All Posts
We all know by now that keeping a business running smoothly is the secret to success. You’ve got to really know how things work behind the scenes, whether you're looking to grow, fix issues, or just boost efficiency.
And here is when techniques like process mining and analysis come into play. Both help businesses get to the heart of what's going on—but, at the end of the day, they each do it in their own unique way.
If you are someone who has heard these terms but aren’t sure what they mean, you’re in the right place. We’ll explain them in simple terms and you’ll also learn why it’s important to know the difference. We’ll also show you how each one works, along with their pros and cons.
What is Process Analysis?
First things first, let’s define what process analysis is. Process analysis is a traditional method, which is used to understand how people do things within a business. The goal here is to analyze current processes, so you can check if you can improve them.
Documenting each step in a process, finding areas of delay or inefficiency, and offering measures to improve the process's effectiveness could be examples of such activities.
The traditional approach
As you can probably tell by now, process analysis typically involves a lot of manual work. Some examples of manual work could be interviews, observations, and document reviews.
Analysts may sit down with staff to ask how things are done or observe workflows to determine where things are slowing down. The goal here is to have a thorough understanding of the process from beginning to end.
Such an approach, most of the time, produces documents like process maps or flowcharts that outline every step in a workflow. These documents serve as a reference to identify:
Potential bottlenecks;
Reductant steps, or;
Areas for improvement.
Focus on Documentation
One of the main characteristics of business process analysis is its focus on documentation. Every aspect of the process is carefully written down and examined. The documentation can serve as a blueprint for further improvement.
For example, if a business wants to streamline how customer orders are processed, a detailed process map can help spot steps that could be automated or skipped.
Limitations of Process Analysis
While process analysis is useful, it does come with limitations. Because it often relies on human observation and interviews, it can be subjective. People may have different interpretations of how a process works, or they might unintentionally leave out important details.
To add to that, process analysis can also be very time-consuming. When you document and analyze processes manually, especially in large organizations, it does take a lot of time and effort. Such a thing is especially true when the processes are complex or spread across multiple departments.
What is Process Mining?
Process mining is a relatively newer approach compared to process analysis. It is a data-driven approach that is based on the real data created by a company's systems. Instead of manually recording procedures, process mining employs software tools to automatically analyze data and identify trends.
A Data-Driven Approach
The main difference with process mining is that it does not rely on humans to describe how processes work. Instead, it uses data—typically from event logs in IT systems—to automatically map out what's going on in a process. Every time a system task is completed, such as placing an order or processing an invoice, event logs are generated.
The event logs provide all the necessary details about the sequence of events in a process. With this data, process mining tools can reconstruct the actual flow of activities and highlight how things are working in real life, not just how people think they work.
Event Logs as the Foundation
Event logs are essential for process mining since they include a wealth of information about what happens in a company's processes. These logs document each step in a process, along with a timestamp. For example, the system logs each invoice that is created, approved, or paid.
Process mining tools then examine this data to determine how the process progresses. This data-driven method assures that the analysis is based on facts, not preconceptions.
Key Capabilities of Process Mining
Process mining tools come with several powerful capabilities. Three of the most important ones are:
Process discovery: This feature automatically maps out a process by analyzing the data. Instead of spending weeks documenting a process manually, process mining tools can generate a complete process map in minutes.
Conformance checking: This allows businesses to compare the discovered processes against an ideal or predefined process. It helps identify deviations or compliance issues.
Performance analysis: Process mining tools can also analyze how efficiently a process runs by looking at key performance indicators like processing times or bottlenecks.
Why is differentiating important?
Understanding the distinctions between process mining and process analysis is crucial since each method is suitable for different situations.
Using the improper technique could result in inadequate or inaccurate insights, which might have a negative impact on company decisions.
When to Use Process Analysis
Process analysis is appropriate for scenarios involving little data or the requirement to gain a general picture of a process. For example, if a company is still in the process of establishing its systems and does not yet have substantial event logs, process analysis is an excellent place to begin.
It also works well when the process involves a lot of human activities that aren’t captured by systems, such as manual tasks or face-to-face customer interactions.
In addition, process analysis can be helpful in the early stages of process understanding. When you’re first trying to document and analyze how things are done, interviewing people and observing workflows can give you a broad overview.
When to Use Process Mining
Process mining, on the other hand, works best when there is a lot of data accessible. It's especially handy for huge businesses that create a lot of event logs.
If a corporation wishes to optimize processes that span numerous departments and systems, process mining might reveal hidden inefficiencies that human analysis would miss.
Moreover, process mining is extremely effective when businesses want to automate processes or need real-time insights. It provides a much more detailed and accurate view of how processes work because it’s based on actual data rather than assumptions.
Combining Both Approaches
Process analysis and mining are not mutually exclusive. In reality, combining both methodologies can result in a thorough grasp of a company's procedures. Process analysis can assist describe a process's overall structure and purpose, whereas process mining can provide comprehensive, data-driven insights.
For instance, a business may begin by outlining the high-level steps of a workflow using process analysis. Process mining can be used to validate the process and find inefficiencies that were missed during manual analysis once it has been recorded. This combination method produces a process image that is more precise and comprehensive.
Final Thoughts
Finally, while process analysis and process mining are distinct methodologies for analyzing corporate processes, they complement one another. Process analysis is a more traditional, manual approach that can provide a wide knowledge, particularly when data is in short supply. Process mining, on the other hand, is a data-driven process that generates deep insights from actual event logs.
