Data Quality: Framework Design for Validation, Contracts & Monitoring

Generic, brand-neutral engineering capability; deep, sourced playbooks live in references/ and knowledge/. [DOC]

Generic, brand-neutral engineering capability; sourced playbooks in references//knowledge/. [DOC]

TL;DR

Data quality architecture defines how organizations detect, prevent, and remediate data issues through profiling, validation rules, anomaly detection, contracts between teams, and SLA monitoring. This skill produces data quality documentation that enables teams to build trust in their data through systematic quality management [EXPLICIT]

When to Use

• Designing data quality frameworks from profiling through remediation
• Establishing data contracts between producer and consumer teams
• Setting up anomaly detection for data pipelines
• Defining validation rule engines with severity and escalation
• Building remediation workflows (quarantine, dead-letter, auto-fix)
• Creating SLA monitoring dashboards for freshness, completeness, accuracy

When NOT to Use

• Data pipeline orchestration and ingestion (data-engineering skill)
• dbt model testing and schema validation (analytics-engineering skill)
• ML model drift detection (data-science-architecture skill)
• Dashboard design and reporting (bi-architecture skill)

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Sub-capabilities (resource map)

Deep, evidence-tagged playbooks — open the one the task needs (ICM Layer 3, on-demand). [INFERENCE]

Procedure

1. Resolve the sub-capability; open the matching references/ playbook. [EXPLICIT]
2. Apply its decision tables; pick the strategy explicitly. [EXPLICIT]
3. Validate against the Quality Criteria and tag every claim. [EXPLICIT]

Quality Criteria

• Sub-capability resolved to one playbook. [INFERENCE]
• Claims evidence-tagged. [EXPLICIT]

Contract

• Aceptación: capability resolved to its reference playbook, applied, validated, evidence-tagged. [EXPLICIT]
• Límites: scoped to this capability; deep procedure in references/. [EXPLICIT]
• Casos borde: Caso Estrategia de Manejo --- --- Sin baseline historico Usar primeros 30 dias como baseline con thresholds amplios (4-sigma); ajustar gradualmente; aceptar mayor tasa. [EXPLICIT]
• Supuestos: · Assumes access to data profiling tools or raw data samples for metric calculation · Statistical thresholds (3-sigma, 4-sigma) assume approximately normal distributions; skewed da. [SUPUESTO]
• Trade-off: Decision Enables Constrains When to Use --- --- --- --- Strict Data Contracts Reliability, upstream accountability Slower iteration, producer friction Produ. [EXPLICIT]

Packet

Capas del packet, cargables bajo demanda (disciplina ICM: una capa por vez, nunca todas juntas): references/ guías de profundidad (cargar UNA por etapa) · knowledge/ cuerpo de conocimiento · prompts/ prompts listos · examples/ salida de ejemplo · agents/ subagentes del packet · assets/ recursos estáticos.