Business Application Data Dictionary

A comprehensive, open-source business data dictionary stored in SQLite (datadict.db, with a full schema + data dump in datadict.sql). It collects standardized business data items across major industries and functional areas, extracted only from public / open-source resources.

3,051 data items · 12 categories · 9 open-source standards  |  31 items corroborated across multiple sources.

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Contents by category

Category	Items	Category	Items
Finance / Accounting	612	Inventory / Warehouse	131
Sales / Order Management	538	Procurement / Purchasing	111
Customer Relationship Management (CRM)	484	Quality Management	81
Healthcare	327	Maintenance / Asset Management	32
Product Master Data	299	Manufacturing	15
Supply Chain / Logistics	226
Human Resources	195

Sources (all public / open-source)

Standard	License	Items
Microsoft Common Data Model (CDM)	CDLA-Permissive-2.0	712
Tryton	GPL-3.0	559
Stripe API (OpenAPI)	MIT	393
Schema.org	CC BY-SA 3.0	373
ERPNext / Frappe Health	GPL-3.0	319
Odoo	LGPL-3.0	275
GS1 (Barcode Syntax Dictionary)	Apache-2.0	220
ISA-95 / B2MML	Royalty-free (MESA)	122
HL7 FHIR (R4)	CC0 (public domain)	116

Full provenance and extraction notes: sources.md.

Diagrams

Schema and source/category maps live in DATA_MODEL.md (Mermaid, renders on GitHub) with static SVG/PNG exports in diagrams/:

ER diagram	two-table star: Categories → DataItems
Categories	items per category (pie)
Source→Category map	which standards feed which categories

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Quick start

# Build / refresh datadict.db and datadict.sql from the seed modules
python3 build_dict.py

# Print summary statistics only (no rebuild)
python3 build_dict.py --stats

# Query the database
sqlite3 datadict.db "SELECT * FROM DataItems WHERE CategoryID = 1 LIMIT 10;"

The build is idempotent — re-running never creates duplicates. To rebuild from scratch, delete datadict.db first (it is fully regenerable from the seeds). See the Query Cookbook for ready-to-run SQL.

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Schema

CREATE TABLE Categories (
    CategoryID  INTEGER PRIMARY KEY,
    Name        TEXT NOT NULL UNIQUE,   -- "Manufacturing", "Finance", ...
    Description TEXT,
    Source      TEXT
);

CREATE TABLE DataItems (
    DataItemID   INTEGER PRIMARY KEY,
    CategoryID   INTEGER NOT NULL REFERENCES Categories(CategoryID),
    Name         TEXT NOT NULL,         -- normalized "entity.field" (snake_case)
    Title        TEXT,
    Description  TEXT,
    DataType     TEXT,                  -- VARCHAR, INTEGER, DECIMAL, DATE, ...
    ByteLength   INTEGER,
    DecimalScale INTEGER,
    IsRequired   BOOLEAN DEFAULT FALSE,
    IsNullable   BOOLEAN DEFAULT TRUE,
    DefaultValue TEXT,
    AllowedValues TEXT,                 -- JSON array, or per-source JSON object
    FormatMask    TEXT,                 -- e.g. "YYYY-MM-DD", GS1 "N14,csum"
    SourceStandard TEXT,                -- one or more "; "-joined standards
    SourceURL      TEXT,                -- one or more " | "-joined URLs
    Version        TEXT,
    CreatedAt DATETIME DEFAULT CURRENT_TIMESTAMP,
    UpdatedAt DATETIME DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_dataitems_category ON DataItems(CategoryID);
CREATE INDEX idx_dataitems_name     ON DataItems(Name);
-- Idempotency key for upserts:
CREATE UNIQUE INDEX ux_dataitems_natural
    ON DataItems(CategoryID, Name, SourceStandard);

Two field conventions worth knowing

• Name is normalized to entity.field in snake_case (e.g. account.account_id, product.gtin). The entity is everything before the last dot.
• SourceStandard / SourceURL hold a ; - / |-joined list when an item was corroborated by more than one source (a cross-source merge).
• AllowedValues is usually a JSON array (["male","female","other"]). When merged sources have divergent enums, it becomes a JSON object keyed by source (e.g. purchase_order.state). Consumers should handle both.

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How it's built

build_dict.py          # orchestrator: schema, load seeds, normalize, export, stats
normalize.py           # Phase 3: snake_case naming, entity/field aliases,
                        #          cross-source merge, per-source enum union
seeds/                 # one module per source (CATEGORIES + ITEMS); some
                        #   are auto-generated by the tools below
tools/fetch_*.py       # generators that (re)build seed modules from upstream
datadict.db            # the SQLite database (build output)
datadict.sql           # full schema + INSERTs (build output)
sources.md             # every source, license, and extraction method
NORMALIZATION_REPORT.md # auto-generated: aliases, merges, related concepts
DATA_MODEL.md          # auto-generated Mermaid ER + category/source diagrams
QUERY_COOKBOOK.md      # ready-to-run SQL recipes
PROGRESS.md            # running build log

Pipeline (4 phases)

1. Discovery — explore each open repo/spec for business entities.
2. Extraction — tools/fetch_*.py parse upstream schemas (XSD, JSON-LD, Python ast, DocType JSON, OpenAPI, FHIR StructureDefinitions) into seed modules. Each is re-runnable to refresh from upstream.
3. Normalization (normalize.py) — consistent snake_case names; deliberate, reviewable entity aliases (e.g. account.invoice → invoice) and field aliases (e.g. gs1.gtin → product.gtin); conservative cross-source merge (same entity.field in the same category) that keeps the richest metadata and records all sources; per-source enum union.
4. SQLite generation — datadict.db + datadict.sql, plus stats and the normalization report.

Regenerating a single source

python3 tools/fetch_cdm.py        # Microsoft CDM   -> seeds/cdm_microsoft.py
python3 tools/fetch_odoo.py       # Odoo            -> seeds/odoo.py
python3 tools/fetch_frappe.py     # ERPNext+Health  -> seeds/frappe.py
python3 tools/fetch_schemaorg.py  # Schema.org      -> seeds/schemaorg.py
python3 tools/fetch_tryton.py     # Tryton          -> seeds/tryton.py
python3 tools/fetch_gs1.py        # GS1             -> seeds/gs1.py
python3 tools/fetch_fhir.py       # HL7 FHIR        -> seeds/fhir.py
python3 tools/fetch_openapi.py    # OpenAPI (Stripe)-> seeds/openapi.py
python3 build_dict.py             # then rebuild the DB
python3 tools/gen_diagram.py      # refresh DATA_MODEL.md from the DB
python3 tools/render_diagrams.py  # render diagrams/*.svg + *.png (Chromium or mmdc)

(ISA-95 / B2MML lives in a hand-curated seeds/isa95_b2mml.py.)

Adding a new source

Create a seeds/<name>.py exposing two lists — CATEGORIES and ITEMS (each item a dict with at least category and name) — then run python3 build_dict.py. For OpenAPI/Swagger specs, just add an entry to SPECS in tools/fetch_openapi.py.

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Requirements

Python 3 standard library only (sqlite3, json, ast, urllib, re). No third-party packages. The tools/fetch_*.py generators need network access to reach the upstream repos; build_dict.py works fully offline from the committed seed modules.

Contributing

Contributions — new open-source sources, corrections, better descriptions, tooling — are welcome. See CONTRIBUTING.md for the seed-module contract, how to add a source, the aliasing/merge rules, and the pre-PR checklist. Core rule: public/open-source sources only, accuracy over quantity, always record provenance.

License & attribution

This repository is dual-licensed to reflect its two layers:

• Code — the build/generator scripts (build_dict.py, normalize.py, tools/*.py) and the project's documentation are licensed under the MIT License.
• Data — datadict.db / datadict.sql is a compilation derived from multiple open sources. The original compilation, arrangement, and this project's own descriptions are licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Upstream terms still apply per item. Individual data items remain subject to the license of the source they came from, recorded in each row's SourceStandard / SourceURL and summarized in sources.md: HL7 FHIR (CC0), Stripe (MIT), GS1 (Apache-2.0), Microsoft CDM (CDLA-Permissive-2.0), Schema.org (CC BY-SA 3.0), Odoo (LGPL-3.0), ERPNext/Frappe Health & Tryton (GPL-3.0), ISA-95/B2MML (MESA royalty-free). If you redistribute the data, provide attribution (the per-item source fields + sources.md satisfy this) and honor each upstream license — particularly Schema.org's ShareAlike and the copyleft (GPL) sources.

No paywalled, proprietary, or scraped commercial content is included.

Not legal advice. This dual-license setup is a good-faith, conservative reflection of the sources; consult a professional for your specific use.