What is Process Mining? See how your processes actually run.
Process mining is a data-driven discipline that uses event logs from business information systems to discover, monitor and improve processes. Where traditional analysis relies on interviews, workshops and assumptions, process mining reads the timestamps your systems already record and reconstructs the real flow of work. The result is a factual picture of every variant, every bottleneck and every loop that manual analysis tends to miss.
The three types of process mining (discovery, conformance checking and enhancement) and the three mandatory event log fields (case identifier, activity, timestamp) are definitions from the discipline itself. The six pillars are the sections of this article. Everything starts with one artefact: the event log.
Six pillars of process intelligence
From discovery to AI-driven automation, these six techniques make up the modern process mining toolkit.
Process discovery
Reconstruct a process model straight from raw event data, with no prior assumption about how the work is supposed to run. Discovery shows the real flow, including every alternative path, every loop and every exception that never made it into the documentation.
Conformance checking
Compare what actually happened against the reference model or the policy that is supposed to apply. Conformance checking names the deviation per case, so a control finding becomes a list of specific cases instead of a general concern.
Process enhancement
Enrich the discovered model with performance data: throughput time per step, waiting time between steps, cost per case and resource utilisation. This is the layer that turns a flow chart into a ranked list of where the time and the money actually go.
Predictive analysis
Models trained on historical cases estimate the likely outcome and remaining duration of the cases that are still open. That moves the conversation from reporting last month to intervening in the case that is about to breach its deadline this week.
Task mining
Record interaction at the desktop level, such as clicks, keystrokes and screen switches, to see the detail inside a single activity. Task mining explains the step that the event log can only measure: the copying between two screens that makes one approval take twenty minutes.
AI-driven automation
Combine the mined picture with AI to rank automation candidates by volume, variation and handling time, and to say which of them suits rule-based automation, which suits an agentic system, and which simply needs the process redesigned instead.
The process mining pipeline
From raw event logs to continuous optimisation, these are the five stages of process intelligence.
Five steps to process intelligence
A pragmatic route to getting process mining working inside your organisation.
Define goals and scope
Start with the business question, not the data. Pick one process that matters, agree what a good answer looks like, and name the decision the analysis is meant to inform. Scope discipline here is what keeps a mining project from turning into a data project.
Extract and prepare the event data
Connect to the source systems, pull the event log, and settle the hard questions: what counts as a case, which tables hold the timestamps, and how to handle events recorded in bulk overnight. Expect this step to take the largest share of the effort.
Discover and analyse
Run discovery to map the real flow, then look at variants, waiting times and conformance gaps. Walk the findings through with the people who own the process: they explain the exceptions, and their explanations decide which gaps are worth fixing.
Implement the improvements
Translate insight into change: remove a handover, reorder two steps, fix a system configuration that forces rework, or automate a repetitive task. Change one thing at a time so the event log can show you what each intervention did.
Monitor and scale
Put the mined metrics on a dashboard that refreshes from the same pipeline, so regression is visible rather than rediscovered. Once one process runs on live data, the extraction work for the next process becomes a fraction of the first.
Continuous optimisation
Process mining earns its keep as a standing capability, not a one-off study. Keep the pipeline running, add prediction where the volume justifies it, and let each cycle of measurement set the agenda for the next round of change.
Everything about process mining
Process mining is a data-driven discipline that uses event logs from business information systems to discover, monitor and improve processes. It shows how work actually flows through an organisation, including the variants, bottlenecks and rework that interview-based analysis tends to miss.
A discovery algorithm reads the event log, groups all events that belong to the same case, sorts them by timestamp and derives the sequences that occur in practice. Those sequences are then merged into a single process model in which every path, loop and waiting time is visible and countable.
Three fields are mandatory: a case identifier that says which process instance an event belongs to, an activity name that says what happened, and a timestamp that says when it happened. Optional fields such as the resource who performed the step, the department, the amount or the customer segment make the analysis considerably richer.
Business intelligence aggregates data into reports and dashboards that show what the numbers are. Process mining reconstructs the sequence of events, so it shows how the result came about: which route a case took, where it waited, how often it looped back and where it deviated from the agreed way of working.
Any sector in which work leaves a digital trace. It is used most widely in financial services, healthcare, logistics, manufacturing, insurance and government, because those organisations run high volumes of structured cases through ERP, CRM and service management systems that already record timestamps.
With a single well-scoped process and accessible source data, a first usable analysis is realistic within four to six weeks. Data extraction and cleaning normally take longer than the analysis itself, so the quality of the source systems is the main factor in the timeline.
Cost depends on scope, the number of processes in play and the tooling you choose. Cloud-based platforms with consumption-based pricing have made a single-process pilot affordable, and open-source libraries make a first exploration possible with no licence cost at all. Budget for data engineering effort as well as for the tool.
Yes, and that is where most of the current value sits. Machine learning adds predictions for cases that are still running, automated root cause analysis across hundreds of variants, anomaly detection in near real time, and a ranked shortlist of steps that are worth automating with rule-based automation or agentic AI.
Process mining reads event logs from business systems and maps the end-to-end flow between departments. Task mining records desktop interactions such as clicks, keystrokes and screen switches, and maps what a person actually does inside one activity. Process mining shows where the time goes, task mining shows why that step takes so long.
Set a baseline from the event log before you change anything: throughput time per case, rework rate, number of variants, cost per case and share of cases that follow the agreed path. Those five figures come out of the same data you already mined, so the effect of every intervention can be measured against the starting point rather than estimated.
Ready to see what your processes really look like?
W69 AI Consultancy helps organisations put process mining and AI-driven process intelligence to work, from the first event log extraction to a pipeline that keeps measuring after the project ends. W69 AI Consultancy is ISO/IEC 27001:2022 certified for its management system for AI advisory and implementation consultancy.
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Also available in Dutch: Wat is Process Mining? and the service page Process Intelligence & Automation.