Classification and ownership
Identify PII and other sensitive information. Define business terms and the people accountable for each dataset.
Data & AI Engineering
Implement Data Governance in systems, not just policies
Make data usable for analytics and AI without inconsistent definitions, unreliable metrics or uncontrolled access. Valanor engineers governance controls into data platforms, pipelines and applications.
Policies alone do not control data
When teams combine information from CRM, ERP and operational applications, inconsistent definitions, unclear ownership and overly broad permissions can undermine the results. Governance needs to work in data contracts, pipelines and actual access controls.
We start with a concrete use case: What information is needed, and why? Who owns its meaning, quality and changes? Which person or application is allowed to use which view?
Controls from source systems to data products
Permissions and data masking
Enforce roles, row-level and column-level access, masking and export restrictions.
Data quality checks
Automate schema, completeness, relationship, freshness and business-rule checks.
Data lineage
Trace the origins, transformations and dependencies behind reports, metrics and models.
Retention and deletion
Account for retention and deletion paths in replicas, downstream data products and exported datasets.
Technical architecture and tool selection
OpenMetadata and DataHub can support cataloguing, ownership and lineage. Microsoft Purview and Databricks Unity Catalog cover governance requirements in their respective platform environments. Microsoft Presidio can help identify personal information, although automated detection still needs validation.
Access enforcement happens in databases, storage, identity systems, APIs and export controls. A metadata catalogue is not an access boundary. For specific analytical workloads, ClickHouse Cloud masking policies can add a role-aware protection layer.
Explore PII Data Masking and Data Governance for Insurance. Our engineering service: Data & AI Engineering.
Governance in regulated industries
Insurers need dependable policy and claims data with traceable origins and controlled permissions. Healthcare adds particularly sensitive information, purpose limitations and the need to distinguish clinical from administrative data flows.
We begin with one data product, assess its sensitivity, define quality checks and implement roles, data contracts, monitoring and audit logs. Legal requirements must be assessed by the responsible specialists; choosing a tool does not establish compliance.
Related work: Healthcare Data Governance and Healthcare Administrative Data Integration.
Frequently asked questions
What is Data Governance?
Clear responsibility for data definitions, quality, access, lineage and lifecycle, implemented in ways that can be verified technically.
Is a data catalogue enough?
No. A catalogue describes data. Permissions and output restrictions must be enforced where the data is actually accessed.
When should data masking be introduced?
When a team needs to analyse data but not see sensitive values in their original form. Masking does not replace data minimisation or access restrictions.
Do we need a central data platform first?
Not necessarily. Distributed data products can also operate with consistent governance controls.
Next step
Review the data architecture and access model.
We will review your data sources, responsibilities, access paths and the scope of a practical implementation pilot.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
