What Your Industrial Data Quality Tool Won't Tell You?

In the world of operational data, what “looks” like good data to IT systems can be fundamentally broken.

A sensor sends data on time - ✅
The schema hasn’t changed - ✅
No null values - ✅

Perfect, right?

Except…

That flow sensor has flatlined.
That temperature reading is drifting ever so slightly.
That pressure gauge is oscillating abnormally.

These are not database issues. They're physical-world issues, but they manifest in your data.

Why does this happen?

Because traditional data quality tools were built to check data structure, not behavior.

They weren’t built to ask:

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⇨ “Is this sensor behaving in a way that aligns with first principles?”
⇨ “Does this pattern indicate degradation, failure, or fouling?”

And most importantly:
⇨  “Is this a data issue… or a real-world operational failure?”

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