Water flows through every aspect of life, yet the story of its delivery is only as reliable as the data that records it. In global benchmarking, such data is often uneven, incomplete, and rarely subjected to systematic validation, allowing anomalies to shape perceptions of performance before they are critically examined. This thesis addresses that gap by developing and evaluating a multi‐stage, data‐driven anomaly detection framework within the World Bank’s New International Benchmarking Network for Water and Sanitation Utilities (NewIBNET), situated at the intersection of data science, water governance, and digital ethics.

The framework weaves together four complementary layers – structural validation, rule‐based logical checks, peer comparison, and weighted prioritisation – transforming anomaly detection from a surface‐level cleaning task into a structured process of active quality assurance. Developed through an iterative, expert‐informed process, it is reproducible and adaptable, balancing statistical rigour with the contextual realities of the water sector so that each flag raised carries both analytical credibility and practical relevance.

Applied to the 2022–2024 NewIBNET dataset, the framework is assessed through robustness checks, a national case study of Indonesian utilities, and an expert survey. Results show that it improves anomaly interpretability, limits the propagation of flawed data into comparative analyses, and reduces review time from 75 hours to under 2 minutes – earning unanimous expert endorsement for operational deployment.

By translating the principles of automated, ethically grounded validation into a scalable methodology, this work advances the state of practice in anomaly detection for data‐scarce sectors. In shifting from red flags to real solutions, it demonstrates how automated validation can turn detection into action, building trust where data meets water, and enabling more transparent, equitable decisions in global water governance.

A learning curve can serve as an indicator of the “performance of trained models versus the training set size” [1]. Recent research on learning curve analysis has been carried out within the Learning Curve Database (LCDB) [2] This paper will investigate if there are similarities amongst these curves by clustering those provided by the LCDB. The experiment employs two distinct input parameters: point vectors and statistical vectors. By conducting individual learner analysis, individual dataset analysis, principal component analysis, and other experiments, patterns are isolated for both input sets. Upon further exploration of shapes and distributions, the concluding remark is that the point vector clustering produced one key concrete pattern amongst certain learning techniques. In contrast, the statistical vector findings are more inconclusive and do not exhibit a clear distinction that could be linked to any dominant patterns.