A data dictionary is a practical guide to the fields in a dataset. It names each field, explains its meaning and records the rules needed to interpret it. A short maintained dictionary is more useful than a detailed one nobody trusts.
Include units, allowed values, date conventions and the meaning of blanks. State whether monetary amounts include tax and which currency they use. For calculated fields, keep the calculation with the definition. Record who can answer questions when a definition is unclear.
A small-business example
A service company's spreadsheet has a column called “Closed”. Sales staff use it for work won; operations staff understand it as work completed. An AI summary could confidently interpret either meaning from the label alone.
The team replaces the ambiguous field with separately defined sales and delivery statuses. The dictionary explains each allowed value, when staff should select it and what remains unknown. Older records are flagged for review instead of silently converted using guesswork.
Try this
Choose five fields from a spreadsheet you use weekly. Define them for a new colleague who cannot ask you questions. Include one example value and one edge case for each. Ask the colleague to explain a sample row back to you, then revise the definitions wherever their interpretation differs.
Choose a useful measure covers calculated values. Consistent categories defines status choices, while Context carries these definitions into an AI task.