Overview
Describe the dataset purpose, source systems, refresh cadence, and primary consumers.
4.8
on G2
Free Data, AI & Analytics Template
Download a free data dictionary template in Word, PDF, or Markdown. Or turn any video into data dictionary template with Docsie AI — auto-fills every required field.
Use this template to field-level reference for [dataset], table, or reporting model.
| Field | Details |
|---|---|
| Category | Data, AI & Analytics |
| Owner | [Team or owner] |
| Version | [Version number] |
| Effective Date | [Date] |
| Review Cycle | [Monthly / Quarterly / Annual / Event-based] |
| Status | [Draft / In Review / Approved] |
Describe the dataset purpose, source systems, refresh cadence, and primary consumers.
| Item | Details | Owner | Status |
|---|---|---|---|
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
[Add context, assumptions, exceptions, evidence links, screenshots, calculations, or reviewer comments.]
Define included entities, excluded records, date range, and grain.
| Item | Details | Owner | Status |
|---|---|---|---|
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
[Add context, assumptions, exceptions, evidence links, screenshots, calculations, or reviewer comments.]
Create a table with Field, Type, Required, Definition, Example, and Notes columns.
| Item | Details | Owner | Status |
|---|---|---|---|
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
[Add context, assumptions, exceptions, evidence links, screenshots, calculations, or reviewer comments.]
Document primary keys, foreign keys, joins, and related datasets.
| Item | Details | Owner | Status |
|---|---|---|---|
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
[Add context, assumptions, exceptions, evidence links, screenshots, calculations, or reviewer comments.]
List accepted values, null handling, range checks, and uniqueness rules.
| Item | Details | Owner | Status |
|---|---|---|---|
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
[Add context, assumptions, exceptions, evidence links, screenshots, calculations, or reviewer comments.]
Identify data owner, steward, support channel, and review cadence. Use concise Markdown tables and make definitions unambiguous.
| Item | Details | Owner | Status |
|---|---|---|---|
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
| [Item or requirement] | [Describe the relevant detail, evidence, or decision] | [Owner] | [Open / Complete] |
[Add context, assumptions, exceptions, evidence links, screenshots, calculations, or reviewer comments.]
Document review conclusions, approvals, unresolved items, and next review date.
| Role | Name | Date | Notes |
|---|---|---|---|
| Preparer | [Name] | [Date] | [Notes] |
| Reviewer | [Name] | [Date] | [Notes] |
| Approver | [Name] | [Date] | [Notes] |
Deploy this template when onboarding analysts, documenting new tables, or meeting compliance audit requirements.
This template produces a complete field-level reference with ownership, validation rules, and system relationships.
Teams often leave definitions vague, skip null-handling rules, or forget to assign data stewards.
Template Structure
Use this data, ai & analytics template as a starting point, then customize each section to match your internal workflow, evidence, and signoff needs.
Describe the dataset purpose, source systems, refresh cadence, and primary consumers.
Define included entities, excluded records, date range, and grain.
Create a table with Field, Type, Required, Definition, Example, and Notes columns.
Document primary keys, foreign keys, joins, and related datasets.
List accepted values, null handling, range checks, and uniqueness rules.
Identify data owner, steward, support channel, and review cadence. Use concise Markdown tables and make definitions unambiguous.
Write a Data Dictionary for a dataset or table. Structure with:
Describe the dataset purpose, source systems, refresh cadence, and primary consumers.
Define included entities, excluded records, date range, and grain.
Create a table with Field, Type, Required, Definition, Example, and Notes columns.
Document primary keys, foreign keys, joins, and related datasets.
List accepted values, null handling, range checks, and uniqueness rules.
Identify data owner, steward, support channel, and review cadence.
Use concise Markdown tables and make definitions unambiguous.
The analytics.customer_orders table supports revenue, retention, and fulfillment dashboards. It refreshes hourly from the commerce warehouse.
| Field | Type | Required | Definition | Example |
|---|---|---|---|---|
| order_id | string | Yes | Unique order identifier | ord_10492 |
| customer_id | string | Yes | Customer account identifier | cus_731 |
| order_status | string | Yes | Current lifecycle state | shipped |
| net_revenue_usd | decimal | Yes | Revenue after discounts and refunds | 129.50 |
order_id must be unique.net_revenue_usd must be zero or greater.order_status must be one of: pending, paid, shipped, refunded.Already have a walkthrough or training video covering this process? Skip manual drafting. Upload the video and Docsie AI generates data dictionary template with every required field populated — ready for review, signoff, or export.
Use the template manually, or let Docsie generate the first draft from source footage.
Plan, metrics, and decision rules for [experiment]
Definition and acceptance criteria for a [dashboard] build
Release notes for [dashboard], metric, model, or dataset changes
Policy for classifying, accessing, and retaining [data domain]
Reusable checks for validating [dataset] before release
Operational runbook for [ETL pipeline] failures and reruns
Template FAQ
Common questions about downloading and generating a data dictionary template.
Q: What is a data dictionary template?
A: A data dictionary template is a structured document for field-level reference for [dataset], table, or reporting model.
Q: Is the data dictionary template really free?
A: Yes. The data dictionary template is completely free to download in Word (DOCX), PDF, and Markdown formats. No signup or credit card required to download.
Q: How do I turn a video into a data Dictionary?
A: Upload a process walkthrough, training recording, or screen capture to Docsie. The AI analyzes the video and generates a complete data Dictionary using this template's structure — every required field auto-filled from the footage.
Q: Can I edit the data dictionary template after downloading?
A: Yes. The DOCX format opens in Microsoft Word or Google Docs. The Markdown format imports into Notion, Confluence, Docsie, or any markdown editor. Customize fields, add your branding, and adapt to your internal workflow.