Research question
Can anomaly detection and pre-model data controls be designed as one operational workflow?
Core idea
Combine statistical anomaly detection with automated validation so that unusual behavior is interpreted alongside data completeness and quality evidence.
Why it matters
A model alert is less useful when the input itself is stale, incomplete, or inconsistent. Operational systems need to distinguish data failure from genuine behavioral change.
Contribution
The work connects statistical anomaly detection with automated data-quality validation so operational teams can interpret unusual behavior alongside the condition of the input data.
Limitations
The evaluation demonstrates the proposed workflow in the reported study context. It should not be read as evidence that one detector, threshold, or validation strategy transfers unchanged to every domain.
Status
Published in the International Journal of Data Intelligence and Modeling, 2025.