By Team Hypomnema · Data
Build an editable field dictionary with definitions, units and ownership.
Use the guide alongside the working tool.
Open Data Dictionary Builder →Explicit working sample
customer,order,date,product,amount
A,100,2026-01-01,Notebook,30
A,100,2026-01-01,Pen,10
B,101,2026-01-03,Notebook,25
A,102,2026-02-01,Pen,20
C,103,2026-02-02,Notebook,50
C,103,2026-02-02,Pen,15
B,104,2026-03-01,Pen,35Build an editable field dictionary with definitions, units and ownership. One dictionary entry per observed column. Inferred storage/type examples are suggestions. Supply optional metadata as JSON keyed by exact column names, with description, unit, owner and allowed fields. Outputs Markdown documentation and a CSV dictionary; never an invented glossary.
Prepare the input
Build an editable field dictionary with definitions, units and ownership.
CSV, TSV and arrays of scalar JSON objects are supported. Identifiers stay text. Empty cells are missing by default; add special missing tokens only if their meaning is certain. Use the sample to inspect the exact workflow.
Review the method
One dictionary entry per observed column. Inferred storage/type examples are suggestions. Supply optional metadata as JSON keyed by exact column names, with description, unit, owner and allowed fields. Outputs Markdown documentation and a CSV dictionary; never an invented glossary.
Keep the useful output
Review the summary and result records. Transformation tools retain separate change, rejected-record or diagnostics datasets where relevant. Choose the dataset before exporting; a report and a cleaned dataset serve different purposes.
CSV exports protect formula-like text. JSON keeps data portable without making an Excel workbook. Reports and editable backups may contain private source values; download only what you intend to share. No automatic account storage or server processing is used.
Ready to apply this workflow?
Try Data Dictionary Builder →Explore a related workflow
- JSON Schema Validator: methods and data limits
Validate JSON against a supplied schema with precise error paths.
- Dataset Drift Detector: methods and data limits
Compare numeric and categorical distributions across two datasets.
- Dataset Schema Change Checker: methods and data limits
Review observed field and type changes between JSON datasets.
- Data Quality Profiler: methods and data limits
Inspect missingness, uniqueness, inferred types and numeric summaries.
Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.