Which data analysis method is used to map the actual workflow of a process by examining event data?

Which data analysis method is used to map the actual workflow of a process by examining event data?

• A. Root cause analysis
• B. Time series analysis
• C. Process mining
• D. Forecasting

Answer: (C)

Process mining focuses on uncovering how a process actually operates by analyzing event data from information systems. When events are logged with details like the activity name, the time it occurred, and the case or instance it belongs to, you can reconstruct the real sequence of steps that people or systems follow. This lets you see the true workflow, identify variations in how cases flow, spot bottlenecks and delays, and check whether the observed process conforms to the intended design. It’s data-driven discovery: you can build a model directly from the event logs, compare it to the designed process to find deviations, and use those insights to improve efficiency. This differs from root cause analysis, which aims to identify underlying causes of problems after they occur; time series analysis, which looks at data points over time to understand trends or seasonality; and forecasting, which uses past data to predict future values.

Process mining focuses on uncovering how a process actually operates by analyzing event data from information systems. When events are logged with details like the activity name, the time it occurred, and the case or instance it belongs to, you can reconstruct the real sequence of steps that people or systems follow. This lets you see the true workflow, identify variations in how cases flow, spot bottlenecks and delays, and check whether the observed process conforms to the intended design. It’s data-driven discovery: you can build a model directly from the event logs, compare it to the designed process to find deviations, and use those insights to improve efficiency.

This differs from root cause analysis, which aims to identify underlying causes of problems after they occur; time series analysis, which looks at data points over time to understand trends or seasonality; and forecasting, which uses past data to predict future values.