Data Catalog Tools List & Evolution

Introduction

In the past decades, organizations have come to realize the importance of leveraging data efficiently. We are witnessing a "data race", in which businesses seek to hire the best data talents. The result? businesses are now equipped with data engineers, data scientists, and data analysts, mastering cutting-edge tools to produce meaningful data analysis.

These talented data people are expected to conduct high-quality and valuable data analysis, but the story often unfolds differently. They encounter a great deal of frustration when they realize they spent most of their time dealing with boring questions:

• Where is the best data to answer my question?
• What does the column name "XXXX" mean?
• Can I trust it?
• When was it last updated? What is the process to create it?
• Who can I contact if I see something wrong?
• Has someone already worked on this question?

That is, data people are spending more time on metadata management than on meaningful value-generating data analytics work. Thankfully, the enterprise data catalog is a tool that can help with all these questions, allowing data people to focus on the core of their work. This is why data catalog tools have flourished in the past 10 years, and there are now so many tools to choose from that businesses have a hard time making up their minds. Today, we take on the difficult task of untangling the vibrant data catalog ecosystem.

What is a data catalog?

Gartner, a specialized research business, defines a data catalog as follows:

“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 other data consumers to find and understand relevant datasets for the purpose of extracting business value.”

Gartner, Augmented Data Catalogs 2019.

A data catalog is a centralized repository that allows organizations to manage their data assets. It provides a systematic and organized way for data professionals and business users to find, access, and manage data. Here are some key features and benefits of a data catalog:

1. Metadata Management: A data catalog collects and stores metadata, which is data about data. This includes information like data source, data type, when the data was last updated, who owns the data, and more.
2. Searchability: Users can search for data assets using keywords, tags, or other attributes. This makes it easier to find relevant datasets without having to sift through multiple databases or file systems.
3. Data Lineage: Many data catalogs provide a visual representation of data lineage, showing where data comes from, how it's processed, and where it's used. This helps users understand the flow of data and its transformations.
4. Data Quality Indicators: Some data catalogs offer insights into the quality of data, such as missing values, duplicates, or inconsistencies. This helps users trust the data they're using.
5. Collaboration: Data catalogs often have features that allow users to comment on, rate, or tag datasets. This fosters collaboration and knowledge sharing among teams.
6. Access Control: Data catalogs can integrate with organizational security protocols, ensuring that sensitive data is only accessible to authorized users.
7. Integration with Data Tools: Many data catalogs can integrate with other data tools like data lakes, databases, BI tools, and ETL platforms. This allows for seamless data discovery and usage across the data ecosystem.
8. Business Glossary: A data catalog might include a business glossary, which defines business terms and links them to technical data assets. This bridges the gap between technical and non-technical users.

In essence, a data catalog serves as a "Google" for an organization's data assets, making it easier for users to discover, understand, and use data effectively. As data volumes grow and become more complex, the importance of having a robust data catalog becomes even more critical for organizations aiming to be data-driven.

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Which data catalog should I choose?

There are four generations of data catalog tools:

• 1st generation: basic software, similar to Excel, that syncs with your data warehouse.
• 2nd generation: software designed to help the data steward in maintaining data documentation (metadata), lineage, and treatments.
• 3rd generation: software designed to deliver business value to end-users automatically hours after the deployment. It then guides users to document in a collaborative painless way.
• 4th generation: Decentralized and intelligent platforms that integrate directly into the user’s workflow with advanced AI capabilities, offering a more personalized and automated approach to data documentation and discovery.

After outlining the characteristics of each category, we propose a benchmark of the current players in the market.

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Data Catalog 0.0: No Dedicated Tool

Companies that deal with very small amounts of data often don't use specific data cataloging tools. If this is your case, you can use whatever tool to describe columns and tables you have in your data infrastructure. Excel and Word can be used to write definitions of your data assets and columns. The good news is that it takes 1 minute to get started. The bad news is that it takes 1 minute to be depreciated. It is hard to maintain and not scalable.

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Data Catalog 1.0: Synced Metadata Inventory

The first data catalogs came to existence in the 1990's and early 2000's. They are basic softwares, similar to an Excel, that syncs with your data warehouse. The concept is dead simple: with this tool, the times where you had to write on an excel document the name of the different tables and columns manually was over. Those tools were syncing automatically the content data warehouse, allowing you to escape the painful and time consuming task of updating what is created or deleted in your data infrastructure.

Data cataloging tools belonging to this category propose basic documentation features: plain text documentation, manual tagging, ownership, metadata curation, and maintenance of governance practice. The search for data assets, if any, is not really powerful. Data catalog 1.0s demand high setup and maintenance efforts, not to mention high costs.

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Data Catalog 2.0: Data Steward Centered Catalogs

As data assets grew exponentially and more people used the data catalog, companies realised all this data had to be managed in terms of meaning, quality and admin rights. This was the birth of the data steward role.

Data Catalogs 2.0 were designed for this new role. They help data stewards maintain data documentation, treatments, lineage, personal information mapping, ownership, etc.

In this context, the second-generation data catalog displays more advanced features:

Search and discovery

Data catalogs 2.0 allow business and data analysts to find and understand the data assets they need. They allow allows you to contextualize information, and to build a Wikipedia-like page for each data asset in the company.

Strong process embedded in the catalog tool

A good documentation strategy revolves around three things: tools, people, and processes. People need to know what the process is to document and make sure it is actionable. For instance, before releasing a table in the production database, it needs to: have an identified owner,  all columns well-documented, several data quality tests.

Advanced rights management features

This allows you to restrict access to data assets. This works by granting data people specific roles in practice, a user will only manage to access a data asset if he has the permission to do so.

Project management features

Data governance teams need to have an overview of the data documentation advancement. You want them to organize the workload efficiently.

New additional data features emerge : data lineage, data quality, SQL editor

There are two levels of data documentation: level 1 of documentation is concerned with writing column and table definitions. Level 2 engages with adding business context around data : what are the tables used to create data asset? what is the code behind? how often is it refreshed? etc.Data catalogs 2.0 made level 2 accessible, but mostly in a manual way. You still have to declare upstream and downstream dependencies for lineage, choose refresh frequency from a dropdown menu, etc.

These catalogs are still process-based: without the processes, the data catalog doesn't bring any business value. That is, they rely on a data steward, in charge of guiding the documentation and labeling of databases. This is changing with the 3rd generation.

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Data Catalog 3.0: Automated and Collaborative

The advent of the third-generation data catalog marks a pivotal transformation in metadata management. These advanced platforms are engineered to provide immediate business value to users upon deployment, catalyzing documentation through collaborative features.

The manual documentation has given way to automated, value-driven interactions that start at implementation implementation. The Data Catalog 3.0 autonomously capture up to 80% of essential business context—such as data lineage, usage metrics, version history, and quality indicators—thereafter integrating a participative layer to enhance and simplify user-driven documentation.

The most compelling aspect? The organic growth of documentation is propelled by daily user engagement. The platform evolves through active user interaction—comments, discussions, and feedback—eliminating the need for costly and labor-intensive data documentation initiatives. Simply connect the tool, and it becomes a conduit for collective intelligence, yielding value multiplicatively as users engage and contribute.

The Data Catalog 3.0 operates on two foundational principles:

1. Immediate Value via Automated Context Collection

From the outset, a Data Catalog 3.0 begins enriching your analysis by automatically providing crucial business context for data assets—origins, processes, creators, usage patterns, refresh history, quality metrics, and access details.

2. Centralized Collaboration in Metadata Management

Emulating platforms like Github or Notion, the collaborative nature of Data Catalog 3.0 transforms metadata management into a collective endeavor. With features like query history and discussion forums, it promotes collective analysis, allowing for a continuation and enhancement of existing work.

In essence, the third-generation data catalog heralds a new paradigm where data management is not only automated but also intrinsically collaborative, leading to significant gains in productivity and insight.

Data Catalog 4.0: Decentralized & Intelligent

Data Catalog 4.0 builds upon its predecessors, bringing decentralization and intelligence into the picture.

Decentralized

Modern data catalogs have become decentralized in an attempt to improve the data experience. There is a plethora of tools in the data ecosystem and different profiles of stakeholders using data. For the data catalog to serve a variety of users across multiple tools, it had to become decentralized. Decentralization comes in different flavors, but the main features that characterize it are the following:

• A Chrome Extension: A browser extension integrates the power of the catalog directly into the user's web environment, enabling instant access to data insights without disrupting workflow.
• Two-way Syncs: Previous catalog generations were unidirectional, merely aggregating documentation to establish the catalog as the sole documentation hub. Data Catalog 4.0 enhances this by implementing bidirectional synchronization, ensuring that updates are not only centralized but also reflected back across all connected tools. This ensures consistency in documentation, whether accessed through dbt, the catalog itself, or your BI tool – each becomes a reliable source of truth

Intelligent

Data Catalog 4.0 embodies intelligent design, effectively automating the role traditionally held by data stewards. Notable advancements include:

• An AI assistant: The data catalog 4.0 put AI to its advantage. It uses AI to generate documentation, guide search, generate SQL, and many other things. Through AI, Data Catalog 4.0 can provide users with a personalized assistant, who can answer any (meta) data question.
• A Customized User Experience: The catalog adapts to the individual, providing a personalized view that aligns with the user's role, preferences, and data interaction patterns, ensuring a tailored data discovery journey. This allows both technical & non-technical profiles to find what they need in the data catalog.

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Data Catalog Landscape

Below, you will find a data catalog landscape, which can hopefully help you choose a metadata management tool adapted to your needs.

A cloud data catalog integrates with cloud-based data warehouses and business intelligence tools. It compiles metadata from these diverse sources into a centralized search system. This allows users to explore, read, and write documentation directly from the data source, offering insights into what's available in the cloud data warehouse and BI platforms. The core functionalities of a data catalog include:

• Enabling non-technical individuals to utilize technical assets efficiently by leveraging query history.
• Showcasing the technical interdependencies of a data asset via lineage reports and services.
• Offering a repository where KPIs (key performance indicators) and analytical metrics are outlined.
• Providing assistance to data users throughout the organization regarding cloud data infrastructure.
• Delivering insights and data-driven decision-making reports to data leaders and managers.
• Highlighting the usage patterns of data products, including their specific applications.
• Enhancing the process of data discovery within large enterprises, helping users identify relevant technical analyses and reports.

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Are you looking for a modern data catalog?

We write about all the processes involved when leveraging data assets: from the modern data stack to data teams composition, to data governance. Our blog covers the technical and the less technical aspects of creating tangible value from data.

At Castor, we are building a data documentation tool for the Notion, Figma, Slack generation. We designed our catalog software to be easy to use, delightful and friendly.

Want to check it out? Try our data catalog tool for free.

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