Process discovery describes the data-based visualization of a process and is a part of process mining. A process model is usually generated automatically from the data found in process discovery (via event logs). This model is often displayed as a direct follower graph. The primary goals of process discovery are increased transparency and process knowledge.
Are there specific requirements for process discovery?
The basic prerequisite for process discovery is availability of data. The data needed for process discovery include the identification of the data point, name of the activities or events in a process, time stamps for the start and end times of the activities, and any number of other process attributes (depending on the available data).
Without these data points, the generation of a direct follower graph is not possible, because the process mining system does not “know” what the process looks like. If data is available, but not in the right form, a data transformation must be carried out. Discuss the availability of the required data with the system user or data owner. Also, note that the data owner must consent to providing data access. As a rule, the company is considered the data owner. As soon as permission to access data is granted, the data can be exported, transformed if necessary, and imported into the process mining system for process discovery.
How does Process Discovery work?
Process discovery is performed as follows:
1. Check for availability of data points
2. Obtain permission from the data owner and, if applicable, the works committee.
3. Extract data.
4. Transform data, if needed.
5. Import data into the process mining system.
6. Generate a direct follower graph, showing a visualization of the process discovered.
How can a company benefit from process discovery?
Contact the Appian process mining team, and we’ll help you get the most value out of process discovery for your company.