What is Data Enrichment?
Data enrichment refers to the process of adding additional information or context to a dataset in order to improve its quality, value, or usefulness. Data enrichment can be accomplished through a variety of methods, including the addition of external data sources, the application of algorithms or machine learning techniques, or the manual addition of information by human analysts.
Data enrichment is often used to improve the accuracy or completeness of a dataset or to make it more relevant or useful for a specific purpose. For example, data enrichment might be used to add demographic information to a customer database, to add geographic or weather data to a sales dataset, or to add linguistic or cultural context to a dataset of social media posts.
Data enrichment can be an important aspect of data management, as it helps to improve the quality and value of the data and can make it more useful for a variety of purposes, such as decision-making, analysis, or customer segmentation.