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TDWI Upside - Where Data Means Business

How Process Mining Can Aid Your Digital Transformation

New data sources and the spread of real-time data analysis are leading to increased interest in optimizing human and electronic processes.

About 100 years ago, the era of scientific management began with time and motion studies by such notables as Frederick Taylor, and Frank and Lillian Gilbreth. The purpose of these turn-of-the-century studies was to optimize tasks -- from bricklaying to factory floor operations -- to create ideal and efficient human processes. Since those early studies, process optimization has developed significantly and is now poised to come into greater prominence in an era of vastly expanded process understanding, greater computational capabilities, new forms of analysis, and vast new information sources. It will become a core discipline of digital transformation.

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Process mining -- the capability to delve into electronic processes with a fine level of detail and optimize both human and electronic components -- has only recently emerged as a separate discipline. It derives from a confluence of process improvement initiatives and advancing analytics through the work of Dutch scientist Wil van der Alst.

Process mining is closely linked to business process management (BPM) but is concerned more with current processes and how they are actually being performed than with process models. It takes readily available logs from electronic process management systems and analyzes this potentially vast amount of data to determine how current processes are actually performed, whether they deviate from the models meant to govern them, and whether structural improvements can be made to those models in order to provide more efficient or more effective outcomes.

As real-time data analysis becomes prevalent, the capability to perform deep analysis of business processes and draw substantive benefit improves. Process mining can now incorporate a big data approach and optimize data through real-time resources such as transaction logs and recorded operations. Despite being relatively new, it is growing in importance as real-time analysis and big data solutions become more common -- and as emerging analytics techniques based on AI and machine learning begin to influence the science of optimizing outcomes.

Process mining resides within a galaxy of process-related tools including business intelligence, business activity monitoring, complex event processing, and corporate performance management, plus quality-related initiatives such as CPI, BPI, TQM, and Six Sigma. By looking at current processes, potentially in real time, process mining can be a strong aid for all of these process-related tools. Backed by artificial intelligence, such a system can create a backbone for continuous improvement as well as providing advance warning when processes might be subject to new pressures or requirements or when they fail.

Fit for Digital Transformation

The links between process mining and digital transformation are compelling. It has long been understood that effective business change depends on current knowledge of actual performance. The finer the detail, the better. Optimization of business processes is a critical goal of digital transformation. Insights from process mining that are advantageous to digital transformation include:

  • Improved ability to bring processes together by understanding components, behavior, blockages, and interactions between them
  • Better visualization of composite processes to determine overlaps and potential roadblocks
  • Better understand the effects of dynamic environments and how changes are affecting actual performance of digital processes
  • Increasing agility to ensure process changes are understood and can be accommodated

Process mining brings the efficiency goals of the past century into today's digital context, providing an important tool for bringing human and digital processes together and evaluating how to reconcile the two for a more efficient and effective result. For digital transformations, it can help you set goals and prioritize targets, and can provide a means for continual improvement.

Development Trajectory

Process mining, like analysis of the Internet of Things, depends on immediate access to logs and real-time analysis of logs and other digital outputs from management systems. Although very different in their approach and content, the methodology of creating continuous analysis of log data that is common to both process mining and the Internet of Things ensures that this area will evolve quickly.

Log analysis is a feedback system that can provide a virtuous cycle of improvement. Such a cycle, when applied to business processes, is likely to provide extremely valuable benefits but also require a high degree of digitization. Logs must be created, processed, and stored. More data must also be created from digital, hybrid digital/human, and robotic processes so that they can be effectively analyzed.

Current interest in process mining has been raised by leading analysis firms including Gartner and IDC, and there are early software platforms available, such as Celonis (founded by an der Alst) and Fluxicon. This approach is still relatively recent, but it does offer a new way to consider business process optimization as we move into a complex era of hybrid human/machine operations and the digitization of everything.

About the Author

Brian J. Dooley is an author, analyst, and journalist with more than 30 years' experience in analyzing and writing about trends in IT. He has written six books, numerous user manuals, hundreds of reports, and more than 1,000 magazine features. You can contact the author at [email protected].

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