Overview
Describe the dataset purpose, source systems, refresh cadence, and primary consumers.
4.8
on G2
Free Data, AI & Analytics Template
Field-level reference for [dataset], table, or reporting model
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] |
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.Record a walkthrough, training session, or process demonstration. Docsie AI turns it into structured documentation using this template as the starting framework.
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 using and generating a data Dictionary.
Q: What is a data Dictionary?
A: A data Dictionary is a structured document for field-level reference for [dataset], table, or reporting model.
Q: Can I download this data Dictionary as Word or PDF?
A: Yes. This page includes free downloads in DOCX, PDF, and Markdown formats so you can edit, share, or import the template into your documentation system.
Q: Can Docsie generate this from a video?
A: Yes. Upload a process walkthrough, training recording, or screen capture to Docsie, then use this template structure to generate a first draft automatically.