 Process Mining is a discipline that uses business process models to analyze event data recorded by information systems. It provides insights into the 'as-is' processes, helping organizations optimize their operations. Three major areas of focus in process mining are Process Discovery, Conformance Checking, and Process Enhancement.

1. Process Discovery: This is the primary function of process mining, where algorithms automatically construct process models from event logs without any prior knowledge. It helps reveal the actual sequence of activities that take place in a process and uncovers deviations from expected behavior.

2. Conformance Checking: After discovering a process model, conformance checking is used to analyze if an event log adheres to this model or not. By measuring the degree of compliance between the models and the logs, organizations can identify bottlenecks, inefficiencies, or non-conformities that need attention.

3. Process Enhancement: Unlike process discovery and conformance checking which focus on understanding and assessing existing processes, process enhancement aims to improve them based on the insights gained from process mining. This could involve redesigning parts of the process for better performance, implementing automation, or optimizing resource allocation.

In summary, while Process Discovery and Conformance Checking are primarily diagnostic tools used to understand and evaluate existing processes, Process Enhancement is a prescriptive approach that uses the insights derived from the first two steps to proactively improve processes. It's about translating those insights into actionable improvements, making the process more efficient and effective.