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Solving Data Quality Issues

We implemented automated data validation within the finance data pipeline to resolve quality issues and prevent them from recurring in new systems.

The situation

The client was upgrading their finance and accounting systems and uncovered a data quality issue. Left unresolved, the same problems would continue inside the new systems.

The opportunity

This was a chance not just to clean historical data, but to stop the problem recurring, and move forward working with accurate data.

How we made it happen

We consolidated financial data from across the business and put automated checks and cleaning in place as part of the ingestion process. Data was standardised, verified, and validated as it entered the platform, removing the need for manual fixes downstream.

The same automated pipeline was then used to support ongoing financial reporting and analytics, ensuring consistency across systems and reports.

What changed

  • Revenue assurance processes automated, recovering development costs within months
  • Historical data cleaned and new data verified automatically
  • Month-end close reduced from five days to one
  • The same clean data used to power Power BI reporting
  • Ongoing data quality maintained without manual intervention

Why it matters

Poor data quality quietly undermines financial control and profitability. By preventing issues before they reach live systems, businesses reduce risk, save time, and improve financial performance.

Data Sources

  • Premier Spa Core
  • Guestline
  • Tevalis

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