In an age where technologies like AI and ML are playing a significant role in process analytics, it’s imperative that companies leverage these evolving tools to optimize efficiency. Process mining does just that; it helps improve the performance of real processes by identifying bottlenecks and other areas of improvement.

What is Process Mining?

Process mining is a process analysis method that aims to discover, monitor, and improve real processes. It functions by extracting knowledge from available event logs in the systems of current information of an organization. The global process analytics market size is expected to grow to USD 21.9 Billion by 2030 at a Compound Annual Growth Rate of 48.5% during the forecast period.

There are three types of process mining: discovery, conformance, and enhancement. Discovery, the most widely adopted, uses event logs to create a model without outside influence; no previous process models exist to inform the development of the new process model. Conformance identifies any deviations from the expected model by comparing expectation with reality. Lastly, enhancement, sometimes known as performance mining, provides guidance to improve an existing process model. Together, they provide leaders with objective evidence, allowing for businesses to reduce costs, optimize resource allocation, and drive innovation.

Process Mining Capabilities

Process mining offers the following operational capabilities:

• Automated discovery of process models, exceptions, and instances of processes (cases) together with basic frequencies and statistics
• Automated discovery and analysis of customer interactions, as well as alignment with internal processes
• Monitoring of key performance indicators using dashboards in real time
• Compliance verification capabilities and gap analysis
• Predictive analysis, prescriptive analysis, scenario testing and simulation with contextual data
• Improvement of existing or previous process models using additional data from saved records
• Data preparation and data cleansing support
• Combination of different process models that interact with each other in a single process mining panel
• Support for the visualization of how processes contribute to business value (such as business operating models) — contextualization of processes
• Effective cooperation between Business and IT
  • For example, software developers might utilize process mining to provide IT admins with an automated documented record to oversee system progress 
• Standardization of business processes
• Improvement of operational excellence by optimizing processes
  • For example, in manufacturing, process mining can help improve allocation of resources such as storage, machines, and workers 

Identifying the processes to be improved is not simple. This process faces several threats and risks, especially in an increasingly digital environment constantly being filled with new data. This information is typically not stored in an orderly fashion. What’s more, process models within large organizations are typically of very low quality. It also is inevitable that processes change while being analyzed, adding an extra layer of complexity known as concept drift. With AI and ML on the rise, feeding these applications clean data needs to be a top priority.

If you’re interested in chatting more about process mining and its benefits, connect with our analytics experts today.

Contributions by Megan Weldon, Alexandria Hatsios, and Aaron Messer