Читать книгу Self-Service Data Analytics and Governance for Managers - Nathan E. Myers - Страница 17

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.