Uncovering gaps and opportunities in water distribution system operations through graph-database-driven data utilization and process mapping

The water industry faces increasing challenges in ensuring efficient and sustainable delivery of safe drinking water through ageing infrastructure while meeting operational, regulatory, and customer demands. Large volumes of data are generated but are stored and utilised in disconnected silos. Such fragmentation limits the potential for comprehensive analysis, informed decision-making, and operational optimisation. To better understand the impact of this fragmentation, we developed a three-layer data and process connectivity framework, a novel graph-database-driven approach designed to identify, analyse, and address gaps in data utilisation and data process efficiency. The framework systematically maps critical data flows and reveals interdependencies between data, intermediary processes, and business applications. We validated the framework through a case study with Scottish Water, constructing a graph comprising 127 nodes and 911 relationships across water distribution data and processes. Using node centrality measures, relationship-based graph querying, and impact simulations, we demonstrate how this approach exposes inefficiencies, uncovers overlooked data relationships, and generates actionable insights for process integration. The findings highlight the potential of graph-database-driven approaches to overcome data fragmentation, bridge silos, and enable more data-driven and strategically optimised operations, with applicability across the wider water sector and other industries.