Data Quality Validation

Overview#

An intelligence analyst running a search for persons of interest in a specific region should be able to trust that every record with that region value actually belongs to that region, that dates are real dates rather than placeholder strings, and that identifier fields are not blank. When those guarantees are absent, analysts spend time chasing data problems instead of doing analysis. Worse, automated risk scoring pipelines that process low-quality data produce scores that look authoritative but are built on errors that were never caught at the source.

Argus Data Quality Validation catches these problems before they reach PostgreSQL. It applies a configurable rules engine against every record as it enters the platform, combining 200-plus pre-built validation rules, custom rule authoring, automated data profiling, and ML-powered anomaly detection. Real-time validation operates in the ingestion pipeline with minimal latency overhead; batch validation covers historical datasets and scheduled quality monitoring. For intelligence agencies, financial crime units, healthcare data controllers, and government registries, the result is that data entering the platform meets a documented quality standard, and any deviation is surfaced immediately rather than discovered months later during an audit.

Key Features#

• Validation Rules Engine: Define and execute 200-plus pre-built validation rules covering data types, formats, ranges, referential integrity, business logic, and completeness checks. Rules are applied in configurable order with short-circuit logic for efficiency.
• Custom Rule Authoring: Build custom validation rules using an expression language or custom functions for complex multi-field and domain-specific validations that pre-built rules do not cover.
• Automated Data Profiling: Generate quality metrics, statistical summaries, and pattern analysis for all datasets with real-time profiling and historical trend tracking. Profiling results are stored in PostgreSQL for longitudinal analysis.
• Data Quality Scoring: Calculate composite quality scores across completeness, validity, uniqueness, consistency, and timeliness dimensions for every dataset. Scores give a single comparable measure of dataset health.
• ML-Powered Anomaly Detection: Identify data quality issues that rule-based validation might miss using statistical models, time-series analysis, and pattern recognition. Subtle distribution shifts are caught before they affect analysis.
• Real-Time Validation Pipeline: Validate data in real-time as it flows through ingestion pipelines with immediate feedback and minimal processing overhead. Failed records are routed to the dead letter queue without blocking the rest of the batch.
• Domain-Specific Validations: Apply industry-specific rules for financial data (IBAN, SWIFT codes), healthcare (ICD-10, CPT codes), geospatial data, and identity verification. Rules are maintained as the standards evolve.
• Temporal and Conditional Logic: Enforce date sequence validations, business day calculations, state machine transitions, and context-dependent rules that simple type checks cannot handle.
• Distribution and Correlation Analysis: Detect statistical distribution shifts, unexpected correlations, and seasonal pattern anomalies across datasets. A change in the distribution of a field that was previously stable is a signal worth investigating.
• Duplicate Detection: Identify exact, fuzzy, partial, and temporal duplicates with configurable matching strategies and primary key integrity checks, complementing the dedicated Deduplication Engine.

Use Cases#

• Pipeline Data Gatekeeping: Validate every record entering the platform against schema rules, business logic, and statistical baselines before it can propagate to downstream systems. Bad data is stopped at the boundary rather than discovered after it has affected analysis.
• Regulatory Data Compliance: Enforce mandatory field requirements, format standards, and referential integrity rules required by regulatory frameworks. Quality scores and audit-ready reports are generated automatically for each ingestion run.
• ML Model Data Assurance: Profile and validate training datasets before model training, detecting outliers, distribution drift, and feature anomalies that could degrade model performance in production.
• Data Migration Verification: Validate migrated data against source-of-truth rules to confirm completeness, accuracy, and consistency after large-scale data movement operations. Discrepancies are identified before the source system is decommissioned.
• Continuous Quality Monitoring: Track data quality trends over time with automated profiling, anomaly detection, and alerting when quality metrics drop below configured thresholds. Quality degradation is visible before it affects operations.

Integration#

The Data Quality Validation module integrates with all major data sources including databases, APIs, file systems, and message queues. It operates within the Argus ingestion pipeline in both real-time streaming (Kafka Streams) and batch modes. Validation results and quality scores are stored in PostgreSQL. The module connects with the data lineage system to propagate quality metadata downstream and with the compliance reporting infrastructure for audit documentation.

Open Standards#

• GraphQL (June 2018 specification): All data quality queries, mutations, and dashboard metrics are exposed through the platform's GraphQL API, using typed schema definitions for quality reports, drift scores, and baseline management operations.
• ISO 8601: Every quality report timestamp and ingestion run marker is expressed as an ISO 8601 date-time string, ensuring consistent chronological ordering and trend analysis across runs.
• OpenLineage Specification: The ingestion pipeline emits OpenLineage RunEvent records (START, COMPLETE, FAIL) for each pipeline job, giving the data quality layer full provenance and lineage visibility for every dataset that is validated.
• RFC 5322: Email address fields extracted from incoming records during entity normalisation are validated against the RFC 5322 pattern before the record enters the quality scoring pipeline.
• ITU-T E.164: Telephone number fields extracted during ingestion are normalised to E.164 international format as part of the entity extraction step that precedes quality validation.
• Apache Kafka: Real-time validation operates within an Apache Kafka Streams ingestion pipeline, allowing quality rules to be applied to event streams with minimal latency and without blocking downstream consumers.
• JSON / JSONB (RFC 8259): Reference baseline datasets, column type metadata, and quality report payloads are serialised as JSON and stored in PostgreSQL JSONB columns, enabling structured querying of historical quality state.

Last Reviewed: 2026-02-05 Last Updated: 2026-04-14