What creates new data feeds from existing datasets within the environment?

The concept of creating new data feeds from existing datasets is primarily associated with data analytics discovery. This process involves analyzing current data sets to uncover insights, patterns, or trends that may not be immediately apparent. By using various data analytical techniques, organizations can generate new datasets that derive from or enhance the original data. This often involves computations, aggregations, or transformations that provide additional context or information, leading to the creation of new feeds tailored for specific analytical needs or decision-making processes.

For instance, an organization might have raw sales data and uses data analytics to generate monthly performance reports or predictive models about future sales, effectively creating new datasets that can be utilized for further analysis or action.

The other concepts, such as metadata-based discovery, typically focus on identifying datasets based on their metadata attributes rather than generating new data feeds. Similarly, content delivery networks and label-based discovery have distinct functions related to optimizing data delivery and organizing content, respectively, rather than transforming existing datasets into new ones. Hence, the emphasis on data analytics discovery directly aligns with the creation of new data feeds derived from existing data.