What is process mining? definition and use cases

Process mining is an analytical technique that extracts knowledge from event logs in information systems to discover, monitor, and improve real business processes by reconstructing them from system data rather than relying on stakeholder assumptions or documentation. The technique builds a visual map of workflows, identifying deviations, bottlenecks, and inefficiencies, and it informs automation strategies by showing the actual process flow. With a market projected to reach $1.9 billion by 2025, process mining is primarily led by companies like Celonis, which process vast amounts of data for organizations such as Siemens and BMW. Process mining consists of three core capabilities: process discovery, conformance checking, and process enhancement, which together help bridge the gap between intended and actual process flows. AI enhances process mining by providing root cause analysis and predictive monitoring, offering insights into delays and future outcomes. Enterprise platforms like Celonis and SAP Signavio provide comprehensive process mining solutions, while open-source tools such as PM4Py cater to data engineering teams, and AI-assisted platforms like CodeWords offer customizable analysis without requiring extensive coding knowledge. By starting small, businesses can use process mining to uncover unexpected insights into their workflows and optimize automation efforts effectively.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.