What is data harmonization?

Data harmonization is the process of ensuring that data produced by different sources can be integrated and analyzed together without inconsistencies or discrepancies. It involves adjusting data to a common standard or format, reconciling differences in data definitions, and ensuring that data is structured consistently across different datasets.

Data harmonization is particularly important in data-intensive fields such as medical research, economics, and environmental studies, where data from multiple sources must be combined and analyzed to draw meaningful conclusions.

There are various techniques used to harmonize data, including data cleansing, data normalization, and semantic mapping. Data cleansing involves identifying and resolving inconsistencies or errors in data, while data normalization involves converting data into a standardized format. Semantic mapping involves establishing relationships between different data elements and mapping them to a common set of terms and definitions.

Overall, data harmonization helps to improve data quality, accuracy, and reliability, making it easier to draw accurate insights and make informed decisions based on data.