Digital innovation requires enterprises to learn how to understand, manage and change increasingly complicated processes. A new generation of process mining tools promises to make it easier to automatically interpret the digital exhaust of modern enterprises to help improve decision-making, drive innovation, and offer new products and services. "By understanding how processes really operate, companies can create operational fluidity to drive more efficient and productive operations that create better customer experiences," said Alexander Rinke, CEO and co-founder of Celonis, a process mining platform based in Germany. "Instead of simply identifying areas of friction, AI will further evolve process mining by allowing businesses to implement recommended changes with employees, enhancing productivity while also saving resources." The core idea of process mining lies in finding new ways to create and calibrate models of how things work with event logs.
Over the last decade, process mining emerged as a new scientific discipline that provides the missing link between process-oriented (BPM, WFM, etc.) and data-oriented (BI, DM, ML, KDD) approaches. Process mining techniques can be used to learn process models, check compliance, and identify and understand bottlenecks, inefficiencies, deviations, and risks. The movie shows what process mining is, and how it works, in less than 2 minutes! Also see the process mining website (www.processmining.org),
Process mining is the art of turning data into meaningful process models of reality. With only a few mouse clicks, process mining can uncover hidden information about the processes and generate new insights. Process mining is based on event data. Event data can be anything that has a discrete time connected to it, that is, one can determine when something has happened. This is essential to model causality.
Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems).