What method is utilized when the data discovery is aimed at a specific purpose?

Label-based discovery is a method that works particularly well when data discovery is aimed at a specific purpose. This approach involves using labels or tags attached to data sets to classify and identify them based on predetermined criteria or intent. For instance, data might be labeled according to its sensitivity level, type, or intended use, which allows organizations to quickly find and manage data based on specific requirements, such as compliance with regulations or alignment with business objectives.

With label-based discovery, it becomes easier to ensure that the right data is being accessed by authorized individuals for the right reasons. This method can enhance data governance and accountability, simplifying tasks such as data retrieval and auditing since the data is organized in a way that aligns with business needs or compliance mandates.

In contrast, other methods such as content-based discovery focus on the actual content of the data rather than labels, which can be more general and less targeted. Metadata-based discovery relies on the properties and metadata of the files, and data analytics discovery typically involves examining patterns and trends within data rather than specifically aimed at finding data tagged for specific purposes.