In our other articles, we’ve talked extensively about the benefits of Robotic Process Automation in different industries. We’ve also covered the most popular automation tools to help businesses select the best fit for their needs.

However, we’ve also noted that 41% of companies are still reluctant to adopt RPA due to the lack of clarity in their business processes. Their fears are understandable, as RPA implementation is a significant leap you can’t take without preparation. The same can be said about other major business decisions—you can’t just throw money at the problem and hope it will work. The difficulty lies in identifying the issue and then coming up with a solution.

The good news is, the answer to this challenge is already here: it’s process mining. The solution is relatively new, but it’s expected to grow substantially in the years to come with an almost 50% CAGR until 2029. The potential for process mining to streamline discovery and decision-making is very much there.

And the even better news is that the capabilities of process mining can be further enhanced with Robotic Process Automation. In this article, we’ll show you how the two can be used together for the best results. But first, let’s get you started with process mining, how it works, and what its benefits are.

What’s Process Mining?

At the basic level, process mining is exactly what it seems: mining for processes. Process mining goes through the event log data generated by the software systems used by your business (more on that later) to discover workflows that could use some improvement. That data is then visualized to facilitate decision-making. Advanced process mining systems can also suggest upgrades on their own.

There are three main types (or applications) of process mining:

  • Discovery — The algorithm scans the available data in search of patterns that can be used to create new, efficient workflows.
  • Conformance The aim of this process mining type is to check if the current processes are working as intended and find any deviations.
  • Enhancement — In this check, additional data (e.g., yielded from conformance analysis) helps identify potential improvements in the existing processes.

Typically, all three types are used together to find, audit, and optimize workflows.

How does Process Mining work?

As we’ve mentioned earlier, process mining relies on event log data from other systems. Event logs are the digital footprints left after a piece of software processes data. These traces store information about past activities and their use is normally limited to error tracking. This means that each business that runs data-processing systems such as SAP,  Oracle, Salesforce, Microsoft Dynamics, etc, has tons of data that is mostly just lying there, unused…

… unless you do process mining. The three record attributes that are crucial for that are:

Case ID

A unique number is assigned to each event for easier identification. This can be an order number, client name, or any other identifier.

Timestamp

The time when the event was finished. With the event timestamps for all activities within a workflow in place, we can see how long each of them realistically took to complete.

Related activities

All activities are connected with the case ID. For an order number that could be the customer placing the order, you responding to it, or an invoice being sent.

Based on these three data points (the so-called “data triple”) gathered from a statistically significant data sample, the process mining software creates a process graph. This visualization allows you to examine the workflow step-by-step and find inconsistencies, tasks that take too long or are outright redundant, bottlenecks, and other inefficiencies.

Advanced process mining solutions can do even more. Some identify the root cause of the problem or monitor process performance 24/7. Others predict results for the set KPIs or give you instant, automated reports, and insights.

What are the benefits of Process Mining?

Before we go straight into listing the pros of process mining, let’s talk shortly about how businesses traditionally discover inefficiencies and areas for improvement.

Gathering information about workflows is a part of Business Process Management (BPM). This is done mostly manually, through interviews, workshops, and process mapping that spans several days and requires the combined effort of members across different teams. It’s time- and work-intensive, and since so many people are involved, the findings can be a little skewed and not entirely objective. That said, this traditional method has its benefits: it engages various stakeholders and takes a qualitative approach.

Process mining, on the other hand, relies on data rather than human effort. Process mining algorithms can crunch numbers and find patterns much faster than any team of experts. The number and size of data sets don’t matter—unlike humans, process mining systems don’t have to focus on a single point. Bias isn’t a factor, either, since the software draws its conclusions based solely on objective data.

This comparison should give you a good idea of how useful pr