Data contextualization is the process of adding contextual information to raw data in order to enhance its meaning and relevance. It involves the use of additional information like metadata, annotations, and other relevant details to provide a better understanding of the data. Contextualization can help analysts understand the relationships between data points and the environment in which they were collected. For example, contextualization can provide information about the time, location, and other environmental factors that might have influenced the data. In data processing, contextualization is becoming increasingly important as datasets become larger and more complex. Without proper contextualization, it can be difficult to interpret data accurately and make informed decisions based on it.

https://learn.microsoft.com/en-us/azure/architecture/example-scenario/data/data-contextualization-based-on-azure-sql-graph