Utilities are being asked to stretch maintenance dollars across older assets and higher loads. Many utilities have responded by expanding monitoring through SCADA systems, thermal sensors, and power monitors. Yet a pattern we hear consistently from utility operations leaders is that the data exists, but utilities are not yet leveraging it to make maintenance decisions more efficient.
Maintenance is still reactive. When something fails, the post-mortem often finds that an earlier signal was there, buried in a technician note, locked in a siloed system, or never captured in a consistent format to begin with.
This is an operations problem, and predictive maintenance only becomes viable when utilities can rely on structured, traceable, and consistently captured field data.
Reactive Maintenance Has a Price Tag
The cost of staying reactive grows quickly. A single unplanned truck roll in specialized field service can run hundreds of dollars in labor and mileage alone before parts or rework are factored in. When a technician cannot resolve the issue on the first visit, that cost multiplies with each repeat visit (Emerj, 2026).
Unplanned downtime carries an even larger cost. A 2026 Oak Ridge National Laboratory analysis found that major outages cost U.S. customers $121 billion. The same analysis found that the average major outage lengthened from 9.6 hours in 2018 to 11.8 hours in 2024 (ORNL, 2026).
Age-based maintenance can create blind spots. Healthy assets may get attention too early, while stressed assets wait too long because teams do not have accessible data to adjust the maintenance plan.
Predictive maintenance closes that gap, but only when the field data powering it is consistent and complete.
The Missing Piece: Usable Operating Data
A maintenance record may show that work was completed without explaining what condition was found, what changed, or what should be watched next (EC&M, 2026). A crew may describe a transformer issue in shorthand. Another crew may document a similar issue with different terminology. A contractor may upload photos without connecting them to the right asset. This makes it hard to spot patterns.
If inspection notes, photos, asset IDs, and closeout records are inconsistent, the utility may have years of history without a clear view of what it is saying. The next crew may still be starting from scratch.
Utilities need inspection records that are consistent, asset information that is current, work status that is visible, as well as closeout evidence that shows what was found and what was completed.
How Predictive Maintenance Eliminates Blind Spots
Predictive maintenance brings analytics and condition-based maintenance together to identify issues earlier. It helps utilities move from reacting to individual failures to identifying patterns across asset condition, inspection history, operating data, and maintenance records. Teams can see which assets are showing early signs of stress, which issues are repeating across similar equipment, and where maintenance plans may need to change before failure occurs.
Historical maintenance records can show how similar components deteriorate under comparable conditions. For example, utilities can compare wood poles with similar age, material, loading, soil exposure, moisture conditions, and maintenance histories to identify recurring deterioration patterns. These patterns can help teams determine when comparable poles may warrant added inspection, treatment, reinforcement, or earlier replacement review rather than waiting for visible failure.
That visibility gives operations leaders a more practical way to focus on assets showing the highest risk. When predictive analytics is connected to maintenance planning, those insights can become prioritized inspections, targeted repairs, earlier replacement reviews, and better asset decisions.
Make Existing Data Actionable
EKN Engineering connects asset health, quality management, field inspection, and data infrastructure into workflows utilities can use across crews, contractors, regions, and programs. Field observations are captured consistently, asset condition becomes easier to compare, work can be prioritized by risk, and leaders can see progress without chasing updates across disconnected systems.
Connecting current inspection findings with prior maintenance and replacement records also helps utilities evaluate how similar components have performed under comparable conditions. That history can support decisions about which assets may need earlier intervention and which can continue to be monitored.
EKN’s programs have helped utilities achieve:
- $4M in annual savings through prioritized maintenance programs
- 99.8% compliance traceability under CPUC/NERC oversight
- 8K+ projects managed with full data integrity
- supported 20–30% less rework
That kind of performance starts with inspection and maintenance records that are structured, consistent, and usable for asset planning.
Utilities do not have to begin with a full predictive maintenance program. EKN helps utilities build the foundation by structuring the field-to-office workflows, asset records, inspection requirements, and progress visibility needed to turn existing data into better maintenance decisions.
First, audit the data utilities already collect. Identify what teams can actually search, compare, and use versus what is buried in technician notes, spreadsheets, PDFs, or disconnected systems.
Second, standardize field inspection capture for the assets that matter most. Transformers, underground cable, substation equipment, distribution lines, and transmission structures should have clear inspection requirements so crews and contractors document issues the same way.
Third, pilot predictive maintenance on a defined group of assets. A focused pilot gives operations leaders room to test data quality, inspection workflows, asset health scoring, crew adoption, and ROI before scaling.
The data to identify the next failure may already exist in your systems. EKN Engineering helps utilities structure that data, connect it to field execution, apply lessons from comparable assets and prior maintenance cycles, and turn it into maintenance decisions teams can act on before issues become outages.