📤Publish Reference and Master Data

NEW

Also known as: Master Data Publication, Reference Data Distribution, Golden Record Publishing

Deliver curated, authoritative datasets and APIs that expose golden records and code lists for consumption across the enterprise.

DESCRIPTION

Publishing reference and master data provides trusted, consistent assets to consumers. Delivery channels include APIs, views, secure sharing, and catalogs. Versioning and effective dating maintain historical accuracy. SLAs ensure availability and quality. Documentation clarifies semantics and usage. Publishing reduces redundancy and supports analytics, applications, and compliance processes.

ALTERNATIVE DEFINITIONS

Wikipedia

Master data management (MDM) is a comprehensive method of enabling an organization to link all of its critical data to a common point of reference.

Establishes the foundation for authoritative master data publishing.

Source

IBM

MDM ensures uniformity, accuracy, stewardship, and semantic consistency of an organization’s shared master data assets.

Emphasizes stewardship and semantic consistency in published assets.

Source

Snowflake

Secure Data Sharing enables sharing live, read-only data across accounts without copying or moving data.

Highlights no-copy publishing mechanism for reliable access.

Source

RELATED TERMS

Master Data Management Reference Data Management Systems Single Source of Truth Data Discovery Platform Data API Data Product Data Sharing SLA Metadata Management

COMPETING TERMS

Data Virtualization

RELATIONS

• Broader: Master Data

KNOWLEDGE AREAS

Reference & Master Data Data Products Data Services Data Management

TARGET AUDIENCE

Data Steward BI Specialist Data Engineer AI Engineer Data Analyst

EXTERNAL SOURCES

• Wikipedia - Master data management
• Snowflake - Mechanisms for publishing data
• Microsoft - API layer for data products
• Google Cloud - Authorized views for controlled sharing

TOP QUESTIONS

What channels are used to Publish Reference and Master Data?

APIs, secure views, shared datasets, catalogs, and downloadable code lists are common channels, chosen per consumer needs and governance constraints.

Category: Requirements

Why is versioning important in Publish Reference and Master Data?

Versioning preserves historical accuracy and ensures controlled adoption of changes without breaking consumers.

Category: Best practices

How do SLAs apply to Publish Reference and Master Data?

SLAs define availability, latency, and quality commitments, aligning publisher and consumer expectations.

Category: Management

What are common mistakes in Publish Reference and Master Data?

Publishing undocumented assets, breaking schemas without notice, and duplicating sources erode trust and cause integration issues.

Category: Mistakes

How does data virtualization relate to Publish Reference and Master Data?

Virtualization can expose curated views without copying, reducing redundancy while maintaining control.

Category: Opinion

Who consumes Publish Reference and Master Data?

BI teams, analysts, application developers, data scientists, and downstream data products rely on authoritative master and reference datasets.

Category: Relevancy

How to document Publish Reference and Master Data effectively?

Provide semantics, keys, effective dates, usage examples, and deprecation policies in catalogs and API documentation.

Category: Best practices

How to secure Publish Reference and Master Data?

Use least privilege, fine-grained roles, token scopes, encryption, and audit logs to protect sensitive attributes.

Category: Risk

What metrics matter for Publish Reference and Master Data?

Adoption, SLA adherence, quality scores, schema compatibility, and consumer satisfaction signal publishing effectiveness.

Category: Operations