Datagaps Podcast

Welcome to the Deep Dive! In this episode, we unpack why traditional business intelligence dashboards are failing modern regulatory compliance and how data engineering teams can fix it.The Root Cause: Regulatory compliance is fundamentally a data quality challenge, not just a reporting issue. Relying on dashboard-level checks is inherently reactive, catching discrepancies far too late in the data pipeline.The Restaurant Kitchen Metaphor: Checking a dashboard is like inspecting a meal after it has already been served to the customer. To guarantee quality, you must inspect the ingredients as soon as they enter the kitchen. Regulators don't just want correct numbers; they require provable correctness.The 5 Pillars of Provable Correctness: To build defensible regulatory controls, data teams need to enforce five specific checks within their pipelines:Schema and structural consistency.Source-to-target reconciliation.Precision and tolerance validation.Completeness and referential integrity.Historical and trend-based anomaly detection.Controls-as-Code: The ultimate fix is integrating data quality validation directly into DevOps workflows. By treating compliance rules as version-controlled assets, teams can ensure transparency and consistency alongside their ETL logic.Continuous Assurance: Moving away from periodic audits, a continuous assurance model executes validations automatically with every pipeline run, generating always-available, audit-ready evidence.