What is a data catalog?

A data catalog is an inventory of a company’s data assets so users can find the information they need fast. The catalog is mostly metadata that provides basic information about other data and describes what it is. Combined with data management and search tools, you have a data catalog.

In the age of big data, data catalogs are a key component in data management. People who work with data use data catalogs to search for required data assets from the entirety of an organization’s sources, which can be spread out and difficult to navigate. Successful data catalog implementations can make a big difference in the speed and quality of data analysis because they help users find the data they need fast.

Data catalogs offer a number of benefits to the organization. Firstly, a data catalog can give users all the right sources, in the right format, in the right view, at the right time, with the right level of control. Data catalogs make sure that all the information you have across all your different sources in a multi-cloud context can be found and is immediately consumable. This means that users can build and deploy models in a real-time context.

In addition to offering context to the data analysts who need to use the data for business purposes, data catalogs also make it possible to automate metadata management. This automation allows the data catalog to become the single most-trusted source of data in your organization, making it collaborative for stakeholders to curate and harvest the data as they need.

A library is a common analogy used to describe data catalogs. A library proves to be the ideal metaphor as it stocks up on information assets (such as books) and requires a system to organize said information assets. In this analogy, while books act as the information assets, the information about the book such as its title, author, ISBN, and genre acts as its metadata. A catalog maintained to identify the books, its position, and other information is exactly how a data catalog works. It allows the readers to find the list of available books, curate it as per their liking, and pick the ones that they need quickly.

Business needs for a data catalog

Business data is growing tremendously every single day. It’s expected that the global datasphere will expand from 33 Zettabytes (ZB) in 2018 to an enormous 175 ZB in the next five years. Data at this scale is difficult to handle and navigate. Data can be stored on multiple cloud providers, in differing formats, with different storage technologies. The quality of data may degrade over time as data has a shelf-life and datasets are always changing (you are adding new datasets, deriving new datasets from existing datasets, etc). You also have different user types from data scientists to developers to business users, who each have different requirements and skill sets when it comes to data. You can’t always depend on IT to build a new solution every time a business user needs to solve a business problem. You need a way to manage all of this.

A data catalog is a key step towards structuring data in a logical and resourceful manner. It can prove to be an important asset for an organization as it can help:

  • Create a reservoir for the data, including information on the quality, structure, usage, and statistics of the data
  • Users collaborate remotely on the data as they access the metadata alongside the actual data
  • Ensure that the data is accurate and consistent across the datasphere by updating itself automatically and frequently
  • Access the lineage of the data and view information such as the source, modifications, and accesses to the data
  • Share data assets with stakeholders in a secure manner

Key factors of a data catalog

A data catalog can be created in several ways, but to ensure the successful implementation of an efficient data catalog, the following factors are necessary.

Connectors and curation tools

A data catalog serves as a single place of trust for data. Connectors map the physical datasets in your database; therefore, it is important to have a wide array of connectors to reinforce the data catalog. Since metadata can be harvested from multiple sources such as Salesforce, SQL queries, business intelligence, or data integration tools, it is important to curate this data as well. Validation and certification are important processes that enhance the efficiency of a data catalog and make data governance a sustainable process.


Automation in data catalogs allows data users to focus on crucial processes such as validation and correction of data issues. This enhances the speed and agility of the data catalog and enriches the datasets within the organization.

Efficient search options

Search is the primary component of a data catalog. A powerful search capability offers a wide range of selection options to data citizens and offers convenient access to data. Therefore, it is important to have several parameters available to perform advanced searches in a single go.

Lineage or lifecycle tracking

Lineage offers a glimpse into the lifecycle of viewed data. In case of any discrepancies, data users can use the data catalog to easily track lineage to locate the issue and correct it. It also helps to understand the difference between various sources and types of data in the organization.

Universal glossary and data dictionary

An organization’s data is a large part of its value, so it needs to be accessible and easy to understand by all the potential stakeholders. Typically, a data catalog is made up of a data dictionary and a glossary. The data dictionary is a collection of all the metadata (usually stored in tables) about the data in your catalog, including meaning, relationships to other data, origin, usage, and format. The glossary allows the members of the organization to identify the business terms used in the catalog and use them in the same manner throughout the company.


Data profiling is the process of evaluating your data for completeness, accuracy, consistency, and timeliness. Basically data profiling determines the usefulness of the data to solve business problems. This is important for maintaining your data pool when harvesting data from multiple data sources.

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