What is the primary focus of conceptual analytics compared to structured analytics?

The primary focus of conceptual analytics is on related concepts, emphasizing the connections and relationships between different pieces of information rather than strictly adhering to structured data formats. This allows for a more flexible and intuitive analysis, helping users discover insights based on the semantics and meanings behind the data rather than just its structured representation.

Conceptual analytics seeks to understand the broader context of information, enabling the exploration of themes, ideas, and associations that may not be captured through traditional structured analytics, which often relies on well-defined schemas and specific parameters. By focusing on related concepts, users can uncover patterns and relationships that would otherwise remain hidden in a more rigid analytical framework.

In contrast, other options involve characteristics that define structured analytics or limitations. For instance, relying on specific structures of content is typical of structured analytics, which focuses on well-organized data formats. Analyzing raw data usually pertains to processes involved in data discovery or preparation, which may not specifically align with the conceptual understanding. Lastly, user-defined queries are often employed in structured analytics to direct the analysis based on specific criteria, whereas conceptual analytics allows for exploring data more organically without predefined limitations.