metric-reconciliation

Cross-source metric reconciliation normalizes data from multiple sources into a common schema, applies your metric definitions, and compares aggregated values across chosen time windows to identify calculation discrepancies and variances.

Cross-source metric discrepancies often stem from differing metric calculations, time windows, or data lag. Reconciliation workflows surface these variances and classify them as matches, minor, or significant based on your acceptable variance threshold.

Metric reconciliation is the validation of metrics across multiple data sources to expose discrepancies. It is needed for audits and data quality reporting when you require aligned calculations and actionable root-cause insights across systems.

Yes, automation handles data loading and normalization across sources with differing time windows and data lag. You define the metric calculations and acceptable variance threshold, and the workflow automatically generates a discrepancy report.

You need to define two data sources, their specific metric calculations, and an acceptable variance threshold. You also need data access via connections or exports to classify matches, minor, or significant discrepancies.