Knowledge Bytes Session: The Power of Data-Driven Process Analysis

Hi everyone! Thomas Littooij here, Product Owner and Process Mining specialist at Mavim. On March 18, 2025, we had a super fun and informative Knowledge Bytes session about data-driven process analysis and process mining. I'd like to share the key insights we discussed that day.

Why is data-driven process analysis so important?

Simply put, it helps you turn data into action. We talked about:

• Identifying hidden bottlenecks: Often, processes have issues you don't even realize. Data helps us uncover those problems.
• Optimizing workflows: By looking at the data, we can make processes more efficient.
• Making better decisions: Instead of guessing, we can make decisions based on hard data.

We also looked at how this fits into a continuous improvement cycle, and how Gartner's 'Digital Twin' concept helps us create a digital blueprint of our processes.

How do you mature in data-driven work?

We discussed a 'maturity model' with three phases:

• Phase 1: Gaining insight: First, we just want to know how our processes actually run.
• Phase 2: Continuous improvement: Then, we use those insights to continuously improve processes.
• Phase 3: Predicting: Eventually, we can even predict how processes will run and take proactive action.

From business goals to process insights

It's important to start with what you want to achieve as a company. Whether it's speeding up customer processes or improving IT tickets, we can translate those goals into concrete processes and systems.

What do you need for process mining?

To get started with process mining, you need these basic data elements:

• A way to track each process step (a 'Case ID').
• The different actions that occur in the process.
• The timestamps of those actions.

With that data, we can do all sorts of useful things, like see how processes really run, find problems, and measure performance.

Process Conformance Checking

An important part is 'Process Conformance Checking'. This allows you to see if processes are running as you designed them. We can compare the designed process with the actual data and see where there are deviations.

Practical examples

We also looked at a few examples:

• A process engineer analyzing the procurement process to find the most common variants.
• A process analyst finding bottlenecks in the order process.
• A continuous improvement engineer checking whether the claims process is running smoothly.

Conclusion

Data-driven process analysis and process mining with Mavim can really make a difference. It helps you find inefficiencies, improve processes, and make better decisions.

Thanks to everyone who joined us!