Process Mining: The Thing Your Business Should Be Doing But Probably Isn't

You think you know how your business processes work. The data tells a different story.

What Process Mining Is

Process mining is looking at real data from your actual operations to discover how work really flows through your business. Not how you think it flows. Not how the process document says it flows. How it actually flows, with all the detours, delays, rework loops, and exceptions.

It’s a concept that’s been around in enterprise for years. Companies like Celonis charge millions to do it for organizations running SAP and Oracle. But the principles apply at any scale, as long as you have the right data.

Why It Matters

You can’t improve what you can’t see. Most business owners have a mental model of their processes that’s often incomplete. The gaps are where time, money, and quality get lost.

That 30% includes the orders that bounce back and forth between two people before getting processed. The invoices that get created, then edited, then edited again because the data wasn’t right the first time. The supplier orders that sit in a queue for three days because nobody noticed them. The customer requests that take a different path depending on who handles them.

None of this shows up in a process document. It only shows up in the data.

What Process Mining Reveals

Bottlenecks. Which step in the process takes the longest? Is it the same step every time, or does it shift depending on volume? Where are orders getting stuck?

Deviations. How often do orders go off the happy path? What triggers the deviation? Is it a specific customer type, a product category, or a time of week?

Rework loops. How often does something need to be redone? An invoice corrected. An order re-entered. A supplier contacted a second time because the first order was wrong.

Cycle time trends. Are you getting faster or slower over time? Has a recent change improved or degraded your throughput?

How It Works

Every action in an automated system can be logged: what happened, when it happened, who did it, and how long it took.

Export that data in a simple format (case ID, activity, timestamp) and a process mining tool visualizes the actual flow. You see every path orders take through your business, how common each path is, and where time accumulates.

The visualization often surprises people. The “simple, three-step process” turns out to have 12 distinct paths, with the happy path accounting for only 60% of cases. The other 40% involves variations, exceptions, and workarounds that are invisible without the data.

Why This Comes as a Byproduct of Automation

Enterprise companies pay millions for process mining because they’re reverse-engineering event logs from SAP and Oracle. That’s hard. The data is messy, incomplete, and spread across dozens of systems.

When you build automation from scratch with event logging baked in, the data is already clean and structured. Process mining becomes a natural byproduct of the automation, not a separate expensive initiative.

Every automation we build includes a complete event log from day one. It costs almost nothing to implement, but it gives us (and you) the ability to analyse your operations with real data. After a few months of operation, we can sit down and show you your actual process map, your bottlenecks, and where to focus next. Read more: What Happens When the Automation Breaks? (Spoiler: We Already Know)

What to Do with the Insights

Process mining isn’t just about pretty diagrams. It’s about making better decisions.

If the data shows that 30% of orders require manual intervention because of a specific edge case, you can decide whether to automate that edge case, change the business rule, or accept the manual handling. But at least you’re making that decision with data, not guessing.

If cycle times are increasing month over month, you can find out why before it becomes a customer-facing problem. If one team member’s orders process twice as fast as another’s, you can figure out what they’re doing differently and standardise it.

For example, you might discover that 15% of orders need a second approval because the original PO was missing a shipping address. That’s not visible in anyone’s mental model of the process, but the data shows it clearly. Now you can decide: do you automate the address lookup, change the intake form, or accept the manual step? The point is you’re making that decision with evidence.

The point isn’t to optimise everything. It’s to see clearly so you can optimise the right things.

If you’re curious about what your operational data could tell you, let’s talk. If you’re already running automations with us, the data is already there. We just need to look at it together.