AI data catalogs in 2024: what’s changed and why it matters

If you’re working in the data space today, you must have felt the wave of artificial intelligence (AI) innovation reshaping how we manage and access information. One of the areas affected is data catalogs, which are no longer simple tools for organizing metadata. They’ve evolved dramatically into powerful, intelligent systems capable of understanding data on a much deeper level.

In this post, we’ll talk about what AI data catalogs are and dive into how they’ve improved to enhance data management workflows, efficiency, and outcomes, not just for data professionals but for business users across industries.

What is an AI data catalog?

We know that a data catalog stores an organization’s metadata so that everyone can find the data they need to work with. So, does that make an AI data catalog a data catalog powered by AI? Yes and no.

While it’s true that AI enhances the capabilities of a data catalog, simply adding AI isn’t the whole picture. In fact, an AI data catalog is fundamentally different from traditional catalogs. This is because the integration of AI transforms the static repository into a dynamic, self-improving system that not only stores metadata but also enhances data context and accessibility to drive smarter decision-making across the organization.

So, an AI data catalog is still a centralized repository of metadata but one that uses AI to automate metadata management, data discovery, governance, and lineage tracking. It provides a complete view of your organization’s data assets, making it easier for your data teams to find, understand, and utilize data without manual intervention at every step.

The Evolution of AI-Powered Data Catalogs

For a while, data teams have been using AI in data catalogs primarily to automate repetitive tasks: locating and scanning datasets, tagging them with metadata, and improving search functionality. While they help with basic data management, these features don’t quite live up to the “intelligent” promise of AI.

Today, AI data catalogs have moved far beyond traditional automation techniques. We now have AI-driven metadata management that uses machine learning (ML) to learn from user interactions and improve its understanding of data sets. The goal Is to deliver context-aware insights that bridge the gap between raw data and decision-making.

In other words, instead of just classifying what data resides where, a modern, AI-powered data catalog can actively suggest relationships between data sets, understand usage patterns, and even predict what data might be useful for certain business queries.

Another key development is Generative AI, which makes data discovery even smoother by generating recommendations for related datasets or automatically curating data based on previous queries and interactions. What we’re looking at now is a system that learns from you and evolves with your needs.

The benefits of integrating AI into your data catalog

AI catalogs aren’t optional anymore, which means relying on manual or outdated data management techniques will have you leaving value on the table. Here’s why adopting an AI-driven data catalog is necessary today:

Faster and smarter decision-making

What do you get when you put the right data in front of the right people when they need it the most? You equip them to make data-driven decisions faster, and that’s something that you can easily achieve with an AI data catalog.

Data democratization across your organization

To equip your employees with the ability to use the data that they need to work with, you must first streamline access to it. A data catalog that uses AI can help you achieve these goals simultaneously by automating data discovery and classification. And when everyone has easy access to data, they can collaborate and meet demands more effectively.

Increased revenue

Faster data discovery and easy access lead to data-driven and timely business decisions. Many of these decisions will lead to innovative uses of data, which improve the bottom line. For example, marketers can improve conversion rates and drive revenue growth by using predictive analytics to understand customer behavior and personalize marketing strategies.

Improved data quality and trust

People only act on the insights if they know they’re trustworthy, which means they need to be confident that the underlying data sets are accurate. AI data catalogs use automated data quality checks to detect anomalies and ensure that everyone works with accurate, reliable data sets. This builds the necessary trust across the organization and reduces the time spent questioning data integrity.

What an AI data catalog means for people in your organization

With an AI-powered data catalog, you can improve your organization’s data processes and systems. When implemented right, it can very well be the difference between your organization being data-driven and one that remains data-siloed. Here’s how it enhances working with data for users across your organization:

Data professionals

According to a study, people, on average, spend 3.6 hours every day searching for information. An AI catalog can reduce this time to a few minutes with features like natural language search (NLS) to quickly find and understand relevant data. For your data teams, this means they benefit from faster access to deeper insights and spend less time on mundane tasks like data discovery.

Business decision-makers

AI data catalogs democratize data access and allow business leaders in your organization to tap into the information they need without constantly relying on technical teams. For example, a retail executive can pull performance data by simply talking to the system in natural language without having to go through several layers of request processes.

IT and data management teams

For those responsible for managing and securing your organization’s data infrastructure, AI data catalogs bring a host of benefits. With automation and ML, IT personnel are able to make data integration smoother and more consistent. Similarly, data quality checks become more reliable as AI continuously monitors for errors or missing data.

Compliance and governance teams

Data governance is critical for industries that must adhere to strict regulatory standards, such as finance and healthcare. AI data catalogs take over much of the heavy lifting by automating data lineage tracking and ensuring compliance rules are applied consistently across datasets.

Operational teams in various industries

AI data catalogs offer a way to monitor and respond to changes more effectively in industries like manufacturing and supply chain management, where real-time data is crucial for operations. AI works alongside your teams to flag anomalies, such as potential disruptions in supply chains, before they escalate into bigger issues.

Features a modern AI data catalog should have

Modern organizations need data catalogs that actively enhance their data operations to remain competitive and agile. Here are the critical features any truly modern AI-driven data catalog must deliver if it’s going to meet the needs of a forward-thinking enterprise:

• AI-powered metadata management to automatically scan, tag, and categorize data sets based on context.
• Automated data lineage and data governance to help keep track of where data comes from, how it’s transformed, and where it ends up, all the while ensuring compliance.
• Automated data recommendations based on users’ roles, previous interactions, and ongoing business needs.
• Intelligent data discovery and natural language search (NLS) to quickly find the needed information.
• Integration with existing data platforms like data analysis tools, data pipelines tools, or data integration tools to simplify data access.
• Built-in data quality management to monitor data sets for inconsistencies and anomalies and alert the personnel.

Wrap up

As 2024 comes to a close, it’s evident that AI is no longer a mere catchword. Within data management, and data catalogs specifically, it has enhanced how we discover, manage, and leverage data assets. These benefits have enabled businesses to make even better decisions and innovate faster.

In the coming years, the use of AI data catalogs will increase exponentially as more businesses move toward greater data democratization. This is supported by the fact that the global data catalog market is projected to grow from USD 1.05 billion in 2024 to USD 4.68 billion by 2032. The increase represents a CAGR of approximately 18% and will primarily be driven by the incorporation of AI, ML, and advanced analytics. Why? To further enhance operational efficiency by making it easier for non-technical users to navigate complex datasets and make smarter, data-backed decisions without any bottlenecks.

If you’re looking for a modern, AI-powered data management solution, contact Astera to discuss your use case today.