DataGalaxy’s Auto Description: Documenting your data has never been easier

Smarter documentation. Less manual work. Same trusted governance.

Data catalogs are designed to bring clarity, but in practice, they often create clutter instead.

The fields are available and the structure exists, but when users try to understand what a dataset means, where it comes from, or how it should be used, they are often faced with an empty description box or, in some cases, a meaningless placeholder.

Documentation does not scale easily, so it is deprioritized. That gap between a catalog that exists and a catalog that works is exactly where many organizations struggle.

Documentation should be built-in, scalable, and always under steward control. That is why we created Auto Description, a feature that generates clear, consistent, multilingual documentation in seconds.

What is DataGalaxy’s Auto Description?

Auto Description is a DataGalaxy documentation assistant that helps teams populate and maintain high-quality descriptions across the platform.

It generates suggested content for any type of object, whether glossary terms, sources, indicators, domains, or tables, based on context such as naming conventions and common data language.

The feature is designed to accelerate documentation while maintaining accuracy and respecting governance boundaries.

Every description is editable and requires steward validation before being applied, which ensures that automation never compromises quality.

The need for Auto Description: Why this solves a real problem

Most stewards and data owners are not opposed to documenting, but they are often too busy to do it consistently.

When a new domain is onboarded or a new system is integrated, documentation is usually rushed or skipped.

Over time, these gaps accumulate, which erodes trust in the catalog and reduces adoption.

Auto Description is not about automation for its own sake. Instead, it provides teams with a smarter starting point.

Instead of writing hundreds of definitions from scratch, stewards can focus on reviewing, validating, and improving automatically generated suggestions. This saves significant time, improves coverage, and helps maintain consistent standards across large and constantly evolving data landscapes.

Operationalizing

CDEs

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How it works

Using Auto Description is straightforward: Stewards select one or multiple objects and trigger generation. The system then returns a suggested description for each selected object, written in natural, human-readable language.

Each suggestion is clearly labeled, and stewards can review, modify, or reject it. Nothing is published automatically, which ensures that teams always remain in full control of the documentation layer.

Auto Description also supports multilingual documentation. With a single click, translations can be generated, allowing organizations to maintain clarity and consistency across global teams.

Documentation with governance natively built in

Like every feature in DataGalaxy, Auto Description is designed with governance at the core. It is fully self-hosted, no metadata is exposed externally, and all documentation stays securely within your governance perimeter.

This allows organizations to move faster without compromising on data responsibility.

What we do

Since day one, DataGalaxy has been guided by a simple conviction: data creates value when people align on it, adopt it, and turn it into outcomes.

Metadata is not the destination. It is the foundation that makes this possible. That’s why we built the value governance platform, a business-first approach that connects strategy to execution, IT to business, and data to results.

From blank fields to full context

When data is properly documented, it becomes easier to trust, reuse, and scale.

The challenge is that writing quality documentation across hundreds or thousands of objects is rarely sustainable in practice. Auto Description closes this gap by giving data teams and business domains a structured way to keep documentation complete and up to date.

It reduces time-to-value, improves adoption, and ensures that governance efforts are reinforced rather than ignored.

It is not designed to replace stewardship but to enable it to scale effectively, so documentation becomes a natural and reliable part of the platform.

About the author

Jessica Sandifer

With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.