Читать книгу Self-Service Data Analytics and Governance for Managers - Nathan E. Myers - Страница 19

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.