Tableau Metadata and Field Definitions

Metadata is a mandatory requirement under Ohio State data governance policy prior to publishing any Tableau asset to production. Complete, search-compatible metadata ensures that university information is discoverable, transparently sourced, and aligned with institutional guidelines. Documentation must be entered in catalog-compatible fields so it automatically flows into the central Ohio State Data Catalog. Separate worksheets, README tabs, or floating text containers inside workbooks do not satisfy metadata compliance.

Minimum Metadata Standards by Asset Type

Core Metadata Guiding Principals

Clarity over Completeness

Prioritize documenting what is actively used and visible to end-users first. Focus on defining critical business logic, active dashboards filters, and primary KPIs. 

Consistency

Establish and use shared, standardized definitions across your unit’s workbooks to avoid confusing users (e.g., ensure ‘FTE’ is defined identically in all HR-related data sources). 

Reusability

Metadata should serve both human (analysts and business partners) and automated systems (search indexes, lineage, and AI-assisted data tools). 

Maintainability

Schedule routine audits (annually or biannually) to ensure that metadata and lineage descriptions are updated as schema, dashboards, and underlying business rules evolve. 

Description Templates

Projects & Data Sources

To maintain a professional and consistent catalog, use these standardized, fill-in-the-blank templates for projects, folders and published data sources. 

Project Description Template

Questions to Consider: What is the purpose of the project? Are contents for a specific audience? Who has access? How is access granted? What types of content belong here? Who creates objects?

Example: “This project contains data and analytical resources pertaining to [Domain/Topic] and is intended for use by [Target Audience]. Access is secured by the [Tableau Server Group Name] group and requires prior approval. Content is created and maintained by [Development Team Name].”

Data Source Description Template

Questions to Consider: What is the underlying system/origin? Is it joined with other data? Have transformations been performed? Have filters been applied? Granularity? Availability? Who has access? Who is the SME?

Example: “This data is sourced from [System Name] and is joined with data from [Secondary System]. Each record represents [Level of Granularity, e.g., one active student enrollment]. Data is updated on a [Schedule, e.g., daily] basis. For questions, please reach out to the designed subject matter expert, [name.#@osu.edu]”

Description Templates: Workbooks & Calculations

Workbook Description Template

Questions to Consider: What is the purpose of the workbook? What questions does the content attempt to answer? How should users utilize the content? Who should use it? Are there caveats?

Example: “This workbook was developed to answer key business questions regarding [metrics]. Users can navigate through the tabs to explore granular data, applying filters for [campus, fiscal year, etc.]. This tool is intended for use by [user role, e.g., college deans]. For questions, please reach out to [name.#osu.edu or department-email@osu.edu].”

Calculation Description Template

Questions to Consider: What is the purpose of the calculation? Is it a base or dependent calculation? What type of calculation (table calc, LOD, parameter based)? Are there limitations (nulls)? Where is it used?

Example: “Calculates the [metric name, e.g., YoY change] of [base variable] to help analyze [trend]. This calculation utilizes a [LOD expression] and handles nulls by [null handling logic, e.g., converting to 0]. It is actively used as a visual label on the [worksheet name] sheet.”

Creating & Maintaining Field Definitions Workflow

Workflow for Existing Data Sources

• Draft Definitions First: write clear, concise definitions for each field in an Excel spreadsheet or similar tool first to ensure consistency and ease of editing. For Workday connections, copy and paste official Workday definitions directly. If struggling, check the Ohio State Data Catalog for existing definitions. 
• Review and Validate: review definitions for accuracy and clarity with your manager, team leader, or data steward.

Workflow for New Data Sources

• Add Definitions During Development: as you build dashboards or data sources in Tableau Desktop, right-click on each field name and select Default Properties -> Edit Description. Paste or type the approved definition directly into the field’s description box. Adding definitions at the data source level ensures your work carries to all downstream assets automatically and guarantees consistency across all dashboards using that data source. 

Leveraging AI for Metadata Generation

Developers can leverage university-approved AI tools to rapidly generate initial metadata definitions. By copying and pasting SQL queries or field lists into an AI tool, you can request plain-language business definitions. 

Sample Prompt for Copilot: “Analyze the following SQL query and write clear, plain-language business definitions for each output column. Allow me to export the definitions to Excel. Column list: [paste list of columns or SQL query here].”

Note: Carefully review and enhance AI-generated definitions to ensure they fit your organization’s specific standards and data context. 

Central Data Catalog Integration & Governance

Tableau Server descriptions and field comments are ingested by the central Ohio State Data Catalog (administered by datagovernance@osu.edu). This ensures self-discovery and reduces duplicative work.