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

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.

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 a worthy objective to be excited about. If an innovation is developed in one part of their organization that has wide applicability and opportunities for replication across the shop, it is their responsibility to put in place an overarching clearinghouse apparatus to capture and scale these opportunities. Perhaps most critical of all is that executives feel convinced that risks are well documented and understood across the enterprise, and that strong policies and procedures are in place to guide the organization in active risk management.

In Chapter 5, we will discuss further the need to address risk through active risk governance, as operators themselves develop processing solutions with the employ of self-service data analytics tools.

The data analytics toolkit is growing at a rapid pace, with many off-the-shelf tools that can be customized to perform routinized processing tasks. By shoehorning an unstructured process into a self-service data analytics tool, analysts and operators can structure work into a repeatable process that is stable, documented, and robust – even tactically mimicking a system-based process. Self-service analytics is a form of business intelligence (BI) in which line-of-business professionals are enabled to perform queries; extract, transform, and load (ETL) and data enrichment activities; and to structure their work in tools, with only nominal IT support. Self-service analytics is often characterized by simple-to-use BI tools with basic analysis capabilities and an underlying data model that has been simplified or scaled down for ease of understanding and straightforward data access.

Earlier in this chapter, in the section Employee/Analyst/Operator Perspective, we described the plight of end-users who spend a disproportionate amount of their time performing data staging, data preparation, and routinized processing activities, instead of spending their time gleaning meaning and trends from their outputs through value-added analysis. We discussed that they may have little influence over the prioritization queue for technology demand items, let alone an ability to influence the budgeted dollar amounts approved during the annual technology investment cycle, often leaving their efficiency needs unmet by core technology. We discussed the overlapping, but slightly different perspective of managers, surrounding the need to increase control and reduce processing variance and failures, by structuring work in tools. We also discussed their motivation to capture efficiency, in order to meet additional demands being placed on resource-constrained departments. Here again, managers are often at the mercy of the technology investment cycle budgets and priorities, which may be likely to leave their needs unmet in the short term. Finally, we discussed the landscape from the perspective of C-suite and divisional executives, who wish to minimize the number and impact of highly publicized catastrophic processing failures and the number of audit points levied by internal and external auditors, and who could be enticed to embrace any edge offered in strategic decision-making that paves the way for organizational success. The authors submit that a program of “small” automation through self-service data analytics could serve the needs of all of these stakeholders.

End-user analytics tools and business intelligence tooling can be readily deployed to automate small bits and pieces of processes in and around systems. Importantly, the involvement of core technology teams is not required to build them, as they would be for a far larger application rollout. When vendor software licensing costs are weighed up against time savings, the average cost of employees, and the additional productivity that can be enjoyed as a result of tool deployment, a significant return on investment (ROI) is evident. End-user tooling can be engaged by virtually everyone in an organization that is able to identify appropriate use cases and to navigate the increasingly accessible and user-friendly functionality.

Operators and analysts can target the low value-added steps in their processing chain for analytics-assisted automation, allowing them to realize efficiency benefits in short order, even while strategic change requests work their way through the backlog and “wait” queues. Managers gain from the structured, stabilized, and regimented processing that results from centralizing processing steps in a tool. They can improve process controls, while improving cycle time and building capacity. Finally, executive-level strategic leadership can directly benefit from widespread adoption of self-service data analytics. From reduced client and regulatory impact of failed processing incidents to improved audit results, from capturing efficiency to sourcing descriptive and predictive information to improve decision-making – all of these arguments will be persuasive to division-level executives and functional heads. As self-service analytics champions, these leaders can do much to instill a proactive and empowered mindset across the organization. They can influence the reallocation of core technology investment budget dollars to the funding of a centrally sponsored data analytics program. Perhaps most importantly, they can promote and encourage innovative thinking throughout the enterprise.