AI and analytics depend on data.
If the data is incomplete, inconsistent or incorrect, the result can look sophisticated while still being unreliable.
For industrial plants, data quality is therefore not an IT housekeeping issue.
It is an engineering issue.
A model cannot correct a transmitter that consistently gives the wrong value.
Poor calibration, drift or installation problems can create misleading input.
Before analysing data, plants need confidence that the measurements reflect the real process.
Industrial datasets often contain gaps because of:
These gaps need to be understood rather than silently ignored.
Large plants may accumulate thousands of tags over decades.
Different projects may use different naming conventions.
This can make it difficult to know:
Good metadata becomes increasingly important as analytics expands.
Work-order history may contain inconsistent descriptions.
One technician may record “bearing failure.”
Another may write “noise.”
Another may simply close the work order without detail.
This makes historical analysis difficult.
Structured failure codes and disciplined reporting improve the usefulness of maintenance data.
A number without context can be misleading.
Analytics should know:
Errors in these details can distort results.
Collecting every available tag does not automatically improve an analytical model.
Irrelevant or poor-quality variables can add noise.
The objective should be to use reliable and meaningful data, not simply large volumes of data.
Before asking what AI model to use, plants should ask:
Can we trust the data feeding it?
Improving instrumentation, naming, history and records may create more value than introducing a more advanced algorithm on top of poor data.
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