What is a Data Catalog?

Data catalogs: a guide to understanding the essentials

There’s no shortage of data in our data-driven world. You can find it stored in data warehouses, data marts, data lakes, mainframes, cloud CRMs, databases, and, yes, Excel spreadsheets.

The problem is the data you need may not be conveniently stored in just one application. It may be scattered across multiple data sources and exist in a frustrating mix of different formats. Or worse, you can’t be sure if it even exists.

Assuming you can still trace your information to a given data lake, there’s no guarantee it won’t be difficult to locate or understand. Where does one go to easily find trusted data?

This comprehensive guide will walk you through data catalogs from beginning to end. You will learn what a data catalog is, how it works, how to find the best data catalog tools, and so much more.

What is a data catalog?

The simple answer to “what is a data catalog?” is a centralized and searchable inventory of an organization’s most current and reliable datasets.

Data catalogs use metadata to manage and keep record of all data and data sources in an organization. It allows business and technical users to search, request, and receive datasets required to complete daily business tasks, manage projects, and generate analytical reporting.

“..data catalogs offer a fast and inexpensive way to inventory and classify the organization’s increasingly distributed and disorganized data assets.”

Gartner’s Augmented Data Catalogs: Now an Enterprise Must-Have for Data and Analytics Leaders

What does a data catalog do and how does it work?

A data catalog connects to your data sources, extracts information about the data inside, and stores it in an orderly manner, making it easy to filter and locate.

We call this extracted information metadata, often referred to as “data about data.”

What is metadata?

As mentioned above, metadata is the extracted information from a data source. Metadata gives information about a piece of information, but not the information itself. It describes the data making it easier to organize, access, and make use of it in operational settings.

The more advanced the data catalog solution, the more metadata the catalog is capable of capturing, extracting, and storing. If “smart” enough (or AI-enabled), a data catalog can even generate its own metadata.

You can call this process documenting a data source. Here is how a documented data source might look like:

What kind of metadata can a data catalog store?

We can classify metadata into three categories: technical, business, and operational.

Technical metadata

Technical metadata acts like a blueprint for your data. It dives into the structure, revealing details like how data points are organized, how they connect, and how they're indexed. Think rows, columns, tables – the whole layout.

But it goes further, providing data users with insights on how the data is handled (e.g. transformations and analysis pipelines). This transparency lets users quickly grasp how the organization has structured and presented the information.

Technical metadata examples:

• The number of records and columns in a dataset
• Data types as defined in the data source, such as string, integer, varchar(25), etc.
• Names of schemas, partitions, table, and attributes as seen in the data source
• Primary key and foreign key indicators
• Constraints
• Table and attribute descriptions imported from the data source

Business metadata

Business metadata is the inside scoop on your data. It goes beyond the raw numbers to explain how that data benefits the business. For example – regulatory compliance, usage details, and helpful context for everyone who uses it.

Imagine data project notes – things like confidentiality levels, descriptions, where it's stored, who uses it, and what department it belongs to. Businesses define the specific details they need, creating a rich profile for each data set.

Business metadata examples:

• Business terms and definitions
• Titles and descriptions
• User-defined tags
• Business rules
• Data owners

Operational metadata

​​Operational metadata is the data's travel log. It tracks where the data came from, any transformations it went through, updates, and other details about its journey. Think origin story, makeover history, and current location (all for your data).

This metadata lets you see how data entered your system, what changes it went through, and its current status. You can even see who last edited it and who has access for future updates. It's like having a behind-the-scenes view of your data's life cycle.

Operational metadata examples:

• The origin of a data source or dataset
• Tracking any transformation of a dataset
• Current status of a dataset
• Seeing which version if currently being used if there are multiple versions of data
• Data lineage to show the flow of data in your system
• User activity

Here is how this metadata can be displayed for a specific data set in a data catalog:

Additional metadata contains more granular details about data stakeholders, including original source system locations, comments from users on use cases and suitability, and versioning histories that track ongoing edits, modifications, and changes in ownership.

Modern data catalogs can also track data quality, generate data lineage, or enable users to wrangle and prepare data. Using AI for automating various use cases is not uncommon either. These include:

• Detecting irregularities in a data set that has been recently updated
• Adding business terms and tags to new data sets
• Detecting potentially related data sets
• Improving search experience

Here is an example of suggestions for “tagging” data generated by an AI-powered data catalog based on how users tagged other data sets.

Who uses data catalogs?

A data catalog acts as a bridge between the technical and business worlds of data. Data catalog tools empower its users to access, interpret, and apply the data to make strategic, sound decisions leading to improved business outcomes.

Here’s a list of the most common users of data catalog tools:

• Data analysts and Data Scientists
• Business Analysts and Managers
• Data Stewards and Data Owners
• IT Professionals and Data Engineers
• Compliance Officers

Data analysts and data scientists

Data analysts and data scientists heavily rely on data catalogs to find the datasets they need for analysis and modeling. The catalog helps them understand the data's purpose, structure, and quality, allowing them to choose the right data for their projects.

Business analysts and managers

Business users may not be technical experts, but they still need data to make informed decisions. Data catalog tools provide a user-friendly interface for them to search and discover relevant datasets based on business context. They can understand what the data represents and how it's used, without needing deep technical knowledge.

Data stewards and data owners

These users are responsible for the accuracy, quality, and governance of specific datasets. Data catalogs provide them with tools to track data lineage, manage access control, and ensure everyone is using the most up-to-date version of the data.

IT professionals and data engineers

While not the primary users, IT professionals and data engineers can benefit from data catalogs. The catalog provides them with a centralized view of the data landscape, making it easier to understand data dependencies and manage data infrastructure.

Compliance officers

Data catalogs can be a valuable tool for ensuring compliance with data privacy regulations. They can help track data lineage and identify datasets that contain sensitive information, making it easier to manage access and protect sensitive data.

What are the benefits of a data catalog? 6 benefits

As an organization or an individual user, the benefits of a data catalog are extensive. We will highlight the six main benefits that a data catalog offers organizations in enhancing their internal processes and competitive advantage.

1. Reducing the time it takes to find the right data

By now, It’s a well-known fact that data scientists spend 50 to 80% of their time locating, accessing, and preparing data before they can use it. Cataloging critical business data enables data scientists and other data-dependent users to find the right data faster, thanks to all the available metadata.

A key benefit of data catalogs is that they support instant access to the source, data sample, and data quality characteristics to understand whether the data set they found fits their purpose and helps with data management.

2. Better data context

Additionally, they can consult data lineage for more context or use AI-powered relationship detection to find similar or related data assets. This data catalog benefit helps people understand their datasets better — where the datasets came from, the quality of the datasets, who should use them, how they connect to other datasets, and much more.

Having a centralized place for data discovery helps these users eliminate the bottlenecks associated with a lack of trust in data or lack of visibility into the organization’s data landscape. As a result, it supports data catalog users in making stronger business decisions from higher quality analysis.

One such important database is sales leads that can originate on multiple platforms and marketing channels. Exporting leads from LinkedIn Sales Navigator or other such lead generation machinery to a data catalog can save users time by eliminating the need to manually search for and extract data. This can increase productivity and enable users to focus on more important tasks.

3. Accelerating data governance

The data catalog’s stored metadata is key to beginning a data governance framework and initiative. It helps create a baseline for stakeholders and data governance activities by providing insight into the current state and nature of an organization's data — how it is collected, created, managed, and where it overlaps.

These frameworks and policies can be documented (and even enforced) in a data catalog. This brings us to the next benefit.

Learn more about Ataccama’s data governance software and how our automated features can help save your business valuable time and money!

4. Improved data quality

As a result of sophisticated data catalogs and data governance mentioned above, data quality and its importance improve significantly. This means that businesses can reduce their risk of error and trust the data to guide sound business decisions.

One of the greatest benefits of data catalogs is that it acts as a shield against mistakes by providing users with a clear advantage. This is achieved through high-quality information and data descriptions, clearly tracking a dataset’s history, and enforcing rules for improved accuracy and accessibility throughout an organization.

This comprehensive approach empowers users to handle data with precision, leading to fewer errors in analysis and overall usage.

5. Facilitating regulatory compliance and security

In this day and age, businesses face stricter rules and guidelines for data collection and privacy. Additionally, reinforced data privacy and protection is more important than ever with increasing threats and risks.

All businesses can take advantage of this data catalog benefit by using it to enhance their data privacy and security. Data catalogs are a great tool for managing data privacy and protection requirements.

1. One way it helps is by letting data protection officers catalog and manage regulatory requirements like GDPR and CCPA.
2. The second way it helps is by enabling them to generate regular reports of PII (Personally Identifiable Information) data locations.

They can track irregularities and immediately address these issues with data or system owners, i.e., sensitive data appears where it shouldn’t.

6. Impact and root cause analysis

The bigger the data catalog becomes, the greater its outreach in assessing the impact of changes to a given dataset. By closely examining the metadata relationships within a particular dataset, data engineers and IT can determine the impact of change on downstream reporting tools and other systems based on changes to a given dataset.

Likewise, if an adverse event did happen, a data catalog can help track its root cause. For example, the numbers in a new quarterly financial report don’t make sense. In this case, a business analyst can look at the data lineage for this report and spot an anomaly or DQ issue that “broke the report.”

What are the must-have data catalog features? 8 features

Traditionally, data catalogs have been all about collecting as much metadata as possible and making it easy to find with search and filtering. These features are still critical today, but data catalog features have become more advanced and sophisticated.

First, the amount and types of metadata that catalogs can now capture and store. Second, the automation they now support. And third, how they have converged with other tools and activities, such as data quality and data preparation. Thanks to these innovations, data catalogs have not only become more user-friendly but also much more useful.

For a more in-depth look into each of these key features, our data experts created an Essential Features of Data Catalogs guide. Otherwise, make sure the following 8 data catalog features are included in your data catalog tool before you make an investment!

1. Data discovery and metadata capture

Comprehensive data discovery is dependent on flexible connectivity to all necessary source systems, including applications and databases. Given the variety of data sources, modern data catalogs should provide a number of pre-built adapters to enable easy integration.

2. Search and filtering

Search is still arguably the most important data catalog feature. If implemented well, it allows users to productively explore and quickly find the datasets that are relevant to them. While both simple and complex search requests should be supported, it is even better if AI is used to give users relevant suggestions.

3. Business glossary

Business glossaries let organizations document their most important business terms and agree on their meaning, and it’s common for modern data catalogs to come with business glossaries out of the box. This integration enables both business and technical terms to be assigned to any cataloged data assets manually or automatically. Next-generation data catalog features also allow associating data quality rules with business terms to enable automated data quality monitoring.

4. Data quality monitoring

Inventoried datasets benefit from ongoing data quality checks. Who wants to use data riddled with duplicates, missing values, and formatting inconsistencies?This is an advanced data catalog feature that very few solutions can boast.

Check out our Ultimate Guide on “What Is Data Quality and Why Is It Important?” to help you make calculated decisions to support your business goals!

5. Data lineage

Data lineage tracks the origin, destination, and transformation of any data asset in the data catalog. As mentioned earlier, users can use data lineage to help track and understand data changes as part of data impact analysis or root cause analysis. It is also useful for preparing reports mandated by regulations like BCBS-239.

6. Social collaboration

Given the size difference between the typically smaller group of dataset creators and the larger consumer community, collaboration between the two is essential. Data catalog features such as commenting, upvoting, and sharing help speed up data adoption and give users an organic way to provide feedback and curate datasets.

7. Data marketplace

Once opened for business, the data catalog tool is not only a central place for users to find data but also a resource for internal customers to download data for productive use in other applications and reporting. However, it is critical that data access be governed by prescribed policies that have been applied to data domains and role authorizations.

8. Customization

Every organization is different and deals with unique metadata varieties. That’s why data catalogs need to be flexible enough to enable the management of any kind of metadata, not just source systems and data lakes. These could be BI (business intelligence) reports, APIs, or data processing servers. Support for adding custom metadata attributes is critical, too.

What makes Ataccama’s data catalog tool different from the others?

Ataccama’s centralized data catalog software, Ataccama ONE, will take the pressure off data cataloging and make it a more seamless process with its self-improving AI, scheduled system scans, and robust connectivity.

Our self-improving AI is constantly working to suggest new business rules and terms, and it detects new relationships within data sources. Scheduled system scans account for any changes made to data domains and data structures.

And the best part — Ataccama ONE connects to popular data sources, including Amazon S3 and Redshift, Oracle DB, Azure Synapse and Data Lake Storage, Google BigQuery, and Snowflake.

Get in touch or schedule a demo to see it in action for yourself!

Data catalog FAQ

1. What’s the difference between data catalog vs data dictionary?

A data catalog is a giant library of all your data assets that offers a high-level overview of your data. It keeps inventory of your data and makes it organized, easily accessible and searchable, and upholds the integrity of the data.

A data dictionary dives deep into the technical details of a specific data set. It focuses on the technical structure of the data, providing in-depth explanations for a particular data set. This can include:

• Definitions of each data field
• The types of data
• Specific rules for data formatting
• Explanation of data relationships

2. What’s the difference between business glossary vs data catalog?

The main difference between a business glossary vs data catalog is that a business glossary is a data catalog feature. It’s a component of data cataloging.

Business glossaries within a data catalog focus on defining and explaining business terms used across the organization. As a result, it ensures everyone has a clear understanding of what specific data points represent in the context of the business.

3. What’s the difference between data inventory vs data catalog?

There are many similarities between data inventory vs data catalog, but the main difference is that a data catalog offers much more insight into its registry of data whereas a data inventory is just a collection of data.

Data cataloging differs by offering more detailed descriptions into the data, such as:

• What the data represents
• Business context and how it’s used across the organization
• Data formatting
• Different types of data
• Tracking the flow of data in the internal system
• Latest versions and sources to ensure data quality and accuracy
• Incorporation of business terms from the glossary to improve search function
• Ownership details for enhanced transparency and accessibility

4. What’s the difference between data catalog vs data lineage?

Data lineage is a functionality of a data catalog. Modern and quality data catalog tools should allow users to track the data lineage within their system.

Data lineage acts as a detailed map of the data’s journey with a strong focus on data transformation and movement, and what’s happened to bring the data to its current state.

5. What’s the difference between data catalog vs master data management?

Data catalogs vs master data management (MDM) are both vital tools of Data Governance as a whole. Data catalogs provide a comprehensive overview and search functionality for all your data, while MDM focuses on managing the critical, core business data of an organization.

They work together to improve data quality, streamline data management, and ensure everyone in the organization is working with the same trusted information.

Data catalog concluding thoughts

Data catalogs provide a convenient means of locating useful datasets for data people of all kinds. Given the variety of features that modern solutions provide, data catalogs save precious time for data scientists, speed up and automate data governance, facilitate regulatory compliance, enable root cause and impact analysis, and much more.

Looking at the growing demand for data democratization and enablement, Ataccama's next-gen automated data catalog software fulfills all of these needs. It includes features, such as data preparation, data quality monitoring, data marketplaces, and integrating AI- and metadata-based automation.

Get a demo of our platform or get in touch with our data experts for more details on how Ataccama’s solution can benefit your company and data professionals!