Читать книгу Self-Service Data Analytics and Governance for Managers - Nathan E. Myers - Страница 21

Companies pursue digital transformation as part of an overall overhaul of business models, as legacy models no longer apply in the current technology-driven environment. As notable technology companies have disrupted entire industries over the past two decades, service companies have pursued digital transformation to remain relevant, develop a competitive edge, and to integrate successful technology innovations into their overall strategy. Many companies regard data as one of their most valuable assets, and digital transformation initiatives are often centered around creating systems and processes to unlock this value. Some of the top reasons that organizations undertake the effort is to unlock data value, to better understand their customers, and to improve products and services. Within finance, accounting, and operations functions, digital transformation initiatives have been more focused on value creation through process control, efficiency savings, and improved reporting.

The low-hanging fruit in finance, accounting, and operations functions is to automate unstructured manual spreadsheet processes using self-service analytics tooling and robotic. process automation (RPA) to increase control and to capture process efficiencies. Enhanced processing and reporting can be achieved by ingesting high-quality data into self-service analytics tools for enrichment and automated processing, before outputs are communicated visually with low-latency, interactive dashboards. Cost savings can be realized by freeing up human capital from the manual and repetitive performance of routinized processes. Further, manual processes performed in spreadsheets are error-prone, and resulting errors can cascade into other dependent processes and continue to proliferate undetected. The reduced dependence on spreadsheets for manual processing can reduce the possibility of introducing errors and can thereby improve the expected quality of processing outputs. These two goals – control and efficiency – are the key motivations for organizations to adopt automation tooling and will be the predominant focus of this book. However, there are additional motivational factors as well.

Increased job satisfaction and full resource actualization can occur when employees focus on tasks that enhance value for the organization, rather than on mundane, redundant, and low value-added processing steps and activities. By reducing the proportion of time that individuals spend on the manual tail performed outside of systems (data staging, cleansing, enriching, reformatting, and processing), relative to the time spent performing value-added analysis to generate actionable business insights, employees can more readily create value. Organizations benefit through increased process stability and productivity, while employees benefit from increased focus, increased engagement, and true process ownership. In some cases, advanced analytics can be applied to use machine learning and artificial intelligence models for decision-making. New applications of advanced analytics emerge daily and are limited only by the collective imagination, but in large spreadsheet processing plants, “small” automation efforts aimed at improving control and realizing efficiencies and cost savings one process at a time will come to the fore. Self-service data analytics tooling can enable these efforts and will largely be the focus of this book.