Data Catalog Vs. Metadata Management: Differences, and How They Work Together?
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Data catalog vs. metadata management: Key differences
The main difference between metadata management and a data catalog is that metadata management is a strategy or approach to handling your data. In contrast, a data catalog is a tool — a means to support metadata management.
Here’s a table summarizing the difference between data catalog and metadata management.
| Aspect | Data Catalog | Metadata Management |
|---|---|---|
| Definition | A data catalog is an organized list of all the data assets which empower data teams throughout the company. | Metadata management helps organizations decide how to collect, analyze, and maintain contextual information — metadata. |
| Scope | It serves as an organized data inventory for all data sources. It enables data search and discovery of data assets, with the right context. | It ensures that the metadata is used as per the data governance policies. |
| Key difference | It is a tool that enables metadata management, among other things such as data discovery, profiling, quality, and governance. | It is an approach to manage the collection, storage, and use of metadata. |
Table of Contents
- Data catalog vs. metadata management: Key differences
- What is a data catalog?
- What is metadata management?
- Metadata vs. master data vs. reference data
- Does a data catalog include metadata?
- Enable efficient metadata management with a robust data catalog
- Data catalog vs. Metadata management: Related reads
Data catalog vs. metadata management — is there a difference? These concepts are often used interchangeably in the data ecosystem.
While there are several differences between the two terminologies, here’s a brief explanation — data catalogs are tools that enable metadata management. So, rather than an either-or debate, explore ways to set up a catalog that supports effective metadata management.
Instead of data catalog vs. metadata management, think data cataloging + metadata management for better data management.
This article analyzes the concepts of cataloging and metadata management, and their synergies and differences.
First, let’s start with data cataloging.
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What is a data catalog?
A data catalog is a tool that helps set up a comprehensive list of an organization’s data assets. The data catalog makes it easier for data engineers and business analysts to find and use the right data.
A centralized data catalog can effectively break down data silos to enable better data accessibility throughout an organization.
Moreover, data catalogs built for the modern data stack can trace lineage and help set up granular access policies and permissions. This enables better data security and compliance with various data protection regulations and privacy laws.
Interested in learning more about data catalogs? Check out our in-depth guide exploring the value, benefits, and features of data catalogs.
Another thing to note before we move on to metadata — data catalogs differ from data dictionaries. Here’s why.
Data catalog vs. data dictionary
As we’ve already mentioned, data cataloging is the process of organizing or inventorying data assets.
A modern data catalog can include a metadata repository, a business glossary for context, a Google-like search to enable data discoverability, and capabilities to ensure good governance. The scope varies depending on the needs of an organization.
Meanwhile, a data dictionary describes data types and data structures included in a database, data model, or data source.
The IT team use to maintain an organization’s data dictionary to handle metadata management in the past. However, with more data consumers and business users, a data dictionary has become one of the building blocks of a more comprehensive data catalog.
To know more about a data dictionary and its benefits, here’s an article that you might find helpful.
The biggest advantage of integrating a data dictionary, business glossary, and search and discovery capabilities within data catalogs is data democratization.
Learn more: Data catalog vs. Data dictionary — differences, examples, and use cases
The role of data catalog in data democratization
According to Bernard Marr, the bestselling author of “Big Data in Practice”:
“Data democratization implies that data is accessible to everyone or that there are no gatekeepers that create a bottleneck at the gateway or entry point to the data.”
Data democratization empowers everyone within an organization to access the data they want when they want it. So, everyone can quickly find, understand, and use the right data for strategic decision-making.
A robust data catalog platform enables data democratization without compromising data security or privacy by empowering data teams with various features, such as:
- Data search and discovery
- Business glossary and data dictionary
- Automated data profiling and auto-glossary suggestions
- API integrations with the rest of the modern data stack
- In-line chat, annotations, and one-click data sharing for seamless collaboration
- Data lineage and impact analysis
Now let’s look at metadata management.
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What is metadata and what is metadata management?
Metadata is data that describes data.
For example, the metadata of an image includes the image name, description, format, size, author, date created, and date modified.
Similarly, an excel sheet containing employee records has extensive metadata information. The metadata for each column of the excel sheet includes a name, description, data type, and more.
In a modern data stack, business stakeholders often confuse metadata and use it interchangeably with master data and reference data. So let’s understand these concepts.
Metadata vs. master data vs. reference data
The main difference between Metadata, Master data, and reference data is that metadata is descriptive information about data that aids an organization in making sense of its data. Master data is essential business information that you need for transactions. Examples include descriptions of customers, products, parts from suppliers, and other such information from transactional data. Finally, reference data is a subset of master data. It’s the data referred to by various systems within an organization’s business processes.
Metadata is descriptive information about data that aids an organization in making sense of its data.
For example, the metadata for a customer record database would include information such as column names, column data types, and descriptions, format.
To know more about metadata, check out our detailed guide on metadata and using it to unlock the value of your data assets.
Next up is master data.
Master data is essential business information that you need for transactions. Examples include descriptions of customers, products, parts from suppliers, and other such information from transactional data.
Gartner defines master data as:
Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.
You need meta data to manage master data. That’s why master data management and metadata are like two sides of the same coin, according to Forrester:
MDM usually involves some business-focused statement about achieving a single trusted view of a customer, product, or some other critical data, while IT typically looks at metadata to reduce complexity, and increase productivity, reuse, and collaboration, by having a single version of truth about their company’s “data about data.”
Finally, reference data is a subset of master data. It’s the data referred to by various systems within an organization’s business processes.
Some of the reference data is standardized as per the specifications from governing bodies like ISO. Examples include country codes, postal codes, and currency codes.
Others, like customer status or product categories, are defined within an organization.
Coming back to metadata, effectively managing all that information builds a complete picture of the data and makes it more understandable and meaningful.
Metadata management is a set of policies and processes that govern the storage and use of metadata.
If done right, metadata management helps organizations comply with data laws while empowering data democratization within an organization.
Traditionally, you could handle metadata management with excel sheets and structured databases. However, with the rise of big data and cloud computing, metadata management has become challenging.
That’s where a data catalog can help.
Does a data catalog include metadata?
As mentioned earlier, data catalogs are tools that consolidate metadata into a single repository, providing a complete picture of all data assets within an organization. So yes, they include metadata.
Modern data catalogs enable metadata management by offering an overview of all metadata from a single repository, thereby setting up a single source of truth for metadata.
Let’s see how.
Data catalog vs. metadata management: Where do data catalogs fit into metadata management?
Firstly, metadata management is a strategy or approach to handling your data. In contrast, a data catalog is a tool — a means to support metadata management.
Next, choosing a metadata management tool would only handle metadata, which may or may not provide adequate context and make discovery easier.
However, organizations can manage large swathes of data with data catalogs in a centralized, collaborative, and user-friendly manner with capabilities such as:
- Indexing the metadata
- Enabling search and discovery
- Simplifying governance
Here’s a table summarizing the difference between data catalog and metadata management.
| Aspect | Data Catalog | Metadata Management |
|---|---|---|
| Definition | A data catalog is an organized list of all the data assets which empower data teams throughout the company. | Metadata management helps organizations decide how to collect, analyze, and maintain contextual information — metadata. |
| Scope | It serves as an organized data inventory for all data sources. It enables data search and discovery of data assets, with the right context. | It ensures that the metadata is used as per the data governance policies. |
| Key difference | It is a tool that enables metadata management, among other things such as data discovery, profiling, quality, and governance. | It is an approach to manage the collection, storage, and use of metadata. |
Enable efficient metadata management with a robust data catalog
A robust data catalog facilitates metadata management, among other things, to help organizations manage and use their data effectively. That’s why the debate shouldn’t be data catalog vs. metadata management, but data catalog + metadata management.
If you’re wondering what “robust” looks like, consider taking Atlan’s modern data catalog for a test drive.
A Demo of Atlan Enterprise Metadata Management Tool
Data catalog vs. Metadata management: Related reads
- Metadata management: Definition, benefits, best practices, and tools
- Data catalog vs. Data dictionary — Differences, examples, and use cases
- What Is a Data Catalog? & Do You Need One?
- Data catalog benefits: 5 key reasons why you need one
- Open source data catalog software: 5 popular tools to consider in 2024
- Data Catalog vs. Data Dictionary: Definitions, Differences, Benefits & Why Do You Need Them?
- Data Inventory vs. Data Catalog: Definitions, Differences, and Examples
- Data Dictionary vs. Business Glossary: Definitions, Examples & Why Do They Matter?
- Modern Data Catalog: 5 Essential Features and Tool Evaluation Guide
- Data Catalog vs. Data Warehouse: Differences, and How They Work Together?
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