Data classification
Distinguish health records, identity data and operational information by sensitivity and intended purpose.
Data & AI Engineering
Use healthcare data for analytics—with clear access controls
Healthcare organisations need to integrate data from operational systems while protecting particularly sensitive information. We engineer controlled analytics architectures, data quality checks and traceable permissions.
Healthcare data needs different control points
Data from treatment, billing, administration and research cannot simply be pooled for unrestricted reuse. Data categories, lawful purposes, accountable owners and permissions must be established first.
Data integration concerns how systems exchange records. Governance also determines who may access which information, for what purpose and with what audit trail. This complements our healthcare administrative data integration work: https://valanor.co/en/healthcare-data-integration.
Technical components of a controlled platform
Permissions and audit trails
Limit access through roles and, where required, row-level or column-level rules. Trace changes and exports.
Pseudonymisation and masking
Support analytics without direct identifiers, protecting re-identification mechanisms, join risks and residual information.
Data quality and lineage
Check completeness, mappings, freshness and the origins of business-critical attributes.
Minimised data products
Expose only the information necessary for each approved use case.
Architecture: source system, controlled transformation, data product
Source systems remain authoritative for the records they own. Pipelines validate schemas and business rules while sensitive fields are minimised or pseudonymised for agreed purposes. Analytics uses reviewed data products and role-based views instead of unrestricted access to raw tables.
A metadata catalogue can document lineage, but database, storage, API and export systems still need to enforce permissions. See PII Data Masking: https://valanor.co/en/pii-data-masking.
What determines the tool choice
OpenMetadata and DataHub can support lineage and cataloguing; Microsoft Presidio can help discover personal information. Warehouses provide different permission and masking controls. Integration, identity management, data residency and operating requirements are important considerations.
Selecting a tool does not establish legal compliance. Healthcare data is subject to additional obligations, so lawful basis, impact assessments and sector-specific requirements should be reviewed with the responsible specialists.
Start with one bounded analytical use case
Select a clearly defined analysis with an accountable owner, map its data flows, assess sensitivity and quality, then implement permissions and tests. Broaden access only after reviewing the results and the control behaviour under realistic conditions.
Further reading: https://valanor.co/en/data-governance and https://valanor.co/en/data-ai-engineering.
Frequently asked questions
May healthcare data be processed in an analytics platform?
There is no blanket technical answer. Purpose, legal basis, safeguards and applicable sector rules must be reviewed before implementation.
Is pseudonymisation the same as anonymisation?
No. Pseudonymised healthcare data can generally still relate to a person when additional information allows re-identification.
How can uncontrolled exports be prevented?
Use least-privilege roles, reviewed query paths, approved data products, export logging and organisational approvals.
Does a hospital need to replace its existing systems?
No. Existing applications can often be connected through controlled integration and transformation layers.
Next step
Review the data architecture and access model.
We will review your sources, privacy requirements and technical control points and define a feasible implementation scope.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
