A team debates whether machine learning can replace data-quality rules entirely for permit and billing constraints. What balanced stance fits modern DQ practice?
Select an answer to reveal the explanation.
Short Explanation
Hard billing and permit constraints still need seatbelts—explicit rules—even if ML is a sharp lookout for weird spikes. Keep rules and monitoring for must-pass constraints; let ML assist anomaly detection. Do not pretend models erase DQ management.
Full Explanation
Explicit rules remain appropriate for critical hard constraints that must pass or fail deterministically for billing, safety, or regulatory submission. Machine learning can assist by surfacing anomalies and unusual patterns that rigid rules miss. Replacing the entire DQ management stack with models removes auditable controls and clear acceptance criteria. A hybrid stance preserves rules where certainty is required and uses ML as an assistive detection layer.