What Is Metadata?
Metadata contains information about a data asset, such as properties, origin, history, location, creation, ownership, and versions. It offers this additional information about a data asset to inform users of the asset’s meaning, and can be an important element for maintaining compliance with regulatory requirements.
For example, a digital image’s metadata can include information related to the image’s resolution, size, color depth, and time of creation. This information can be used for data classification, labeling, organization, sorting, tracking, searching, and analysis.
This is part of a series of articles about data security.
Why Is Metadata So Important?
Your data is only the sum of all your metadata. Metadata is a resource that enables you to create a more holistic picture of the data, understanding it fully in its context. Metadata helps keep data organized and makes it easy to understand.
For example, medical and pharmaceutical research became more collaborative during the COVID-19 pandemic. Researchers required an effective system for searching, sharing, understanding, peer-reviewing, and replicating experiments. One of the main aspects that enabled this was the quality and availability of metadata.
Scientific research requires metadata about the test designs, test populations, terminologies and definitions, measurements and evaluation methods, and data-gathering schedules.
Enterprises increasingly invest in data to inform decision-making, so the data used by businesses will increase over time. Managing metadata is essential for ensuring data’s usability, searchability, and longevity.
Types of Metadata
Structural Metadata
Structural metadata contains useful information that helps in the establishment of object relationships. The characteristics of structural metadata include:
- Enables people to comprehend and successfully employ the data resource.
- Describes the hierarchical structures that exist between various data resources.
- Improves the display and navigation of obtained information using page-turning software. It depends on how customer images are delivered and stored.
Descriptive Metadata
Descriptive metadata is important for identifying and distinguishing a data resource. It includes details regarding the data’s context and content. Descriptive metadata is structured and frequently follows one or more established standard schemes. At the system level, descriptive metadata enables users to search for and obtain information.
Preservation Metadata
Metadata can assist the process of sustaining a digital object or file. This data may be needed to access a file. Metadata preserves a digital file or object from beginning to end. One of the common patterns is Preservation Metadata Implementation Strategies. It emphasizes preservation and maintenance fundamentals.
Provenance Metadata
Provenance metadata gives useful information about a data resource’s origins. The characteristics of provenance metadata include:
- The data, such as data ownership, transformations, consumption, and archival, facilitates monitoring a resource’s lifecycle.
- Provenance metadata is established when a new version of a set of data is created. It describes the relationship among various versions of data objects.
- Users can query the relationship among versions and provide fine or coarse-grained provenance information on collected data.
Definitional Metadata
Definitional metadata is data that establishes a shared lexicon for comprehending the significance of the data. Semantic and schematic are metadata types. Textual vocabularies can describe organized and unstructured data semantically. Schemas display database data in a structured format.
Administrative Metadata
Administrative metadata describes a file’s constraints. Administrators can restrict file access using this data. Administrative metadata provides complete details regarding data. Users can manage a variety of data files.
Administrative metadata is analogous to a basic version of data. Even if a data set is incredibly complex, its metadata will be significantly more detailed. Therefore, administrative metadata is concerned with control, specifically managing and simplifying these complex elements.
Metadata Examples and Use Cases
Below are common use cases and examples for proper metadata management:
Sensitive Data Discovery and Classification
Organizations that need to manage big data must determine which data points or sets are sensitive. However, manually managing data discovery and classification across large pools is impractical. Instead, employ artificial intelligence (AI) and machine learning (ML) algorithms for discovery and classification.
AI and ML algorithms rely on metadata to analyze data pools and classify data appropriately. Metadata management is critical to enable sensitive data discovery and classification, ensuring that algorithms properly identify and tag sensitive data.
Learn more in our detailed guide to data discovery. See how Imperva can enable data discovery and classification.
Data Access Monitoring
Managing metadata helps monitor data access and oversee who is using the data, when, why, and how. It tracks changes to data because the information about who accessed and changed data is a form of metadata. Access monitoring is essential for generating accurate audit reports that demonstrate data compliance.
Data Analytics
Metadata management enables organizations to implement advanced data analytics. It provides a structure for an organization-wide catalog of data. The goal is to prevent data from being misinterpreted or improperly understood by standardizing data and making it easily searchable.
Enhanced Data Compliance
Data is a highly valuable asset that requires adequate security. Organizations using personal and sensitive data must understand the risks associated with it and comply with the relevant regulations and policies protecting this data. Proper metadata management helps maintain compliance in various ways, such as proving compliance according to data lineage and impact.
What Is Metadata Management?
Metadata management enables organizations to gain more granular control over their data, ensuring business users can discover information more quickly and use it effectively. It typically encompasses various processes, including data labeling, classification, and analysis.
Manual metadata management is tedious and time-consuming. This process involves classifying a data catalog and establishing taxonomies to organize all data. It requires reading and processing information assets, analyzing them, and labeling each asset with the relevant metadata.
A metadata management system should use only metadata that is consistently captured, stored, and governed across the following three elements:
- Terminology for common business language – you can use various sources, such as industry standards, contracts, policy manuals, handbooks, and reference guides.
- Business resource-specific attributes – common attributes include systems or reports. You can use technical documentation, data models, and data dictionaries.
- Data resource-specific elements – you can use database catalogs, data models, spreadsheets, and other sources that include elements like database tables or reports.
You must properly link all three elements to ensure consistency.
Metadata management capabilities
A metadata management solution should provide the following capabilities:
- Data inventory – automatically identifies similar attributes, detects relationships with other data, and resolves ambiguities.
- Data lineage – identifies data provenance and enables impact analysis to help learn what happens if a metadata element changes.
- Automation – actively supports various data management efforts.
- Intuitive user experience – provides a user-friendly user interface and collaborative workflows to support diverse use cases and users.
- Semantic language understanding – supports terminology variations, establishes transparent business rules, and can identify exceptions to data rules.
A metadata management solution helps streamline processes while maintaining compliance and security, automatically governing data in an organized and accessible way.
Protect Data in All Forms with Imperva
Imperva Data Security Fabric protects all data workloads in hybrid multi-cloud environments with a modern and simplified approach to security and compliance automation. Imperva DSF flexible architecture supports a wide range of data repositories and clouds, ensuring security controls and policies are applied consistently everywhere.
