Datalinkage

Datalinkage is the process of linking or merging datasets from multiple sources to create a unified dataset. It aims to improve data completeness, enable cross-source analysis, and support complex research or operational tasks. Key methods include record linkage (matching records across sources using identifiers or similarity metrics) and data integration (combining attributes from different datasets). Challenges include privacy concerns, data quality inconsistencies, and errors in identifier matching. Datalinkage is widely used in public health research (e.g., tracking disease trends), social science studies (e.g., longitudinal surveys), and fraud detection (e.g., identifying duplicate claims). Effective datalinkage requires careful handling of privacy, robust matching algorithms, and validation of merged data to ensure accuracy and reliability.