Data Governance & Quality
Trusted data, by design.
Governance works when it is automated. We build quality checks, lineage, cataloguing and access policies directly into your pipelines, so trust is the default rather than an afterthought.
Sound familiar?
The problems we solve
Nobody trusts the numbers
Different reports disagree and meetings start with reconciling figures.
Audit pressure
Regulators and auditors ask where a number came from and nobody can show the path.
Personal data everywhere
PII copied into analytics tables without masking or access control.
Unclear ownership
When data is wrong, nobody knows who should fix it.
Outcomes
What you get
- Quality issues caught before they reach a dashboard or model
- End-to-end lineage for audit and impact analysis
- Personal data identified, masked and access-controlled
- Clear ownership for every critical dataset
Approach
How we deliver it
- 1
Identify critical data
Focus on the datasets that drive revenue, risk and regulatory reporting.
- 2
Automate controls
Tests, lineage capture and access policies built into pipelines.
- 3
Assign ownership
Lightweight stewardship roles and escalation paths.
- 4
Measure and improve
Quality scorecards reviewed with data owners every month.
Deliverables
What we hand over
Working software, tests and documentation in your repositories and cloud accounts.
Technologies
- Unity Catalog
- Microsoft Purview
- Collibra
- Great Expectations
- dbt tests
- OpenLineage
- Monte Carlo
- Quality gates
- Automated tests on freshness, completeness and business rules at every layer.
- Catalog and lineage
- Searchable catalog with column-level lineage across pipelines and reports.
- Access and privacy
- Role-based access, PII detection and masking aligned to your policies.
- Operating model
- Data ownership, stewardship roles and lightweight governance rituals.
Use cases
Where this makes a difference
Regulatory reporting lineage
Traceable path from source systems to every figure in a regulatory return.
PII discovery and masking
Automated classification of personal data with dynamic masking by role.
Data quality scorecards
Business-facing quality metrics with owners and trends.
FAQ
Common questions
Will governance slow our teams down?
Not when it is automated. Controls run inside pipelines and access is granted through policies, so teams spend less time waiting for approvals.
Do we need a big catalog tool first?
No. Start with automated tests and lineage on critical data; add a catalog when people need to discover data across domains.
Related services
All services →Data Engineering
Reliable batch and streaming pipelines that move data from every source into your platform, tested and observable from day one.
Batch and streaming pipelines you can trust
ETL & Data Integration
Integrate every system and modernize legacy ETL (Informatica, SSIS, DataStage, Talend) into maintainable, cloud-native ELT pipelines.
Modernize ETL to cloud-native ELT
Big Data Migration
Move Hadoop, Teradata, Netezza, Exadata and on-premises warehouses to a cloud lakehouse safely, with parallel runs and zero-surprise cutovers.
Hadoop and legacy warehouses to the cloud
Tell us where your data is today.
A 45-minute conversation with a senior engineer. You leave with an honest view of what to build first, whether or not we work together.