Restoring Mission‑Critical Reporting for a Large Retailer
Problem
A large retailer relied on point‑of‑sale (POS) data for essential business reporting including tracking product performance, analyzing trends, and informing operational decisions. Their existing method of pulling POS data into their analytics environment used a connector that was scheduled to be deprecated, putting all reporting at immediate risk.
The client attempted to rebuild the process internally using a series of API‑driven Power Automate flows, but the effort became far more complex than expected. They struggled with incorrect API documentation, slow and unstable endpoints, and limited internal familiarity with the required data‑landing process. Without a fix, they faced the possibility of losing visibility into core sales data.
Solution
The team stepped in to diagnose the issues, review the client’s partially built approach, and reconstruct a stable, well‑documented process for loading POS data into their Lakehouse environment. This included:
Identifying and correcting the use of an outdated API endpoint.
Rebuilding data ingestion to ensure reliability, proper documentation, and repeatability.
Enhancing the process to support faster and more frequent data refreshes.
Conducting deep research where internal documentation was lacking, ensuring the client understood and could maintain the solution going forward.
The final result restored and strengthened the retailer’s data pipeline before any reporting outage occurred.
Impact
The new data‑landing process delivered immediate operational value:
Continuous access to POS reporting: The retailer avoided any disruption despite the deprecated connector.
More frequent and up‑to‑date reporting: Faster data refresh cycles enabled decision‑makers to work with near‑real‑time sales data.
Efficiency and performance improvements: Modifications to the landing process reduced compute strain and increased overall system reliability.
Improved documentation and maintainability: The client now has clear visibility into their data pipeline, which is something they previously lacked.
Positioned for future AI initiatives: With cleaner, centralized data, the retailer is now better prepared to explore AI‑assisted analytics and automation.
Broader Applicability
Retailers and other data‑driven organizations frequently rely on legacy connectors, undocumented processes, or outdated integrations that quietly underpin critical reporting. When those components break or get deprecated, entire analytics programs can be thrown into crisis.
This project highlights how businesses benefit from:
Modernizing data ingestion pipelines
Documenting data flows for operational continuity
Centralizing data to enable scalable analytics and AI adoption
Bringing in expert support when internal teams lack the time or specialized experience
Organizations with significant sales, operational, or transactional data can dramatically reduce risk and unlock future innovation by ensuring their data infrastructure is stable, current, and well‑managed.