Metadata Management: Definition, Benefits, Best Practices, and Tools

February 21, 2022

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Metadata management is the key to making sense of the vast amount of data that exists throughout our increasingly digital world. According to G2, businesses generate approximately two billion gigabytes of data every day. With so much information surrounding us at all times, it can be difficult to find, interpret, or even trust the data we have that is required to get a job done. That’s where metadata management comes into play.

What is metadata management?

Metadata management is a framework of processes, policies, and technologies that are used to catalog information/data assets within your organization throughout their lifecycles. Metadata management is the practice of cleaning, classifying, and organizing data to ensure its accuracy, integrity, consistency, and usability. Metadata management is the stepping stone to data discovery, search, collaboration, data quality and data governance.

It usually refers to enterprise metadata management (EMM), or the business discipline of managing metadata about the information assets of an organization.


[ebook] What is Active Metadata Management and Why Does it Matter?


What is metadata?

Simply put, metadata is data about data. It describes data and provides important context. Think of an Excel spreadsheet: If the data is the numbers or text contained in the rows and columns, the metadata would be the descriptions of those columns. Metadata helps you understand what your data means or represents.

What are the types of metadata?

While there are no “official” classifications of metadata, it can be categorized as one of many different groups:

  • Technical metadata (e.g., schemas, data types, data models) provides information on data format and structure.
  • Business metadata (e.g., data tags, classifications, mappings) adds business context to data that is created and/or used by an enterprise.
  • Operational metadata (e.g., outputs, ETL, lineage metadata) helps you understand when and how data was created or transformed.
  • Process metadata (e.g., job execution logs, errors logs, audit results) is a type of operational metadata originating from data warehouses and lakes that aids with traceability and lineage mapping.
  • Structural metadata (e.g., data element types, table names, record size) is technical information on the physical organization of a data set, which is often used to create data dictionaries.
  • Administrative metadata (e.g., access control information, user restrictions) is additional information on the lineage of a data set that helps manage and establish its credibility.
  • Rights management metadata (e.g., copyright information, license agreements) is a subset of administrative metadata that refers to intellectual property rights.
  • Provenance metadata (e.g., ownership, change logs, versioning records) tracks when something is created, changes, or is duplicated.
  • Preservation metadata (e.g., technical, rights management, and provenance metadata) helps maintain certain objects and files over time. This includes information that is essentially required for a system to interact with a specific file.
  • Social metadata (e.g., author information, content tags, business user knowledge) gives information about the users who create different types of data.

Learn more →  A Comprehensive guide on the types of enterprise metadata

Architecture of enterprise metadata lake: A unified repository for metadata management from disperate data sources. Source: Atlan

Architecture of enterprise metadata lake: A unified repository for metadata management from disperate data sources. Source: Atlan

Active Metadata vs Passive Metadata

All of these types of metadata can be grouped under two broad categories, so we can better understand how they exist within a system:

  • Passive metadata: Technical metadata that provides basic data definitions such as schema, data types, models, owner name, etc.
  • Active metadata: Descriptive metadata that adds context to data by providing details on everything that happens to the data (e.g. operational, business, and social metadata).

Read more →Active metadata management: The key building block of a modern data stack.

Key components of an enterprise metadata management data stack. Source: Atlan

Key components of an enterprise metadata management data stack. Source: Atlan


[ebook] → Building a Business Case for Metadata Management & Data Catalog

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What is a metadata management platform?

A metadata management tool is a technology, often a SaaS product used to store, organize, track, and edit every type of metadata within a business. A metadata management platform should enable diverse data users to surface relevant metadata when and where they need it.

Traditional metadata management solutions are typically static and rely on humans to curate and document information. However, a new generation of technology — the active metadata management platform — not only aggregates metadata in a single store (such as a metadata lake), it also leverages orchestration to make relevant metadata available to users as seamlessly as possible.

Atlan: An enterprise active metadata management tool for the modern data stack. Source: Atlan

Atlan: An enterprise active metadata management tool for the modern data stack. Source: Atlan

Data-driven organizations and institutions have been eager to embrace active metadata management as a superior way to handle metadata. Last year, Gartner replaced their Magic Quadrant for Metadata Management Solutions with the Market Guide for Active Metadata Management. “The stand-alone metadata management platform will be refocused from augmented data catalogs to a metadata ‘anywhere’ orchestration platform,” says the guide.

Metadata Management: Collection of technical, operational, business, and social metadata in one place. Source: Atlan

Metadata Management: Collection of technical, operational, business, and social metadata in one place. Source: Atlan


A Demo of Atlan Enterprise Metadata Management Tool


Why is metadata management important?

Taking a unified approach to metadata management gives you the ability to trust that your metadata is accurate and reliable.

Metadata connects all of your organization’s data so teams can find exactly what they need, exactly when they need it. But with so much metadata existing within the modern business environment, it can be difficult to analyze data and separate the essential from the extraneous without the right context.

Without a metadata management strategy in place, data silos will emerge through the organization, and users in different departments won’t know which information is best for their needs.

What are the benefits of metadata management?

Here are a few benefits of metadata management to consider:

  • Improved consistency
  • Better data quality
  • Faster access to insights
  • Reduced costs

  • Improved consistency: Creates a consistent definition of metadata across the organization so conflicting terms don’t lead to data retrieval issues.
  • Better data quality: Metadata management solutions almost always leverage automation capable of identifying data issues and inconsistencies in real-time.
  • Faster access to insights: Data scientists have more time to analyze data to extract real business value, and data teams can achieve faster project delivery.
  • Reduced costs: The efficiency gains and repeatable processes of metadata management decrease redundancy and reduce excess costs, such as storage costs.

Learn more:Top 6 best practices for effective enterprise metadata management.


On top of these benefits, active metadata management provides additional advantages that many businesses find appealing.

Additional benefits of active metadata management:

  1. Enhanced context of metadata
  2. Auto-classification of sensitive data
  3. Embedded collaboration
  4. Orchestration of metadata across platforms

  1. Enhanced context of metadata: Business terms can be automatically correlated with any data object (such as tables, columns, saved SQL snippets, and reproducible queries).
  2. Auto-classification of sensitive data: Active metadata algorithms enable businesses to automatically identify sensitive data, such as personally identifiable information (PII), to help with compliance and designing custom access policies.
  3. Embedded collaboration: All data users are able to get on the same page while still using their tools of choice with no disruption.
  4. Orchestration of metadata across platforms: The modern data stack is always evolving to meet new needs — active metadata management allows disparate systems to connect, making data assets across these systems interoperable.

Atlan's vision for active metadata management

Vision for active metadata management. Image by Atlan


Data Catalog 3.0: The Modern Data Stack, Active Metadata and DataOps

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Active Metadata: Key to automated metadata management

Following are some of the examples of how active metadata help in automating metadata management at scale.

  • Programmable bots help automatically identify and tag sensitive columns based on regulatory compliance requirements — HIPAA, GDPR, BCBS 239, etc.
  • Bots help with auto-classification and auto-tagging based on specific naming conventions of data assets.
  • Active metadata management platforms parse SQL queries and automatically create column-level lineage and also auto-classify PII data.
  • Active metadata intelligently suggests recommendations and alerts and thereby making it easier for people to make the right decisions — surfacing commonly used data assets, stopping pipeline workflows when data quality issues are detected.

Custom metadata like pipeline errors, ETL status, DAG logs gives context about the health of the data asset. Source: Atlan

Custom metadata like pipeline errors, ETL status and orchestration DAG logs gives context about the health of the data asset. Source: Atlan

What are metadata management tools?

Metadata management tools are solutions that help extract value from the metadata that is stored by a business. Because the number of data users within a business is often increasing and their level of data expertise may vary, metadata management tools are useful because they make discovering, organizing, and interacting with metadata simple for anyone, regardless of technical proficiency.

While there is no shortage of tools to choose from to facilitate metadata management, there are a handful of capabilities that you should focus on when evaluating your options:

  • Metadata discovery/harvesting
  • Metadata repository (a catalog, inventory, or dictionary)
  • Metadata lineage and governance
  • Metadata automation and intelligence
  • Metadata collaboration and connectivity

Ideally, your metadata management toolset will feature all of the above functionality — considering the challenges you will face if any of these features are missing from your data stack. This may have you wondering, “Is there a comprehensive solution that fulfills all of these needs?”

Metadata Management: Automated data lineage and governance. Source: Atlan

Metadata Management: Automated data lineage and governance. Source: Atlan


Active metadata management platform

Metadata management has come a long way and continues to evolve at a rapid pace. As a result, Gartner notes that “metadata management has shifted from focusing on reports, inventories and static impact analysis, toward intelligent optimization, data discovery and use-case analysis.”

Let's take a look at how the active metadata management platform ticks all of these boxes and satisfies both the current and future needs of the modern business.

What are the elements of an active metadata platform?

An active metadata platform features five key elements:

  1. The metadata lake
  2. Programmable-intelligence bots
  3. Embedded collaboration plugins
  4. Data process automation (DPA)
  5. Reverse metadata

Atlan: An enterprise active metadata management tool for the modern data stack. Source: Atlan

Atlan: An enterprise active metadata management tool for the modern data stack. Source: Atlan


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  1. The metadata lake: A unified repository to store all kinds of metadata, in raw and processed forms, built on open APIs and powered by a knowledge graph. The metadata lake provides much-needed flexibility for limitless use cases such as data discovery, lineage, observability.
  2. Programmable-intelligence bots: A framework that allows teams to create customizable ML or data science algorithms to drive intelligence. AI-driven metadata intelligence increases efficiency across areas such as data organization, quality assurance, and compliance.
  3. Embedded collaboration plugins: A set of integrations, unified by the common metadata layer, that seamlessly integrates data tools with each data team’s daily workflow. Embedded collaboration ensures equitable access and data democratization across the diverse members that make up data teams
  4. Data process automation (DPA): An easy way to build, deploy, and manage workflow automation bots that will emulate human decision-making processes to manage a data ecosystem. DPA is another essential way to increase metadata management efficiency; for example, it could recommend parameters for scaling up a cloud warehouse and assist with resource allocation.
  5. Reverse metadata: Orchestration to make relevant metadata available to the end-user, wherever and whenever they need it, rather than in a standalone catalog. Reverse metadata ensures that data users always get the full context, no matter what tools they are working in.

Modern Metadata Management tools enables collaboration between teams. Source: Atlan

Modern Metadata Management tools enables collaboration between teams. Source: Atlan


Now that you understand the key features to look for in a metadata management tool, let’s take a look at some best practices.

Metadata management best practices

In addition to the technology and processes behind effective metadata management, there are a few high-level principles you should keep in mind whenever planning, executing or measuring metadata management activities:

  • Design a metadata strategy: The foundation of successful metadata management is to develop a strategy that supports business objectives and can be shared with key stakeholders. It should answer the following questions:
    • What is the data asset about?
      • Descriptions (tables, columns)
      • Keywords or tags
      • Themes or categories
    • Why does the data asset exist?
      • Data source
      • Lineage
      • Impact analysis
    • Where is the data asset from?
      • Spatial coverage
      • Language
      • Business domains
    • Who is responsible for the data asset?
      • Creator or owner
      • Contributors or experts
      • Point of contact
    • When was the data asset created and updated?
      • Creation date
      • Last updated or modified date
      • Update frequency
    • How can the data asset be used?
      • License
      • Classification
      • Use cases

5W1H: A framework for a robust metadata management model.

5W1H: A framework for a robust metadata management model. Image by Atlan

  • Leverage all types of metadata: To capture the full value of your data through metadata-driven intelligence, you must use all four categories of metadata outlined above.
  • Democratize metadata: Maximize visibility and usability for all users across the business so they can discover, digest, and trust the information they need.
  • Practice continuous metadata improvement: Metadata management is an organization-wide process — use feedback from diverse data users to refine your metadata strategy on an ongoing basis.

Learn more:6 Metadata Management Best Practices for 2022

The future of metadata management and data catalogs

As metadata itself becomes big data, rapid advances in cloud platforms, such as Snowflake, allow businesses to extract intelligence that would not have been possible just a few years ago. With the ability to store all types of metadata in both raw and processed forms, the metadata lake will drive the next wave of innovation in active metadata management and power use cases that haven’t even been imagined yet.

Active metadata is the driving force behind a number of new and exciting developments in the world of data: autonomous DataOps, augmented data catalogs, data fabric, and the data mesh, among others.

The rapid adoption of active metadata management comes in tandem with the advent of “Data Catalog 3.0.” This new generation of data catalogs is built around diverse data assets, end-to-end data visibility, and embedded collaboration. Currently, most enterprises are managing metadata in a way that just won’t work in the business world of tomorrow. Here’s how a third-generation active metadata platform solves these major roadblocks:

Second-generation data catalogsThird-generation data catalogs
Siloed contextEmbedded context
Generic experiencePersonalized experiences
Minimum automationAutonomous
Top-down governanceFederated governance

While the rest of the modern data stack has evolved over the past few years, metadata management tools are still lagging behind. Data Catalog 3.0 is a much-needed evolution in metadata management, allowing metadata to be searched, analyzed, and maintained the same way as all other types of data.

Atlan: The next generation of metadata management

Atlan is a third-generation, active metadata platform that unifies metadata from every tool and data domain, creates personalized discovery experiences for every data user, and drives automation across every part of the modern data stack through orchestration.

Atlan leverages these four core principles of modern metadata management:

  • Programmable bots
  • Embedded collaboration
  • End-to-end visibility
  • Open by default.

If you’re striving to unlock the full potential of your business’s metadata, you must check out all possibilities that Atlan promises to unlock.



Photo by Vitaly Vlasov from Pexels


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