In the context of ingesting unstructured data for an agent, what specific data characteristic does 'normalization' primarily address to ensure consistent representation?

The specific data characteristic that 'normalization' primarily addresses to ensure consistent representation is variability or inconsistency in how information is expressed within the unstructured data. Unstructured data, such as natural language text from documents, emails, or web pages, inherently lacks a fixed schema or predefined format. This means the same underlying piece of information can be presented in numerous different forms, leading to significant inconsistency. Normalization is the process of transforming this diverse and inconsistent data into a standard, uniform, and canonical representation. Its core function is to reduce or eliminate the inherent variability found across different expressions of the same data point. For example, dates might appear in various formats (e.g., '2023-01-15', 'Jan 15, 2023', '15/1/23'); normalization converts all these into a single, standardized format. Textual case (e.g., 'Apple', 'apple', 'APPLE') can be unified to a consistent form. Different units of measurement (e.g., 'kilometers', 'km') or synonymous terms (e.g., 'United States', 'USA', 'U.S.') are mapped to a single, consistent representation. For an agent, which requires a reliable and unified view of information to perform accurate processing, comparison, reasoning, and decision-making, addressing this variability through normalization ensures that identical concepts are always represented identically, preventing misinterpretations or missed connections due to inconsistent data forms.