What is a data dictionary?
A data dictionary is a centralized reference that documents each element of a dataset, table and column names, data types, definitions, allowed values and relationships, so everyone interprets the data the same way. It is the glossary that explains what each field actually means.
A data dictionary turns a cryptic schema into something understandable. For each field it records the essentials:
- Name and type the column and its data type.
- Definition what the field means in business terms.
- Allowed values valid ranges, codes or categories.
- Relationships keys and links to other tables.
- Notes caveats, units, ownership.
A good data dictionary reduces mistakes and onboarding time, but it is notoriously hard to keep current, and plenty of databases have none at all.
iDBQuery reduces the dependence on a hand-written dictionary. Its schema introspection reads your structure automatically, and its semantic layer infers the meaning of tables and columns, even when they are obscurely named, effectively building and maintaining a working data dictionary for you. You can also record your own definitions in a business glossary that iDBQuery uses when answering. The payoff is that anyone can ask a plain-language question and get a cited answer without first hunting through documentation to decode what 'cust_typ_cd' means, because iDBQuery already understands it.
Updated 2026-06-22