Cloud Migration of Enterprise Reporting Platforms with Automated Validation of Output Data Quality

Abstract

A reporting platform builds trust one report at a time, but in a migration, all of that trust can be lost in a single event. This paper treats cloud migration of enterprise reporting platforms and automated validation of report output data quality as a single engineering problem instead of build and testing phases. Output data quality is defined at the report boundary of a published figure as it matches or does not match what the incumbent platform produced. It is distinct from record-level data quality dimensions. Drawing on practitioner experience building source-to-target reconciliation frameworks for large reporting migrations in regulated financial services, the paper presents a reconciliation ladder mapping validation techniques to architectural layers and stages of migration. Cross-sector findings for finance, commerce and health system reporting include constraints that design validation architecture, with also harness-first sequencing, baseline-freeze discipline and anti-patterns for migration pitfalls. Taking into account machine-assisted migration, in the model LLMs compress the burden of mechanical translation and a named human retains adjudication authority over materiality, tolerance and cutover.