The most dangerous result is a clean one
An analytics report that says "no issues found" is making a strong claim. It says someone looked and found nothing wrong. Too often, it means something much weaker. The data was not there. A sensor stopped reporting last week. The list stopped early. The check covered only part of the network. On the screen, all of those look exactly like a clean result.
Consider a few ordinary cases. A voltage check runs on a feeder where many meters have not reported in days, and it finds no violations among the meters that did. A leak screen runs on a water district whose district meter has gone stale, and it reports no leak. A findings list shows the first page of results, and nobody notices there were more. Each of these produces a clean answer that is not true.
For a utility, the cost is real. Capital plans, regulatory filings, and field work orders all get built on analytics. A result that is silently wrong is worse than one that is openly uncertain, because nobody knows to question it.
Why it happens
Most analytics tools drift into this for ordinary reasons.
Missing data disappears quietly. In most queries and reports, a missing value simply drops out. Averages skip it. Filters skip it. Counts ignore it. The absence of evidence becomes the absence of a problem.
Results get cut short. Large networks produce large result sets, so tools limit what they return. That is reasonable, as long as the limit is visible. Often it is not.
Coverage is uneven. AMI coverage varies by territory. SCADA is dense at the substation and sparse on the feeder. GIS attributes are complete in one district and patchy in the next. A check that runs everywhere does not see everywhere equally.
Inference passes as measurement. Some things the utility cares about are not recorded anywhere. Which water pump station depends on which electric feeder is one example. How old the water is at a given street, and how much chlorine is left in it, is another. Tools that infer or model these often present them with the same authority as metered data.
Nobody checks the checker. Analytics find errors in the utility's records. Far fewer are designed to find errors in their own findings.
Principles of honest analytics
"Not measured" is its own answer. Every check should be able to return three outcomes: a problem was found, the evidence was examined and nothing was found, or the evidence was not there to examine. The third outcome should never be shown as the second.
Name stale evidence. A finding, or a clean result, should carry the age of the evidence behind it. A reading from this morning and a reading from last month should not look the same.
Say when results are truncated or coverage is partial. If a list shows part of a larger set, say so. If a check covered only the meters that reported, say how much of the network that was.
Rank worst-first and link to the map. People act on the top of the list. Findings should be ordered by how much they matter, and each should open directly on the asset it concerns, so an engineer can see it in context.
Label inferred and modeled results for what they are. When a relationship or a value comes from inference or a model rather than measurement, the label should travel with it all the way to the person who acts on it.
Report on the record; do not rewrite it. Analytics should set their findings beside the utility's system of record and leave the correction to the people who own that record. When the record contradicts itself, that is a finding too.
Refuse rather than guess. When a result or a correction cannot be made on the evidence at hand, say so and say why. A gap with a reason is more useful than a number filled in to look complete.
Check the platform against itself. Track how findings hold up once people act on them. Compare independent views of the same fact, and treat disagreement as a finding about the analytics, not only about the grid. A forecast should report its skill against a simple baseline, and say plainly when it does not beat "same as yesterday." A platform that can catch itself being wrong earns more trust than one that never admits it could be.
What it looks like in practice
Grid Data Enhanced Analytics (GDEA) is built on these principles. It joins the utility's GIS connectivity model, SCADA telemetry and events, and AMI reads and events into one live picture across electricity, gas, and water, and runs a library of analysis checks against it, scoped per commodity. Every finding is ranked, linked to the map, and honest about truncation and coverage. GDEA never presents "not measured" as "nothing found," and it names stale evidence when it sees it.
The same discipline runs through the rest of the product. Cross-commodity interdependency, such as a de-energized feeder that was carrying a pump station, is inferred from geometry and labeled as inferred. Water age and chlorine residual across the network are computed from the hydraulic model and labeled as computed, never measured. EV charging found in meter data is set beside the utility's record, and the record is reported on, never rewritten. Gas pipe records that contradict themselves are surfaced as a finding for the records team. Fault location uses independent methods and states whether they agree. The scenario sandbox refuses a result, with reasons, when a proposed change cannot hold. The gas balance does the same with its correction for gas stored in the pipes: when the data it needs is missing, GDEA says why instead of guessing. The water demand forecast is scored against "same as yesterday" and says so when it does not beat it. And GDEA tracks finding quality and checks its own results against each other, so it can catch the platform being wrong.
Illustrative example (hypothetical)
The following scenario is hypothetical. An engineering team reviews the analysis findings for its service territory before a planning meeting. The list opens worst-first, and each item opens on the map.
A water district shows no leak finding, but it is marked "not measured," because the evidence behind its night-flow check is stale. Instead of reporting the district as clean, the team files a request to restore the district meter.
A planned feeder outage is flagged because it would de-energize a pump station. The relationship is marked as inferred from geometry, so the electric planner calls the water team to confirm before rescheduling anything.
A list of transformer findings carries a note that it shows part of a larger set, so nobody mistakes the first page for the whole story.
Finally, finding-quality tracking shows that one category of findings rarely holds up in the field. The team traces the cause to a gap in the source records, fixes the records, and the false findings stop.
Every one of those outcomes came from analytics that were clear about what they knew.
Questions to ask any vendor
When a check cannot run because data is missing, does the product say so, or report nothing?
Does each finding, and each clean result, show how fresh its evidence is?
Are truncated lists and partial coverage clearly labeled?
Are findings ranked by importance and linked to the asset on the map?
Which results are inferred or modeled rather than measured, and are they labeled that way?
Do forecasts show their skill against a simple baseline, even when it is poor?
Does the product ever overwrite your system of record?
How does the product detect its own errors, and can it show you?
Closing
Trust in analytics is built on the edge cases: the stale sensor, the partial list, the inferred link, the forecast that is no better than yesterday. A platform that is honest about those is one engineers, executives, and regulators can rely on for everything else.
Learn more about Grid Data Enhanced Analytics at perinimble.com/grid-data-enhanced-analytics/, or talk with our team at perinimble.com/contact/.