Online utility
Report Data Dictionary Generator
Upload a back-office CSV or Excel export and generate a clean data dictionary for reporting, migration, cleanup, or AI-assisted documentation.
Browser-only processing
Generate your data dictionary
Upload CSV or Excel, review detected fields, edit the table, then export CSV.
Your file is used only to generate the data dictionary in this browser session. It is not stored permanently or sent to analytics.
For best results, upload a raw export from your back-office system before manual cleanup. The tool works best when the first row contains column headers.
1. Upload CSV or Excel export2. Review detected columns and sample values3. Edit suggested field names and business logic4. Export CSV or copy the AI review prompt
Tool discovery
Other Data Tools
Three slots reserved for future data utilities in this standalone tool ecosystem.
What is a data dictionary?
A data dictionary is a structured reference for the fields in a dataset. It explains what each column is called, the type of value it appears to contain, sample values, whether the field looks required, and the business logic that still needs stakeholder confirmation.
How to generate a data dictionary from CSV or Excel
- Upload CSV or Excel exportChoose or drag in a CSV or XLSX export from your back-office system.
- Review detected columnsCheck the detected columns, sample values, data types, empty counts, and unique counts.
- Edit the dictionaryRefine suggested field names, business_logic notes, migration notes, reporting use, and review status.
- Export or copyExport the final data dictionary as CSV, copy it as Markdown, or copy an AI review prompt.
Why data dictionaries matter before data migration
Migration work fails when teams move fields without understanding identifiers, dates, statuses, amounts, and free-text fields. A data migration dictionary gives analysts, developers, and business owners a shared starting point before mapping fields into a new system or reporting model.
What should a data dictionary include?
Source file and sheet name
Keep this information reviewable so source-system owners can confirm definitions before migration, reporting, or cleanup decisions are made.
Original column name and suggested field name
Keep this information reviewable so source-system owners can confirm definitions before migration, reporting, or cleanup decisions are made.
Detected data type with example values
Keep this information reviewable so source-system owners can confirm definitions before migration, reporting, or cleanup decisions are made.
Empty and unique value counts
Keep this information reviewable so source-system owners can confirm definitions before migration, reporting, or cleanup decisions are made.
Likely key and required field flags
Keep this information reviewable so source-system owners can confirm definitions before migration, reporting, or cleanup decisions are made.
Draft business_logic notes, migration notes, reporting use, and review status
Keep this information reviewable so source-system owners can confirm definitions before migration, reporting, or cleanup decisions are made.
How business logic helps migration and reporting
The generated business_logic field is a draft note, not a final conclusion. It highlights likely follow-up questions such as whether an ID is stable, what a status value means, which date represents a business event, or whether a free-text field should be cleaned or excluded.
Data dictionary vs field mapping
A data dictionary documents what the source fields appear to contain. A data migration field mapping connects those source fields to target-system fields, transformations, and ownership decisions.
Using AI to review a data dictionary
After reviewing the generated table, copy the AI review prompt and paste it into ChatGPT, Claude, Gemini, or another assistant. The prompt asks the assistant to flag unclear fields, risky assumptions, duplicate concepts, and practical business_logic improvements without inventing rules as fact.
Frequently asked questions
What is a data dictionary?
A data dictionary documents dataset fields, including column names, data types, examples, empty values, key-field signals, and business definitions that need confirmation.
Is this data dictionary generator free?
Yes. The Report Data Dictionary Generator is free to use.
Do I need to create an account?
No. You can upload a supported file and generate a dictionary without creating an account.
What file formats can I upload?
You can upload CSV and XLSX files up to 10 MB.
Can I export the data dictionary as CSV?
Yes. The generated and edited dictionary can be exported as a CSV file.
Does the tool use AI?
No. The tool does not call an AI API. It only creates a copy-ready prompt you can use elsewhere.
Can I use this with ChatGPT or Claude?
Yes. Copy the AI review prompt and paste it into ChatGPT, Claude, Gemini, or another assistant after you review the generated dictionary.
Are uploaded files stored?
No. Your file is used only in the browser session to generate the dictionary. It is not stored permanently or sent to analytics.
What is business logic in a data dictionary?
Business logic explains how a field is used in the source system or reporting process. This tool drafts confirmation notes instead of inventing rules as fact.
Why is a data dictionary useful for data migration?
It helps teams identify key fields, statuses, dates, amounts, free-text fields, and unclear definitions before mapping data into a target system.
Can this replace manual data mapping?
No. It helps prepare source documentation, but stakeholders still need to confirm business logic and target-field mappings.
Can I edit the generated dictionary?
Yes. Every generated dictionary field is editable before export.
What happens if my file has messy columns?
The tool warns about duplicate or empty columns and still creates editable rows where columns can be detected.
Can I use this for back-office exports?
Yes. It is designed for raw back-office exports used in reporting, cleanup, documentation, or migration preparation.
Can I use this for reporting cleanup?
Yes. The dictionary helps analysts review column meaning, likely categories, sample values, and reporting use before cleanup.