What is Metadata Management and How to Build a Successful Framework

This blog was last updated in November 2023

What is metadata management?

Metadata management is a crucial aspect of handling and organizing data effectively. It acts as a “librarian” to catalog, store, and provide information about your data.

In more technical terms, metadata management is the systematic process of collecting, organizing, and leveraging metadata to maximize the value and utility of an organization's data assets. It plays a pivotal role in ensuring that data is not just a passive resource but a strategic asset that drives informed decision-making, operational efficiency, and compliance.

Why is metadata management important?

In addition to maximizing the value of data assets, metadata management helps businesses in various ways:

Data discovery and accessibility

Metadata provides valuable context about data, making it easier for users to discover, access, and understand data assets. This accessibility enhances data-driven decision-making and encourages collaboration among teams.

Data quality and trust

By tracking data sources, transformations, and updates, businesses can monitor and enhance data quality. High-quality data fosters trust among stakeholders and supports reliable analytics and reporting.

Data governance and compliance

Metadata provides essential information about data lineage, ownership, quality, and sensitivity. This is crucial for maintaining data integrity, ensuring regulatory compliance, and safeguarding sensitive information, such as customer data or financial records.

Data lineage and impact analysis

It’s important to understand how data flows through the organization. Metadata management provides a visual map of data lineage, showing how data moves from source to destination. This helps with tracking the impact of changes, auditing processes, and ensuring data accuracy.

Business intelligence and decision-making

By enhancing the effectiveness of business intelligence and analytics initiatives, metadata provides context to data. This enables data analysts and decision-makers to interpret and use data more effectively for strategic planning, forecasting, and identifying opportunities and risks.

Why should organizations document and manage their metadata?

Collecting and using data to make informed decisions is the new foundation for businesses. The key term here is usable: Anyone can be data rich, and collect vast troves of data. The real challenge lies in getting people to access, manage, and search for it appropriately.

This is where metadata, or the data about data, comes into play. Having a data catalog is the cornerstone of your data governance strategy, but what supports your data catalog? Your metadata management framework provides the underlying structure that makes your data accessible and manageable.

What is a metadata management framework?

A metadata management framework combines organizational structure and a set of tools to create a data asset taxonomy. Your framework should include the following:

• Global metadata: applies to all information

• Local metadata: focuses on departments, functions, or processes

• Document type: describes creation, storage, and use during business processes

By organizing the metadata, people will be able to obtain the data they need in an accessible and manageable way in order to improve business outcomes.

What are the benefits of a metadata management framework?

Your metadata gives users context. Consider how your favorite streaming service informs you about movies and shows. It gives you information that might include:

• Which category (horror, comedy, documentary, etc.)

• Who’s starring in it

• When it was released

This information is metadata. It makes it easier for the streaming service to make suggestions about what you might like based on what you’ve already liked. A metadata management framework does the same for your data analysts.

With a metadata management framework, your data analysts:

Optimize search and findability

Create a single portal using role-based access for rapid data access based on job function and need.

Establish business glossaries

Define business terms and create standard relationships for data governance.

Collaborate more effectively

Break down data silos for better understanding of data assets across all business units.

Create taxonomies and modeling languages

Enrich data analytics by enhancing relationships between data for ensuring consistent modeling outcomes when new data is introduced.

Scale effectively

Leverage taxonomies to ensure consistent modeling outcomes when introducing new data sets or changing business demands.

Improve manageability

Automate administration of business rules to facilitate management.

Track data lineage

Document data origins, record data transformation and movement, and visualize flow throughout the entire data lifecycle.

Enhance the user experience

Create a shared source of truth for all users to build confidence in data.

Key considerations for a metadata management framework

The organization and tools you implement should be flexible so you can scale as your business adopts new technologies. They should also be customizable, to help achieve your team’s unique data governance goals. When building out your metadata management framework there are a few things to consider.

Capabilities for a metadata management framework

Automates active metadata management

Automation saves time, eliminates manual processing activities, and reduces the risk of human error. An active metadata management approach allows the metadata management framework to incorporate artificial intelligence and machine learning, removing the need for siloed workflows, typically contained in spreadsheets.

Supports strong data culture

Your metadata management framework establishes a common language for everyone across the organization. This supports a strong data culture by enabling:

• Data search and discovery: Breaking down silos for better collaboration so everyone can find the relevant data when making decisions.

• Data literacy: Employees can correctly interpret and analyze data for drawing logical conclusions for more informed decision-making.

• Data governance: Setting clear expectations for how to appropriately manage data makes sure it is used the right way when making business decisions.

Classifies data to mitigate risk of non-compliance

Classification of data within a metadata management framework helps meet the compliance requirements many organizations face. By setting role-based access to sensitive information, data leaders can troubleshoot insider threats and meet privacy requirements at scale.

For example, metadata tags identify personally identifiable information (PII) to comply with the General Data Privacy Regulation (GDPR); metadata tags can also identify protected health information (PHI) to comply with the Health Insurance Portability and Accountability Act (HIPAA).

Simple navigation for business users

When you deploy a metadata management tool, you’re able to leverage artificial intelligence and machine learning for guided navigation. When the tool’s search engine supports natural language, a wider range of non-technical users can leverage the tool to self-service data, as well. In this way, search with natural language empowers organizations to gain the full benefit of the metadata management framework’s analytics capabilities.

A metadata management framework supports smarter data curation, which delivers contextualized data assets to people who need them.

3 Critical steps to building a metadata management framework

An active metadata management framework guides users so they can unlock the full value of their metadata.

1. Keep the process simple and flexible

If you start building it in the weeds, your metadata management framework will turn into a behemoth. Instead, create a taxonomy that meets your primary goals while limiting the number of review cycles.

Major changes often require reviewers at the business and enterprise levels. To manage this, you can outline minor changes that have customized approval or change processes that speed up implementation. In the end, you’ll have fewer cooks in the data kitchen and more flexibility to meet everyone’s needs.

2. Assign clear roles and responsibilities

Your metadata management framework acts as the foundation of your data governance strategy. Therefore, it is important for people to have assigned roles and responsibilities within the metadata management framework in order to have accountability. Your metadata management framework should have an administration team that develops the processes and strategy, coordinates the rollout of said processes and policies, and guides the selection of the metadata management tool.

3. Implement automation to streamline processes

Data isn’t static. Automation helps you maintain your metadata governance initiatives dynamically.

When you implement automation, your team doesn’t have to worry about whether people have learned the processes. You’re able to leverage technology to:

• Regularly clean up data with system flags

• Detect unused data that can be archived/removed

• Ensure the longevity of metadata models for accurate representation and better analytics

• Create a centralized location for tracking changes across different databases

• Automatically categorize content as you add new databases and scale your analytics

How Alation supports a metadata management framework

The Alation Data Catalog provides automated data search and discovery capabilities that elevate metadata management. Managing metadata becomes easy with our automated tools for more efficient discovery that maps across diverse sources, including data sets, business intelligence reports, visualizations, and conversations. With the Alation Data Catalog, you can simplify your data governance strategy by building metadata management right from the start.

Our machine learning adds context to, identifies, and tags sensitive data, translating technical metadata into popular business terms so data analysts can communicate and collaborate easily. Our natural language search capabilities combine with a built-in SQL editor to automate recommendations, flags, and policies during the query process so you have the right information — at the right time — to make data-driven decisions.