Process Mining versus Process Discovery

Process mining and process discovery both aim to understand and optimize business processes, but they have different methods, scopes, and applications. Here's a comparison to help elucidate the distinctions and similarities:

Process Mining:

  1. Definition:
    • Process mining uses event logs generated by enterprise systems (like ERP, CRM, etc.) to analyze and visualize the actual flow of a process.
  2. Data Source:
    • Primarily depends on event logs, which are data points generated by IT systems every time a process step is executed.
  3. Scope:
    • Focuses on processes that are already digital in nature and are recorded in system logs.
    • Reveals bottlenecks, inefficiencies, and deviations in the actual vs. intended process.
  4. Strengths:
    • Accurate visualization of processes based on real data.
    • Offers deep insights into process performance metrics like throughput times or waiting times.
    • Can detect and visualize process variants.
  5. Limitations:
    • Only captures processes that generate event logs in IT systems.
    • May not provide insights into granular, human-level tasks that aren't logged.

Process Discovery:

  1. Definition:
    • Process discovery is a broader term that refers to techniques and tools used to identify, understand, and document business processes. This can involve a mix of manual and automated methods, including observation, workshops, interviews, and software tools.
  2. Data Source:
    • Can range from human observations and stakeholder inputs to automated tools like task mining software.
  3. Scope:
    • Encompasses a wider range of processes, both digital and non-digital.
    • Aims to capture and understand processes as they exist in their natural state, irrespective of whether they're recorded in any system.
  4. Strengths:
    • Provides a holistic view of processes, capturing nuances, human tasks, and undocumented steps.
    • Can be applied even when processes are not entirely digital or do not produce event logs.
    • Sets the foundation for automation by identifying which processes are suitable for tools like RPA.
  5. Limitations:
    • Depending on the method, it might be more time-consuming and less precise than process mining.
    • Can be influenced by subjective human inputs, which might not always represent the process's true state.

Common Ground:

  1. Objective:
    • Both aim to understand, document, and optimize business processes.
  2. Foundation for Automation:
    • They provide valuable insights that can guide automation initiatives, ensuring that the right processes are targeted for tools like RPA.
  3. Continuous Improvement:
    • Both methodologies promote the idea of ongoing process analysis and optimization.

In summary, while process mining is a subset of process discovery that focuses on analyzing event logs from IT systems, process discovery is a broader concept that uses various methods to understand and document processes. Both play vital roles in process optimization and automation, but they have distinct applications and strengths. Often, they are used in tandem to provide a comprehensive view of business operations.