Читать книгу Self-Service Data Analytics and Governance for Managers - Nathan E. Myers - Страница 14

However, a game-changing add-on is now available. Data analytics is coming to the fore and emerging as an exciting strategic and tactical enabler of higher-order analysis and value creation through insight generation. Data analytics includes a number of analytics tools, technologies, and buzzwords you have heard thrown about over the last decade (perhaps not at parties or on date night, but you have heard them nonetheless): robotic process automation (RPA), machine learning (ML), artificial intelligence (AI), text mining technologies like natural language processing (NLP), optical character recognition (OCR), and intelligent character recognition (ICR), along with neural networks, logistic and linear regression analysis, and many more. At the most basic level, these are tools meant to enable descriptive techniques to understand past events and drivers, to gain knowledge through the extraction of trends, to forge more intelligent processing steps, and to inform decisions. Analytics at its sexiest enables predictions to be made and even for actions to be recommended, in response to scenarios encountered.

The ability to detect anomalies in financial data, the ability to detect and flag high-risk patient test results for further review by medical practitioners, and any of a host of proven use cases for descriptive and predictive analytics – these represent huge opportunities to employ data analytics. In many cases, these are enabling models run outside of core technology systems. Data science and data analytics encompass these and many other solutions. It is not always a theoretical predictive rocket science employing complex code-based technology and advanced algorithms to solve complex problems. It is the body of solutions that organizational leaders need to cultivate and have at hand to forge forward in their digital journey, at whatever pace is appropriate to meet digital transformation goals and objectives. This pursuit has led to billions of dollars of investment in technology across many service industries, aimed at building core competencies, increasing competitive advantage and organizational efficiency, doing more with fewer employees, or reducing employee costs and footprint.

It is this last goal that the authors predict will prompt a surge in adoption of data analytics tooling in the next five years, across medium-to-large scale enterprises. Managers are looking to structure their spreadsheet-based processes in a more mature and robust way. By reducing the amount of unstructured processing performed manually in Excel, managers can stabilize and lock down spreadsheet-driven processes into more automated, repeatable, structured, and time-efficient processing steps. By minimizing time spent in performing routinized processing steps, and by minimizing process variance through emulating system processing, the spreadsheet-based jobs of the past will evolve to remove the least value-added steps in the processing chain. While this book cannot but acknowledge many of these data analytics capabilities and technologies, its focus will be around one subset of the wide body of data analytics disciplines – self-service data analytics.

See Exhibit 1-1, which illustrates how productivity can be built through the reduction of time spent for the performance of routinized processes. This can be achieved by enlisting self-service analytics capabilities to address many of the routine steps performed daily, which are highlighted in the list at the left of the diagram. Remember that it is realistic to assume that there will always be some measure of a manual processing tail that is expensive or even impossible to eliminate, but the idea is to move as far to the right along the continuum as possible. The end result is to recapture processing time spent on low value-added steps, to allow for a greater proportion of the day to be spent on value creation.

EXHIBIT 1-1 Building Daily Productivity