Data catalog and data dictionary are complementary but distinct capabilities that enable data management. While both serve a similar purpose, these are terms that can't be interchangeably used.
It's understandable as to why a term like data catalog generates a bit of confusion in both seasoned practitioners and beginners in the ecosystem. The concept of a data catalog and what it can do have evolved drastically over the years. We've discussed extensively about it in this blog: Data Catalog 3.0
To appreciate the difference between a data catalog and a data dictionary, and for an instinctive understanding on when you need either or both of the tools, let us first rehash our basics.
What is a data catalog?
A data catalog is a composite inventory of all data assets that exist across data sources in your organization. Fundamentally, what does a data catalog do? A data catalog reduces the time to insight for data users. It ensures:
- Anyone in the organization can quickly find data that's relevant to their work with a simple search.
- People with no context of the data can learn more about it to ascertain that they have the right data.
- Users have full visibility of the origin of data assets and how they have changed over time to develop confidence about using it.
- If a certain data asset requires permissions for use, users have visibility of who to reach out to request access.
Now, that we have sort of eased into the concept of a data catalog, let's look at a more technical definition from Gartner, to further our understanding of it.
"A data catalog creates and maintains an inventory of data assets through the discovery, description, and organization of distributed datasets. The data catalog provides context to enable data stewards, data/business analysts, data engineers, data scientists, and another line of business (LOB) data consumers to find and understand relevant datasets for the purpose of extracting business value."
When does an organization need a data catalog?
It's safe to say, deploying a data catalog is a right of passage for an organization to be truly data-driven. As a business, you can collect all the data that you want, set up best-in-class infrastructure to store that data, but data in itself is nothing. Just numbers. You need the right data to reach the right person at the right time - for it to really move the needle on your business. Modern Data Catalogs are being designed to ensure that the complexities and scale of data do not deter "non-data" folks from using data in their day-to-day work.
Read in-depth about data catalogs: here
What is a data dictionary?
A data dictionary is a metadata repository of a database. It contains detailed information, attributes, and technical descriptions that provide more context about data. A data dictionary focuses on ensuring common knowledge about all data assets available within the organization.
Data dictionaries like data catalogs have gone through their own evolution curve over the years. Earlier data dictionaries could only be understood by technical users of data - like data scientists, data engineers, and data analysts. They were largely illegible for business users who would want to make sense of available data.
Modern data dictionaries are more than just metadata repositories. They provide a 360-degree view of a data asset - apart from defining each column of a data asset, modern data dictionaries also provide insight into the column's data type, business glossary terms attached to it, classifications linked, and other relevant stats like missing values.
When does an organization need a data dictionary?
Data Dictionaries ensure that there's sufficient documentation and context about data existing in the organization. So, even if data owners or experts leave the system, people still know how to make sense of them and use them. Other than that, data dictionaries also ensure the following:
- Quick detection of anomalies
- Evaluation of data quality
- Instill trust in data
- More transparency within data teams
Read in-depth about data dictionaries: here.
Data Catalog vs. Data Dictionary
|Parameter||Data Catalog||Data Dictionary|
|Definition||Inventory of all data assets in an organization||Repository of technical descriptions and attributes about data|
|Scope||Ensure access, context, quality and trust in data||Ensure context about data|
|Manifestation||As a software platform||As a metadata repository|
|Purpose||All data users can find, understand, trust and use data||All data users can understand and trust data|
Why do people get confused between data catalogs and data dictionaries?
A lot of people still get confused between data catalogs and data dictionaries because a lot of the core capabilities of data catalogs are generated from having a great data dictionary within. Data is useless to people if they can't understand it or trust that it's in a form that can be used.
Often in data catalogs once a data catalog is used to discover data, data dictionary finds usage to build more context and trust around it.
To further this point, let's say
Most modern data catalogs will have data dictionaries, but data dictionaries don't have data catalog capability.
Real-world interaction between data catalogs and data dictionaries
Let's take Atlan for example. It's a third-generation data catalog. Atlan comes with a data dictionary that doesn't just stop at defining the column, it also provides the following added information:
- Data type
- Column level metrics
- Classification and glossary terms
Deep dive into this in the Atlan documentation: here.
Data catalogs and data discovery are both instrumental tools of data management. They both have a common goal of making data more inclusive to the culture of organizations - making sure that everyone in an organization, irrespective of the type of work they do, can make data-driven decisions or fashion data-powered products.
Why not get an in-depth understanding of how modern data catalogs and data dictionaries can make your life easy as a data user.