No data is not zero: the danger of a smooth line

A chart with a hole in it looks like something is broken, and a dashboard full of holes looks like a product that does not work. So there is constant pressure — from users, from designers, sometimes from ourselves — to make the line continuous. The two easy ways to do that are to drop the missing value to zero, or to bridge it with a smooth interpolation. Both make the picture look finished. Both quietly destroy its meaning.

When an inverter or meter stops reporting, the honest statement is "we do not know what happened here." Zero says something entirely different: it asserts that nothing happened — no generation, no consumption. On a solar plant at midday, a string that reads zero because its data is missing looks identical to a string that has genuinely failed. So one of two errors follows: you raise a false alarm for a plant that was actually fine, or — far worse — a real dead string hides inside a sea of data-gap zeros and never gets found.

This is why SPC treats one sentence as non-negotiable: no data does not equal zero generation. A missing signal is labelled as missing. It never becomes a tidy zero that a chart, an average, or an alert can mistake for a measurement.

Interpolation is more dangerous than zero precisely because it is more convincing. A straight or smoothed line across a gap looks exactly like real telemetry, and nothing about it warns the reader that no instrument recorded any of it. It launders a guess into apparent fact. Downstream, an engineer sizing a battery, a CFO checking a bill, or an alerting rule looking for deviation all treat that invented curve as ground truth — and inherit an error they cannot see.

The failure is compounding: every calculation built on a fabricated segment carries the fabrication forward, and none of them know to distrust it.

Both platforms are built on the same refusal. SPC surfaces gaps rather than hiding them, and judges a string against a weather-adjusted expectation only when it has real data to judge. WattEY carries a total it can recover but marks the timing it cannot know, rather than smoothing across it. The shared principle, in WattEY’s words, is that a number that looks complete is not necessarily a number you can trust — so the platform shows when it does not know rather than silently filling the missing period.

The pay-off is subtle but total: because the platform never fakes data, the data it does show can be acted on without a second guess. Honesty about the gaps is what makes everything else believable.