Why Process Mining Isn't Just Another Analytics Tool

I've spent an extraordinary amount of time trying to explain process mining to people. Smart people. People who cared about fixing problems in their processes. They liked what they saw - the visualisations, the flows, the process insights. But again and again, I'd hear the same reaction: "Isn't this just another data analytics tool?" They'd already invested in reporting and business intelligence tools. Surely those could ultimately be used to do the same thing.

Process mining is often mistaken for yet another business intelligence tool, another way to analyse and visualise your data. And at first glance, it can look that way. You still get charts, tables and filters. It also draws data from the same systems your existing analytics tools already use, so it's easy to assume it works in the same way too. But it doesn't.

It's not just a fancier dashboard with a process map. It's designed to solve a fundamentally different problem from regular business intelligence tools, and that is understanding how work actually flows through your business.

Why Traditional Analytics Tools Fall Short for Analysing Process Flows

Tools like Power BI, Tableau, and Looker are great for summarising data. But they weren't designed to understand sequences, flows, or how behaviour unfolds over time.

So if you want to calculate something like average handling time, rework rates, or skipped steps, you have to rebuild the logic of your process each time. For every new process question, you have to start over and define the joins, pick the right timestamps, apply the correct SQL functions and filter the data correctly. It's slow, complex and fragile.

Process Mining Introduces Two Additional Layers

Process mining separates the hard part (structuring the process data) from the fun part (asking questions). It introduces two core layers that traditional analytics tools don't have:

A Structured Event Log linked to Objects

A time-ordered log of every event that ever happened to objects in your business. For example, an order being placed, an invoice being approved or a support ticket being escalated.

Each event is timestamped, described in natural language, and linked to all objects it affects, along with contextual data like which system recorded the event, who triggered it, or where it happened.

Each object also carries its own contextual data, like its ID, value or category.

The result is a complete and connected data structure that captures how work flows from start to finish, enriched with context to turn these flows into insights.

A Process-Aware Query Layer

A higher-level process query language built on the event-and-object data layer that lets you ask process-specific questions like: What are the most common paths? Where did a delivery happen before approval? How often did a support ticket switch agents more than twice? Or which customer orders triggered finance activity before fulfilment?

These questions are painful to ask using SQL or traditional reporting because they require understanding sequences, time, and event-object relationships. Factor in time zones, cross-system events, and multi-object interactions, and the complexity escalates fast.

Once the event and objects data layer is in place, you can reuse it endlessly with a language designed specifically for querying process flows. No more reinventing complex logic every time you want to answer a simple flow-related question. Structure once, query forever.

When Process Mining isn't the right approach

The line between process mining and traditional analytics tools can sometimes feel blurred. Process mining tools often display aggregated metrics alongside views of flows, such as the number of orders processed, order value, rejection rates, or average resolution times, with each metric typically broken down by dimensions like product type or customer.

But process mining isn't always the right approach. If you're doing financial reporting, customer segmentation or aggregations that don't involve understanding sequences or process behaviour, traditional business intelligence tools are often simpler and more cost-effective. Process mining only shines when you need to understand how work actually moves through your business over time - key for uncovering inefficiencies, delays, and opportunities to improve how things get done.

If you've been using standard tools for process insights, process mining will feel like a superpower.

Final Thought

If you're still wrestling with complex SQL to calculate lead times or rework, process mining will feel like a super-power. It delivers visibility and reusability without making you rebuild your process logic every time.

In a previous role, my team didn't just surface process insights, they surfaced them live - in meetings or during coffee chats. Once the events and objects layer was loaded, they could explore flows, test ideas and give people novel process insights and improvement opportunities on the spot. It made the team look awesome, which they absolutely were, but they were also using process mining 😉