Shifting Data Quality Left with Contract-Driven Data Platforms
Introduction
Traditional data quality approaches focus on detecting issues after data has already entered the platform. This reactive model leads to delayed insights, costly reprocessing, and reduced trust in data.
At Datilis, we take a different approach: Data quality starts at the source.
With Data Contracts, we shift quality validation upstream—ensuring that data is validated before ingestion, during ingestion, and throughout the data platform lifecycle.
This transforms data quality from a reactive process into a proactive, enforceable standard.
The Problem: Reactive Data Quality
Most organizations today:
- Validate data only inside the data platform
- Discover issues too late (after ingestion or transformation)
- Lack clear ownership of data quality
- Have no formal agreement between data producers and consumers
- Rely on fragmented validation logic
The result:
- Broken pipelines
- Unreliable analytics
- Increased operational costs
- Low trust in data
The Datilis Approach: Data Contracts as a Core Platform Pillar
We introduce Data Contracts as a first-class component of the data platform.
A data contract defines:
- Schema (structure of the data)
- Ownership (who is responsible)
- Expectations (data quality rules)
- Test cases (validation logic)
These contracts are stored in a central repository and enforced across the entire data lifecycle.
Architecture Overview
The Data Contract Framework operates across three key stages:
1. Contract Definition (Data Contract Repository)
Contracts are defined centrally and include:
- Schema definitions
- Data ownership
- Business expectations
- Quality rules and test cases
This creates:
- Clear accountability
- Shared understanding between producers and consumers
- Version-controlled data agreements
2. Validation at the Source (Shift Left)
Data is validated directly in the source systems:
- Test frameworks execute validation against source tables
- dbt tests or custom checks ensure contract compliance
- Results are published to an observability platform (e.g., DataHub)
Benefits:
- Issues detected early
- Prevents bad data from entering pipelines
- Reduces downstream failures
3. Validation During Ingestion
The ingestion framework integrates with data contracts:
- Validates incoming data against contract definitions
- Blocks or flags invalid data
- Publishes validation results
This ensures:
- Data quality is enforced at ingestion boundaries
- Consistent validation across all pipelines
4. Validation Inside the Data Platform
Data contracts continue to be enforced within the platform:
- dbt transformations include contract-based tests
- Data products validate their outputs
- Results are continuously monitored
Outcome:
- End-to-end data quality assurance
- Reliable, trusted datasets
Observability & Governance Layer
All validation results are centralized in an observability plane (e.g., DataHub):
- Data quality metrics
- Test results
- Contract compliance status
This provides:
- Full visibility across the data lifecycle
- Auditability and governance
- Transparency for all stakeholders
Key Capabilities
Contract-Driven Data Quality
- Quality rules defined once, enforced everywhere
Shift-Left Validation
- Detect issues at the source before ingestion
End-to-End Enforcement
- Source → Ingestion → Platform
Centralized Observability
- Unified view of data quality across systems
Ownership & Accountability
- Clear responsibility for data quality
Business Benefits
Organizations adopting data contracts achieve:
- 70% reduction in downstream data issues
- Faster time to detect and resolve quality problems
- Increased trust in data across teams
- Reduced pipeline failures and reprocessing
- Strong alignment between data producers and consumers
Strategic Value
Data Contracts transform your platform into: A governed, reliable, and scalable data ecosystem
They enable:
- Data as a product mindset
- Clear SLAs between teams
- Scalable data platform operations
- Foundation for AI and advanced analytics
Why Datilis
Datilis combines:
- Deep expertise in data platform architecture
- Integration with tools like dbt, Airflow/Dagster, DataHub, Collibra
- Proven frameworks for data quality and governance
- Focus on standardization and scalability
Conclusion
Data quality cannot be an afterthought.
With Data Contracts, organizations can:
- Prevent bad data at the source
- Enforce quality throughout the pipeline
- Build trust in their data platform
Next Steps
- Identify critical data domains
- Define initial data contracts
- Integrate validation into ingestion pipelines
Contact Datilis to implement contract-driven data quality