Does your company need a data catalog?

Data catalogs have become essential for improving data quality, access, and insights for businesses of all sizes. Data catalogs provide a comprehensive view of a company’s data assets. Modern catalogs identify where data comes from, who produces it, and how and where it flows through an entire organization.

Data catalogs provide easy access to information for business intelligence, data teams, and IT, and with companies practically drowning in information, they offer an automated solution for data lineage, governance, and other aspects of data management.

Businesses are collecting and analyzing more substantial amounts of data than ever. And there’s little question that yours would benefit significantly from a data catalog. A more practical question might be whether your company should attempt to build a catalog or buy a proprietary one.

What is a data catalog?

A data catalog is a centralized platform that enables organizations to discover, understand, govern, and trust their data assets.

It serves as the “knowledge layer” that sits on top of your data ecosystem and provides a unified view of metadata — the information about your data.

Modern data catalogs go far beyond traditional metadata repositories. They incorporate active metadata, automation, governance workflows, and AI-assisted insights to accelerate decision-making and improve data quality.

Core capabilities of a modern data catalog

• Metadata Management: Automatically collects and organizes technical, business, and operational metadata.
• Business Glossary: Standardizes definitions, KPIs, and shared business terms to eliminate ambiguity.
• Data Lineage: Visual maps of data flows from origin to destination, across pipelines, apps, and warehouses.
• Data Governance Workflows: Support stewardship, quality checks, certifications, and compliance requirements.
• Search & Discovery: Context-rich, AI-powered search to help users quickly find and understand data.
• Collaboration Tools: Discussions, tagging, roles, and ownership to improve cross-team communication.
• AI & Analytics Enablement: Provides the context and governance needed to build trustworthy AI models.

In platforms like DataGalaxy, these capabilities are unified in a user-friendly interface designed to help everyone leverage data confidently.

Who needs a data catalog?

In today’s data-driven organizations, almost every team interacts with data.

A data catalog ensures everyone has consistent definitions, context, and access, which increases trust and accelerates decision-making across the business.

Teams that benefit the most from data catalogs

1. Data Engineers & Architects

• Need lineage and metadata visibility to manage pipelines
• Diagnose issues faster with impact analysis
• Use standardized metadata to design scalable architectures

2. Data Stewards & Governance Teams

• Require end-to-end governance workflows
• Use business glossaries to enforce definitions
• Ensure compliance with GDPR, CCPA, and internal policies

3. Business Analysts & BI Teams

• Save hours searching for datasets
• Understand data quality, meaning, ownership, and usage
• Build more trustworthy dashboards and reports

4. Data Scientists & AI Teams

• Need high-quality, well-documented training data
• Rely on lineage to validate model inputs
• Accelerate experimentation with trusted datasets

5. Product Owners & Domain Experts

• Gain visibility into the data behind their products
• Collaborate on KPIs and semantic definitions
• Make decisions based on consistent business logic

Industries where data catalogs are essential

• Financial services & insurance
• Retail & e-commerce
• Healthcare & pharmaceuticals
• Manufacturing & supply chain
• Technology & SaaS
• Energy & utilities
• Public sector & government organizations

Any business that relies on analytics, compliance, automation, or AI is now a candidate for a data catalog.

In other words, nearly every organization today.

Why data catalogs matter more than ever in the age of AI

Data catalogs have transformed from simple metadata repositories into intelligent, collaborative hubs that unify data knowledge, governance, and usage across an organization.

Today’s leading platforms, such as DataGalaxy, provide:

• Enterprise-wide visibility of all data assets
• Metadata management and active metadata automation
• Data lineage across pipelines, tools, and systems
• Business glossary, semantic modeling, and KPI definitions
• Data governance workflows for stewardship, quality, and compliance
• Context-aware search for all teams
• AI-readiness through structured, governed data knowledge

As businesses accelerate digital transformation, analytics adoption, and AI initiatives, the volume of data and the need for governance have exploded.

Companies now require a centralized “source of truth” that enables teams to understand:

• What data exists
• Where it comes from
• Who owns it
• How it flows
• How trustworthy it is
• How it should be used responsibly

This need leads to the inevitable question:

Should you build your own data catalog or buy a proven enterprise solution?

Build vs. buy: 4 critical factors to consider

1. Technical expertise & leadership requirements

Building an internal data catalog is far more complex than many organizations anticipate.

Modern catalogs require deep expertise in metadata engineering, UI/UX design, security architecture, data governance frameworks, and integration ecosystems.

Challenges when building a data catalog internally

• Many IT teams lack specialized metadata management expertise
• Internal development time competes with other high-priority initiatives
• Skilled resources (data engineers, architects, product managers) are costly
• Technical debt accumulates quickly without continuous upkeep
• Internal teams often underestimate the complexity of lineage mapping, connector development, and governance workflows

Advantages of buying a proven solution

A mature data catalog vendor provides:

• Metadata engineering experts
• Pre-built connectors for cloud, warehouse, BI, and governance tools
• Proven implementation methodologies
• Support teams, trainers, and industry best practices
• Ongoing product innovation, you don’t have to build yourself

With DataGalaxy, organizations benefit from a dedicated success team and a platform purpose-built to support Data & AI governance at scale.

2. Start-up costs & timelines

The true cost of building

Even with a small internal team, organizations incur:

• Significant engineering hours
• Architectural design and infrastructure costs
• UI/UX development
• Governance workflow design
• Integration with cloud, warehouse, and BI systems
• Continuous maintenance and roadmap development

For most organizations, internal builds reach six-figure investments before the catalog is even functional.

Time-to-value

A build-from-scratch catalog can take 6–18 months.

Even then, there is no guarantee the solution will meet the evolving needs of data, governance, and AI teams.

Why buying wins

Vendor platforms reduce implementation time from months to weeks, thanks to:

• Ready-to-use connectors
• Pre-built governance modules
• Automated metadata discovery
• Scalable architecture
• Dedicated implementation teams

With an enterprise catalog, organizations achieve ROI faster and avoid surprise development costs.

3. User experience & adoption

A data catalog is only valuable if your teams actively use it. Adoption is driven primarily by user experience, searchability, and intuitive design.

Challenges of in-house UX

Internal development teams rarely specialize in:

• Human-centered design
• Data democratization UX patterns
• Collaborative knowledge workflows
• Contextual search optimization
• Stewardship and governance interfaces

As a result, homegrown catalogs often feel clunky and lack the polish required to encourage daily use.

Why vendor UX matters

Professional data catalog platforms invest heavily in:

• Clean, intuitive interface design
• Search experiences modeled after consumer apps
• AI-assisted discovery
• Collaborative features like discussions, roles, tagging, and quality indicators
• Governance workflows and versioning

After deployment, vendors continue to support adoption through:

• Training & certification
• Best practice guidance
• Ongoing UX improvements

A strong UX is essential for true data democratization.

4. Managed services & long-term sustainability

The hidden reality of internal solutions

Building is just the beginning. A data catalog requires:

• Continuous updates
• Bug fixes
• New connectors
• Security patches
• Upgraded architectures
• Role-based access model maintenance
• Change management & training for new employees

Internal teams may struggle to maintain momentum while balancing competing priorities.

Vendor-provided managed services

Buying a catalog provides sustained support and scalability through:

• A dedicated team of specialists
• Ongoing product development
• Continuous feature release cycles
• Expert guidance from data governance professionals
• Flexible support models

In short, vendors absorb the long-term operational complexity so your teams can focus on delivering business value.

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Conclusion: Why buying a data catalog is the best long-term strategy

When evaluating the costs, risks, timelines, and capabilities required to build a modern data catalog, the conclusion is clear:

Buying an enterprise-grade catalog is the most cost-effective, scalable, and future-proof option.

Organizations benefit from:

• Expert implementation
• Proven governance workflows
• A modern user experience
• Pre-built integrations
• Security and compliance assurance
• Continuous innovation
• Lower total cost of ownership

Homegrown solutions may suffice for very basic metadata needs, but they fall short in today’s AI-driven landscape where catalog platforms serve as the foundation for governance, trust, and cross-team collaboration.

If your goal is to unlock the full value of your data—confidently and at speed—an enterprise catalog is the only sustainable choice.

FAQ

How does a data catalog work? –

It connects to your data sources and tools, ingests metadata automatically, and creates a centralized, searchable inventory of your assets. Advanced catalogs like DataGalaxy also provide lineage, collaboration, and governance capabilities.
👉 Want to go deeper? Check out:
https://www.datagalaxy.com/en/blog/utilizing-the-semantic-layer/

Do I need a data catalog? +

If your teams are struggling to find data, understand its meaning, or trust its source — then yes. A data catalog helps you centralize, document, and connect data assets across your ecosystem. It’s the foundation of any data-driven organization.
👉 Want to go deeper? Check out:
https://www.datagalaxy.com/en/blog/what-is-a-data-catalog/

Can I build my own data catalog? +

You could, but you shouldn’t. Custom solutions are hard to scale, difficult to maintain, and lack governance features. Off-the-shelf platforms like DataGalaxy are purpose-built, continuously updated, and ready for enterprise complexity.

How does a data catalog help with AI risk management? +

A modern data catalog helps identify and track sensitive data, document lineage, and ensure data quality — all of which reduce AI-related risks. It also improves traceability across AI pipelines and enables proactive monitoring.

How do I migrate from another data catalog like Atlan or Collibra? +

Switching platforms can feel complex, but it doesn’t have to be. DataGalaxy offers dedicated support, metadata import features, and automated connectors to help teams smoothly transition from tools like Atlan, Alation, Collibra, or Informatica.

👉 Talk to us about your current setup

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.

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