From ERP Data to Process Mining Insights: Building an Automated Pipeline for Real-Time Process Visibility
Summary
- Clean ERP data sitting in a data lake doesn’t answer the question every operations leader eventually asks: where exactly is our process breaking down?
- We built an automated pipeline that connects a client-facing web portal, Azure Table Storage, and Azure Databricks to a leading process mining platform, turning validated ERP data into a living view of how work actually flows.
- The pipeline is fully status-driven: every record is tracked from submission through processing to completion, with no manual exports or spreadsheet hand-offs.
- Purchase order data is modeled through a medallion architecture and delivered to the process mining platform, where AI-driven analysis automatically surfaces bottlenecks and deviations from the expected process.
- Business impact: process owners moved from static, after-the-fact reporting to a near real-time, evidence-based view of process performance.
Table of Contents
About the Customer
Customer Spotlight
A Leading Digital Transformation Partner — Europe
Our customer is a leading enterprise headquartered in Europe, operating across diverse manufacturing and supply chain divisions. Having already standardized their ERP data through a medallion architecture on Databricks, leadership wanted to go a step further: not only manage ERP data at scale, but also connect it seamlessly into process mining tools to uncover how core processes truly perform in practice. The focus was on gaining operational clarity into workflows such as purchase order management, invoice handling, and procurement cycles.
The Challenge
Standardized, clean data answers “what happened.” It rarely answers “why is this taking so long” or “where exactly is this process breaking down.” The business kept running into the same limitations:
The Solution
We extended the existing Databricks-based data platform with an automated, status-driven delivery layer connecting a client web portal, Azure Table Storage, Azure Databricks, and a leading process mining platform, orchestrated end-to-end with minimal manual intervention.
Status-Driven Orchestration
Every record carries a live status, from initial submission through sync completion, tracked in Azure Table Storage.
Automated Bulk Processing
Azure Logic Apps trigger the pipeline through APIs, so batches of records are processed without manual intervention.
Reusable Databricks Framework
The same medallion pipeline used for data standardization models Purchase Order data for process mining.
AI-Driven Process Analysis
The process mining platform’s AI reconstructs the real, as-executed process and highlights bottlenecks automatically.
The Six-Step Pipeline
Here’s how a single record moves from submission to a fully synced, process-mining-ready state:
1) Website Input
The user submits data via the client web portal, a form or API request initiates the pipeline.
2) Azure Table Sync
Incoming data is written and synced into Azure Table Storage.
3) Status Filter
Records from Azure Table are filtered where status matches:
4) Databricks Pipeline
The framework is executed through the Databricks pipeline, processing all filtered records in batch.
5) Azure Table Update
Once the Databricks sync completes, status is updated in Azure Table:
6) UI Reflection
Synced data is reflected back to the client web portal UI for the end user.
Architecture Overview
Once records reach the “Synced” state, the same medallion architecture used for data standardization models Purchase Order Details and Purchase Order Lines and delivers them into the process mining platform:
Extracts
Lake
Mining
Because the framework is configuration-driven, the same architecture can extend to additional ERP data lake sources, SFTP feeds, or other cloud storage without a redesign.
AI-Driven Process Mining Analysis
With Purchase Order Details and Purchase Order Lines modeled and delivered on a reliable, automated cadence, the process mining platform’s AI reconstructs the real, as-executed purchase order process directly from the underlying event data. Instead of relying on assumptions about how the process should work, process owners see how it actually works: where orders stall, which approval paths deviate from the intended flow, and where cycle time is quietly being lost.
Business Impact
| Before | After |
|---|---|
| Manual exports required to analyze process performance | Fully automated, status-driven pipeline from intake to process mining |
| No visibility into where a record stood in processing | Live status tracking from submission through sync completion |
| Process bottlenecks discovered through manual investigation | AI-driven analysis surfaces deviations and delays automatically |
| Static, after-the-fact process reporting | Near real-time, evidence-based process visibility |
| One-off integration effort per process area | Reusable framework, extendable to other business processes |
Frequently Asked Questions
Does this require a specific process mining platform?
No. The pipeline delivers modeled, business-ready data through Delta Lake and Parquet, which can be connected to most modern process mining platforms.
How often is data refreshed in the process mining platform?
The pipeline is designed for batch processing on a defined schedule, and can be tuned toward near real-time delivery depending on business needs and source system constraints.
Can this be extended beyond Purchase Order data?
Yes. Because the framework is configuration-driven, the same approach can extend to other process areas such as order-to-cash or procure-to-pay.
What happens if a record fails validation?
Records that don’t meet the status criteria simply remain in a pending state and are not passed downstream, so failures are visible and traceable rather than silently dropped.
Conclusion
Clean data is the foundation, but process visibility is where the business value shows up. By connecting a status-driven web portal, Azure Table Storage, and a reusable Databricks framework to a process mining platform, this organization moved from static process reports to a living, AI-analyzed view of how work actually flows through the business.
Turn Your Process Data into Actionable Insights
Looking to move from static ERP reporting to real-time process visibility? Our team helps organizations build automated, auditable pipelines that make process mining tools genuinely useful, not just another dashboard.
Connect with Us