The problem this solves
Data programs often validate only target tables or dashboards while missing quality risks earlier in ingestion, transformation, lineage, scheduling, and recovery.
AskAQA six-checkpoint framework
1. Requirements & Testability
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2. Source / Ingestion Readiness
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3. Transformation Validation
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4. Curated Data Quality
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5. Business Consumption Validation
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6. Release & Operational Readiness
What the framework covers
- Data-quality dimensions
- Source-to-target validation
- ETL/ELT rules
- Reconciliation
- Incremental loads
- Pipeline failures
- Dashboard validation
- Evidence
- Alerting
- Ownership
Typical use cases
Suitable for data warehouses, Databricks/Fabric programs, Azure data platforms, analytics modernization, BI transformation, and enterprise reporting.
Typical deliverables
- Data QA framework
- Checkpoint definitions
- DQ rule model
- Reconciliation approach
- Evidence standards
- Operational-readiness controls
Expected outcomes
Stronger confidence that trusted business data survives every hop from source to consumption.