If you had to find a specific book in a library that has no labels or categories, what would you do? There will be no way to find that book, right?
That’s what it’s like to find relevant data in a pool of cluttered resources when data is not cataloged properly. To avoid that, use a data catalog, which is a centralized, searchable inventory of all your organization’s data assets.
In this blog, we will explain what a data catalog is and its surrounding concepts to help you use data catalogs to their fullest.
What is a data catalog?
A data catalog is a centralized storage area where data assets are saved with business metadata, data about data. Metadata adds context to each asset, which helps search for and organize relevant assets in the right categories. But beyond metadata, catalogs also contain information about lineage (where data came from and where it flowed) and usage (who, when, how it was used).
For example, imagine you work at a retailer like “Demo Mart”. Their data catalog stores sales data and customer feedback, each tagged with business metadata, such as product category and region in which the product is sold. This makes it super easy for Mart’s teams to search for “Q1 shoe sales in the Midwest” and find the right datasets instantly.
But there’s always this one common misconception that data catalogs are like a database or a warehouse. In reality, it doesn’t store the actual data, but instead, it connects to your existing data sources and organizes everything in one searchable place.
Note: Data catalogs are not the same as data dictionaries. Read our blog on data catalogs vs. data dictionaries to understand the main differences.
Why are data catalogs important?
As global data volume is expected to reach more than 394 zettabytes by 2028, storage capacity would also increase rapidly.
But the problem is we don’t want to store data only, we need it to be ready for analysis. And modern data catalogs help with that. They are the best way to clean, organize, structure, and analyze your data.
That’s why data-driven organizations are investing in data catalogs, and its market size is projected to reach $4.54 billion by 2032.
If you also run a large or even mid-size business like our “Demo Mart,” here’s why you should consider using a data catalog:
Data discovery: You and other data analysts can locate the exact datasets you need in seconds without wasting time digging through systems.
Data governance: You can meet global data security and privacy standards by clarifying data ownership and lineage.
Collaboration: You and other data users can access and share data in a consistent format to avoid confusion and make decisions based on reliable information.
Productivity boost: By eliminating duplicate and outdated records, you free up your data teams to spend more time analyzing and less time finding the right data.
White Lion Interactive, a creative agency, gained these benefits for real. Before using a data catalog, their data was stuck in different systems (or silos), so they couldn’t easily access what others were working with.
This means team members often pulled reports or did analyses spontaneously, without following any consistent method.
The result?
Different teams produced different answers to the same business questions. So leaders struggled to get a clear, unified view of performance across the agency.
Then they started using data.world’s data catalog.
It brought together more than 467 million lines of client data from different systems and added valuable context such as how the data was being used and how it had changed over time. All of this was stored in one place, so their teams could easily search for and trust the data they found.
The catalog also handled tasks that used to take hours, like automatically tagging data with metadata and keeping track of who used what data and when. This helped White Lion’s teams stay on top of privacy rules and made audits much smoother, even as their data kept growing.
Because of this, their teams could focus on delivering insights and creative work instead of wasting time tracking down or cleaning up data. And they could do it with confidence that the data was accurate and well-maintained.
Common use cases for data catalogs
Data catalogs are widely used across industries for multiple purposes. So let’s look at some of their main use cases:
Business intelligence and reporting
Business intelligence (BI) means turning raw data into helpful insights so you can make smarter decisions, like tracking sales performance or understanding customer behavior. Reporting is how these insights are shared, either through dashboards or charts.
In many companies, analysts spend more time searching for the right data than analyzing it. That’s because data is present in disconnected systems like sales records in one tool and customer feedback in another. That’s why it’s not easy to extract consistent data.
But data catalogs avoid all this.
Take our example company, “Demo Mart”, a national retailer. They use a smart data catalog to bring all of their data together—sales, inventory, and customer data—into a single, searchable hub. Each dataset is tagged with business metadata so its analysts can quickly find what they need.
Instead of worrying about duplication, they build reports that everyone understands. The data catalog also shows data lineage, so if a number looks off, analysts can trace it back to the source and see how it was processed.
That means you can also use a data catalog to create consistent, accurate BI reports without second-guessing data quality.
Data governance and compliance
When you work in a regulated industry like healthcare or finance, you handle sensitive data daily—patient records, financial transactions, and personal details.
If your business fails to meet security standards like HIPAA (Health Insurance Portability and Accountability Act), you could face fines ranging from $141 to over $2 million. And it can even damage your company’s reputation.
But a data catalog helps you stay compliant.
How?
It keeps detailed records about your data—where it comes from, how it’s transformed, and who uses it. When auditors ask how your data is managed, you can easily show proof through the catalog.
Data science and AI
Data science and AI are becoming part of daily business operations across industries. In fact, 92% of companies plan to increase their AI investments. But AI systems are only as good as the data they’re built on. The results can be inaccurate if ML models are trained on unreliable data.
So you should use a data catalog to store and manage all the data used to train these models. It will help you:
Find and evaluate datasets without digging through disconnected systems.
Check how data has been used in previous projects to reduce the risk of duplicating work or introducing errors.
Understand relationships between different datasets to build models that reflect the whole picture.
Learn how to automate your data management processes with a machine learning data catalog.
Cross-team collaboration
When multiple teams pull data from different systems, it’s no surprise that they don’t always agree on the data. Your sales team's reports may not match another’s customer analysis. And that misalignment creates confusion.
But a data catalog can help overcome this problem. It is a shared data hub that everyone can use as a single source of truth.
For example, when the marketing team at “Demo Mart” launches a new campaign or the analytics team builds a report, they’re all looking at the same, trusted information using a data catalog.
This way, both teams stay aligned on the facts and reach decisions faster because they’re working with consistent, reliable data.
How does a data catalog work?
Data catalogs might sound complex and hard to learn, but that’s not true. Most data catalogs are easy to understand.
So, let’s see how they work on the back end.
First, a data catalog connects to various data sources, such as databases, data lakes, spreadsheets, on-premises, or SaaS tools. Then it automatically pulls in technical metadata for complete data curation (organizing and adding helpful details about your data). This can include table names, column types, data owners, and the frequency of data usage.
Data classification and organization
Once the data is extracted, the catalog classifies and tags it. For example, it might label customer data as PII (personally identifiable information) or tag it by department (e.g., Marketing or Finance).
This structured tagging makes it easier to filter and search for exactly what you need. And this whole process is monitored by data stewards.
Search and discovery features
Modern data catalogs come with AI/ML-powered search capabilities that work just like Google but for your data. You can type natural language queries like sales data for Q1 2024, and the catalog suggests the most relevant datasets. It also learns from user behavior to provide intelligent recommendations.
Data lineage and relationships
A data catalog tracks data lineage, so you can see where your data came from, how it’s been transformed, and where it’s used. It keeps a version history, too, so teams can trace the root of any issue and feel confident in the data-driven decisions.
Data catalogs integrate seamlessly with the rest of your data stack, whether that’s BI tools like Tableau or Power BI, ETL tools like dbt or Talend, or data warehouses like Snowflake or BigQuery. That means your team can work with trusted data directly inside their usual platforms.
Why build a data catalog on a knowledge graph?
Regular data catalogs do all the necessary things for data management, but what if there’s a way to get much more enhanced results? And that is by using knowledge graphs as the foundation of a data catalog.
Here’s what makes knowledge-graph-powered data catalogs different:
They make it easy to see how your data connects across systems, like following customer data from your CRM to reports and dashboards.
They help both technical and non-technical teams find and understand data faster by linking complex metadata to real-world business concepts your team already knows.
They keep your catalog organized no matter how much your data grows.
They save time with smart features like automated tagging, predictive search, and data quality alerts, all powered by built-in AI and machine learning.
They ensure your catalog can support modern data strategies like data mesh or data fabric.
When choosing the right data catalog for your business, make sure it has the following key features:
A strong data catalog should automatically gather metadata from all your data sources, including databases, cloud storage, data lakes, or SaaS tools. This way, you don’t waste time doing it manually. And it creates a complete, transparent profile for every dataset, where you can easily trace data lineage and see where your data came from and how it’s being used.
It should also include a dynamic business glossary that keeps terminology consistent across your organization. This bridges the gap between business and technical teams and ensures everyone speaks the same data language.
Data discovery capabilities
When your data is spread across systems, finding what you need can feel overwhelming. A smart data catalog changes that. It allows you to search for and access the right data easily.
So, look for a catalog that offers natural language search powered by machine learning. This way, your team can search using plain keywords or filter by tags, domains, or other business-friendly categories. And they can quickly locate the data they need, along with helpful business context about each dataset.
Data lineage features
Lineage tells the story of your data, so a catalog without lineage tracking is fruitless. Your catalog should provide visual representations of data pipelines and dependencies. This way, you could understand the entire data transformation journey and trust its reliability.
Data profiling functionalities
Data profiling scans datasets for quality metrics such as null values, outliers, and data types. It’s like a health check for your data that shows teams where the weak points are in your data. So, make sure you choose a catalog that has this capability.
Self-service analytics capabilities
A good data catalog should make it easy for business users to explore and work with data. So, look for features like natural language queries and dataset previews that help non-technical team members understand the meaning and impact of the data without relying on IT.
The best catalogs also integrate smoothly with BI tools like Power BI or Tableau. This way, teams can analyze and visualize data independently, which saves time and speeds up decision-making.
Knowledge sharing features
Look for tools that allow your team members to leave comments, attach documentation, share queries, and even flag trusted datasets. This kind of built-in knowledge sharing keeps teams aligned and reduces redundant efforts on data management tasks that could be automated.
The future of data catalogs
Every year, we’re seeing more and more advancements in data catalogs, like every other technology. So, here’s what the future of data catalogs looks like:
Knowledge graph adoption will increase, with an expected market size of $6.93bn by 2030. And more and more enterprise data catalogs could leverage knowledge graphs to map data relationships dynamically.
Modern organizations are already shifting toward data fabrics and data meshes because they can blend knowledge graphs, real-time connections, and analytics.
Data catalogs will focus on embedding AI and ML to change metadata management and compliance from reactive to proactive.
data.world’s enterprise data catalog
If you want to make your data easier to find, trust, and manage while staying compliant, data.world is built for you.
What sets it apart?
It’s powered by a knowledge graph that connects and organizes your data, so you see how everything fits together. Its AI-powered search lets your teams ask questions in plain language and quickly find the right, trusted data. And with built-in automation for governance, you can manage data faster without giving up control or quality.
In short, data.world gives you the modern data catalog you need to simplify discovery and improve collaboration, no matter how your data grows.
Book a demo today and see data.world in action.
