Process Mining is a field that has gained attention in recent years as a way to improve business processes.
It involves analyzing data generated by business systems to understand how processes are implemented and identifying areas for improvement. The field surfaced in the early 2000s due to the combination of several areas of research and practice.
One of the critical origins of process mining is the field of business process management (BPM). BPM is a method of optimizing and improving business processes. It uses process modeling, -analysis, and -improvement techniques to identify inefficiencies and bottlenecks in business processes. By doing this, BPM aims to simplify operations, increase efficiency, and reduce costs. Process mining builds on the foundations laid by BPM by using data to analyze business processes and find areas for improvement.
Another fundamental origin of process mining is the field of data mining. Data mining is a technique that involves using statistical and machine learning methods to obtain insights from large datasets. In the context of process mining, data mining techniques are used to analyze event logs generated by business systems such as enterprise resource planning (ERP) and customer relationship management (CRM) systems. These event logs provide a detailed record of how business processes are performed, and data mining can be used to identify patterns and trends in the data.
Overall, process mining is an interdisciplinary field that combines ideas and techniques from BPM, data mining, and distributed systems to improve business process efficiency and effectiveness. By using data-driven approaches, organizations can gain insights into their processes and identify areas for improvement, ultimately leading to better performance and increased profitability.
Reference: The business leader’s guide to process mining
Process Mining is a field that has gained attention in recent years as a way to improve business processes.
It involves analyzing data generated by business systems to understand how processes are implemented and identifying areas for improvement. The field surfaced in the early 2000s due to the combination of several areas of research and practice.
One of the critical origins of process mining is the field of business process management (BPM). BPM is a method of optimizing and improving business processes. It uses process modeling, -analysis, and -improvement techniques to identify inefficiencies and bottlenecks in business processes. By doing this, BPM aims to simplify operations, increase efficiency, and reduce costs. Process mining builds on the foundations laid by BPM by using data to analyze business processes and find areas for improvement.
Another fundamental origin of process mining is the field of data mining. Data mining is a technique that involves using statistical and machine learning methods to obtain insights from large datasets. In the context of process mining, data mining techniques are used to analyze event logs generated by business systems such as enterprise resource planning (ERP) and customer relationship management (CRM) systems. These event logs provide a detailed record of how business processes are performed, and data mining can be used to identify patterns and trends in the data.
Overall, process mining is an interdisciplinary field that combines ideas and techniques from BPM, data mining, and distributed systems to improve business process efficiency and effectiveness. By using data-driven approaches, organizations can gain insights into their processes and identify areas for improvement, ultimately leading to better performance and increased profitability.
Reference: The business leader’s guide to process mining