Ensuring Data Quality in the Water Sector: Challenges, Framework, and Best Practices

Dat a quality poses a significant challenge in the w ater sector.Inconsistent, fragmented, and poor-qu ality data slow down digit al transformation, making algorithms and w ater models less reliable and harder to validate.This paper introduces the WAT ERVERSE Dat a Q uality Framew ork, a domain-specifi c approach tailored to the curation, assessment and improvement of time-seri es data in the w ater sector.By quantifying data quality across completeness, consistency, timeliness, uniqueness, and validity, the framew ork 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 Q uality Assessment tool and a Data Validation and Reconciliation tool.We demonstrate the framew ork on two pilots: hourly Spanish metering data and Dutch conductivit y time series.The iterative assessment-reconciliation cycl e demonstrated measurable improvements in data quality scores; for example, timeliness for a problematic Spanish meter increases from 52% to 100%, whil e validity for Dutch conductivit y sensors rises from 67%/59% to 96%/92% respectively.These results show that the framew ork enhances data reliabilit y and that its architecture is sufficiently flexible and scalable to support adaptation to other domains that depend on high-qu ality time-seri es data. Impact StatementThe WAT ERVERSE Data Q uality Framew ork advances how data are managed and used in the w ater sector, where reliable information is essential for effective decision-making, regul atory compliance and operational efficiency.By addressing persistent challenges such as inconsistent records, fragmented systems and outdated monitoring practices, the framew ork offers a structured w ay to improve the quality of time-seri es data from smart meters, online w ater quality sensors and other operational sources.Thro ugh its iterative process, whi ch combines automated assessment and data reconciliation, the framew ork enables w ater utilities to systematically detect and correct issues such as missing, duplicated or erroneous data.This supports more trustworthy indicators for leakage, consumption, w ater quality and asset performance, helping utilities prioritise interv entions, optimise resource allocation and justify investments in infrastructure and digit alisation.The deployment of the framew ork in pilot projects in Spain and The Netherl ands demonstrates that these benefits are attainable in real operational contexts, not just in theory.In both cases, data quality scores improved substantially, strengthening the basis for analytics, foreca sting and digit al-t win applications.Beca use the framew ork is gen eric at the architectural level and tool-supported, it can be adapted to other organisati ons and even other sectors that depend on high-qu ality timeseries data.By offering a domain-specifi c yet transferable approach to data governance, assessment and continuous improvement, the framew ork contributes to more transparent, reliable and sustainable management of critical infrastructure systems, and can ultimately enhance trust among customers, regul ators and other stakehol ders.