Dataforenkling

Dataforenkling is a term used to describe a structured approach to reducing the complexity of data ecosystems. It involves simplifying data models, reducing data volumes, standardizing formats and definitions, and improving data quality to make data easier to find, understand, and use.

The aims include enabling faster data processing, easier sharing between systems, reducing maintenance costs, and strengthening privacy by limiting the amount of data stored and processed.

Common methods encompass data minimization, schema simplification, standardization of field names and data types, deduplication, and master data management. Data governance, metadata catalogs, and data lineage help ensure consistency, transparency, and compliance. API-first design and careful data architecture decisions (when to normalize or denormalize) also play a role.

In public sector and enterprise contexts, dataforenkling can support easier citizen and customer interactions, streamline reporting, and accelerate analytics. It often involves collaboration across domains to adopt common data models and shared services.

Benefits include reduced processing time, lower storage and maintenance costs, smoother data integration, improved data quality and trust, and easier privacy compliance. It can also reduce the cognitive load on data users.

Challenges include risk of information loss through over-simplification, the need for ongoing governance, stakeholder alignment, and the upfront effort required to redesign data architectures. Successful implementation relies on clear objectives, measurable outcomes, and ongoing stewardship.