Process discovery is a process mining technique used to create a data-based visualization of process workflows. Using data found in event logs, process mining automatically generates a discovered model for analysis, giving users a visual representation of their business process for the first time. The primary goal of process discovery is increased transparency and process knowledge. Process discovery outcomes are visualized in the discovered model.
How does process discovery work?
Process discovery needs data to work. This includes the identification of a data point, the name of activities or events in a process, at least one time stamp for the start and end times of activities, and any number of other process attributes (depending on the available data).
Data needs to be transformed before process discovery can be carried out. Once accessed, data can be exported, transformed, and imported into the process mining system for process discovery.
Process discovery is performed as follows:
- Check for availability of data points.
- Extract and transform data.
- Import data into the process mining system.
- A visualization of the discovered process is generated automatically.
Why do we use process discovery?
Not all business processes work as expected. With bottlenecks and missed or skipped activities, companies waste time and money running inefficient processes. Yet, without process transparency, or the ability to see exactly where inefficiencies slow progress, companies are left guessing where to make improvements. Analysis is often subjective, slow, and tedious. Process discovery takes the guesswork out of a process improvement project, delivering objective, data-driven insights.