Process Mining: Application of Data Science by Nivarthana Sandeepani | Track 2

Process mining is a collection of approaches for learning and unlocking insights from processes through the analysis of event data produced throughout the course of the process. Discover, model, monitor, and optimize the underlying processes are the ultimate objectives of process mining.

The advantages of process mining could include:

- Creating a process model from an event log is known as process discovery.
- Investigating discrepancies between the model and what really occurs is known as conformance checking. The businesses can then assess the severity and expense of model deviations by identifying them.
- Throughput analysis and bottleneck detection take into consideration the intensity of an event's execution (defined by the amount of time it takes to execute an individual event) to spot potential bottlenecks.

Simply process mining is the intersection of Data mining and business process management.

By providing answers to both compliance-related and performance-related queries, process mining provides objective, fact-based insights generated from actual data that assist you in auditing, analysing, and improving your current business processes.

In my session I will be discussing how data science is applied in Process Mining to increase efficiency in process automation to ensure the above advantages of process mining are gained in the most efficient way. Under that I will discuss four main techniques that we can use to make process mining more efficient and achieve business goals. Namely;

- Data extraction and management
- Data preparation
- Visual analysis
- Optimization of Processes

And will also cover broader use cases on the topic to make the session more understandable and informative.

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[nivarthanasandeepani]

Thank you for having me ! It was a pleasure to be a part of Automation Days Asia 2022