The challenge
Critical revenue and sales reporting relied on fragmented manual workflows spread across disconnected files, extracts, and analyst-owned processes.
The analytics team carried a 75% time tax on recurring data preparation. This slowed reporting cycles, increased operational dependency on individual knowledge, and amplified the risk of serious data errors reaching decision-makers.
What we did
We architected and deployed end-to-end automated ETL pipelines within an Azure SQL environment.
The new architecture standardised ingestion, transformation, validation, and reporting outputs. Manual oversight was replaced with an exception-based model, where the team only needed to intervene when data quality checks or process controls surfaced a genuine issue.
The implementation also introduced repeatable pipeline monitoring, clearer data ownership, and stronger reconciliation across revenue and sales datasets.
Outcome
Monthly data-processing latency was reduced by 75% while maintaining a 95% first-run success rate.
The analytics team was released from repetitive manual processing and could redirect effort toward higher-value strategic analysis, performance interpretation, and commercial decision support.