e have extensive experience in modern data architectures and analytics, delivering customer, financial and process analytics to a wide range of clients. Yet, with traditional process analytics, we began to encounter the limits of tools such as statistical process control, operations scorecards and business process improvement. While these tools provide valuable insights into intended processes and outcomes, they often fail to reveal what is actually happening in the organization. By ingesting, preparing, and analyzing event logs from processes and tasks - both inside and outside standard process flows - Process Mining takes analytics to the next level.

Advanced Outcomes Through Familiar Use Cases

The use cases for Process Mining are closely aligned with those of Business Intelligence (BI) and traditional process analytics. By uncovering the as-is situation, Process Mining delivers inputs for process improvement, enhances data capture and quality, and strengthens decision-making in areas such as research and development, product management, distribution, and operations.

Totally Different From BI And Yet So Identical

At first glance, BI and Process Mining may appear similar: both analyze business data and rely on visualizations to simplify insights. Yet they tackle the problem from very different perspectives. BI focuses on KPIs and metrics, often based on predefined assumptions, while Process Mining reveals how processes truly operate in practice.

The Critical Role of Data Engineering

Any process mining specialist will tell you that data acquisition, cleansing, and preparation is the largest part of a successful project. That’s where our expertise in data engineering comes in: we provide scalable and sustainable modern data architectures that enable accurate, reliable, and high-impact process mining.

Common Pitfalls and Lessons from BI

Process Mining, like BI, faces similar challenges:

 Poor data quality

 Gaps between business and technology alignment

 Lack of executive sponsorship or a clear business case

 Insufficient project team competencies

A structured, careful approach is essential to ensure that Process Mining delivers actionable insights and measurable business value.

Final Thoughts

For data engineers, automation isn't about replacing expertise—it's about amplifying productivity and reducing repetitive work. Whether you're planning a simple lift-and-shift or a full DW re-architecture, incorporating automation into your workflow is the fastest way to success.