Why Process Mining Belongs at the Heart of Digital Transformation

One project in particular shaped how I think about the role of process mining alongside a digital transformation. We connected process mining before a new system went live, initially working with test data and then observing the process in real time on launch day.

The rollout happened market by market. After each launch, we took what we had learned and applied it to the next. This created a powerful feedback loop. For example, we were able to spot on day one that a handful of invoices weren't getting synced to a downstream system, an issue that would otherwise have taken days to surface, perhaps only when a vendor raised a query.

Instead of reacting late, we could intervene early because we had full visibility into how the end-to-end process and system integrations were playing out.

Digital Transformation Needs Active Monitoring

The term “digital transformation” is used frequently, whether to describe the rollout of new technology or an attempt to realise greater value from an existing one. In both cases, work is being done differently. Processes are redesigned, automations are introduced, responsibilities shift, and integrations are reconfigured. What is often missing is a structured way to monitor how these changes play out.

Process mining provides that structure. It uses system data to show how work flows end to end, making it possible to observe how the intended systems perform once the planned changes meet reality.

Is the new workflow being followed? Is automation realising the expected gains in efficiency? Are integrations behaving consistently across markets? As discussed in The Hidden Story in Your Data, there is always a gap between how technology is designed to work, and how people use it.

If you want your transformation to succeed, you need visibility into that gap while it is forming.

Process Mining as a Steering Mechanism

Process mining should not be thought of as something applied only once a transformation is complete. Its value lies in being used alongside change, when there is still an opportunity to steer outcomes rather than simply measure them.

The distinction matters. Measurement tells you what happened. Steering changes what happens next. In the invoice example at the start of this article, we did not just observe the sync issue and subsequent consequences, we acted before the consequences played out and before the next market launch. Each rollout became an opportunity to apply what the previous one had revealed. That is the difference in practice.

Used this way, process mining gives the business a live view of how change is landing as it is introduced. Not as a look back, but as an active mechanism to keep closing the gap between expectations and reality so that the outcomes assumed in the business case are within reach.

Final Thought

For me, this is where process mining really earns its place. Not as an analytics tool you return to after the fact, but as something that sits at the heart of the digital transformation programme.

It helps the business stay on top of change as it unfolds, and significantly increases the likelihood that the outcomes assumed in the business case are actually achieved.