Why Causal Process Mining Is Replacing Traditional Event Logs

For over two decades, process mining has promised to bring transparency to business processes and enable continuous optimization. Yet many companies still face roadblocks in its implementation. The main reason lies in the underlying technology: event logs. These flat data structures often fail to capture the true complexity of real-world processes and limit in-depth analysis.

But there is a way forward: Causal Process Mining. This new approach makes it possible not only to visualize processes but also to understand the underlying causes and effects. In this article, we explore the limitations of traditional event logs and show how causal process mining opens up new perspectives.

The Limitations of Traditional Event Logs

High Implementation Effort

Creating event logs requires significant resources. Data must be extracted, transformed, and harmonized across multiple systems. This process is time-consuming and error-prone. It typically involves several stakeholders, adding to the complexity and driving up implementation costs.

Lack of Flexibility

Event logs are static. Every change to the process perspective or context requires another round of data extraction and transformation. This hinders iterative analysis and slows down agile process improvement.

Limited Realism

Due to their flat structure, event logs lose essential contextual information. Parallel activities, loops, or exceptions are often not adequately represented. The result: incomplete or distorted process models that poorly reflect business reality.

Restricted Analytical Value

Event logs primarily enable correlation-based analysis – not causation. Without a clear view of cause-and-effect relationships, many optimization opportunities remain hidden. Moreover, insights gained are rarely reusable in other analytical contexts.

Causal Process Mining: A New Paradigm

Causal Process Mining directly addresses these shortcomings and introduces an alternative approach:

Direct Crawling and Entity Graphs

Instead of generating flat logs, data is crawled directly from source systems and structured into what’s called an Entity Graph. This graph captures all entities and their relationships, enabling a more accurate representation of business processes in their natural complexity.

Flexibility and Iteration

Thanks to its graph-based structure, various process dimensions can be flexibly analyzed—without the need for repeated data transformations. This supports agile, iterative analysis and faster learning cycles.

Causation over Correlation

Causal Process Mining focuses on identifying cause-and-effect relationships. It enables organizations to address not just the symptoms, but the root causes of process inefficiencies.

Reusability and Integration

Insights are not limited to process analysis. They can be seamlessly integrated into other contexts—such as predictive models or AI applications—boosting the overall value of analysis and supporting holistic optimization.

Conclusion

Event logs have served their purpose but are reaching their limits in today’s complex and fast-moving business environment. Causal Process Mining offers a forward-looking alternative that captures the full complexity of processes and enables deeper, more actionable analysis. Organizations that adopt this approach early will gain a critical competitive edge in data-driven process optimization.