Knowledge of operational processes is an important factor in being able to optimize them. However, processes are often insufficiently documented or there is a discrepancy between the everyday process performed and the record. Process mining can help to better understand and optimize the processes in the company.
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What is Process Mining?
Process mining is a technique that uses existing data to reconstruct and evaluate business processes. Existing data from operational IT systems is used to reconstruct and analyze business processes. The method focuses on implicit process knowledge in order to define, combine and visualize process steps. The goal of process mining is to recognize a process end-to-end with all its steps, interrelationships and dependencies, to compare it with the desired process and to work out how it should actually be or where it can be optimized.
Process Mining vs. Data Mining
Data mining is an analysis technique similar to process mining that analyzes large amounts of data to provide a better understanding. However, data mining focuses on empirical relationships, cross-connections, and trends when analyzing the data and provides individual metrics and point-by-point results. Process mining, on the other hand, analyzes how the data was created and can therefore also make predictions and identify deviations in real time. Process mining makes entire company processes visible and enables the identification of problems and optimization potential.
5 common tools for process mining
Celonis is one of the most popular process mining platforms and offers a wide range of features, including process visualization, process optimization and predictive analytics. The platform is easy to use and can be implemented quickly.
Minit provides an intuitive user interface to visualize and optimize processes. The platform can combine data from various sources and offers functions such as process analysis, process comparison and process optimization.
Disco is a user-friendly process mining software that offers a wide range of features, including process visualization, process analysis and process optimization. The platform also supports a wide range of data sources and can be implemented quickly.
ProcessGold offers a comprehensive suite of functions to visualize, analyze and optimize business processes. The platform supports a wide range of data sources and provides a user-friendly interface.
QPR ProcessAnalyzer offers a comprehensive range of functions to visualize, analyze and optimize business processes. The platform supports a wide
range of data sources and offers integrated process automation and optimization. The platform is easy to use and can be implemented quickly.
The role of data quality
The quality of the data is an important factor when carrying out a process mining project. It is important to think about the data in advance and to understand it including all contextual information. The relevant data must be selected and prepared, which often requires linking data from different sources. Only in this way is it possible to draw the right conclusions from the model at the end and to make fact-based decisions, conclusions or recommendations.
Improving data quality through Document AI
To further improve data quality, the application of Document AI (Artificial Intelligence) can be useful. Document AI is a technology that uses machine learning and artificial intelligence to analyze, extract and classify documents and data.
By applying Document AI, documents can be automatically recognized and analyzed to extract relevant information and structure it in a consistent way. The technology can help merge and cleanse data from disparate sources to ensure higher data quality.
Document AI can be used, for example, to automatically capture and process invoices, delivery bills, purchase orders or contracts. The technology can extract the relevant data such as customer name, amount or payment terms from the documents and structure them in a uniform form. This avoids errors during manual entry and increases data quality.
When combined with process mining, Document AI can help improve the quality of data entered into process mining software. Through the automatic Extraction and structuring of the data, errors and inconsistencies are avoided, leading to better results and insights.
Overall, the application of Document AI provides an opportunity to improve data quality, laying the foundation for successful process mining projects.
Phases of process mining
A process mining project comprises several phases. In the first phase, the project is planned and the relevant data is selected. In the second phase, questions are defined and a model is created. In the third phase, the event logs are related to the model and the model is adjusted if necessary. In the fourth phase, additional factors are added to the model and finally the model can be used to support predictions of events, planning of interventions or recommendations.
Process mining is an important approach to understanding and optimizing business processes. The technique uses existing data to reconstruct and analyze business processes. There are now many software vendors offering process mining tools. Common tools include Celonis, Minit, Disco, ProcessGold and QPR ProcessAnalyzer. When carrying out a process mining project, it is important to think about the quality of the data in advance and to select and prepare the relevant data. Only in this way is it possible to make fact-based decisions, conclusions or recommendations.