With the volumes of generated data increasing, metadata has become an essential component in organizing and comprehending massive datasets. Metadata plays a key role in any modern data strategy, especially among organizations that treat data as one of their most precious assets.
This article dives into all the different metadata types, tools, and frameworks to help you take the first steps in managing your metadata.
What is Metadata?
Metadata describes a data asset, including its properties, origin, history, location, creation, ownership, and versions. It provides additional information about a data asset, which can be a key component in discovering data or maintaining regulatory compliance.
Types of Metadata with Examples
1. Descriptive Metadata
Descriptive metadata is useful for identifying and differentiating between data resources. It offers information about the data’s context and content. Descriptive metadata is structured and often adheres to one or more defined standard schemes, such as Dublin Core or MARC.
Descriptive metadata helps you discover and identify data resources. It specifies a resource’s what, when, where, and who. It may also provide the resource’s physical properties, such as medium type and size.
2. Structural Metadata
Structural metadata provides useful information for determining the link between items, allowing users to better understand and use the data resource. It also provides information about the hierarchical relationships between various data resources, such as a table of contents, page, section, and chapter numbers.
Its primary goal is to improve the display and navigation of collected data, which is aided by page-turning software that specifies the sequence of page visuals. This depends on how images are sent to the user and stored in the repository.
3. Administrative Metadata
Administrative metadata provides essential information for resource management. It provides information about governance, access controls, and security, covering technical details on copyright, rights management, and license agreements. It may include technical data on work creation and quality control, rights management, access control, user needs, and action information preservation.
Administrative metadata is subject to project-specific procedures based on local requirements and may include contract agreements and payment information. It combines preservation and technical understanding. The archiving strategy of administrative metadata can be used to manage resources internally.
This type of metadata is useful in data governance. It helps to control and establish the credibility of the data, specifying who can access the files and how they can be utilized.
4. Preservation Metadata
Preservation metadata is information on the preservation management of collections and information resources. It documents the process of preserving both physical and digital versions of resources, as well as collecting all of the information required to manage and protect digital assets throughout time.
Preservation metadata in digital repositories may deal with rights management and include information on the rights holders who have authorized such acts. It refers to different structures, such as structural and administrative metadata. It’s primarily concerned with the analysis and actions taken on a resource after it has been uploaded to a repository.
5. Provenance Metadata
Provenance metadata contains useful information about data origins. It comprises information about the data’s ownership, any transformations that may have occurred, and its usage – all of which you can use to track the lifecycle of a piece of data.
Provenance metadata is generated anytime a new version of a data collection is created, and it describes the link between different versions of data. This lets users query the relationship between versions and contains fine- or coarse-grained provenance data for data resources.
6. Definitional Metadata
Definitional metadata is data that creates a common lexicon for understanding the relevance of the data. There are two sorts of metadata: semantic and schematic. Textual vocabularies can describe both structured and unstructured data semantically, while schemas represent database data in a structured manner.
Definitional metadata is metadata that provides a consistent vocabulary that allows for a shared understanding of the data’s meaning. The data’s meaning comprises information about its definitions, the rules that govern its context, and calculations. Definitional metadata may also provide details on the logic used to create derived data so that its full meaning is understood.
Definitional metadata is divided into two categories: semantic and schematic. Structured and unstructured datasets can be semantically described using textual descriptions or language. A database schema can display structured data collections.
Metadata in Data Lakes and Version Control
Metadata management in data lakes contextualizes the content of data sets and is essential in making data understandable and accessible in applications.
However, since raw data is regularly supplied into a data lake, many organizations neglect to include methods for verifying the data or applying data standards to it. Because of inadequate metadata management, data in a data lake is less useful for analytics.
To properly manage data in a data lake, you need a framework for recording technical, operational, and business data so that it can be identified and used for numerous purposes.
A metadata management tool is one way to automate metadata management. This type of platform can automate the collection of metadata on arrival while performing transformations and relate it to specific meanings, such as those in an enterprise business lexicon. It ensures that all users are reading the same data in accordance with a set of rules and concepts – and it can be automatically updated as your data changes.
Robust metadata management features simplify and automate ordinary data administration. If the metadata architecture isn’t strong, data lakes might not be able to move from an analytical sandbox or proof of concept (POC) with few datasets and one use case to a modern data architecture, which is a production-ready, enterprise-wide data platform with many users and many use cases. It also significantly impacts the performance of any data analysis using data sets stored in a data lake.
The role of version control in data lake metadata
Live data systems are constantly absorbing new data as different users experiment with the same datasets. These processes might easily result in many versions of the same dataset, which presents a management difficulty.
Data version control assists teams in addressing it by applying a well-known approach to versioning source code to the realm of data. Many data versioning technologies are open-source, allowing teams to experiment with them immediately.
Key Metadata Standards and Frameworks
Data Cataloging Standards
Metadata standards are at the heart of any data governance strategy, providing a set of agreed-upon principles to guarantee data is correctly handled and retains its value and significance over time.
Common metadata standards include Dublin Core, Dublin Core Abstract Model (DCAM), SVG Metadata (SVG), ISO/IEC 11179 Metadata Registry, and RDF/XML. Here are some more details regarding each category.
Dublin Core Abstract Model (DCAM)
DCAM is a globally recognized set of 15 fundamental data items intended to provide a high-level overview of digital resources such as papers, photos, and web pages. It’s widely used in databases and search engines. DCAM defines a framework for representing digital resources that promotes interoperability and consistency.
SVG metadata (SVG)
SVG Metadata (SVG) is a standardized metadata format for Scalable Vector Graphics (SVG) pictures. It offers instructions for rendering the image and supports several resolutions.
ISO/IEC 11179: Metadata Registry
ISO/IEC 11179 The Metadata Registry is a worldwide data element registry that establishes rules and principles for defining and managing data items. It’s designed to facilitate the exchange, categorization, and integration of metadata elements.
RDF/XML
RDF/XML is a markup language that encodes data in XML format and allows it to be transferred across applications.
Lineage Standards
To take use of the benefits of data metadata and data lineage, you must follow some common standards and frameworks:
- Dublin Core provides 15 core metadata elements to describe digital resources;
- ISO 11179 is a standard for managing metadata registries
- DDI is a standard for describing data generated by surveys and other observational methods
- PROV is a standard for representing and exchanging provenance information
- W3C Data Catalog Vocabulary (DCAT) is a standard for describing datasets and data services
These standards can help you specify, record, store, and distribute your data’s information and lineage.
Benefits of Adopting Metadata Management
Metadata management plays a critical role in data and is known by various names, from “the business glossary for data” and “data cataloging” to “a single source of truth.” It explains the meaning and context of data for everyone. To gain a better understanding, consider some of its benefits:
Enhanced Data Consistency and Governance
End-to-end data lineage and governance are key in the world of metadata. Metadata management tracks a dataset from its inception to each change. Such extensive logs help in tracking data for regulatory purposes, data quality assessments, and root cause analysis, which ensure data quality and risk minimization.
Boosted Productivity and Operational Efficiency
Automating and standardizing metadata management enables organizations to focus resources on high-priority projects. This method saves resource time that was previously spent on manual tasks. Additionally, it results in savings in areas such as data design, conversion, and mapping.
Elevated Data Quality and Searchability
Metadata management provides a comprehensive catalog for discovering relevant datasets. This discoverability improves accessibility, allowing for improved data use and informed decision-making.
Data consistency and unification
Another benefit of metadata management is the standardization of data definitions inside an organization. As a result, each department has access to the metadata required for effective understanding and management of its systems, data, and operations. This promotes uniformity, streamlines data governance, unifies the company’s attitude to data, and reduces data retrieval challenges.
Enhanced Collaboration
Metadata management documents data structures, formats, content, and relationships, allowing seamless integration across sources, systems, and applications. It also helps users to understand the relationships and dependencies between various data assets, ensuring departmental integration and collaboration.
Challenges in Managing Metadata
Inconsistent Standards and Definitions
Failure to adhere to recognized metadata standards or best practices will result in poor interoperability and integration with other systems or data sets.
To avoid this, make sure to investigate and apply industry-specific metadata standards. Teach your teams the importance of these standards and how to implement them. And don’t forget to review and update your processes on a regular basis to ensure they meet current standards.
Not Prioritizing Metadata Quality
Allowing erroneous, outdated, or irrelevant metadata to accumulate will eventually jeopardize the integrity and usability of your data.
Carry out regular audits of your metadata to guarantee its accuracy and relevance. You can use automated validation techniques whenever feasible to detect problems early on. And most importantly, build a culture of excellence and accountability for metadata management.
Cost and Scalability Issues
Typically, business units and technical groups require metadata and swiftly develop different metadata repository solutions that address only one or two unique problems/challenges. If executive management understood the expense of these metadata repository point solutions, they would realize that it significantly eclipses the cost of a truly sound enterprise-wide metadata management solution.
Organizations that design single-point metadata solutions are frequently concerned about the cost of developing them. However, doing it right the first time is always less expensive than doing it incorrectly and attempting to rectify it later.
Types of Metadata: Key Use Cases
One can apply several types of metadata in different contexts. The following are the main uses of metadata inside a company:
1. Database administration
In a database management system (DBMS), metadata is a column name and a row number coupled with the piece of data. The SQL standard offers a consistent method for accessing the metadata, or schema, but not all databases follow it. Metadata facilitates data requests, organization, and interpretation.
2. Search on websites
Metadata found in websites greatly influences their ranking and success. If you include metadata elements, like a meta title and a meta description, you’ll help a website rank higher. Search engines classify the webpage using this metadata, which they then interpret as keywords.
In LLM-powered search, metadata acts as semantic signals that guide retrieval and ranking within vector-based systems, enabling models to surface and contextualize the most relevant content for generation.
3. Social media
Users have some control over the distribution of their information on sites like Facebook or X, formerly Twitter, thanks to social media’s metadata. Users who maximize their content experience more interactions from their posts than those without optimization.
4. Marketing languages
Markup languages let users spot individual components of a document, such as a header or a paragraph. They comprise either extensible markup language (XML) or a standard generalized markup language (SGML). SGML makes documents readable by machines.
5. Organizing information
Classification is the logical arrangement of data to locate it as required. Taxonomy is the arrangement of this material into classes or categories; the data linked with the objects is metadata. You can include this material in an external content management system or include it in your own material.
Top Metadata Tools and Technologies
Apache Atlas
Apache Atlas is an open source metadata management platform providing users with a wide range of data definition, storage, and management features. It enables data tagging, entity type definitions, search, and fine-grained access control. Apache Atlas also simplifies the integration of data and applications, allowing users to create data governance workflows using flexible entity models.
Collibra
The solution provides extensive data governance and cataloging capabilities for harvesting and managing data throughout an organization. Collibra is a collaborative platform for managing metadata that focuses on group interactions and assigns user responsibilities for data ownership and consumption.
Collibra was designed with corporate end-users in mind. It serves as a searchable repository for users who need to understand how and where data is kept and how it may be used.
Alation
Alation provides a robust and intelligent data platform that supports various metadata management applications, including search and discovery, data governance activities, and digital transformation.
Key features include machine learning capabilities, an AI-enabled data catalog, ad hoc reporting, and integration with Active Directory. Alation is well-known for its ease of use and advanced AI capabilities. It’s a good choice for companies looking to improve data collaboration and discovery. It enables teams to accelerate self-service, simplify cloud migration, and easily manage data governance.
Informatica Metadata Manager
Informatica Metadata Management streamlines data intelligence gathering, curation, and inference. Its CLAIRE AI Engine allows you to automatically find data domains, classify data, infer relationships, recommend the best next steps, associate business terminology, and more.
The solution supports various file formats, cloud platforms, databases, SaaS applications, and business intelligence tools. It enables you to create a single, end-to-end view of all your essential corporate data with scalable multi-domain MDM and 360 solutions. Informatica is an effective metadata management platform for large organizations with complex information management needs.
Microsoft Purview
Microsoft Purview Unified Catalog aims to provide a platform for data governance and promote commercial value development in your organization.
It makes your data more visible to your users through a single, integrated SaaS architecture. This solution brings everything together in one location, with experiences for data consumers, stewards, and owners.
Integrating lakeFS into Your Data Management Strategy
Managing metadata is difficult, but teams who work with data lakes have even greater challenges.
Because raw data is regularly delivered into a data lake, many teams overlook strategies for validating or creating organizational data standards. Insufficient information management in data lakes makes the data less valuable for analytics.
The four primary challenges of handling metadata in data lakes are:
| Challenge | Description |
|---|---|
| Data silos | In many companies, teams store data in separate buckets, dependent on the path to the data. The same goes for metadata. Keeping data in silos that do not match has a substantial impact on the organization’s ability to use metadata for its most critical purposes. |
| Immutability or mutability | Data lakes are immutable, but data is not. It’s always changing. Metadata is required for recording changes in data collection over time and permitting concurrency across many versions of the data. |
| Versioning | Because data sets vary over time and organizations deal with a large number of datasets, metadata is essential for managing changes to diverse data sets over time while also maintaining consistency across all datasets consumed and produced within the data lake. |
| Abstraction layer | Data lakes are built using object storage, which handles data as objects rather than files. |
Data Lakes and Version Control
Live data systems constantly absorb new data as different users interact with the same resources. This may easily lead to many copies of the same dataset, which would be difficult to manage.
Data version control helps teams deal with it by applying a well-known mechanism for versioning source code to the data domain. Many data versioning tools are open source, allowing teams to experiment with them immediately.
lakeFS is an open-source data version control management system based on Git semantics that runs on top of a data lake. It enables data engineers and scientists to version control their data as they build and manage data pipelines and machine learning models, ensuring reproducibility, smooth collaboration, and high-quality data.
While other approaches need data copies, lakeFS operates entirely at the metadata level, allowing for fast branching, data reproducibility, and atomic commits without duplicating data.
lakeFS views metadata as the ultimate source of truth, allowing teams to version data, experiment in isolation, and communicate effortlessly – whether for machine learning, analytics, or data engineering. This metadata-driven approach makes data accessible, trackable, and controllable at scale while protecting existing storage infrastructures.
Conclusion
Metadata is the cornerstone of many advanced data-driven functionalities, including data meshes, fabrics, data lakes, and warehouses. As humans and machines generate more information around the world, metadata helps keep track of these assets and offers a unique identity to each data set. New technologies will lead to the rise of new metadata management approaches, such as AI metadata management to help organizations enhance their data strategies.
