The importance of data catalogs to data-driven enterprises

A couple of years ago, while working at an established logistics company, there was a boardroom incident where one of the stakeholders identified inconsistencies in the company’s financial reports during a meeting. The team banded together to find the real reason behind the inconsistencies and concluded that the data in the financial reports was sourced from tables with incomplete data.

An extensive investigation revealed several interesting facts.

  • Multiple entities and tables with the same name were in different databases.
  • Difficulty in identifying tables/entities with the correct data as the organization’s data was in silos.
  • Customized security regulations specific to the data silos restricted access to the tables/entities.
  • Databases/schemas were specific to departments/domains and solely managed by them.

To overcome these pitfalls, the data and business teams developed a tool to store all the metadata and provide a searchable inventory of data assets across the organization.

Do these issues sound too familiar to you? So, how do you deal with them?

It’s simple – organizations develop, modernize, or optimize data catalogs to deal with these issues.

What is Data Catalog?  How popular are Data Catalogs?

A data catalog provides a unified view of organization-wide data assets through a collection of enriched metadata connecting data assets using business relationships. It ensures control over data, simplifies data retrieval, and enables intelligent decision-making.

As global data increases exponentially, the role of a data catalog becomes vital to drive enterprise strategic insights. For instance, per a  Mordor Intelligence Report , the data catalog market is expected to grow from USD 2,549 million in 2023 to USD 2,884 million by 2028, at a CAGR of 2.50%. The shift toward digital transformation, increasing adoption of cloud-based solutions, and the global shift towards remote work are some major factors driving the growth of the Data Catalog market. These factors have further increased the demand for solutions that help in enhancing the security and comfort of work.

Today, data and insights are crucial. The adoption of data catalogs is strong and the resources required to implement them are in high demand. Unfortunately, organizations are struggling to find, inventory, and analyze vast, distributed, and diverse data assets. As big data sets become bigger, organizations face increasing metadata management and cataloging challenges.

Key Features of a Data Catalog

Data Awareness: An organizational ecosystem provides metadata transparency that benefits data consumers who want to derive insights from business data.

Understanding: This feature refers to the knowledge that you have about the data. It includes data definitions, synonyms, key business attributes, and how and where to use them.

Data Discovery: This feature touches on the process that identifies all the data stored in an organization and centralizes the metadata – making it easier to work with.

Data Analysis: Analysis entails understanding underlying data relationships across multiple data assets and arranging the information into a simple, easy-to-understand format.

Data Realization: The results should be deployed to support decision-making, change behavior, and actualize potential benefits.


Evolution of the Data Catalog

Data catalogs have evolved greatly since their inception over 50 years ago. Today, just because you have a “data catalog” of some level of maturity does not guarantee that you have the needed business benefits of current, modern data catalogs.

First Generation Data Catalogs

Metadata has been around for a long time. The original dictionaries were used to store metadata about the structure and contents of databases, including the names and descriptions of database tables and columns, data types, and other details about the data.

Second Generation Data Catalogs

As enterprise data became huge and spread beyond the IT team, the idea of data stewardship took root. Data stewards were the dedicated suite of data owners responsible for taking care of an organization’s data. For instance, they handled metadata, maintained governance policies and practices, manually documented data, and so on.

In this era, tools were built fundamentally on monolithic architectures and were deployed on-premise. Each data management system would have its unique installation, and companies could not easily roll out software changes.

Modern Data Catalog

Today, the metadata itself has become huge. It is much more comprehensive than in earlier generations. To make it practical to maintain this more comprehensive metadata, automation and synchronization processes have become important.  Processing and understanding metadata are key driving aspects to building a powerful, truly data-driven enterprise.


Characteristics of a Modern Data Catalog

  • All data assets under a single roof
    A data catalog should be sufficiently flexible to collect, preserve and connect different types of data assets under a single logical roof and serve as an integrated service layer for all data sources. Data catalogs are driven by visual querying capabilities to enable democratic access to data users. Apart from tables and field descriptions, metadata can include APIs, code snippets, models, BI dashboards, and more.
  • End-to-end data visibility
    A data catalog needs to understand and keep track of the data journey to provide a clear understanding of the flow and dependencies of the data, i.e., data lineage for business users. It also strengthens business process relationships, provides data quality scores and metrics, and discloses usage restrictions.
  • Automatic metadata generation & synchronization
    Organizing and preserving technical metadata is insufficient. We need Data Catalogs that automatically generate and synchronize metadata. With the widespread usage of Artificial Intelligence (AI), Machine Learning (ML), semantic interference, tags, patterns, connections, and so forth, modern data catalogs can systematically scan data stores and automatically update the necessary information in the data catalog.
  • Data Governance
    Data Catalogs are the result of organization-wide data governance programs that control and audit how users consume data. Data governance defines authority and control over data assets within an organization and evaluates the data quality and movement. It is feasible to follow the company’s acceptable compliance criteria while also taking into consideration regulatory requirements.
  • Data Democracy
    A data catalog maintains a reliable and robust data asset landscape at the enterprise level enabling metadata synchronization with data sources and further enforcing documentation by data stewards, data owners, users, and so on. It becomes a reference data tool for all data users. It allows all people with appropriate security permission in their organizations to easily utilize data assets and better collaborate on those assets, regardless of their technical skill.  This is fundamentally, what is meant by ”data democracy”.


Data Catalog Tools for Enterprises

Choosing the right data catalog tool is very important for driving business vision. A data catalog tool should be selected that satisfies current needs and aligns with future goals.  Today, there are several options to choose from including all-in-one data ecosystems, stand-alone data catalog products, open-source data catalogs, and data catalog-as-a-service.

  • All-in-one data ecosystems
    All-in-one data ecosystems are typically useful for enterprises that want to integrate all data aspects within a single platform.  The data catalog is simply a feature of the integrated platform. The strengths of data ecosystems are that the data catalog is part of a larger product and has built-in features like data documentation, data quality, data governance, etc.  The weaknesses of these solutions are vendor lock-in and high prices.
    A data ecosystem is a good choice for organizations that use products like Informatica, Tableau, SAP, Oracle, Qlik, and Cloudera—all of which products include a data catalog as a feature.
  • Stand-alone data catalog products
    Stand-alone data catalog products support data cataloging with new alternatives focused on enabling the use of data through collaboration and knowledge sharing.  These products are less expensive and can integrate many data tools across all sections of the data stack.  The weaknesses of these solutions are that they may lack enterprise-level security and self-hosting capabilities. Furthermore, the flexibility to integrate with tools across the data stack can also be a weakness in the sense that integration is required.  Stand-alone data catalog products are most appropriate for organizations that have a limited budget and that need to unify metadata across a variety of data tools.  Collibra, Atlan, and Alex Solutions are some popular tools in this category.
  • Open-source data catalogs
    Open-source data catalogs tools are more targeted for software engineers rather than for functional and business users.  Their strengths include their ability to be customized, self-hosting (to address security concerns), and community support.  Their weakness is that setup requires time & effort for configuration. On-going maintenance concerns can also be a concern.  Open-source data catalogs can be a good choice for organizations with a large team of data engineers and for organizations that need to self-host for security reasons.  Examples of this category include Atlas, Amundsen, DataHub, and Metacat.
  • Data Catalog as-a-Service
    Data Catalog as-a-Service uses a popular cloud environment to increase broad adoption and continuous value creation across your data ecosystem.  Strengths of these solutions include no upfront cost and the ease of building a robust data catalog system by integrating other services. Weaknesses of these solutions include vendor lock-in and needed customization.  These solutions are most appropriate when one cloud vendor maintains most of the organization’s data.  Key examples include Azure Data Catalog, AWS Glue Catalog, and GCP Data Catalog.



Returning to my experience at the logistics company, inconsistent financial reports are serious, board-level issues, and the company made the necessary investments to fix the problems.

In particular, the company modernized its data catalog and made a variety of other related changes that enabled not just financial compliance, but also helped the company become a data-driven enterprise.

Adoption or modernization of a data catalog as well as continuous collaboration with emerging technologies, organizational data assets, and data teams, can play a vital role in helping companies derive true value from their entire data landscape.  With clean and well-understood data that is easily available to all employees with a need-to-know, companies can fully leverage their data to reduce costs, better serve customers, and increase competitive advantage.

Coming soon will be my next blog on the Significant Influence of AI and Machine Learning on Modern Data Catalogs.


About the Author:

SiddharthaReddy Maddi
Senior Data Architect

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