Process Mining vs Automated Process Discovery in '24

Process mining is applied to numerous sectors such as  HR, logistics, and healthcare. Despite the increasing interest in adopting process mining, business leaders are confused about the definitions of process mining and process discovery. In this article, we go over the techniques that are used for process discovery and how process discovery augments process mining, and what you need to do when comparing process mining tools.

What is process discovery?

Process discovery is defined as the first step at process mining in the academic literature. It helps grasp a full understanding of actual processes by extracting of control-flow models from raw event logs. With cause-and-effect analysis, they can connect different events to create process models, including their deviations. Process discovery follows the steps below:

• Extract data: Event logs and performance metrics are collected from the company’s enterprise and departmental software systems.
• Process and map events: The collected data is analyzed and are mapped for each case from the event logs. This step is also where the process deviations become apparent. The variations mostly happen due to manual changes or errors in the process.
• Combine events to create “as-is” processes: The generated process maps are combined and visualized to see who, what, when, and where” of each process variation, including the related subprocesses.

Manual process discovery vs. automated process discovery

Process discovery is available as manual process discovery and automated process discovery. The automated process discovery applies ML and AI technologies (e.g., deep learning) to detect the the tasks, applications, and human interactions in processes. Automated process discovery sophistications have been mostly shown to work for academic research.

Today, some vendors and academics use the term of automated process discovery to distinguish the AI capabilities and applications of the tools. Therefore, there are both RPA and analytics companies that offer automated process discovery tools without including other attributes of PM (e.g. conformance checking and enhancement). Yet, there are other PM companies that offer automated process discovery tools. To learn more on the process discovery tools, do not hesitate reading our brief article on the process discovery tools.

Automated process discovery vs. process mining

Automated process discovery and process mining are similar because:

• Both process mining and processing discovery employ automation technology to identify and map processes.
• Both techniques can be applied to numerous business sectors.
• Both can be integrated with enterprise solutions (e.g., ERP and CRM).

Nonetheless, AI-powered process discovery can advance process mining by:

• Identify human interactions with systems that are missing in logs. This is done with the help of computer vision and machine learning.
• Leveraging automation, therefore, minimizing human involvement, biases, and errors.
• Automating discovery of workflows which is typically done manually in process mining.
• Shortening the time to complete process mining.

How does AI-powered process discovery augment process mining?

AI-powered process discovery improves and completes process mining in these areas:

Monitoring user interactions

A process discovery solution can monitor user interactions by using uses a variety of automation techniques to create event logs from human interactions and create a business process model. Process models replace the necessity of process mining to rely on event logs and human supervision to generate an ideal process representation.

Data extraction from productivity applications

Process mining works only for systems that produce logs, failing to support data extraction from Excel, Teams chat, or other personal productivity tools. AI-powered process discovery tools can record users’ interactions, enabling the tools to support data extraction from personal productivity tools (e.g. Slack, Excel).

Real-time analysis

Process mining tools typically work with historical data. Therefore, these tools do not provide results based on real-time data. Whereas, AI-powered process discovery tools continuously monitor the processes to assure real-time analysis and rapid retraining in cases of changes.

Integration

Businesses that do not want to integrate process mining but still want to discover their processes can benefit from process discovery as process discovery does not need any integration to the system application.

Further Reading

To dip into AI-enabled process mining features, feel free to read:

• Top 6 Applications of Machine Learning (ML) in Process Mining
• 7 Ways AI Solves Process Mining Challenges

If you believe your business can benefit from process mining tools, you can check our data-driven list of software.

And you can let us find you the right vendor: