Most utilities know their GIS data isn’t perfect. What they don’t know is how much that imperfection is costing them.
The cost rarely shows up as a single line item on a budget. Instead, it hides in delayed projects, inaccurate bills, inefficient field dispatches, and analytics that produce results no one fully trusts. It’s a tax utilities pay every day—silently, and often without realizing the source of the problem.
The Invisible Foundation
GIS data is the backbone of the modern distribution grid. It defines which assets exist, where they are located, how they connect to each other, and how power flows through the network — no longer just from substation to meter, but increasingly in both directions as rooftop solar, batteries, and EV chargers push energy back upstream. Every system that utilities rely on — ADMS, DERMS, outage management, billing, asset management — depends on this foundation being accurate.
When it isn’t, the consequences cascade. And the erosion is measurable: even GIS data checked to near-100% accuracy when it’s entered typically drifts down to 75–90% accuracy over time as it’s used and updated, according to EPRI research on utility data quality.¹
A transformer mapped to the wrong feeder produces load calculations that don’t match reality. A meter associated with the wrong phase generates billing anomalies that take hours to investigate. A switch recorded as open when it’s actually closed means an outage restoration team dispatches to the wrong location. Individually, each error seems manageable. Collectively, they represent a significant and largely invisible operational burden.
What Utilities Actually Lose
The costs of poor GIS data fall into several categories, most of which are difficult to measure precisely — which is part of why they persist.
Capital project delays. Advanced systems like ADMS and next-generation network models are only as reliable as the data that feeds them. When a utility embarks on a major modernization initiative and discovers mid-implementation that the underlying GIS data is unreliable, the consequences are significant: project timelines slip, vendor costs increase, and the capital tied up in the delayed project carries a real opportunity cost — money that could have been deployed elsewhere. The promised benefits of the new system are deferred as well. A data quality assessment that should have happened before the project started ends up happening in the middle of it — at maximum disruption and cost.
Operational inefficiencies. Field technicians work from grid diagrams. When those diagrams don’t reflect reality, crews spend time in the field figuring out what’s actually there rather than executing the task they were dispatched to do. In outage scenarios, minutes matter. Incorrect connectivity data can mean the difference between a 30-minute restoration and a three-hour one.
Revenue leakage. Incorrect meter-to-transformer mappings, phase mismatches, and orphaned meters don’t just affect planning — they affect billing. The core mechanism here is energy balancing: comparing what flows into a transformer or feeder against what’s actually billed downstream. That comparison only works if the connectivity model is correct. When GIS data doesn’t reflect the real network, energy balancing becomes unreliable, and without reliable balancing, a utility has no solid basis for monitoring non-technical losses at all. Energy that can’t be properly attributed gets logged as non-technical loss by default — even when the real cause is a bad connectivity record, not theft. The utility ends up managing a non-technical loss number that may be inflated by its own data gaps, with no clear way to tell how much of it is genuine and how much is simply GIS error.
Analytics that can’t be trusted. When a utility invests in grid analytics — load forecasting, capacity planning, DER impact assessment — those analytics are only as good as the data model underneath them. GIS errors don’t just affect the specific assets involved. They propagate through topology calculations, affecting every downstream analysis. The result is teams that have learned to distrust their own data, and insights that get second-guessed rather than acted on.
DER integration risk. As distributed energy resources — rooftop solar, batteries, EV chargers — connect to the distribution grid in growing numbers, accurate GIS connectivity becomes even more critical. A utility that doesn’t know precisely which transformer serves which meters, or which phase a new EV charger is connected to, cannot effectively manage the load and generation variability that DERs introduce. The grid of 2030 requires a level of data fidelity that most utilities have not yet achieved.
Why the Problem Persists
If the costs are real, why doesn’t every utility prioritize GIS data quality?
Part of the answer is that the costs are distributed and hard to attribute. No one budget line says “GIS data errors cost us $X this quarter.” The project delay shows up in the capital budget. The field inefficiency shows up in operations. The revenue leakage shows up in non-technical loss. Each department experiences a symptom without seeing the common cause.
Part of the answer is also that fixing GIS data has traditionally been expensive and slow. Manual verification of network connectivity — the classic “walk the line” approach, checking asset by asset, connection by connection — is a multi-year effort that requires significant resources and produces results that can be outdated before they’re complete.
A Different Approach
The emergence of automated data quality assessment changes the calculus significantly. Rather than treating GIS data quality as a one-time manual project, utilities can now ingest their GIS data into a platform that automatically detects topology errors, validates connectivity, flags orphaned meters and phase mismatches, and generates structured reports with prioritized corrections.
The process doesn’t require years. It requires data access and a defined scope — and it produces a clear, quantified picture of where the errors are and what fixing them would enable.
At Awesense, this is exactly what our Energy Data Engine was built to do. We’ve worked with utilities across North America and Europe to assess GIS data quality as a first step toward grid modernization — and in every engagement, the findings reveal errors that were costing the utility more than anyone had estimated.
The cost of bad GIS data is hidden. But it’s not small.
