01
Preserve meaning at ingestion
Carry source identity, timestamp, engineering unit, quality status and relevant equipment hierarchy with each value. Normalize carefully and retain the original meaning needed to trace a result back to its source.
02
Make data quality observable
Track missing, delayed, substituted and out-of-range values. Distinguish a measured zero from a missing sample and record transformations so downstream users can judge fitness for purpose.
03
Connect models to decisions
Join process values to production state, orders, materials or quality events when the use case requires them. Keep the lineage and assumptions visible in reports, analytics and applications.