Requirements for Process Mining Variante 2
Process Mining is a technology designed for process modeling, analysis, and optimization. By fully visualizing and evaluating business processes, important data is no longer overlooked but instead used to enhance process optimization. This helps companies boost their productivity and efficiency. Despite advanced and user-friendly Process Mining tools, implementing and using Process Mining is far from straightforward. This page outlines the requirements and challenges for Process Mining. The information applies to all methods of Process Mining, including traditional Process Mining and Object-Centric Process Mining (OCPM).
Digitized Process Flow as the Key Requirement
Since Process Mining tools work with digital data, digitized process flows are essential for their applicability. Business processes are considered digitized when process data is collected digitally from start to finish via software applications (e.g., SAP) and all process steps are captured.
Even if companies have partially analog business processes, Process Mining can still be applied. In such cases, analog processes are excluded, and the application focuses solely on digital processes.
Digitized processes are the foundational criterion for the applicability of Process Mining technology. Once such processes exist, the following additional requirements must be met for efficient and error-free use of Process Mining:
- Availability of Process Mining software
- Implementation of the software within the company
- Customization of the software to specific business processes
- Integration of the software with relevant systems
- Training of users and IT specialists in the company
In summary, the core requirement is that processes must first run digitally (via IT systems). These IT systems are then linked to a Process Mining software tailored to the company to reconstruct the processes.
Clear Identification and Delimitation of Processes
To reconstruct processes accurately and generate qualitative data in Process Mining, clear identification and delimitation of processes are necessary. This requirement is fulfilled through the following three aspects:
- Assignment of an identification criterion (e.g., part number in a production process; order number in an order process)
- Timestamping for the correct chronological mapping of individual steps in the business process
- Clear naming of the process (e.g., placing an order; sending an order)
Using these three attributes for individual process activities, event logs can be created, which are then captured by the Process Mining tool. Event logs are systematic tables that include these three mandatory attributes for process activities and other optional categories.
Once business processes are clearly distinguishable from unrelated activities within the company, there is no risk of collecting incorrect data, which could impair or even make process analysis impossible. A clearly defined start and end to business processes enables comprehensive process analysis, such as calculating process cycle times. The collected data facilitates precise analysis of the current state and helps identify a broader range of optimization potential.
Confidential Handling of Personal Data in IT Systems
A key legal consideration in Process Mining is data protection. Without the consent of individuals, no personal performance data may be collected. If individuals and their contributions are part of processes, their roles must be anonymized.
Potentially collected data includes personal data, activity data, and interaction data during business processes. It is important to inform employee representatives about the type of personal data being collected in Process Mining. Solutions should focus on the following:
- Limit analysis of personal data solely to the relevant processes.
- Ensure anonymization of data, such as using roles or permissions instead of employee names. Data collection and analysis should then focus on roles or permissions.
- Only collect personal data that is truly relevant to the processes.
Process Mining, as part of digital transformation and a method for process optimization, entails changes for the workforce. These changes should be communicated early through open change management, gradually preparing employees for the adjustments. Supporting employees in using Process Mining data to analyze and optimize processes is crucial. Providing direct access to this data increases transparency and empowers employees to address issues independently based on newfound process knowledge.
Competencies Required for Process Mining: Achieving the Target Process
To optimize current processes and achieve target processes through Process Mining, users need specific competencies. These are grouped into three key areas:
Analytical Competencies
For qualitative analysis, skills in visualizing and interpreting generated data are necessary. A broad knowledge of statistical models is also advantageous.
Technological Competencies
Successful application of Process Mining requires technological skills, including data modeling, data provisioning, and understanding data flows within the IT landscape. This includes proficiency in using the Process Mining tool. Technological competencies are often a challenge during implementation. To address this, we at mpmX provide comprehensive training, covering data analysis with our software and deriving actionable measures for process improvement.
Organizational Competencies
Lastly, it is crucial to integrate analytical and technological competencies with the company’s organizational aspects. Process improvement can only be achieved through data analysis and optimization tailored to the company. Implementing changes without considering their impact on the organization risks failure. Companies must account for system component interactions and potential effects of process changes.
Example of Process Mining Implementation and Application
After selecting a Process Mining software, the provider integrates it into the business processes based on agreements with the company. All workflows to be modeled and analyzed are documented within the software.
Pilot projects can help evaluate whether Process Mining is suitable for the company’s specific processes. These pilots assess the applicability and value of Process Mining. If successful, workflows are documented in the Process Mining tool, and implementation begins.
During workflow documentation, processes are clearly named to collect all relevant process data. Software integration with the company’s systems and data preparation typically takes about two weeks. During or before this time, employees and IT specialists can be trained in using the software and adapting to company changes.
FAQ: Questions and answers on the topic of “Process Mining and its requirements”
OCPM enables cross-process modeling of business processes, uncovering optimization potential at the interfaces of multiple processes. Classical Process Mining, on the other hand, is focused solely on analyzing and optimizing individual processes with a clearly defined beginning and end.
Yes. We are happy to assist in extending conventional Process Mining to a system with OCPM. Using an appropriate tool, the transition from traditional Process Mining to the more advanced and comprehensive OCPM becomes possible.
Business Intelligence is used to measure the performance of the entire company, using automated data collection similar to Process Mining. However, Business Intelligence assumes that there is no need to optimize the business processes themselves.Ultimately, the use of both solutions is recommended: Process Mining for visualizing and analyzing process data, and Business Intelligence for optimizing company performance based on various KPIs, some of which are influenced by processes.
Yes, our BI tools can be used even if another BI tool (e.g., Qlik, Power BI, Tableau) is already in use. However, for better synergy between tools and seamless automation, it is recommended to use tools from a single provider.
Compared to Data Mining, Process Mining offers several advantages. It not only uses static data for analysis but also examines how data is generated during processes. Deviations from the target process can be identified in real time with Process Mining, unlike Data Mining.
Learn more about Process Mining
Discover how Process Mining can revolutionize your business operations. By analyzing real-time data from your existing systems, Process Mining uncovers inefficiencies, optimizes workflows, and provides actionable insights to streamline processes. Whether you're looking to reduce costs, improve compliance, or enhance overall performance, Process Mining empowers you to make data-driven decisions. Take the first step toward transforming your operations—explore the power of Process Mining today!
