What is Hybrid Process Mining?
As organizations look for ways to streamline their operations, one solution gaining attention is hybrid process mining. According to Fortune Business Insights, process mining is experiencing significant growth, with the global process mining software market set to reach USD 15,546.4 million by 2029.
Hybrid process mining is defined as a technique that combines process mining approaches, including traditional process mining, cognitive process mining, and machine learning-based process mining. By integrating these techniques, hybrid process mining provides more comprehensive and accurate insights into business processes.
Learn More: What is Process Mining?
Hybrid Process Mining: An Overview
Traditional process mining techniques involve analyzing event logs to discover process models, evaluate performance, and identify bottlenecks. However, these techniques have limitations, such as difficulty handling complex and unstructured data, which can result in inaccurate findings.
To address this issue, organizations turn to automation and task mining to streamline their processes. Task mining involves capturing and analyzing user interactions with software to identify areas for automation and process improvement.
In addition, cognitive process mining is becoming increasingly popular as it involves analyzing event logs using natural language processing and semantic analysis to uncover hidden information about business processes. By applying cognitive process mining, more accurate and detailed insights into business processes are obtained.
Machine learning-based process mining is another technique that uses machine learning algorithms to analyze event logs and discover patterns in the data. This technique can provide more accurate and reliable results than traditional process mining approaches.
Understanding Hybrid Process Mining
Hybrid process mining is valuable for organizations looking to implement business process automation. By identifying process bottlenecks and areas for improvement, hybrid process mining can help organizations streamline their processes and identify which tasks to automate.
Here’s how it works:
Step 1: Preprocessing Event Logs
The first step in hybrid process mining is to process event logs and remove noise and irrelevant data. This step helps to ensure that the analysis is based on reliable and relevant data.
Step 2: Analyzing Data Using Multiple Techniques
Traditional process mining helps identify process flows and bottlenecks, while cognitive process mining uncovers hidden information. And machine learning-based process mining identifies patterns in the data.
Step 3: Combining the Results
The results of each analysis technique are combined to create a more comprehensive and accurate picture of the business processes. This provides a deeper understanding of the processes and helps to identify areas for improvement in achieving Intelligent Process Automation (IPA).
Benefits of Hybrid Process Mining
- Detailed Insights into Business Processes
Hybrid process mining provides more accurate and detailed insights into business processes than traditional process mining. Combining multiple techniques offers a comprehensive understanding of process behavior and performance, enabling businesses to achieve successful automation outcomes.
- Improved Decision-Making
By providing more accurate and detailed insights into business processes, hybrid process mining can help organizations to make more informed decisions about process improvements and resource allocation.
- Monitoring Process Performance Over Time
By analyzing event logs, hybrid process mining can identify changes in process behavior and performance. This helps to identify areas where process improvements are needed and supports continuous process optimization for automation.
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