It’s the paradox of big data: The more you have, the more difficult it can be to locate and use it effectively. This is where metadata can help. Metadata is “data describing context, content and structure of records and their management through time”, as defined by the International Organization for Standardization. In theory, there is as much metadata as there is content, which makes its management a must if companies hope to locate and extract the full value of their growing volumes of valuable information assets for business advantage.
While many of us may associate metadata with web searches, there are many other use cases. Metadata management lets users, systems and applications discover relevant data in a more automated fashion. Metadata management means data stored in different physical locations, applications and formats can be accessed and understood across platforms.
What Is Metadata?
Metadata is information that summarizes each piece of data to make it easier for businesses to find and understand. In essence, metadata answers the who, what, when, where, why and how of any given data asset and is often used for computer files, images, spreadsheets, audio or video files, web pages and relational databases. The metadata for a document file, for example, would likely include author, date, file size and the content’s keywords. Metadata may reside in multiple locations, including emails, data collection instructions or spreadsheets. Metadata for web pages exists within the code as metadata tags, page titles and page headers. Metadata for databases may be stored in tables or fields. Some data files include both raw data and metadata. In some cases, metadata is stored in a special metadata document called a metadata repository or data dictionary.
In 2020 alone, a staggering 64.2 zettabytes (that’s 64.2 trillion gigabytes) of data were created or replicated; by 2025, that number is forecast to reach 175 zettabytes, according to research firm IDC. Locating relevant needles of data within ever bigger haystacks of information is nearly impossible for humans to accomplish on their own. This is where metadata comes to the rescue.
Providing context, meaning and usability for the growing trove of data that companies gather allows organizations to solve business problems and answer business questions with greater speed, agility and accuracy. However, because data continues to accumulate and often exists in different formats or within disparate systems, many organizations fall short when making business decisions if they are unable to capture the full value of available data because it is too difficult to find the relevant information. This is where metadata management comes in.
What Is Metadata Management?
Metadata management is the administration of metadata within an organization. It refers to the policies and practices that guarantee effective data management and maintenance. The focus of metadata management is on how data assets relate to one another, where data assets have come from and what has been done to those data assets over time. This helps to ensure that any analysis performed using data will be accurate and actionable.
- Metadata — data that describes other data — can be generated (manually or automatically) every time data is touched, from capture to analysis.
- Metadata management is the administration of that information. It involves creating policies and processes that enable metadata to be maintained, integrated, accessed, shared and analyzed appropriately and effectively.
- A basic framework for metadata management integrates metadata discovery, collection, governance, storage and distribution.
- Benefits of effective metadata management include better data governance, improved data quality, faster speed to insight, more effective regulatory compliance, greater productivity and reduced costs.
Metadata Management Explained
Metadata is generated throughout the data life cycle (whether by automated information processing or manual entry) — from when it is first captured, to when it is moved or integrated with other data, to when it is accessed or analyzed by users. And thank goodness: It’s this metadata that lets organizations, IT functions and business units understand how various data might work together to create insight and, thus, promote better decision-making.
Metadata management refers to the policies and processes for managing this “data about data”. This kind of governance of data involves developing standards, policies and best practices that then move along with the data assets wherever they go. Metadata management has a number of applications. It can be used to:
- Better organize data.
- Optimize data utilization.
- Improve the integration and interoperability of data.
- Enable effective analysis of data and ensure the outcomes are correct.
- Protect and control access to sensitive or high-value information.
- Track data for regulatory purposes.
How Does Metadata Management Work?
The aim of metadata management is to make it easier for a person — or, more likely, a computer program — to identify and search the key attributes of data assets within an organization’s inventory of data assets, called its “data catalog”.
To accomplish that, metadata management involves creating policies and processes that maintain, integrate, access, share and analyze information appropriately and effectively. A basic framework for metadata management integrates metadata discovery, collection, governance, storage and distribution.
The goal of metadata management is to make it easier to locate and make the best use of specific data assets. This requires designing a metadata repository, populating the repository and making it user-friendly.
Why Is Metadata Management Important?
Metadata management is a foundational aspect of data governance — and fundamental to digital transformation initiatives. Along with capabilities in the areas of master data management and data quality (which deal with data assets themselves), metadata management is part of a holistic data governance program, integrating metadata at the enterprise level.
Without metadata management and tools, data stewards would be forced to spend the majority of their time parsing metadata rather than extracting business value from it. In fact, it would be impossible for organizations to use and analyze their growing troves of data effectively — if at all — without metadata management policies and tools.
Who Needs Metadata Management?
While metadata management benefits everyone in the organization who relies on data for tasks and decision-making, there are two groups of people who work with metadata most directly: metadata users and creators. In many cases, individuals will find themselves involved as both metadata creators and users, with both functions playing a critical part in effective metadata management.
- Metadata creators: While some basic metadata may be captured in an automated fashion (for example, marking the point when a data asset was created, who created it, file size), other more nuanced metadata is entered by humans. When we post this article, for example, we will tag it with metadata so you can discover it more easily.
- Metadata users: Business and technical users rely on metadata management to discover and define data, collect and consolidate metadata from multiple sources and systems, track the trajectory and transformation of data, and govern data using standards, policies or best practices. As a result, they are able to manage and analyze data more easily within the context of their needs and roles.
Types of Metadata
Metadata comes in a variety of forms. It can provide information about technical processes and data structures, outline access and usage rules and protocols, and describe keywords and business terms included in the associated information. Some of the most common type of metadata, as they apply within the enterprise context, include:
- Administrative metadata: This often technical metadata details information about the format and structure of the data (as required by computer systems), as well as when and how the asset was created, usage rights, data ownership and when and how the information asset may be used. Examples include access permissions, where data originated and what has been done to it, and backup rules for an information asset.
- Operational metadata: This type of metadata is also rather technical and describes the processing and accessing of data. Examples include error logs, data retention rules and version-maintenance plans.
- Business metadata: This is a plain-English type of metadata. It provides information on the meaning of data in everyday business terms, such as the owner of the data or data classifications. Unlike administrative and operational metadata, business metadata adds context to data created or used by business users.
- Behavioral metadata: This type of metadata notes and records reactions and behaviors of the data set’s users, such as rating or user analytics. This metadata is generated, for example, when users navigate a website, view content or create social media posts.
Benefits of Metadata Management
Management of data, or data governance, is more important than ever as data stores continue to multiply exponentially. Indeed, it is a strategic priority in any organization pursuing digital initiatives. Metadata management is a key aspect of any data-governance program. Metadata policies and processes make it possible for an organization’s data users to find, access, trust and use data effectively and efficiently.
As a key pillar in a mature data-governance program, metadata management offers a number of benefits related to data management and beyond. Some of the advantages of metadata management include:
- Improved data quality. Metadata management, particularly when automated by increasingly advanced and user-friendly tools, ensures that data quality is regulated through the data life cycle. Consistent use of common metadata definitions eliminates data-retrieval problems, and data quality rules can be set for data assets.
- Better regulatory compliance. Metadata makes it easier for organizations to keep up with increasing government regulations surrounding the handling and use of data, such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) and Basel Committee on Banking Supervision (BCBS).
- Greater confidence in data analysis. Metadata management is critical as organizations embrace more advanced analytics and artificial intelligence (AI) capabilities, so that the data driving these intelligent capabilities is accurate and appropriate.
- Increased productivity and reduced costs. Metadata management tools greatly reduce the costs and effort required for effective data management by automating aspects of data organization, discovery, integration and delivery.
- Quicker insights. Enterprise metadata management provides not only greater accuracy and efficiency, but also faster analysis. The result is significantly accelerated project delivery.
- Greater use and reuse of data. A good metadata management program reduces redundancy in the enterprise because it grants consistency across multiple types of data and allows data to be used and reused appropriately.
Five Levels of Metadata Management Maturity
Metadata management is a discipline — one that can take some time, effort and expense to master. Organizations may find it helpful to understand the increasing levels of metadata management maturity as they set out to improve their own capabilities.
- Level 1: Metadata is not in a central repository. Metadata management, if practiced at all, is a labor-intensive and error-prone process.
- Level 2: There is some awareness that metadata management is necessary and the organization is exploring more formal metadata management and tools. However, any metadata management is done using rudimentary tools already available, such as spreadsheets and databases.
- Level 3: There is a recognition of the value of metadata management with specific systems and processes in place. There is no enterprise-level metadata management function or repository, however, so these efforts remain siloed.
- Level 4: There is recognition of the importance of metadata management at the executive level. Often, there is an enterprise-level data management team led by a C-level executive (e.g., chief data officer), implementation and adoption of metadata management tools and processes across the organization, and increasing automation.
- Level 5: Metadata is recognized as the organization’s most important data asset. There are strict and universal metadata standards, formats, and usage; a centralized data repository; and a high level of automation.
Metadata Management Best Practices
As advanced data analytics has become a primary driver of business growth, metadata management has a strategic role to play in ensuring that the data fueling those efforts is used to greatest effect. Metadata management can clarify the context of data assets across an enterprise, resulting in better, faster and more secure data analysis for decision-making.
However, metadata management takes effort to set up properly. There are several best practices an organization can adopt for successful implementation of a metadata management program:
- Develop a metadata strategy. Importantly, an organization’s metadata management strategy should align with its strategic vision and business objectives. Making sure the metadata strategy supports key business priorities encourages business engagement when a metadata management process is launched.
- Outline the scope. Identify key purposes for using metadata and understand the resulting metadata requirements that will define the goals and parameters for the metadata management program.
- Define key metadata roles. Key stakeholders in metadata management include creators, managers and users. Clearly outline their metadata management roles. Consider establishing metadata stewards who can join the data-governance team.
- Agree on data vocabulary and taxonomy. Before establishing metadata management policies, key stakeholders must reach consensus about how to categorize and organize metadata, rationalizing any intra-enterprise variations.
- Explore and adopt standards. Metadata standards continue to evolve. They vary in terms of detail and complexity. Determine which standards are appropriate for your organization to work well with customers and partners.
- Invest in the right metadata management tool. Once you’ve settled on strategy, scope, roles and standards, your organization will have a better idea of the key capabilities necessary in a metadata management system. You may decide you need to buy a new metadata management tool, whether it’s a new tool or one that explores use of the metadata repositories available within existing data management or business intelligence tools.
- Make metadata management a core enterprise capability. Engage users in data governance and metadata management by communicating objectives and benefits early, and championing enterprisewide processes. Plan for ongoing reassessment of metadata management practices to make sure they continue to meet business needs.
Metadata Management Tools
Metadata management tools help an organization record, identify and track metadata associated with data assets present within disparate systems inside and outside of the enterprise. Modern metadata management and data cataloging tools can gather metadata in databases, data warehouses and data lakes, as well as from data in motion.
The market for metadata management tools is growing, as vendors develop products that are increasingly capable and user-friendly. Some key features to look for include:
- Data lineage. Metadata management tools should be able to track where data originated, where it has traveled and what changes have been made to it. Lineage can also indicate where else data is being used.
- Data inventory. Sometimes called data mapping, data inventory is an essential function. An inventory lists all data assets in an organization and their locations.
- Metadata communication. Tracking communication is key when managing metadata, so the ability to pull together conversations related to metadata (comments, remarks, etc.) is vital.
- Data connectivity. Metadata management systems are built to ingest metadata from various sources, but they should also be able to share metadata with analytics systems and business intelligence software. Metadata exchange with third-party tools ensures that this valuable information is accessible for analytics purposes.
- Tagging. This is the process of systematically attributing a digital asset to the appropriate nugget of metadata. The ability to add metadata to a data inventory is invaluable, especially when business requirements are apt to change. Tagging capabilities make this possible. Some tools may offer the ability to add tags based on patterns.
- Data matching. Understanding relationships among types of metadata facilitates data searching. Metadata management tools may offer automated solutions to this function, such as metadata semantic matching or data matching.
Implementing Metadata Management
Implementing a metadata management program and adopting tools that match an organization’s data goals should be a top priority for any organization with digital ambitions. Indeed, the vast majority of businesses either have a metadata management strategy in place or are developing one.
Organizations that want to establish robust enterprise metadata management must do more than invest in a metadata management tool. They need to adopt a metadata management framework that guides the organization’s efforts to make data and metadata assets accessible and usable to achieve business objectives. An overarching framework at the enterprise infrastructure level can mobilize the organization and gather the resources necessary to underpin ongoing metadata management. Successful metadata management implementations have executive-level buy-in and support, a metadata strategy aligned to business strategy and goals, and clear ownership and responsibilities to guide the program long term.
The multiple benefits of metadata management are clear. At a time when data stores are not only growing in volume but in importance to business strategy and performance, metadata management is emerging as a mandatory capability for the digitally focused enterprise. Organizations that want to pave the way for increasingly intelligent platforms for data-driven insight are investing in the metadata management processes, policies and tools that underlie effective data governance.
Metadata Management FAQs
Why do we need to manage metadata?
Metadata is a foundational element of overall data governance and ensures that the data used for analytics and decision-making is sound. Metadata management may be used in a number of ways, including optimizing data utilization; improving the integration of data; better data organization, enabling effective data analytics; and protecting, controlling and tracking sensitive, high-value or regulated data.
What does metadata management do?
Data governance is more important than ever as data stores continue to multiply exponentially. Indeed, it is a strategic priority in any organization pursuing digital initiatives. Metadata management is a key aspect of a data-governance program. Metadata policies and processes make it possible for an organization’s data users to find, access, trust and use data effectively and efficiently.
What are metadata management tools?
Metadata management tools automate aspects of the metadata management process. Metadata management tools can record, identify and track metadata associated with data assets within disparate systems. They can gather metadata stored within databases, data warehouses and data lakes, as well as from data in motion from one location to another. They may function like data inventory management, data lineage, tagging and data matching.
What is a metadata strategy?
A metadata strategy lays out not only how an organization will track and use metadata, but also why metadata management is important to the business and how the organization will refine its metadata management over time, based on feedback from business users.
What is metadata in data governance?
Data governance is the management of data assets to identify them and extract their full business value. Metadata (and its management) is how organizations can identify, define and classify their data assets so business users and technology leaders can manage them more effectively.