Let’s say you want to watch a movie. You go to your favorite OTT platform be it Netflix, Hotstar, AmazonPrime, etc., and find hundreds of suggestions to choose from. It confuses you as to which movie will be worth watching. So a simple solution to this is to filter the suggestions. You can type which genre of movie you prefer to watch like horror, romance, comedy, etc., or you can mention the name of the actor or actress whose movie you want to watch. You will then get a filtered amount of suggestions from which you can choose a movie you will enjoy. Additionally, you will also get recommendations of the movie’s sequel or “similar movies� that can be watched next.

Now suppose it’s not about a movie but about finding the right data. A Data Catalog works like these OTT platforms, providing us with ample data to preview and search from. The data catalog gives us a preview of all the data that is present to us. It is to present a description of data such as the kind of data present, the source of data, and in which form it is stored like databases, cloud, files, etc. The availability of such information makes it extremely convenient for users to access accurate data for performing operations related to data analysis.


Techopedia defines a data catalog as: A data catalog belongs to a database instance and is comprised of metadata containing database object definitions like base tables, synonyms, views or synonyms, and indexes.

In other words, Data Catalog is an inventory of all the data present in an organization with the help of metadata. It aids in data management and other processes like collecting, accessing and organizing data efficiently. With the exponential increase in data generation, Data Catalogs have become a core component of data management resulting in improved data search, and access and consequently enhancing data analysis and decision-making.


A data catalog uses metadata to store information about various data collected from numerous sources like databases, data warehouses, or lakes. Before diving in, let us first understand metadata. In simple words, Metadata is data about data, i.e. it represents a brief description of the data which assists in understanding the data better and gives an improved searching experience.

Metadata includes details like the date and time of the creation of data, where the data came from, and sometimes information related to the whole lineage of data, in what system the data is stored, the author, file size, etc.

There are three main types of Metadata:

  1. Technical Metadata: It includes information about the source systems like the schema followed, the column name, file size, data types, etc.
  2. Business Metadata: It includes the details related to the business point of view of that data asset, like comments, annotations, ratings, etc. giving an understanding of what the data presents.
  3. Operational Metadata: It includes information regarding the operation performed on data assets, like when it was created, updated, or transformed. It also consists of details about access rights and about the owner of the data.

This classification of data through Metadata is used to store data information in Data Catalogs to give a comprehensive view of the available data to make it easier for data users to browse, access, and use relevant data.


Data catalogs are used by different members of the data management team handling different responsibilities.

  1. Data Engineer: The role of data engineers is to collect appropriate data and set it up in data catalogs. Their task is to make sure accurate, clean and quality data is added to the catalog, and if there’s any error residing in any of the data assets they make sure to cleanse it.
  2. Data Steward: Data stewards are responsible for maintaining Data Governance. Their task is to manage the data properly and make sure the data governance policies and regulations are strictly followed. Through accessing the Data catalog, data stewards can make sure that the data is correctly organized, Data Quality is maintained, and data is up to date, etc.
  3. Data Consumers/Users: Users like Data scientists and Data analysts require data for performing analysis for making informed decisions. For this task, data catalogs are actively used in order to access information conveniently and accurately. Users require a platform where data can be easily accessible and understood by them so that they can directly pick out relevant data without much hassle and the Data catalog provides that platform.


Just as there are multiple users of Data Catalog, there are numerous use cases of Data catalog as well:

  1. Ease of Accessing Data: Data cataloging is done to ensure the easy availability of data whenever required. To get a quick preview of what type of data is available, and in which form and system, it makes it convenient to find the right data. It reduces the time and effort put forward to search for the required data and rather uses it for better data analysis.
  2. Data Curation: Data curation involves the process of data collection, indexing, and structuring data and for this data, catalogs are used.  Data curation is an important component as it allows the storage of sustainable data and the application of self-service analytics. Explore SCIKIQ Curate as one of the Data Curation tools.
  3. Self-service Data Analytics: With the help of Data Catalogs, it’s much easier to automate data analytics systems and use machine learning to make the process advanced and self-regulating. Assistance from IT management team members reduces and fewer human efforts are called for.
  4. Data Governance: We read how Data stewards use data catalogs to make well-informed decisions related to data governance practices. Better policies can be developed, compliance with governance policies can be assured, and we can also track changes and access controls.


We realize by now how data catalogs impact overall data management, as a result of which it has become an integral part of the process. Some of the advantages revolving around the usage of data catalogs are:

  1. Improves Data Analysis: Searching, accessing, and using the right data for the analysis process becomes easier for data analysts, which consequently uplifts the accuracy of the process.
  2. Enhances Decision Making: As analysis improves it improves the decisions generated by them. It improves strategies, policies, and other functional decisions.
  3. Improves Data Quality:  Data cataloging improves and ensures the quality of data collected and used in an organization.
  4. Reduces time and effort: Data cataloging makes it easier to trust the data and reduces time in examining the data’s accuracy. It also makes it easier to search and access the data required by users for specific tasks.
  5. Upgrades Productivity: By aiding different types of data users like data stewards, data engineers, and analysts in various processes of data management, governance, and security, it ensures that decisions taken are in favor of the organization and hence increases productivity.

Now that you know about data Catalogues, Explore Data Governance

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