metodologia-data-quality

Build a data quality framework by profiling baselines, enforcing centralized validation rules, codifying data contracts, and monitoring SLAs across ingestion and transformation pipeline stages.

Data contracts are codified agreements between producers and consumers that enforce schema validation and versioning. They fit into a data quality framework by preventing upstream pipeline changes from breaking downstream analytics.

Set up anomaly detection by configuring baseline profiles and health scores for core datasets, then mapping detected deviations to automated remediation workflows that restore data quality before SLA breaches occur.

Yes, the data quality framework supports SLA monitoring dashboards designed for both executives and operators, providing visibility into pipeline health, data contract enforcement, and validation rule outcomes.

The best way to establish initial validation rules and data contracts is to analyze data sources to generate profiling baselines, then draft YAML contract templates defining schema expectations for key producers and consumers.

Yes, the centralized validation rule engine supports both schema checks and cross-dataset checks, enabling comprehensive data quality enforcement across multiple data sources within your pipelines.