Enterprise data semantics: teach AI what the business already knows
Data semantics is a maintainable connection among fields, values, terminology, rules, and analytical preferences—not just technical comments.
Start with table and field descriptions
Explain the business object represented by each table, how fields are produced, and when they update. Clear structure reduces wrong table and stale data choices.
Document values and synonyms
Status codes, channel IDs, product categories, abbreviations, and internal language must be translated into definitions AI can use.
Connect business documents and domain knowledge
Policies, metric manuals, operating rules, and analysis methods can provide context when their scope, owner, and update date are explicit.
Improve semantics with real questions
Use failed or ambiguous questions to identify whether the missing context is a field description, value index, business rule, or analytical preference.