What metadata standards and models are most effective for data governance?

1 Common metadata standards

Metadata management is often challenged by the diversity and complexity of data sources and formats. To combat this, adopting common metadata standards can help to facilitate data integration, interoperability, and exchange. The most widely used metadata standards include Dublin Core, ISO 19115, Data Documentation Initiative (DDI), and Resource Description Framework (RDF). Dublin Core is a simple and flexible standard that defines 15 basic elements for describing any type of resource. ISO 19115 defines the schema and elements for describing geographic information and services. DDI is a standard that defines the structure and content of metadata for social and behavioral sciences data. Lastly, RDF is a standard that defines a model and syntax for representing metadata as triples of subject, predicate, and object which can be linked using Semantic Web technologies.

2 Metadata models

In addition to adopting common metadata standards, you also need to design and implement metadata models that capture the relevant information and relationships of your data assets. Metadata models are conceptual and logical representations of metadata, which can vary depending on the purpose and scope of your data governance. Examples include a business metadata model that describes the meaning, context, and usage of data from a business perspective, a technical metadata model that outlines the structure, format, and location of data from a technical perspective, and an operational metadata model that highlights processes, activities, and status of data from an operational perspective.

3 Metadata repositories

When considering metadata management, you should also think about a metadata repository. This centralized storage and access point can help you organize, catalog, and share your metadata across your organization, enabling data discovery, analysis, and governance. It's important to choose a repository that supports your metadata standards and models, as well as your data governance requirements. Features to look for include metadata integration (the ability to collect and synchronize metadata from various data sources and formats), metadata search (the ability to search and browse metadata using keywords, filters, facets, and categories), metadata governance (the ability to define and enforce policies, rules, and workflows), and metadata collaboration (the ability to share and communicate with other users).

4 Metadata best practices

To maximize the benefits of your metadata standards and models, as well as ensure the effectiveness of your data governance, you should adhere to some metadata best practices. These include documenting the standards and models you use, ensuring the accuracy and validity of your metadata, protecting its confidentiality, integrity, and availability, as well as regularly updating and refreshing it. Additionally, you should archive and delete obsolete or redundant metadata.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?