What Is an AI Data Catalog? And Its Business Impact Potential in 2025

Updated December 09th, 2024

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An AI data catalog automates metadata management by leveraging artificial intelligence to optimize data discovery, documentation, and governance.
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It enhances workflows with intelligent recommendations, automated tagging, and natural language search.

AI data catalogs improve efficiency by reducing manual effort, enabling faster insights, and ensuring compliance with governance policies.

These tools offer scalable, user-friendly platforms that integrate seamlessly with diverse data systems, providing a unified view of metadata.

Organizations benefit from better data quality, secure access, and real-time collaboration, empowering teams to make informed decisions and uncover new opportunities.

An AI data catalog scours your data estate for metadata and then processes it to automate data workflows and offer intelligent recommendations to enrich data discovery, exploration, documentation, and governance.

With an AI data catalog, you can get all the data and context you need in seconds so that you can make better and more insightful decisions. Let’s understand the possibilities of AI in data cataloging, and then explore the business impact of AI data catalogs.

Table of contents #

What is an AI data catalog?
What is the business impact of AI data catalogs?
How organizations making the most out of their data using Atlan
Summing up
FAQs about AI Data Catalogs
Related Reads

What is an AI data catalog? #

An AI data catalog is a modern data catalog that uses automation and intelligent recommendations to crawl, collect, and process metadata, optimizing data documentation, search, discovery, and exploration. It has the ability to draw context from an asset’s metadata to help data practitioners think more, work less and be more efficient.

As per Data Catalog Statistics 2024 by Scoop, The global data catalog market has experienced significant growth, with revenues increasing from USD 718.1 million in 2022 to an anticipated USD 5,235.2 million by 2032, reflecting a Compound Annual Growth Rate (CAGR) of 22.6%.

According to Gartner, AI data catalogs “automate various tedious tasks involved in data cataloging, including metadata discovery, ingestion, translation, enrichment, and the creation of semantic relationships between metadata.”

For a data catalog to be considered an AI data catalog, it should:

Offer automatic suggestions for data documentation — business glossaries, data asset descriptions, READMEs
Suggest questions you can ask about data
Autocomplete and write SQL queries, enhance existing query scripts, and fix bugs, so that everyone can explore data sets
Support natural language search across your data estate
Recommend similar assets when you search for data
Automatically suggest and update data tags so that you can classify data easily at scale
Run automated quality checks and alert the right people whenever there’s an issue with an asset or a pipeline

The world of AI is constantly evolving and breakthroughs in innovation are being made every day. For example, Microsoft recently invested $10 billion in OpenAI, GitHub launched a pair programming AI called Copilot, and text and image processing chatbot GPT-4 is now available. These incredible advancements in AI have opened up new possibilities and have shown the potential of AI in transforming and revolutionizing approaches to various workflows.

So, the possibilities of how AI can transform your data cataloging experience are also endless.

Why do you need to care about your data catalog having AI capabilities? #

An AI data catalog can help data practitioners be more productive and efficient with their daily workflows.

How Is AI Impacting The Development of Next-Gen Metadata And What Does It Mean For Data Teams? - Source: Data Cloud Now on Youtube.

According to Gartner, “AI can assist with data preparation, insight generation, and insight explanation. It supports the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment.”

Here’s how Forbes emphasizes the possibilities of AI in cataloging. With AI data catalogs, CMOs can ask questions like “What is the return on our advertising spend in both print and digital, over the last eighteen months for our newest product line?”

Without AI data catalogs, even identifying the data points necessary to answer these questions required the involvement of IT, who —backlogged with other priorities — might get back with the answer in a few weeks. With modern data catalogs powered by AI, users can look for and find the answers to such questions by themselves, within minutes.

What is the business impact of AI data catalogs? #

AI is already changing the way we work, and it’s easy to imagine the possibilities it can bring to our data interactions and experiences.

As we get excited, let’s consider how we can use AI data catalogs to drive business outcomes.

Save costs with faster and more efficient data discovery
Uncover new opportunities at scale to drive revenue growth
Work less with automation and intelligent recommendations for data documentation
Reduce data chaos by ensuring data consistency across all applications
Reduce time-to-insight with no-code data exploration
Improve data security, privacy, and regulatory compliance to avoid costly fines and build trust

Let’s see how.

1. Save costs with faster and more efficient data discovery #

AI data catalogs can scour metadata across your entire data estate to increase the accuracy and relevance of your data search and discovery efforts.

Data practitioners may spend hours searching for the data they want — an average employee spends 3.6 hours daily searching for information. IT teams spend half their day (4.2 hours) looking for relevant information to support business user requests.

With an AI data catalog, data practitioners can reduce the effort required to find the right data. They can ask questions about data using a Google-like search interface and get lightning-fast search results.

That’s at least 3 hours saved per day, resulting in significant time and cost savings.

2. Uncover new opportunities at scale to drive revenue growth #

Data practitioners can discover new opportunities at scale as AI data catalogs can analyze and interpret metadata across your data estate.

So, whenever you look for a certain asset, AI will also offer suggestions on similar data assets, so that you can understand data asset relationships better. Think of it along the lines of the “People also ask” and “Searches related to…” sections on Google search.

Without AI, making such connections might have taken days. In some cases, you might completely miss them — “unknown unknowns”, i.e., things you’re not even aware you don’t know.

HBR explains the “unknown unknowns” with this case study:

“GNS Healthcare applies machine-learning software to find overlooked relationships among data in patients’ health records and elsewhere. After identifying a relationship, the software churns out numerous hypotheses to explain it and then suggests which of those are the most likely. So, GNS uncovered a new drug interaction hidden in unstructured patient notes.”

How can AI data catalogs help do something similar?

AI data catalogs can study metadata on customer behavior to make connections and offer insights. So, for instance, if you search for the purchase history of a certain customer, an AI data catalog might recommend reviewing data on the same customer’s service requests or feedback offered.

3. Work less with automation and intelligent recommendations for data documentation #

AI data catalogs can study metadata from related assets to offer automatic suggestions for data descriptions, glossaries, READMEs, and more. Data practitioners can then choose to accept, modify, or reject these suggestions.

This process could take hours and lead to inconsistencies. For example, different teams can interpret “revenue” in different ways:

The sales team may consider revenue to be the amount of money received from customers
The marketing team may interpret revenue as the money generated from marketing campaigns
The finance team’s interpretation could be the amount after deducting cancellations or refunds
The C-suite may interpret revenue as the overall financial health of their organization

Standardizing such terms and ensuring that all teams are aligned is crucial to make sure that everyone’s on the same page.

AI can crawl metadata from similar data assets to auto-populate data descriptions and definitions to avoid such conundrums altogether. So, data practitioners spend less time and effort manually documenting data and rely on intelligent recommendations to document at scale.

4. Reduce data chaos by ensuring data consistency across all applications #

AI data catalogs, especially the ones powered by active metadata, also enable a two-way flow of metadata. As mentioned earlier, AI can speed up data documentation with intelligent suggestions.

Data practitioners can also perform bulk updates on data descriptions, certifications, owners, classifications, status, and other such metadata across all applications. So, your entire data estate is in sync and everyone has access to consistent and fresh data.

5. Reduce time-to-insight with no-code data exploration #

AI data catalogs can help business users write SQL queries and understand existing scripts with English prompts. AI can also review code, find errors, and offer suggestions on fixing them so that you don’t have to rely on IT to query data.

Without AI, this process involves submitting a request to IT or engineering. They may take hours, even days, to get back.

Even after they get back, the results might not be what you needed as business and IT teams query data with a different lens. While business users try to connect insights from data with project metrics directly affecting the overall business, IT might not have that context.

Forbes summarises this problem on how business and IT teams see data:

“Executives approach business questions through the filter of strategy, sales growth, target markets, competitive threats, the customer experience, and company mission. IT professionals see the world through a vastly different lens.”

AI empowers business users to explore data by themselves, which also frees up the time IT or a central data team has to spend supporting business requests so that they can focus on data quality, security, and availability.

6. Improve data security, privacy, and regulatory compliance to avoid costly fines and build trust #

AI data catalogs can help you automatically tag data and propagate it via lineage, based on the labeling for similar data assets. So, if you’ve identified patient records as PII, then an AI data catalog would compile related assets and recommend tagging them as PII too.

The catalog would also ensure that the right data encryption, masking, and anonymization policies are applied to the PII assets by tracking an asset’s journey through various workflows. This helps you avoid compliance issues and ensure data privacy and integrity.

Just like classification and encryption, an AI data catalog can also propagate access control policies via lineage. Moreover, it can offer suggestions on people who can own or modify data assets by studying metadata from similar assets. This helps with monitoring data access and security.

With AI data catalogs, you can reduce the costs associated with data errors, compliance issues, and inefficient workflows.

Moreover, since data is properly classified, labeled, and tracked throughout its lifecycle, you know where the data came from, how it has been processed, and who has had access to it. This builds trust in the reliability and quality of data.

Here’s a peek into what an AI data catalog can do

Snippet from the demo of AI capabilities of Atlan data catalog. Video by: Atlan

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

Automatic cataloging of the entire technology, data, and AI ecosystem
Enabling the data ecosystem AI and automation first
Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

Summing up #

AI in data cataloging is akin to the iPhone moment for the telecom industry. From data discovery to exploration, AI can power numerous use cases, automate recurring tasks, and take data teams a step closer to 100% self-serve analytics.

Think of an AI data catalog as an always-on, intelligent workspace that acts as the context, control, collaboration, and action plane for your data estate.

AI data catalogs can free up data practitioners to spend less time doing grunt work and more time-solving problems that can drive revenue growth, discover business opportunities and improve efficiency.

Ready to get your hands on AI data catalogs? Sign up to Learn More.

FAQs about AI Data Catalogs #

1. What is an AI data catalog, and how does it work? #

An AI data catalog is a modern tool that leverages artificial intelligence to automate metadata collection, discovery, and enrichment. It works by crawling data assets, offering intelligent recommendations for metadata management, and enabling natural language searches, making it easier for users to discover and utilize data effectively.

2. How can an AI data catalog improve data discovery? #

AI data catalogs enhance data discovery by providing faster and more accurate results through metadata crawling and intelligent search. Features like natural language queries and recommendations for related data assets significantly reduce the time spent searching for information, helping teams make data-driven decisions quickly.

3. What are the benefits of using an AI-powered data catalog? #

Using an AI-powered data catalog provides several benefits, including automated metadata management, enhanced data discovery, improved compliance with governance policies, and the ability to uncover new business opportunities. These tools also save time by reducing manual efforts in data documentation and metadata tagging.

4. How does AI enhance data catalog automation? #

AI enhances automation in data catalogs by processing metadata to suggest classifications, descriptions, and tags. It also automates quality checks, alerts users to issues, and updates metadata dynamically, ensuring consistent and reliable data management across the organization.

5. What industries benefit the most from AI data catalogs? #

Industries such as finance, healthcare, e-commerce, and technology benefit significantly from AI data catalogs. These tools help in regulatory compliance, improve data accessibility, and uncover insights that drive innovation and operational efficiency.

6. How do AI data catalogs ensure compliance with data governance policies? #

AI data catalogs ensure compliance by automating the tagging and classification of sensitive data, such as Personally Identifiable Information (PII). They apply appropriate security measures, track data lineage, and enforce access controls, reducing the risk of non-compliance with regulatory standards.