Data Management Glossary
Metadata
Metadata means “data about data” or data that describes other data. The prefix “meta” typically means “an underlying definition or description” in technology circles. Standard metadata are storage system attributes such as: when the file was created, who created it, what type of file it is, its size, when it was last accessed, and when it was last modified.
Metadata makes finding and using data easier so that the user can quickly find and categorize specific documents. Some examples of basic metadata are author, date created, date modified, and file size. Metadata is also used for unstructured data such as images, video, web pages, spreadsheets, etc.
Web pages often include metadata in the form of meta tags. Description and keywords meta tags are commonly used to describe content within a web page. Search engines can use this data to help understand the content within a page.
Metadata can be created manually or through automation. Accuracy is increased using manual creation as it allows the user to input relevant information. Automated metadata creation can be more elementary, usually only displaying basic information such as file size, file extension, when the file was created, for example.
Metadata can be stored and managed in a database, however, without context, it may be impossible to identify metadata just by looking at it. Metadata is useful in managing unstructured data since it provides a common framework to identify and classify a variety of data including videos, audios, genomics data, seismic data, user data, documents, logs.
Top Benefits of Metadata
- Metadata brings structure to unstructured data, valuable for search, data mobility, management, and analytics;
- Metadata delivers deeper insights on your data, such as: top data owners, top file types and sizes, and usage information such as last access date;
- It improves cost savings and decision-making for data storage;
- It supports compliance and AI data governance by tagging regulated or audited data sets;
- Users can find key data sets faster and move them to the right location for AI and research projects.
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What is Metadata?
Metadata is “data about data.” It is structured data that references and identifies data to give an essential extra layer of shorthand information. Metadata schema can be simple or complex but it provides an important underlying definition or description.
Types of Metadata
There are three main types of metadata:
Structural Metadata – examples include:
- Page Numbers
- Sections
- Chapters
- Indexes
- Tables of Contents
Administrative Metadata – examples include:
- Technical Metadata – Decoding and rendering files information
- Preservation Metadata – Information necessary for the long-term management and archiving of digital assets
- Rights Metadata – Information relating to intellectual property and usage rights
Descriptive Metadata – examples include:
- Unique identifiers (eg ISBN)
- Physical attributes (eg file dimensions or Pantone colors)
- Bibliographic attributes (eg author or creator, title, and keywords)
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Metadata Management
Metadata management includes both standard metadata that most storage systems create and track as well as more custom metadata that gives more context about the contents of the file. Metadata management is the administration of data that describes other data and can include metadata enrichment, via tagging. AI tools can help enrich metadata by inspecting file contents and identifying new tags to indicate demographics, project keywords, sensitive data, individuals and objects discussed or included in the file. Metadata management is important for understanding, aggregating, grouping and sorting data for use. Over the last decade, the rapid growth of data has created the need for metadata management to provide a clear insight into what data to produce and what data to consume. This ensures data becomes a valuable enterprise asset.
Advanced metadata is handled differently by file storage and object storage systems:
- File storage organizes data in directory hierarchies, making it hard to add custom metadata attributes.
- Object storage lacks the hierarchical directory structure of file storage, but you can customize it.
For instance, a clinical image file would only contain metadata such as creation date, owner, location, and size. But if it is stored as an object, a user can enrich the metadata with demographics such as patient’s name, age, and diagnosis.
Managing metadata requires strategy and automation: Choosing the best path forward can be difficult when business needs are constantly changing, data is growing explosively and data types are morphing from the collection of new data types such as IoT data, surveillance data, geospatial data and instrument data.
Read more about metadata and its role in unstructured data management in this two-part blog series.
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