Introduction In the digital age, data is often collected from multiple sources, leading to variability in formats, standards, and quality. Data harmonisation addresses these issues by transforming disparate data into a cohesive dataset, enabling better analysis, insights, and decision-making. It is essential for organisations looking to leverage their data assets across diverse systems and platforms. Data harmonisation involves several key steps: preparing, transforming, and validating data. Additionally, it’s built on a foundation of best practices that ensure the integrity, accuracy, and usability of the harmonised data.
Data Standardisation vs Harmonisation: The Right Things at the Right Times In the evolving landscape of data management, two concepts often come to the forefront: data standardisation and data harmonisation. Both play critical roles in how organisations manage and utilise their data, but they serve different purposes and are applicable in various contexts. This article delves into the nuances of each concept, particularly focusing on their significance in business and scientific environments.
Data Harmonisation: Unifying Data for Deeper Insights What is Data Harmonisation? In today’s data-driven world, data harmonisation has become increasingly important. With data coming from disparate sources, it’s essential to ensure that this information is consistent, accurate, and usable. For example, in a large study in social sciences, such as a longitudinal study or meta-analysis, it is common that a researcher may want to combine data from different studies. We can make data comparable by recoding variables from different studies, modifying them, or identifying which variables in one study match variables in another study.
