How to use the Data Dictionary Generator.
A data dictionary should describe meaning as well as type. Include units, allowed values and how missing information is represented.
Make the workflow fit your task.
For each column, document its business meaning, data type, units, allowed values, missing-value convention and source. Include relationships to other fields and unresolved questions. Check definitions against examples rather than inferring everything from a short header.
- What you provide
- Schema, sample values and business definitions.
- What you get
- Column definitions, types and allowed values.
See the input and the result.
Illustrative input and output · a teaching example, not a live WebAct run
Example input
Document lead_time_days. Confirmed facts: integer; expected calendar days from order acceptance to delivery; nonnegative; blank means estimate unavailable; source is supplier estimate.
Completed example
Field: lead_time_days Meaning: estimated calendar days from accepted order to delivery. Type: integer. Unit: calendar days. Allowed values: 0 or greater. Missing value: blank = estimate unavailable. Source: supplier estimate. Example: 7 means an estimated seven calendar days, not seven working days.
Load this input into the prompt, then copy it to WebAct to try the task. Your result may differ from the illustration.
Decisions and troubleshooting.
Should a dictionary record whether lead time uses working days?
Yes. Units and calendar conventions affect interpretation and belong in the field definition.
Why do two teams interpret the same status field differently?
The allowed values or transition meanings may be undocumented. Resolve these with the data owner and record the agreed definitions.
Try it with your own source.
Replace the example with your material in the task prompt. Keep the requirements you need, then copy the task into WebAct.
Customize and copy the task ↑