Process Mining – Definition, Use Cases and Best Practices at a Glance
Process mining uses digital event data from systems like ERP, CRM or ticketing to make real process flows visible. It shows the difference between target and actual process and uncovers bottlenecks and automation potential.
Process Mining: Definition & Benefits | Glossary
How a process should run is often in a diagram – how it really runs only becomes clear when you look into the data.
Process mining does exactly that: from the digital traces in ERP, CRM or ticketing, it reconstructs the actual flow, including loops, waiting times and workarounds. This turns gut feeling into a fact-based basis for improvement and automation.
This glossary entry for Process Mining gives you a clear Definition, practical Use Cases and Best Practices at a glance – with examples, pros and cons, and FAQs.
What is Process Mining?
- Process Mining – Process mining uses digital event data from systems like ERP, CRM or ticketing to make real process flows visible. It shows the difference between target and actual process and uncovers bottlenecks and automation potential.
Process mining is a data-based method that makes real business processes visible based on digital event data. The basis is the traces that operations leave in systems such as ERP, CRM, online shop, ticketing or specialist applications – such as timestamps, statuses and activities.
From this event data, process mining reconstructs the actual flow (actual process) and contrasts it with the desired flow (target process). This makes bottlenecks, loops, waiting times, deviations, variants and automation potential visible.
Process mining clearly differs from related topics: RPA automates tasks, business intelligence visualises key figures, classic process consulting usually works with interviews and workshops. Process mining, by contrast, provides an objective, data-based view of the real flow.
It is often used as a precursor to workflow automation, API integration or RPA because it shows where automation brings the greatest benefit. Related topics are automation, data pipelines, ETL processes and ROI.
How does Process Mining work?
Process mining begins with extracting the event data from the systems involved. This data is cleansed and brought into a uniform format – often via ETL processes or data pipelines. Each operation is described by a unique identifier, activities and timestamps.
From this data, a process mining tool reconstructs the actual process flow and visualises it as a process graph with all occurring variants.
Then anomalies are analysed: where do waiting times arise, where are steps repeated, which workarounds deviate from the target, where do manual interventions accumulate. From these insights, targeted measures can be derived – such as process improvements, automation via workflow tools, API integration or RPA.
Data protection and a clean data base matter: only with correct, complete event data does process mining deliver sound results. Repeated analyses show whether implemented measures actually work.
Practical Examples
An analysis of the order process shows that approvals regularly sit for days and form a bottleneck.
In the invoicing process, frequent correction loops become visible that stem from unclear inputs.
Process mining reveals that a supposedly standardised process runs in many variants in reality.
Before an RPA project, the analysis shows which steps truly suit automation.
After a process improvement, a renewed analysis proves that cycle times have measurably dropped.
Typical Use Cases
Making real process flows visible from ERP, CRM and ticketing data
Detecting bottlenecks, loops and waiting times
Uncovering automation potential before RPA or workflow projects
Comparing target and actual process for process improvement
Identifying process variants and deviations
Verifying the success of implemented process and automation measures
Advantages and Disadvantages
Advantages
- An objective, data-based view of real processes instead of assumptions
- Uncovers bottlenecks, loops and waiting times based on facts
- Shows where automation brings the greatest benefit
- A good basis for ROI considerations of improvements
- Success control possible through repeated analyses
Disadvantages
- Requires available, complete and clean event data
- Data protection and access rights must be carefully observed
- Data extraction and preparation can be effortful
- Results require professional interpretation, not just visualisation
- Without subsequent measures, the analysis remains inconsequential
Frequently Asked Questions about Process Mining
What is process mining?
Process mining makes real business processes visible based on digital event data from systems like ERP, CRM or ticketing. It reconstructs the actual process, compares it with the target and uncovers bottlenecks and automation potential.
How does process mining differ from RPA?
Process mining analyses and visualises how processes actually run. RPA automates concrete tasks. Process mining is often used as a precursor to identify which steps suit RPA or other automation.
How does process mining differ from business intelligence?
Business intelligence visualises key figures and evaluations. Process mining reconstructs the temporal flow of processes including variants, loops and waiting times. It is about the process flow, not just key figures.
Which data does process mining need?
Digital event data from the systems involved: a unique operation identifier, activities and timestamps. This data must be available, complete and clean, and data protection must be observed.
When is process mining worthwhile?
Especially before automation and improvement projects, for unclear or grown processes and when bottlenecks or inefficient flows are suspected. It provides a fact-based basis for targeted measures.
Direct next steps
If you want to apply or evaluate Process Mining in a real project, start with these transactional pages:
Process Mining in the Context of Modern IT Projects
What this glossary entry gives you
This page gives a concise definition of Process Mining. You also get practical use cases and best practices at a glance.
You can use it to evaluate the technology for your next project. Process Mining sits in the domain of Automation. It plays a significant role across many IT projects.
Look beyond isolated technical merits
When you judge whether Process Mining is the right fit, look beyond isolated technical merits. You should weigh the full project context.
Consider the following factors:
- Existing team expertise
- Current infrastructure
- Long-term maintainability
- Total cost of ownership (TCO)
Drawing on our experience from over 250 software projects, we have found that correctly positioning a technology or methodology within the broader project context often matters more than its isolated strengths.
How we help you decide
At Groenewold IT Solutions, we have worked with Process Mining across multiple client engagements. We know its advantages and the typical challenges during adoption.
If you are unsure whether Process Mining suits your requirements, ask us for an honest, no-obligation assessment. We analyze your situation. We recommend the approach that delivers the most value. We may suggest an alternative solution if that fits better.
Where to go next
For more terms in Automation and related topics, open our IT Glossary.
For concrete applications, costs and processes, use our service pages and topic pages. There you will see many of the concepts from this entry applied in practice.
Related Terms
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