Data Classification and Normalization

Data classification and normalization tools use AI to automatically tag unstructured data, such as documents, emails, and records, with structured metadata, typically by applying keywords or concepts from a defined taxonomy, so that information becomes easier to find, filter, and analyze. Normalization ensures the same underlying concept is tagged consistently across different systems and sources, even when the source material uses different terminology, so that data from disparate places can be compared and queried on equal terms. Organizations use these tools for tasks such as e-discovery, records management, regulatory compliance, and preparing data sets for search or analytics platforms. This work once depended on people manually tagging documents, which was slow and produced inconsistent results as different reviewers applied judgment differently. Machine learning made this faster by training classifiers on labeled examples, and large language models have advanced it further by classifying based on context and meaning rather than surface keyword matches, often requiring little or no task-specific training data and handling ambiguous or unfamiliar terminology more reliably. Human review typically remains part of the workflow, particularly for edge cases, but the bulk of classification and clean-up work can now run at scale without manual tagging