Water companies have no shortage of data – just a shortage of ways to make sense of it all in an auditable way. From individual household monitors to bulk metered areas, small area monitors, district metered areas, traditional property meters and the ever-expanding world of smart meters, there’s plenty of numbers flying around. The problem? Each source tells a slightly different story, and blending them into a single, trustworthy picture is no small feat. Some datasets are precise but limited in scope, others are broad but prone to biases. And when auditors come knocking, having a clear, defensible approach is essential.
That’s where Artesia’s Hybrid Data Integration (HDI) framework comes in. Think of it as a clever way to combine multiple estimation techniques, ensuring a solid, transparent, and auditable result. It’s a methodology that works today—helping companies manage their reporting challenges—and lays the groundwork for the future when smart metering is everywhere.
Water companies serve an eclectic mix of customers, shaped not just by property type but also by a range of demographic and socio-economic factors. Household size, income, lifestyle, and even cultural habits all influence consumption patterns, and smart meters, while revolutionary, don’t provide a magic bullet. Their data is shaped by where they’re installed, who opts in, and what happens to leaks along the way. Relying on smart meter readings alone would be like trying to understand the entire population’s commuting habits by only studying train passengers—valuable insights, but missing those who drive, cycle, or walk.
At present, there’s little industry-wide guidance on how to integrate multiple estimation sources, leaving companies to piece things together as best they can. The risk? That their well-intentioned, pragmatic methods look like a pick-and-mix approach rather than a robust system. HDI provides a structured, transparent way to bring all this data together while maintaining flexibility and the possibility to adapt to each company’s unique features.
The HDI process works in two stages. First, it splits the population into groups — those with direct measurement data and those still lurking in the unmeasured shadows. For the first, we take raw data as the foundation. For the latter, we use a data assimilation (DA) approach, carefully weighting different estimation sources based on their uncertainty.
The second stage is about bringing everything together. Here, another weighted averaging process ensures the final estimate accurately reflects the total customer base while ironing out overlaps between measurement techniques—no double-counting, no inconsistencies.
The HDI approach can be applied to a variety of estimations, particularly for reporting key figures like unmeasured per capita consumption (uPCC), household night use (HHNU), non-household night use (NHHNU), non-household consumption (NHH), or leakage components. However, its flexibility means it can potentially be used for any estimated quantity, making it a valuable tool beyond just regulatory reporting.
Beyond helping individual companies, HDI has the potential to shape broader industry guidance. A consistent, repeatable method for data blending could provide a much-needed framework for regulators and auditors alike, replacing the current patchwork of bespoke solutions with a more unified approach.
With smart metering on the rise, the way we estimate water consumption is evolving fast. But for the foreseeable future, companies will need to juggle a mix of old and new methods. HDI helps bridge this gap, ensuring consistency between historical data and emerging insights.
At Artesia, we believe in clear, transparent methodologies that stand up to scrutiny. HDI isn’t just a technical fix—it’s a step towards a smarter, more standardised way of handling water consumption data. If you want to hear more, you know where to find us!
