What is a data catalog? Examples from Airbnb + GE Aviation

A data catalog is a centralized inventory that helps organizations discover, govern, and use their data effectively.

Data is central to business operations, yet finding the right piece of information at the right can be a frustrating, time-consuming experience. A data catalog is an intelligent solution that helps make sense of all your data. 

Think of a data catalog as a guide to your data. For each dataset, it provides:

• Where it lives
• Its quality
• How it can be accessed

But more than just a list or inventory, it’s a tool that allows your team to leverage the data at its disposal without getting mired in confusion.  

If you’re searching for data catalog solutions, you’re probably at a pivotal moment — you’ve realized you need a better way to find, understand, and utilize your data, and you know you need to do so to gain a competitive edge. 

Your pain points are real: scattered data assets, inefficient processes, and a vacuum when there should be data-driven insights. 

With a revamped data catalog, you can say goodbye to these pain points in favor of enhanced data governance for a more agile, better-informed business.

Why are data catalogs important?

Data catalogs are critical since they turn the mountains of disparate information modern businesses accumulate into a structured and accessible data environment. They create order out of data chaos.

This order enables business users and data stewards to locate whatever data they need efficiently, while ensuring it's used securely and in compliance with data governance policies. 

You can look at a data catalog’s importance as part of a drive toward a better data culture in your business, a culture that encourages data sharing in a trustworthy, efficient, innovative manner. 

What are the key components of a data catalog?

So, what exactly is a data catalog? It’s actually made of several critical components. Let’s break them down to understand how a data catalog functions. 

1. Metadata

Think about metadata as the data about your data. We can break metadata down into three elements to help explain:

Technical metadata 

This covers information like data formats, schema, data sources, and more. Think of technical metadata as the blueprint of your data — it explains what kind of data you have, how it's structured, and where it's stored. Technical metadata is essential for understanding the nuts and bolts of your data assets, and providing insights into the databases or cloud services that hold your data.

Business metadata

Business metadata translates technical terms into business-friendly language, providing context and definitions that make the data useful for business users. This part of the data catalog is key to helping even non-technical stakeholders make sense of what data means. 

Operational metadata

Operational metadata includes details about the movement of data (data lineage). This metadata tracks the data lineage from creation to its current form. It provides transparency into your data’s journey. This can play a pivotal role in ensuring regulatory compliance and data integrity. 

2. Data assets

Data assets are what a data catalog manages:

Data sources

These include databases, data warehouses, and data lakes — any place your data lives.

• Database: An organized collection of data that you can easily access and manage. A digital filing cabinet.
• Data warehouse: A large system used for storing and analyzing data from multiple sources. 
• Data lakes: A data lake is a storage system that holds vast amounts of raw data in its original format. It’s like a giant container where data is stored until you need it for processing or analysis.

Data formats

Modern data catalogs can manage structured, unstructured, and semi-structured data. From a fully relational database to a video file, your catalog knows where it is and how to handle it.

• Structured data: Structured data is highly organized and often stored in tables or spreadsheets.
• Unstructured Data: Unstructured data doesn’t have a fixed format — think of emails, documents, or video files. Due to its lack of consistent structure, this type of data can be notoriously challenging to store and analyze.
• Semi-structured Data: Semi-structured data falls somewhere in between; it has some organizational structure but doesn’t fit neatly into a table. Examples include JSON or XML files.

Data quality metrics

By monitoring metrics like data quality, the data catalog ensures users are accessing trustworthy information. Poor data quality is one of the biggest obstacles to effective data use, and catalogs help mitigate this.

3. Data governance

Data governance features ensure proper data use by managing access control and compliance management. These features help your organization use data responsibly and maintain trust. 

4. Collaboration and user features

Many data catalogs include helpful, collaborative features that enable users within a data team to add comments and tags. This can transform your catalog into something interactive, increasing its value as a central knowledge hub. 

5. Search capabilities

A good data catalog will have search features that make it easy for users to locate the data they need. This provides a much-needed boost to data discovery. 

How do data catalogs work?

The purpose of a data catalog is to simplify the complexity of large data ecosystems by using the key features outlined above. Here’s a closer look at how the features come together.

Step 1: Data ingestion and metadata harvesting

The first step in a data catalog’s operation is to ingest data from various sources. During this process, the catalog extracts metadata from each source.

Step 2: Indexing and organizing data

Next, the catalog indexes all the data assets. This indexing makes it possible to organize the data logically. It classifies assets by type, content, and relationships, allowing you to find data when you need it. 

Step 3: Data discovery

With the metadata indexed, users can explore and locate data assets through search features. Think of it like a Google search for your company’s data, allowing employees to find data based on names, keywords, types, or specific business contexts. 

Step 4: Data lineage mapping

The data catalog maps out the lineage of each data asset. This helps users understand how data flows through the organization and supports auditing and compliance needs.

Step 5: Access control

Effective data governance is built into how the catalog works. The catalog provides a centralized platform for managing access control to data assets. This means determining who can view, edit, or use the data. It also tracks user activities and how they interact with the data. This is key for compliance with both industry regulations and your own internal policies for data security. 

Step 6: Collaboration/data enrichment

Your data team interacts with and collaborates through the data with annotations/tags/comments. We call this an “enrichment process” since it improves the metadata, making it easier for others in the organization to understand and use the data. 

Step 7: Data quality monitoring

A critical aspect of how data catalogs work is their ability to assess data quality continuously. The catalog uses quality metrics to profile data assets. If a data quality issue arises, the catalog can trigger alerts that notify data stewards they need to take some kind of corrective action. 

Finally, all of these behind-the-scenes actions culminate in a user-friendly interface. Users interact with the catalog to find, understand, and use data. The interface provides simplified tools for searching, browsing, and filtering through data. There will also be features for documenting and sharing knowledge about all your data sets. 

A data catalog works by attempting to bring a sense of navigable order from potential mountains of data chaos.

What are some of the challenges to creating a data catalog?

We’ve spelled out the benefits of implementing a data catalog, but it’s not as simple as a one-day turnaround, and all your data troubles melt away in an instant. Implementation comes with its own set of challenges:

• Data quality issues: Your data catalog is still only as good as the data it's built from. Don’t forget — garbage in, garbage out. If your data quality is compromised, your data catalog can’t provide reliable information. 
• Data silos: Data stored in different silos across departments can be hard to integrate into a single catalog.
• Lack of data ownership: Data stewards play a vital role in keeping data assets current and well-documented. You need these team members to maintain the catalog. Without this clear ownership structure, data can quickly become outdated, making it unreliable.
• Resistance to change: All businesses encounter some resistance whenever there is change. Change is challenging and requires effort — teams might be reluctant to switch from established processes. Overcoming this requires showing the value of the catalog and ensuring smooth onboarding.
• Technical complexities: Setting up a data catalog often involves integrating multiple systems and connecting different data sources, which can be a technically complex process, especially for large enterprises.

Data catalog examples

Let’s spotlight two companies to see how implementing a data catalog can enhance an organization’s data management practices:

Example 1: Airbnb

Airbnb created a data catalog called Dataportal to try to solve their challenge of scattered, siloed data across its vast global operations. 

As the company experienced quick growth, its data became challenging to pin down for employees to access and use effectively. There was a lack of a single source of truth and too much of what they called “tribal knowledge” — information only known to a handful of data experts. 

Dataportal was the company’s solution. It aimed to enable data democratization.

By making data easily searchable and adding context through metadata, such as who created the data, who consumed it, and when it was last updated. Airbnb empowered all employees, not just data scientists, to explore and leverage data for decision-making.

They knew that employees needed to be confident in using data and that their business would benefit from this institutional knowledge. In response, they created a Data University, an internal program that teaches data literacy. This helps employees to understand, interpret, and use data effectively in their roles.

The intuitive design of the data catalog, which features a search engine that mimicks Google’s simplicity, encouraged adoption and trust across different departments. As a result, Airbnb enhanced its data-driven culture and streamlined decision-making processes.

Example 2: GE Aviation

GE Aviation initiated a program called Self-Service Data (SSD) to tackle the challenges of scattered, disparate data sources. Again, they knew they had a lot of useful data on hand, but they needed to make it more accessible and to improve reliability. 

A key aim was to improve functionality with a data catalog while upholding proper data governance practicewrs. 

To achieve this, GE Aviation established two teams:

• The SSD data team focused on user enablement, creating things like dashboards to improve processes for employees.
• The database admin team was responsible for data governance. It was their job to document data products and define owners and workflows. 

Working collaboratively, they established a four-step process for deploying better data products.

1. Users can only access datasets for which they have permissions set.
2. Users created projects in a design environment to explore data and test data pipelines. This testing step upholds to compliance rules.
3. The SSD and database admin teams performed further checks to verify naming conventions, schemas, data distribution methods, etc. 
4. Only after these three steps, and once the product was ‘approved,’ would they proceed with production. 

This approach ensured they adhered to data governance policies while fashioning solutions that empowered employees to explore the data held within the organization. 

The initiative instilled a strong sense of data ownership among all employees. 

This example demonstrates how strict governance policies needn’t be a chore or something you reluctantly have to follow. Instead, use them as an opportunity to tinker with your data catalog, which will lead to better data enablement.

And, just to round off the logic here, this is a win-win for the organization; GE Aviation improved both safety and operational efficiency, in one motion. 

5 tips for choosing a data catalog solution

Choosing the right data catalog solution is a critical step toward leveraging the full potential of your data. Here are a few factors to consider:

1. Scalability: Make sure the data catalog has the capacity to grow with your business's expanding data needs.
2. Security: Protection is paramount, so look for features like access control and data governance. RecordPoint places a strong emphasis on security, offering the capabilities needed to secure your sensitive information.
3. Integration: Look for tools that easily integrate with your existing systems. RecordPoint’s data catalog is designed for seamless integration across your data ecosystem.
4. User experience: Successful adoption depends heavily on usability. Choose a catalog that is intuitive and user-friendly. RecordPoint provides a straightforward interface that makes data discovery accessible to everyone in your organization.
5. AI advancements: As data catalog technology evolves, look for tools at the forefront of AI-powered data discovery and automation. RecordPoint integrates cutting-edge AI features, ensuring you stay ahead of the curve with smarter data discovery and proactive insights

The bottom line

As modern organizations increasingly rely on data to offer better customer experiences, the need for data catalogs has never been greater. Those who don’t leverage the data at their disposal risk losing a competitive edge. 

A well-implemented data catalog can transform the way your organization uses data, from enhancing data discoverability and data governance to providing all-important context for decision-making.

If you're ready to take control of your data and unlock its full potential, RecordPoint is here to help. With over 15 years of expertise, RecordPoint's solutions, customized to your business needs, are designed to help you discover, govern, and control your data.

Schedule a demo today to see how we make data more accessible, turning it into something meaningful and creating opportunities for your business to thrive. 

FAQs

What is data cataloging?

Data cataloging is the process of organizing and properly documenting all data assets across an organization. The purpose is to make data easier to find and use for everybody within an organization.

How can I measure the ROI of a data catalog?

There are several ways to measure the ROI of your new data catalog:

• Compare time saved in data discovery
• Check for an improvement in data quality
• Check for a boost in data-driven decision-making

What is the difference between metadata and a data catalog?

Metadata provides details about individual data sets. Data catalogs then use this metadata to organize data assets, helping users discover, understand, and manage data.

What is the difference between a data catalog and a data dictionary?

A data dictionary provides definitions and descriptions of data elements. Data catalogs offer a much broader perspective, including metadata management, data lineage, and access details.