At a glance
- Provides an objective basis for process improvements and automation initiatives.
- Based on real process data rather than on assumptions or subjective assessments.
- Three core methods: Discovery, Conformance, Enhancement.
- Applicable across industries: manufacturing, logistics, finance, e-commerce, telecommunications.
- Competitive advantage through cost savings, higher productivity, and better compliance.
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Process mining definition
Process mining refers to a data-driven analysis method for examining business processes. Based on the insights gained, these processes can be optimized. Using specialized algorithms, digital traces from IT systems are evaluated to recognize how workflows actually function. This enables companies to create transparency in order to identify and optimize bottlenecks, errors, or inefficient steps. Since process mining is based on real data, it provides an objective foundation for well-founded decisions and lays the groundwork for automation initiatives.
By integrating process mining with business intelligence solutions such as DeltaMaster from Bissantz, process analyses can be seamlessly combined with powerful visualization and control capabilities. This enables companies not only to recognize process bottlenecks but also to intuitively derive data-based measures for optimization.
„Using DeltaMaster, we translate the insights from process mining into control-relevant KPIs. This way we detect variances in order processing directly and can take targeted corrective action.“Head of Controlling
Industrial company
How does process mining work?
In summary, process mining works by analyzing digital traces from IT systems such as ERP and CRM software in order to reconstruct and visualize real business processes. The specific process mining steps are:
- Data capture: Business processes leave digital traces in IT systems such as ERP or CRM software. This transaction data is stored in event logs and contains information such as timestamps, processors, and lead times.
- Data extraction: A process mining software reads the event logs from the various IT systems and prepares them for analysis.
- Data analysis: Using mathematical and statistical methods, patterns and relationships in the process flow become recognizable based on the collected data.
- Process visualization: The actual flow of a business process is presented graphically so that companies get a clear overview of their processes.
- Identification of problems: By analyzing real process flows, bottlenecks, inefficient process steps, and, where applicable, deviations from target processes become visible, so that potential for improvement can be identified.
- Optimization & monitoring: Based on the insights, measures for process improvement can be developed and the optimized process can be continuously monitored.
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What are the different types of process mining?
There are three types of process mining: Discovery for the initial modeling of a process, Conformance for checking alignment with target processes, and Enhancement for optimizing existing workflows. More precisely, the three main types of process mining can be described as follows:
- Discovery (process discovery): This method serves to reconstruct process models from existing event log data and present them visually. There are no predefined models; instead, the mining is performed exclusively based on the recorded process steps. This allows companies to gain transparency about real workflows for the first time.
- Conformance Checking: Here, real process flows are compared with an existing target model. The goal is to uncover deviations and inefficiencies in order to identify weaknesses. This method is often used to check compliance requirements or to detect process deviations in practice.
- Enhancement (process optimization): This type of process mining builds on existing process models and uses event log data to recognize and realize optimization potential. This can be done by adjusting workflows or automating inefficient steps. Bottlenecks, redundant steps, or unnecessary waiting times can thus be reduced to make the process more efficient.
What is the difference between data mining and process mining?
Data mining and process mining are both data-based analysis methods, but they differ in their focus and application. Data mining refers to the automatic identification of patterns, trends, and relationships in large data sets, for example to detect patterns and make predictions. It is used in areas such as customer behavior, fraud detection, or market analyses. Process mining, on the other hand, focuses on the reconstruction, visualization, and analysis of real business processes based on event data (event logs) from IT systems.
So while data mining extracts general insights from data, process mining delivers detailed insights into operational workflows and supports targeted process improvements. Because of these differences, companies use both methods to make data-driven decisions and increase their efficiency.
| Characteristic | Process Mining | Data Mining |
|---|---|---|
| Focus | Reconstruction and analysis of real business processes | Identification of patterns and relationships in data sets |
| Data basis | Event logs from IT systems (ERP, CRM) | Structured and unstructured data sets |
| Result | Process models, bottlenecks, deviations | Patterns, predictions, segmentations |
| Typical applications | Process optimization, compliance checking | Customer behavior, fraud detection, market analyses |
| Goal | Insights into operational workflows | General insights from data |
What are the advantages and disadvantages of process mining?
Process mining provides companies with in-depth insights into their workflows and enables data-based optimization of business processes. As a result, companies benefit from numerous opportunities, but they also face challenges. The specific advantages and disadvantages of process mining include, for example:
Advantages of process mining
- Transparency: Process mining shows the actual flow of processes and makes optimization potential visible. By integrating with business intelligence tools such as DeltaMaster, companies can further increase transparency and transfer process mining insights into interactive reports and dashboards.
- Efficient analyses: Processes are analyzed automatically, which is faster, more cost-effective, and more accurate than manual and traditional methods such as workshops or interviews.
- Optimized use of resources: Bottlenecks, delays, redundancies, and inefficient process steps can be identified and eliminated through process mining.
- Objective decision-making: Decisions are based on real data instead of subjective assessments, which makes process flows more transparent and traceable.
- Better compliance: Deviations from defined processes are detected early, which helps to better comply with regulatory requirements.
- No system change required: Process mining can be integrated into existing IT systems without the need for a complete system change.
Disadvantages of process mining
- Demanding implementation: The integration into existing IT systems, particularly with regard to data formats and interfaces, requires technical know-how and careful data preparation.
- Complexity in large companies: With highly branched processes, the analysis and modeling can be time-consuming.
- High demands on data quality: Error-free analyses require accurate, complete, and correct event logs, which can be a challenge in practice.
- Limited capture of manually performed activities: Processes outside digital systems often remain unconsidered in process mining.
- Interpretation effort: The insights gained must be interpreted correctly in order to derive meaningful measures.
Despite some challenges, process mining is a powerful method for optimizing company processes and developing them further on a data basis.
Process mining examples and use cases
The method of process mining is applied across industries to make workflows more transparent, identify bottlenecks, and make workflows more efficient. Concrete process mining examples include:
Where would you like to use Process Mining?
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Bissantz and process mining
Bissantz & Company sees process mining as a valuable complement to classical business intelligence – but not as a replacement. While process mining helps reconstruct processes and make their actual workflows visible, the strength of DeltaMaster begins where process data is translated into control-relevant KPIs and action signals.
In BI projects with DeltaMaster, process mining can be used as preparation, for example to identify lead times, loops, or deviations in ERP processes such as purchasing, order processing, or invoice verification. The KPIs derived from this – such as delivery times, booking rates, or process costs per case – can then be analyzed, visualized, and monitored in DeltaMaster.
Unlike many specialized process mining tools, DeltaMaster does not offer an animated process graphic, but instead relies on typographically scaled numbers, color logic, and graphical tables to make deviations and bottlenecks directly recognizable and controllable. This combination of analytical depth, intuitive visualization, and KPI-based control makes it possible to translate the insights from process mining into operational and strategic management.
Bissantz recommends process mining where processes are complex, variant-rich, or compliance-critical – as a data-driven basis for BI-supported decision-making with DeltaMaster.
| Function | Benefit |
|---|---|
| Analyze and visualize KPIs | converts process metrics such as delivery times or process costs per case into analyzable reports |
| KPI monitoring | continuously keeps track of lead times, booking rates, and variances |
| Typographically scaled numbers and color logic | makes bottlenecks and variances directly recognizable, without animated process graphics |
| Graphical tables | present process metrics in a controllable way and combine analysis with visualization |
| Transfer into operational and strategic management | turns process mining insights into action signals relevant for management |
„We made cycle times and loops in invoice verification visible and continuously monitor the derived process KPIs in DeltaMaster. Decisions are now based on real process data instead of assumptions.“Head of IT
Logistics service provider
Frequently asked questions
What is process mining explained simply?
Process mining is like an X-ray of business processes: it reveals how workflows in a company actually function – not how they were planned on paper. To do this, digital traces from IT systems are analyzed: When was an order placed? Who approved it? How long did each step take? The result is an objective, data-based picture of reality – with visible bottlenecks, variances, and optimization potential.
What is the difference between process mining and conventional process analysis?
Classic process analysis is based on workshops, interviews, and manual observations – it shows how processes are supposed to be or how employees perceive them. Process mining shows how processes actually run – based on real system data. This eliminates subjective distortions and provides an objective, complete data basis for optimization decisions.
What data do you need for process mining?
Process mining requires so-called event logs from IT systems such as ERP or CRM. An event log contains at least three pieces of information per event: a unique process ID (e.g., order number), the activity name (e.g., “approval granted”), and a timestamp. The more complete and consistent this data is, the more precise the analyses are.
How does process mining differ from RPA (robotic process automation)?
Process mining analyzes and visualizes processes – it shows where optimization potential exists. RPA automates repetitive process steps – it implements improvements. The two methods complement each other: process mining identifies which steps should be automated, and RPA carries out the automation. Process mining is often used as a preliminary stage to RPA implementation.
Summary
Process mining makes business processes transparent by analyzing and visualizing real process data from IT systems – objectively, data-based, and without subjective distortions. The three methods Discovery, Conformance, and Enhancement cover the entire process life cycle: from initial discovery through conformance checking to continuous optimization. In combination with business intelligence solutions such as DeltaMaster, process mining delivers its greatest benefit – when process insights are not only visualized but also translated into control-relevant KPIs and action signals.