Business 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 and unbiased representation of their business processes. The primary goal of process discovery is to increase transparency and process knowledge for enhancement.
[ Read the Process Mining Guide for tips on getting started with process discovery. ]
Process discovery needs data to work. These data points include names and descriptions of the activities or events in a process, time stamps for the start and end times of activities, and other process attributes such as case IDs (depending on the available data).
Once identified, data can be exported, transformed, and imported into the process mining system for process discovery. Business process discovery outcomes are then visualized in the discovered model to uncover optimization and process automation opportunities.
Checking for availability of data points.
Extracting and transforming data.
Importing data into the process mining system.
Generating a visualization of the discovered process, or process model.
Thanks to the rise of process mining tools, process discovery can be completed much more quickly than with traditional, manual approaches. The result is a less biased and more data-driven process model that provides business users with an actual representation of how their processes run.
Not all business processes run as expected. Riddled with bottlenecks and missed or skipped activities, inefficient processes waste companies’ time and money. 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 business process improvement project, delivering objective, data-driven insights. And it's a key part of business process management.
Identified inefficiencies: With process discovery, organizations can easily identify bottlenecks, process deviations, and other inefficiencies. This insight allows them to take action and make improvements that can lead to greater efficiency and cost savings.
Data-driven insights: Process discovery uses data to provide objective insights into how a business process is functioning. This helps to remove any subjective biases that may be present when analyzing a process manually, leading to more accurate conclusions and recommendations.
Fast analysis: Compared to manual analysis, process discovery with process mining is often much faster. This means that companies can identify inefficiencies and opportunities for improvement in a shorter amount of time, allowing them to take action and see results sooner.
Collaboration: Process discovery involves stakeholders from across the organization, leading to greater collaboration and a shared understanding of the process. This can help break down silos and improve communication between teams.
Nearly any type of process can be a good candidate for process discovery. Some of the most common applications of business process discovery include:
Financial management
Risk management
Compliance management
Quality assurance and control
Customer experience and onboarding
Employee training and development
Digital transformation
Business continuity planning
After a model of the actual process has been generated, you can then compare it to the target process. In process mining, this technique is called conformance checking, and it helps you uncover deviations between the actual and target process and identify areas for optimization. You can also use root cause analysis to drill deeper into the cause of unwanted process behavior. With this new visibility into your process, you can then implement and monitor changes on an ongoing basis for continuous improvement.
Get started on your process discovery journey. Read the Process Mining Guide for tips and best practices.
Related terms: Process Model, Target Model
Reach the optimized process with the Process Mining Guide.
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