What is Metadata and How is it Used?

Often described as data about data, metadata is a foundational element used to transform data into an enterprise-wide asset. Metadata helps understand the data behind it, metadata reflects how data is used and is key to supporting data governance initiatives, regulatory compliance demands and data management processes.

Metadata is critical to data management because it provides essential details about an organization’s data assets —what is that data, when the data was created, where it resides, how it’s been altered, who has access and who owns it. Simply defined, metadata is the summary and the description about your data that is used to classify, organize, label and understand data, making sorting and searching for data much easier.

Without metadata, companies can’t manage the huge amounts of data created and collected across an enterprise. They need metadata to understand and effectively deploy information resources to support different business processes and enable advanced analytics.

However, different departments have various metadata perspectives on how to organize and interpret metadata.

Defining Diverging Metadata Perspectives 

Metadata provides a comprehensive understanding of where data resides in an organization and how it is deployed. To ensure all data users understand organizational metadata, businesses must collect, arrange and manage metadata from three different perspectives – physical, logical and conceptual.

1. Physical metadata covers the specifics of: within which system data resides, the schema, table and column or key-value level of detail. This information is machine generated and automatically pulled from software systems. 

2. Logical metadata provides details on how data is linked together to form larger sets. It also outlines how data flows through systems and processes, from creation, to storage, transformation and consumption. Logical metadata essentially establishes a roadmap on data’s path through the data supply chain, including its usage and alterations over time. 

3. Conceptual metadata provides the business context for data–it details data’s meaning and purpose within an enterprise. It provides critical information about the data usage, including the acquired knowledge of subject matter experts within the organization. This type of metadata is derived from people. As a result, it is the most difficult type of metadata to collect and update because it requires human intervention and management processes to continuously refresh.

Once an organization identifies these three types of metadata, they can empower the business to create a metadata glossary and make it readily available to all users. 

Building a Metadata Glossary 

A metadata glossary provides transparency into data assets for both business and technical users. Building the glossary starts with an enterprise-wide data governance strategy that emphasizes data quality.

A comprehensive data governance program helps encourage communication between data owners, data stewards and users to cultivate a collaborative approach for establishing common data descriptions. When everyone works together to interpret and document metadata, organizations can institute a mutual understanding of data assets, minimizing any confusion business users face when looking through the metadata glossary. By automating and tracking these processes, alerts are generated when a data element falls out of compliance.

With full participation across the enterprise, data governance delivers complete transparency into an organization’s data supply chain so business users can easily define, measure, track and manage their data assets.

Data governance also provides accountability for individual data assets. By establishing clear lines of responsibility, companies ensure that metadata is always consistent and precise. Data integrity components can then connect metadata descriptors and data quality scores to ensure data remains accurate, reliable, trustworthy and fit for use.

In addition, data governance programs help to keep pace with the ever-growing supply and demand for data. Today, machine learning capabilities embedded into a data governance program automates the capture and curation of metadata, removing some manual efforts that save businesses time and money.

Managing Metadata

Once metadata has been defined within the context of a greater data governance program, the final critical step is to identify a metadata management tool to help capture, curate, evaluate, and store metadata. This should be an automated process to facilitate data tracking and accountability. Of course this can be done using spreadsheets, keeping the information up-to-date will cause major headaches. Capturing, publishing and updating metadata on every current and future data project will yield decreased time to insights, helping the business make better informed business decisions, faster.

By understanding and evaluating metadata from various perspectives, together with an integrated data governance program, organizations can successfully build a comprehensive metadata glossary, secure a metadata management solution and empower all data users to take advantage of trustworthy data.

Are you looking for more information about metadata? Check out the webinar above or below.

For additional about metadata and metadata management, read this article from Dataversity, featuring our EVP of product management, Emily Washington: https://www.dataversity.net/metadata-management-drives-the-future-of-data-management/