Fix the Real Bottleneck: the Data Preparation, then your Process Mining can Finally Take Off

Why Process Mining Is Finally Taking Off — and Why Data Is Now the Real Bottleneck.

From growing demand for process visibility to the hidden challenge of data readiness.
For years, process mining was seen as an interesting, somewhat academic capability that originated in Europe and lived mostly inside SAP-heavy organizations. It promised deep insight into how processes actually ran versus how they were designed.
The idea was powerful. The adoption, however, was limited.
That has changed dramatically over the past five years.
Today, process mining is no longer a niche capability. It is becoming a foundational component of ERP transformations, automation programs, compliance initiatives, and operational excellence efforts across industries and geographies.
What changed is not the technology. What changed is the enterprise environment around it.

The bottleneck investigated

1The Question Many Organizations Are Now Asking

Across industries, leadership teams are running into the same frustrating realization.
* They are investing heavily in ERP transformations, automation programs, compliance initiatives, and operational excellence efforts.
* They have more data than ever before.
* They have modern platforms, advanced analytics tools, and transformation roadmaps.
And yet, when asked a simple question
— “How does this process actually run in our organization today?”
— the answer is often unclear, debated, or based on assumptions.
Process documentation exists. System data exists. Dashboards exist.
But a clear, factual, end-to-end view of how work really flows across systems, departments, and handovers is missing.
This gap between what organizations think happens and what actually happens is becoming impossible to ignore.
And it is precisely this gap that has brought process mining into the spotlight.

The Market Finally Caught Up to the Idea

Several structural shifts have made organizations ready for process mining:
• ERP modernization programs (especially SAP S/4HANA and Oracle transformations) force companies to confront a hard truth: they do not fully understand how their current processes actually run.
• RPA and intelligent automation showed organizations that automating poorly understood processes delivers limited value. Companies now need to know where to automate.
• Increased compliance and regulatory pressure requires demonstrable visibility into how processes execute in reality, not how they are documented.
• Platform vendors such as SAP, Microsoft, ServiceNow, UiPath, and IBM have embedded process mining into their ecosystems, legitimizing it as a standard enterprise capability.
• Maturing data-driven management has increased the appetite for turning system data into operational insight.
In short, organizations now face problems that process mining is uniquely suited to solve

The Reality Most Organizations Encounter

Once companies decide to adopt process mining, they quickly discover the real challenge is not analytics. It is data.
Process mining depends on:
• High-quality event logs
• Consistent case identifiers across systems
• Harmonized timestamps and attributes
• Clean transactional data from ERP, CRM, service platforms, and legacy systems
What organizations typically have instead is:
• Fragmented data across multiple platforms
• Inconsistent data models
• Limited data lineage
• Significant manual effort required to extract and prepare data
As a result, many process mining initiatives stall. Initial pilots succeed, but scaling across processes, systems, or business units becomes slow, expensive, and heavily dependent on custom data engineering.
The promise of process mining is clear. The data foundation required to support it is often missing

Why Data Transformation Has Become the Critical Enabler

This is where the conversation shifts from process mining as a tool to the broader question of how organizations prepare their enterprise data for process visibility.
Evidant’s Data Transform Refinery addresses this exact gap.
Rather than treating each process mining use case as a separate data project, the Refinery creates a structured, repeatable way to extract, transform, and harmonize transactional data into process-ready formats across systems.
This enables:
• Standardized preparation of event data for process mining platforms
• Alignment of case and event data across ERP, CRM, and service systems
• Faster time to insight by reducing months of data preparation to weeks
• The ability to scale process mining beyond pilots into enterprise-wide capability
• Improved data quality, governance, and lineage
Importantly, this does not replace process mining platforms. It enables them to deliver their full value.

From Isolated Insights to Scalable Capability

As organizations globally invest in ERP transformation, automation, and operational transparency, process mining is becoming a key part of the solution landscape.
But without an industrialized way to prepare enterprise data, its impact remains limited to isolated use cases.
By turning fragmented system data into consistent, process-ready intelligence, Evidant’s Data Transform Refinery allows organizations to move from experimental process analysis to scalable, data-driven operational improvement.

The Bigger Picture

Process mining is taking off because enterprises are finally ready for it.
Evidant’s Data Transform Refinery matters because most enterprise data environments are not.
Together, they enable organizations to not only understand how work really happens, but to systematically improve it at scale.