Drinking water distribution systems operate to transport high-quality treated water across large distances to entire populations, yet water quality and contamination events occur between treatment and tap. Deployment of water quality instrumentation within drinking water distribution systems enables such events to be better understood. Specifically, in-network turbidity sensors offer a unique opportunity to measure network discolouration events, which are difficult to predict and may pose health risks to end users. However, extracting actionable information from the increasing volume of water quality data represents a major challenge to realising the true benefits of digitalisation. Typically this involves manual interpretation of time series plots, which is time-consuming and impractical for larger sensor networks. There is therefore a need to develop automated algorithmic approaches to process and integrate the turbidity signals. However, the information that is of interest and such algorithms should detect is uncertain. This study employed crowd-sourcing exercises with groups of domain experts to identify significant features within turbidity time series data from real-world distribution systems. The labelled data derived from these exercises delivers valuable insights and a critical benchmark for evaluating algorithmic methods designed to replicate human interpretation. Reflecting on the outcomes of the labelling tasks led to the development of a turbidity event scale that differentiates between advisory (< 2 NTU), alert (2 < NTU < 4), and alarm (> 4 NTU) level events. This event scale provides network operators with tools required to manage discolouration events both reactively and, crucially, proactively. A time-based averaging method, centred on data from the same time each day, proved most effective in identifying the advisory events, when compared to popular time series forecasting approaches. The event scale is demonstrated on a real-world example not included in the labelling exercises, showcasing the practical benefits and scalability of this data-driven approach. The automation of event detection and categorisation developed here offers the potential to obtain actionable insights to protect the quality of drinking water as it passes through ageing network infrastructure.