Difference between Master Data Management(MDM) and Metadata Management

Updated June 02nd, 2023

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Quick answer:

TL;DR? We’ve got you covered with this quick summary of the difference between master data management vs. metadata management:

The main difference between master data management and metadata management is that:
- Master data management is building a unified view of master data — a master list of key business attributes, which includes metadata.
- Metadata management involves organizing, structuring, and storing metadata — data about data.
This article explores the differences between master data management and metadata management, followed by the setup process.
Need a metadata management solution? Check out Atlan - the only data catalog to activate your metadata. Book a demo or take a guided product tour.

Forrester’s Rob Karel states that metadata management and master data management are two sides of the same coin. Metadata management is central to the success of master data management (MDM) and is the only way to build a trustworthy single source of truth.

We’ll explore the reasons behind this interrelationship in this article while addressing commonly asked questions such as:

What is the difference between metadata and master data?
What is the difference between master data management and metadata management?
What is the role of metadata in master data management?

Let’s begin by recapping the terms metadata management and master data management.

What is metadata management?

Metadata management, also known as enterprise metadata management (EMM), is a strategy or approach for organizing, structuring, and storing metadata — contextual information or data about data.

Examples of metadata include data types, classifications, table names, content tags, and more.

Gartner defines EMM as a business discipline for managing information that describes various facets of an information asset to improve its usability throughout its life cycle.

To know more about metadata management architecture, check out our in-depth explainer article here.

Why is metadata management important to the modern data stack?

Raghotham Murthy, the Corporate VP at Cloudera, highlights the state of the modern data stack and the role of metadata management succinctly:

“Companies across the globe are building data stacks and investing heavily in data teams, leading to a proliferation of tools for managing data processing, state management, and configuration. These tools are excellent for building pipelines that are ingesting and transforming data, but they end up creating metadata silos.”

That’s the premise of metadata management — it ensures there are no metadata silos. Instead, metadata from various tools in the modern data stack is compiled, assimilated, and stored in a way that’s easy to access and understand.

However, traditional metadata management suffers from a silo problem too. Previously, organizations relied on data catalogs to solve the “too many silos” problem.

However, instead of getting rid of silos, these data catalogs compiling passive metadata from various sources built yet another silo — and got tagged as expensive shelfware (software that never gets used). That’s because data users had to visit a separate data catalog to get context on data from their business systems like CRMs or BI.

That’s why active metadata management is vital — rather than gathering data from the rest of the stack and organizing it with a passive data catalog, active metadata sends enriched metadata back into every tool in the data stack.

Active metadata management also enables end-to-end lineage as your teams can keep track of changes or updates to datasets as and when they occur, making it easier to trust and use data. To know more about active metadata management, check out this article on the future of data catalogs.

Finally, you need solid metadata management for effective master data management. Before we delve into the specifics, let’s quickly recap “master data management”.

What is master data management?

According to Danette McGilvray who’s known for her Ten Steps™ approach to extracting value from data:

“Master data describe the people, places, and things that are involved in an organization’s business. Examples include people (e.g., customers, employees, vendors, suppliers), places (e.g., locations, sales territories, offices), and things (e.g., accounts, products, assets, document sets).”

So, master data is a master list — a set of identifiers — of key business attributes. It isn’t transactional in nature and doesn’t change frequently

Master data management (MDM) involves building a single, trusted view of master data. Gartner defines it as, “a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.”

For instance, product, sales, and marketing teams maintain master lists of their core data. However, each department can define the attributes of that data differently. Sales might refer to customers as deals, whereas product might call them users. Meanwhile, marketing might refer to its leads as users.

When all the stakeholders involved work together and agree on how to define master data, managing it becomes simpler.

That’s why before building a trusted view of master data, research director at Gartner Micheal Moran recommends organizations should establish:

What constitutes master data
How to define master data in a way that it supports the views of each business unit or stakeholder

What is the difference between master data and metadata?

Both master data and metadata are used in data management. They require collaboration between business and IT and play a crucial role in data governance and compliance. Nailing metadata and master data management is vital to extracting value from accurate and trustworthy data.

However, here’s the technical difference between metadata vs. master data — metadata describes the contents of master data. It doesn’t contain the actual data. Meanwhile, master data contains the actual information as well as metadata. You can think of metadata as a subset of master data.

So, is metadata important to master data management?

Absolutely.

As mentioned earlier, metadata describes the contents of master data lists by adding context in the form of data types, models, transformations, and other such characteristics. Such context helps in understanding, categorizing, and sorting master data.

Master data with contextual information is necessary to:

Help you understand and explore the data you have
Know how to use it in analysis and decision-making

That’s why Forrester’s Rob Karel declared, “there’s no MDM without metadata.”

Metadata management vs. master data management: Summarizing the differences

Aspect	Metadata management	Master data management (MDM)
Definition	A strategy to organize contextual information about data from various tools and systems across the modern data stack	A business function to identify, create, and manage master data in an organization
Example	For an mp3 audio file, metadata can include audio format, size, bit rate, release date, and name.	Master data includes unique, business-critical information such as product name, bill of materials, and company branches.
Importance	It adds context and meaning to data.	It helps build a single source of truth for business-critical data.
Who’s responsible	Data engineers and data stewards	Data stewards and data domain owners

Setting up metadata management and master data management to establish trust in data

To quickly recap, metadata gives you essential information about data. Master data provides you with all the information you need about business-critical data on customers, products, contracts, etc. and that includes the metadata for these assets.

Together, they can help organizations have a better understanding of their data and extract business value from it, while ensuring data security, integrity, and privacy.

That’s why the first step to implementing master data management is ensuring metadata management across all tools and data domains of the modern data stack. The best way to do that is to harness the power of active metadata. Active metadata sends metadata back into every tool in the data stack, giving the humans of data context wherever and whenever they need it.

To know more about getting started with metadata management for the modern data stack, check out our article on active metadata management and its role in operationalizing the modern data stack.

Atlan: The next generation of metadata management

Atlan leverages these four core principles of modern metadata management:

Programmable bots
Embedded collaboration with all your favourite tools like Slack and Jira
End-to-end visibility, both upstream and downstream
Open API 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.

What is metadata: The key to unlocking the value of your data assets
What is active metadata: & Why does it matter?
Metadata management 101: Benefits, tools, and best practices
Enterprise metadata management and its importance in the modern data stack
6 metadata management best practices to follow in 2024