerging technologies marching under the broad banner of data analytics.
Internet of Things
One of your authors was a houseguest in a luxury Manhattan apartment roughly 15 years ago, in 2005. He recalls being shown around and impressed that previously stand-alone items were now connected and controlled by the internet. One specific appliance that caught his eye was the refrigerator, which featured a small TV monitor on its door. The homeowner explained that with his “connected” fridge, he was able to maintain an inventory of what necessities were on hand and could easily make a list of the items he needed to purchase. He could even place an online order to have those items delivered. This was an amazing step forward at that time.
Now, any number of items in our homes are connected to the internet – smart TVs, thermostats, security cameras and home alarms, door locks and garage door openers, lightbulbs, a variety of Amazon Alexa and Google Home hubs, stereos and speaker systems, and far more. Looking forward another 15 years, we predict that readers of future editions of this book may not even recall the age when these connected “things” were not in our homes and relied on to provide the weather forecast, to recommend items for our shopping lists, and to consume streaming podcasts, music stations, and video content. Clearly, we are interacting with and consuming data at an unseen level, but on the flip side, we are generating consumer data at an explosive clip.
The “internet of things” (IoT) is one of the factors that is driving the data explosion. All of these connected items are generating colorful data on consumer choices, our buying patterns, and our individual routines. When do we check the weather? When do we leave for work and typically return at home after our workday? When do we turn up the heat or cool our homes down? How many times did the garage door go up and down throughout the day? What movement was captured and logged near the back door on our connected security
