El Paso Water Reduces Unknown Service Lines by 90% Using Predictive Modeling

Overview

El Paso Water, a large Texas utility serving approximately 240,000 service lines, entered 2025 with nearly 130,000 lines classified as unknown. This substantial number of unknowns created regulatory pressure and operational challenges as compliance requirements intensified. The utility had already verified approximately 60,000 lines in six months, but inaccessible infrastructure and diminishing returns from manual inspections made it clear that a more scalable solution was needed.

The Challenge

Despite significant fieldwork, traditional verification methods alone could not efficiently eliminate the remaining unknowns. Continued reliance on manual inspections would require additional time, funding, and staff resources, while also increasing customer notifications. El Paso Water needed a cost-effective, data-driven approach that could accelerate progress without overextending operational capacity.

The Solution

El Paso Water partnered with 120Water to implement predictive modeling to supplement its field efforts. By leveraging historical data and advanced analytics, the model classified more than 40,000 previously unknown service lines as Non-Lead at an extremely low per-line cost. This allowed the utility to strategically prioritize fieldwork only where risk remained highest, reducing unnecessary inspections and communications.

The Results

Within one year, El Paso Water reduced its unknown service lines from approximately 130,000 to fewer than 13,000—an estimated 90% reduction. The approach saved the utility more than $150,000 in avoided fieldwork and associated costs while significantly streamlining compliance efforts. By combining targeted field verification with predictive modeling, El Paso Water dramatically improved inventory accuracy and positioned itself for long-term regulatory success.