RUBIKSCORPAI in factories
Why factory data is not ready for machine learning
Production data is often incomplete, inconsistent across machines or locked in separate systems. Models built on it learn the gaps instead of the process.
Audit the data before selecting a model
Inventory the sources needed for one proposed use case. Record what each field means, its unit, timestamp, ownership and collection frequency. Review whether identifiers match across systems and whether clocks are synchronized. A large file can contain very little relevant information about the decision you want to improve. Look for changes in how measurements were recorded over time.
What to check
- Measure missing values, duplicate records, impossible readings and gaps by machine and shift.
- Check whether maintenance, quality and production events can be linked reliably.
- Confirm that the required labels exist and that the data can be used for the proposed purpose.
What to change
Create a small, documented dataset with a reproducible extraction method. Keep a record of corrections and avoid silently filling gaps with assumptions. Establish a baseline using simple rules or existing reports. If essential data is unavailable, propose a limited collection exercise with a clear decision about whether to continue after reviewing it.
How to evaluate the result
Report coverage, freshness, label quality and the proportion of records that can be linked across sources. Explain how gaps affect the particular use case rather than issuing a generic readiness score.
What the result does not establish
Some projects should pause until measurement improves. Identifying that limitation early is a useful outcome: it can prevent investment in a model that cannot be tested or operated reliably with the available data.
Further reading
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