datavervlechting

Datavervlechting is a concept in data management describing the process of intertwining data from multiple sources to create a more interconnected and informative dataset. It involves linking entities (such as people, organizations, items) and attributes across datasets to reveal relationships that are not visible within single sources.

Methods used in datavervlechting rely on data integration, entity resolution, record linkage, schema matching, and data fusion. Techniques include probabilistic matching, deterministic identifiers, machine learning, ontology alignment, and graph-based representations. Provenance and lineage are tracked to document data origins and transformations, supporting traceability and accountability.

Applications of datavervlechting span various domains. In business analytics, it enables a comprehensive view of customers and operations by combining data from different departments. In research, it allows the integration of diverse datasets to support broader analyses. In healthcare, it can correlate patient information across systems to improve care coordination. In urban planning and public sector work, it helps connect sensor data, demographics, and services to inform decisions. Fraud detection and risk assessment also benefit from cross-source pattern discovery.

Challenges and risks accompany datavervlechting. Data quality, semantic heterogeneity, and inconsistent metadata can hinder integration. Privacy, consent, and governance are critical, as data fusion may increase re-identification risk and raise regulatory concerns. Data provenance, versioning, and access controls are essential to maintain trust and compliance.

Tools and standards commonly used include graph databases, data integration platforms, and knowledge graphs built with RDF/OWL or JSON-LD. Metadata standards and data catalogs support interoperability, while privacy-preserving techniques are increasingly incorporated to balance insight with protection.