Future of Data Driven Process Optimisation

How can organizations analyze and improve their processes today in the context of emerging technologies like GenAI etc? No matter what technologies are brewing in the labs, the story of process optimization will always starts from event log. Event logs represent raw data of executed processes, capturing what happened, when, and in what sequence. Process mining techniques turn these logs into insights, revealing inefficiencies, bottlenecks, and opportunities for optimization. But here's the catch: the logs don’t always tell the full story.

The process mining community has spent decades refining tools for discovering, checking, and enhancing processes based on event logs. Yet these methods struggle when the logs are messy—full of gaps, noise, and ambiguities—or when the processes themselves are unstructured and complex, like those in healthcare. What you get from the algorithms in these cases is often what’s called a "spaghetti model"—a tangled mess that’s as hard to interpret as it is to act on.

The fundamental issue is this: traditional process mining methods focus narrowly on observed behavior. They extract insights from what’s explicitly recorded but fail to reason beyond it. They lack what researchers call common-sense reasoning, the ability to infer relationships or patterns that aren't directly logged but are nonetheless obvious to a human observer. This limitation creates a gap in understanding—one that leaves process analysts guessing, abstracting, or even discarding key insights.

The Limits of Process Logs

Imagine trying to analyze a hospital’s clinical workflows. The event logs might tell you when a patient arrived, what tests were performed, and when they were discharged. But they probably won’t capture why certain decisions were made, the cascading effects of those decisions, or the informal handoffs and workarounds that happen in practice. Without this context, even the most sophisticated process mining algorithms can only provide a partial picture.

And it’s not just about missing context. Logs are often incomplete or biased, skewed by errors in data recording or by systemic gaps in what’s logged in the first place. The result is a process model that might fit the data but misses the mark in reality. It’s like trying to reverse-engineer a symphony from a handful of notes—technically accurate, perhaps, but far from complete.

This limitation becomes even more pronounced in complex domains like healthcare, where understanding process behavior isn’t just about efficiency—it’s about improving outcomes that directly affect people’s lives. The stakes are too high for guesswork or overly simplistic models.

Toward Knowledge-Centric Process Mining

If event logs alone can’t give us the full picture, what can? One promising answer lies in knowledge graphs. These structures represent relationships between entities—like people, processes, or decisions—in a way that’s both flexible and richly detailed. They’re not just about storing data; they’re about modeling knowledge, including the implicit relationships that traditional process mining methods overlook.

Think of a knowledge graph as a map, where nodes represent concepts (e.g., “Patient,” “Test,” “Diagnosis”) and edges represent relationships between them (e.g., “undergoes,” “leads to,” “associated with”). This map can encode the hierarchical, cascading, and often abstract relationships that make real-world processes so complex. More importantly, it can provide the reasoning capabilities that traditional methods lack.

For instance, a knowledge graph could help infer missing links in a process: “If a patient undergoes a test and the test indicates a risk, then a follow-up procedure is likely.” These kinds of inferences aren’t explicitly logged but are critical for understanding the broader dynamics of a process.

Adding Intelligence: LLMs and Knowledge Graphs

The other exciting development is the rise of Large Language Models (LLMs) fine-tuned on domain-specific data. Unlike traditional process mining algorithms, LLMs can process and interpret unstructured data—like text descriptions, clinical notes, or business rules—and integrate it into a structured understanding of the process. Paired with knowledge graphs, they can enrich process analytics with reasoning capabilities that go beyond pattern recognition.

Imagine this scenario: You’re analyzing a hospital’s patient journey. The event logs give you a timeline of actions, but the LLM, trained on the organization’s documentation, adds context. It identifies that a delay in a specific test is linked to resource constraints and that these delays disproportionately affect certain patient groups. The knowledge graph then ties it all together, showing how these delays cascade through the system and identifying where interventions would have the most impact.

This isn’t just about automating insights; it’s about augmenting human understanding. By combining the data-driven rigor of process mining with the contextual intelligence of LLMs and knowledge graphs, we can give process analysts tools that are both more powerful and more intuitive.

Challenges and Opportunities

Of course, this vision comes with challenges. Building and maintaining knowledge graphs requires significant effort, especially in dynamic environments where processes and relationships are constantly evolving. And while LLMs are incredibly versatile, they’re not infallible—they’re only as good as the data they’re trained on, and they can introduce their own biases if not carefully managed.

But the potential payoff is enormous. By bridging the gap between data and knowledge, we can move from simply observing processes to truly understanding them. This shift could transform how organizations approach everything from operational efficiency to customer experience, unlocking insights that were previously out of reach.

Rethinking Process Analytics

Process analytics has always been about making sense of complexity. But as processes grow more intricate and the data we collect becomes more fragmented, the limitations of traditional methods become harder to ignore. The next frontier lies in embracing a knowledge-centric approach, one that integrates the strengths of process mining, knowledge graphs, and LLMs.

This isn’t just an incremental improvement; it’s a rethinking of how we approach process discovery and optimization. It’s about recognizing that processes are more than the sum of their logged events—they’re dynamic, contextual, and deeply human. And understanding them fully requires tools that are just as dynamic, just as contextual, and just as human-centered.

The future of process analytics isn’t about replacing the old tools; it’s about expanding what’s possible. By leveraging knowledge graphs and LLMs, we can move closer to that ideal—where process mining doesn’t just model what we’ve done but helps us imagine what we could do next.