MIPODIS: A Migration-Ready Operational Decision-Intelligence Framework for Safe Data-Source Transitions

Abstract

Operational decision-intelligence systems routinely outlive the data sources they were built on. When an organization migrates from a legacy or development data environment to a new production source, a pipeline can pass conventional schema, type, and distributional checks while the meaning of the data has changed. We present MIPODIS (Migration-Ready Industrial Predictive Operations & Decision Intelligence System), a framework that integrates data validation, provenance tracking, predictive analytics, explainability, and operational decision support into an auditable lifecycle centered on a Migration Compatibility and Semantic Safety Gate. In a leakage-repaired controlled benchmark with known ground truth, full MIPODIS detected 48 of 57 (84.2%; Wilson 95% CI 72.6–91.5%) latent-track, empirically silent, operationally unsafe migrations, compared with 3 of 57 (5.3%) for B4, the strongest nested comparator combining schema, data-quality, drift, and model-output checks. The paired improvement was 78.9 percentage points (cluster-bootstrap 95% CI 70.7–88.0 points; exact seed-level sign-flip p = 6.10×10⁻⁵ across 15 held-out seed clusters). Full MIPODIS produced 0 observed alarms among 180 benchmark safe controls (Wilson 95% upper bound 2.1%). MIPODIS contributes an evaluated systems integration, an auditable migration lifecycle, and a reproducible benchmark for measuring migration-safety failures that remain silent under simpler layered safeguards.