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

they will strive to get faster, better, and more efficient at assembling and processing data. Those who are able to get their head above water enough to truly understand what the processing outputs are telling them, and those who can glean critical insights surrounding the business, may gain visibility and be recognized as true process owners. With luck, they may graduate to being a reviewer, supervisor, or manager – sampling the butter with a critical and experienced eye, instead of churning all day. This is the aspirational path to advancement for many in the analyst ranks, whether across finance or accounting functions, operations functions, or any of the business analytics or reporting roles that pepper the ranks of large organizations.

      In times of great flux from business growth or volatility, departmental reorganizations, regulatory demands, or other external pressures, operators may find themselves quickening the pace to get their heads above water, only to be rewarded with more of the same work to drown them anew. Take a deep breath, because you are about to be pushed right back under the surf, until you bed things down yet again at a higher plateau of utilization, with even less time to perform actual analysis, to learn, and to add value to your business. Just when the operator begins to get to a point where they have learned enough about what their own deliverables and end-products are telling them about the business to begin to add value, they may be asked to cover another 20 accounts, or take on 20 more processes, or to produce five more weekly deliverables.

Very often, processing pain points to the need for additional system features and functionality. Organizationally astute analysts will articulate the need and route the demand item to the core technology demand queue, with the hope that it will introduce time savings, when it is eventually delivered. Such hopes can be dashed with the slash of a red marker, as requests are buried at the bottom of an interminable wish list, when higher priority initiatives take precedence, or when requestors lack the influence to argue for the relative priority of their requests. This is the reality of the environment in which many operators find themselves each day. Later, we will propose that self-service data analytics is one of the few tactical levers that can be pulled to lock down the data preparation, transformation, and processing steps in a stable, controlled manner, and to capture efficiency in the form of time savings. For now, let's look at the same environment from the perspective of managers.

Managers' Perspectives

      Some managers have come up the ranks in the career progression outlined above; they may have started as an analyst and sharpened their technical skills at a faster rate than others, such that they were able to successfully execute their workload, but even more, they learned from their outputs, demonstrated value to internal clients, and ultimately moved up. Others may have been hired externally and brought into the organization, and may be less aware of the processing steps and rigors that their teams undergo each day. Similarly, existing managers within the organization may have been asked to assume ownership of a function, and may again be less familiar with the processes required to generate departmental deliverables. Irrespective of which of these profiles is most applicable, managers will be expected to deliver an increasing number of accurate and conforming deliverables, these days without the free hand to hire additional resources to meet increasingly stringent demands.

Most managers are focused on minimizing process variance to ensure consistent quality of outputs. In the mature systems-based environment, they insist that as much processing as possible is performed within systems, and that system outputs require little manipulation, in order to generate deliverables. Deliverables requiring complex, multi-step, and unstructured Excel-based operations introduce significant risks. Accordingly, astute managers track the progress of the technology backlog, ensure that they weigh in on the prioritization queue, and shepherd their must-haves through project stages to a scheduled release. In this way, they can ensure that the systems environment supports their processing needs. They would prefer to use well-documented, prescribed and controlled system features and functionality to perform the lion's share of processing, rather than relying on unstructured manual processing steps. The goal is to extract output from systems that is as close as possible to final form for departmental deliverables.

      However, often the core technology systems have a lengthy backlog of competing priorities that may have been built up over years, that can be difficult to navigate, and which can result in significant delays in the delivery of needed features and functionality. Many readers will have felt the disappointment when they learned that a promised sprint or release has been postponed, or when they learned that the all-important and long-awaited Phase 2 of a large-scale strategic technology delivery is below-the-line for the year, left unfunded on the shelf. Does that mean that teams must continue to work in an inefficient and unstructured way, until such time as the technology investment is revisited in the next investment cycle? Perhaps not. In the section Arguments for Self-Service Data Analytics Tooling, presented later in this chapter, we will provide a preview of self-service data analytics options and introduce an approach that managers can take to structuring work with analytics-assisted tooling while they await the needed system enhancements.

      Control is not the only concern of today's managers. In an environment where increasing work demands are being placed on the talent pool, with downward pressures on the organizational cost-base and footprint, managers are preoccupied with the capture of efficiency. Across large departments, each daily hour saved can contribute to headcount avoidance, in the event that the increased productivity allows existing staff to accommodate additional demands without making a hire. Even in a stable demand environment, efficiency is a prime motivator. In the event that the hours saved sum up to a full headcount equivalent, one full-time employee can then be redeployed to another function altogether.

      Now, let's look at the organization from the perspective of executives.

Executives' Strategic Perspectives

To get a full perspective, it is useful to understand the concerns of divisional executives or even C-suite executives who are charged with leading efforts to unlock the value of data, managing divisional footprints, and driving organizational efficiency. They have a keen interest in setting the pace of digital technology adoption and deployment. They can directly influence the approach, course, and speed of the organization's digital journey progress. Of course, they have more control over resource pools and technology budgets across functions than do managers. While often removed from the day-to-day processing operations of their constituent teams, they share an interest in structuring unstructured work across the plant, and in minimizing the likelihood and business impact of process failure, given their accountability to internal auditors, external auditors, regulators, clients, and investors.

      Divisional executives will be interested in all key measurements that communicate the health of their business. From sales and market share on the revenue side, to the cost and expense side of profitability metrics, they will be motivated by data points and trends that point to organizational fitness and longer-term value creation. In service organizations, efficiency is measured not by inventory turns and asset turnover but by productivity measures like cycle times, process completion times, failure rates, and straight through processing (STP) ratios, just to name a few. Of course, executives spend much of their time managing and remediating failures and exceptions, which impact the business considerably, when they are bubbled up to visibility. We are speaking in broad terms here, and in no way are we minimizing other important metrics that executives may actively manage like social responsibility, employee diversity, employee satisfaction, and the many other critical measures they consider. The point is that, to the extent that executives can be brought to see the potential for introducing processing efficiency across an organization, to the extent that they understand the very real impact of process failure on client relationships, audit results, and even on their stream of information for decision-making, they can be brought into the tent as active champions and sponsors of a digital course that drives the organization forward in leaps and bounds.

Due to the organizational resources they have at their disposal, there are many levers they can pull to increase control and to drive efficiency, and to unlock data value to enhance decision-making. If the total cost of the many processing departments measures in the millions of dollars, a fractional savings is