Читать книгу Self-Service Data Analytics and Governance for Managers - Nathan E. Myers - Страница 16

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 layered, a pecking order emerges. More junior, if not entry-level, staff will be buried in the assembly of information and information processing. Over time, if they are good, 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.