Ensuring data quality in the water sector: Challenges, framework and best practices

“Data quality poses a significant challenge in the water sector. Inconsistent, fragmented and poor-quality data slow down digital transformation, making algorithms and water models less reliable and harder to validate. This article introduces the WATERVERSE Data Quality Framework, a domain-specific approach tailored to the curation, assessment and improvement of time-series data in the water sector. By quantifying data quality across completeness, consistency, timeliness, uniqueness, and validity, the framework provides a structured reference for future initiatives. It encompasses essential elements, including data governance, assessment, improvement, reporting, and monitoring, and is operationalised through two tools integrated in a Water Data Management Ecosystem: a Data Quality Assessment tool and a Data Validation and Reconciliation tool. We demonstrate the framework on two pilots: hourly Spanish metering data and Dutch conductivity time series. The iterative assessment–reconciliation cycle demonstrated measurable improvements in data quality scores; for example, timeliness for a problematic Spanish meter increases from 52% to 100%, while validity for Dutch conductivity sensors rises from 67%/59% to 96%/92%, respectively. These results show that the framework enhances data reliability and that its architecture is sufficiently flexible and scalable to support adaptation to other domains that depend on high-quality time-series data.”

(Citation: Baena Miret S, Vrijhoeven T, Akson A, et al. – Ensuring data quality in the water sector: Challenges, framework and best practices – Cambridge Prisms: Water 4(2026)e15 – https://doi.org/10.1017/wat.2026.10023 – (Open Access))

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© The Author(s), 2026. Published by Cambridge University Press