What type of data requires contextual interpretation?

Unstructured data requires contextual interpretation because it consists of information that does not have a predefined structure or organization. Examples of unstructured data include text documents, images, videos, and social media posts. This type of data can vary significantly in format and content, making it more complex to analyze and interpret.

To derive meaningful insights from unstructured data, analysts often need to understand the context surrounding the data, such as the source, intent, and underlying themes or patterns. For instance, analyzing customer reviews involves interpreting the emotions, sentiments, and nuances conveyed in text, which may not be immediately apparent without considering the context in which the data was created.

In contrast, structured data is organized in a predefined manner, typically in databases or spreadsheets, making it easier to analyze with standard tools. Semi-structured data, while having some organizational properties, still contains elements that are not strictly defined and might require some level of contextual interpretation but not to the extent of fully unstructured data. Processed data refers to data that has been cleaned and transformed for analysis, which does not necessarily influence its need for context interpretation.