How to Pick the Best Enterprise Data Catalog? Experts Recommend These 11 Key Criteria for Your Evaluation Checklist

Updated August 18th, 2024

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If you’re looking for the best enterprise data catalog, then you’ve come to the right place. Having worked with the foremost data teams, and bearing the latest Forrester report in mind, we’ve put together the list of key criteria that you must consider while choosing the best enterprise data catalog for your use cases.

The best enterprise data catalog for your organization should align with your specific requirements, use cases, and tech stack, while also keeping pace with emerging market trends and technological advancements. Adaptability and a forward-thinking vision are essential as the rate of technological change accelerates.

When industry experts evaluate the top enterprise data catalogs (EDCs) on the market—a process that spans several months and involves enterprise teams across functions—they focus on these factors, with a strong emphasis on future-centricity and innovation.

“The best enterprise data catalogs transcend traditional metadata repositories to catch up with the exponential surge in the velocity, variety, veracity, and volume of data.” - The Forrester Wave™: Enterprise Data Catalogs, Q3 2024

So, let’s look at the topmost evaluation criteria to help you find the best enterprise data catalog for your organization.

Table of contents #

  1. Enterprise data catalog key criteria: 11 factors to consider
  2. 1. No-code, native integrations to connect your entire data estate
  3. 2. Metadata management that ensures relevant, real-time context
  4. 3. Natural language search and automated data profiling for faster data discovery
  5. 4. Automated, cross-system, actionable lineage for a single pane of glass
  6. 5. Automated data governance, compliance, and risk management that build trust
  7. 6. Intuitive and collaborative UX, personalized for different personas and backgrounds
  8. 7. Deployment options catering to your unique use cases, infrastructure, and tech stack
  9. 8. Data quality and observability capabilities to ensure accuracy of your data
  10. 9. A vision that considers emerging use cases and future needs of data teams
  11. 10. A solid adoption strategy to maximize product use and build a data culture
  12. 11. A supportive and interoperable partner ecosystem
  13. How to evaluate an enterprise data catalog in 5 steps
  14. Atlan: A Leader in The Forrester Wave™ Enterprise Data Catalogs, Q3 2024
  15. Related reads

Enterprise data catalog key criteria: 11 factors to consider #

The best enterprise data catalogs for modern data teams should bridge the gap between complex datasets, business insights, data governance, and innovation. Here are some essential factors that can help:

  1. No-code, native integrations to connect your entire data estate
  2. Metadata management that ensures relevant, real-time context
  3. Natural language search and automated data profiling for faster data discovery
  4. Automated, cross-system, actionable lineage for a single pane of glass
  5. Automated data governance, compliance, and risk management that build trust
  6. Intuitive and collaborative UX, personalized for different personas and backgrounds
  7. Deployment options catering to your unique use cases, infrastructure, and tech stack
  8. Data quality and observability capabilities to ensure accuracy of your data
  9. A vision that considers emerging use cases and future needs of data teams
  10. A solid adoption strategy to maximize product use and build a data culture
  11. A supportive and interoperable partner ecosystem

Let’s explore each criterion further.

1. No-code, native integrations to connect your entire data estate #

The enterprise data catalog must automatically help you integrate your entire data estate – data management tools, BI solutions, AI/ML platforms, data quality tools, third party apps, etc. Moreover, it should keep accurate and updated metadata for every asset.

No-code, native integrations that quickly connect and auto-ingest metadata are essential for this purpose.

2. Metadata management that ensures relevant, real-time context #

An enterprise data catalog should act as a metadata and AI control plane, mapping different types of metadata (technical, operational, governance, usage, custom), data models, and relationships.

According to Forrester, it should also:

  • Automatically update and synchronize metadata changes across your connected data ecosystem
  • Handle metadata conflicts and inconsistencies between different data sources
  • Customize to fit your specific needs

3. Natural language search and automated data profiling for faster data discovery #

A major problem with enterprise data catalogs is that they’re seldom used outside of the technical teams. Natural language search with filters, role-based recommended assets, and popularity or usage stats can make business teams adopt and rely on EDCs for insights.

Meanwhile, automated profiling can collect and present all kinds of data assets along with their relationships, patterns, quality scores, etc. This helps in monitoring and improving data quality, ensuring that your data assets are trustworthy.

Using AI, EDCs can further support data search and discovery with automatic recommendations based on metadata.

4. Automated, cross-system, actionable lineage for a single pane of glass #

Several data catalogs mention lineage, but don’t necessarily map your entire data estate, support downstream impact analysis, anomaly detection, or root-cause analysis.

The best enterprise data catalog would set up data lineage quickly with automated no-code lineage miners and reach every corner of the data estate with out-of-the-box connectors. It would illustrate lineage at a granular level – mapping each column, table, and transformation across systems.

Moreover, the UI should support actionable lineage, i.e., navigating, exploring, filtering, searching, and highlighting specific lineage elements along with overlayed metadata.

Also, read -> Automated data lineage 101

5. Automated data governance, compliance, and risk management that build trust #

Forrester specifies that a solid enterprise data catalog solution should support:

  • Governance – data, analytics, BI, AI, and ML
  • Risk management – identification, assessment, and mitigation
  • Compliance with regulatory policies, AI ethics, industry standards, and rules

The EDC should also help in measuring and communicating governance, risk, and compliance for your enterprise.

The best EDCs simplify governance by automatically identifying, flagging, remediating, and recommending non-compliant data assets.

They also offer rule-based enrichment and tagging (using playbooks) upstream and downstream (using lineage), integrate with risk management tools, and offer built-in support for compliance frameworks (GDPR, CCPA, HIPAA).

Also, read -> Automated data governance 101

6. Intuitive and collaborative UX, personalized for different personas and backgrounds #

Since every organization’s organization structure and data estate are unique, an enterprise data catalog must support a diversity of users, use cases, and tools through a customizable and personalized approach.

For instance, the solution could address diversity in data users with:

  • Personalized experiences for different roles and types of users
  • Customized granular access policies for diverse users and groups
  • Tool tips, help documentation, and training resources for all users

Meanwhile, it can tackle diverse data assets from numerous sources with:

  • Custom metadata to capture the fields that matter
  • Custom classifications and tags for different types of data
  • Custom masking and hashing policies for different types of sensitive data

The EDC could also facilitate user collaboration with features such as threads, comments, tags, mentions, alerts, and notifications, among others. Plus, it should seamlessly integrate with popular communication and collaboration tools (Jira, Microsoft Teams, Slack, etc.).

7. Deployment options catering to your unique use cases, infrastructure, and tech stack #

Deploying the EDC should be seamless and customizable, with options for all kinds of data infrastructure – on-premises, public cloud, SaaS, multi-cloud, hybrid cloud, etc.

With native connectors and capabilities that expedite time-to-value, the data catalog should be quick (going from kickoff to go-live within weeks) and easy to use for all teams within your enterprise.

8. Data quality and observability capabilities to ensure accuracy of your data #

The enterprise data catalog should clearly define, communicate, and enforce the quality and state of data, complete with data anomaly reporting, tracking inconsistencies, measuring data estate health, etc.

It should support real-time monitoring of data quality metrics and KPIs, and also integrate with popular data quality and observability tools to automatically exchange metadata.

9. A vision that considers emerging use cases and future needs of data teams #

An ambitious vision affects the direction a product will take in terms of governance, data quality, observability, etc.

Moreover, as technology evolves rapidly (gen AI, data mesh, etc.), EDCs must adapt to stay relevant.

How does all that translate into a vision that’s relevant to you while also future-focused?

Here’s an example. Atlan’s vision is to become a market leader by building a data and AI control plane, powered by active metadata, with complete configurability, interoperability, and openness to power every data team in every industry, however unique and complex their needs may be.

This vision has landed Atlan top scores from Forrester along with this noteworthy quote, “Atlan is a visionary player with a clear, ambitious goal: to become the data and AI control plane enabling complex business use cases.”

10. A solid adoption strategy to maximize product use and build a data culture #

Adoption is a major problem with most enterprise data catalogs in the market, leading to most of them being deemed as ‘expensive shelfware’. The right EDC provider would partner up with you to ensure widespread adoption of well-defined use cases in less than 90 days, delivering significant business value.

For instance, here’s what Atlan does for its customers:

  • Roll out additional use cases throughout the year, based on your plan/roadmap
  • Provide guidance on adopting paradigms like data mesh, data products, and data contracts
  • Offer insight on scaling adoption through user interviews, workshops, and gamification
  • Give access to Atlan University for on-demand courses and best practices
  • Conduct masterclasses and workshops from customers to inspire and guide

11. A supportive and interoperable partner ecosystem #

A supportive and interoperable partner ecosystem is defined by the catalog solution provider’s approach to:

  • Prioritizing collaboration and seamless integration – easy exchange of data and capabilities between various solutions in enterprise tech stacks
  • Developing and maintaining relevant partnerships
  • Building alliances and gaining industry recognitions
  • Co-creating solutions, sharing expertise, and expanding market reach

How to evaluate an enterprise data catalog in 5 steps #

Here are five steps to help guide you in your evaluation process:

  1. Identify your organizational needs and budget
  2. Create evaluation criteria (including, but not limited to, the criteria listed above)
  3. Understand the providers and offerings in the market (you could use the Forrester report)
  4. Shortlist and demo the prospective solutions
  5. Execute proofs of concept (POCs)

Dig deeper -> 5 steps to evaluating a data catalog

Atlan: A Leader in The Forrester Wave™ Enterprise Data Catalogs, Q3 2024 #

According to the latest Forrester Wave™: Enterprise Data Catalogs, Q3 2024 report, which evaluates the 12 most significant enterprise data catalogs providers on 20+ criteria, enterprise data catalogs must:

  • Automatically catalog the entire technology, data, and AI ecosystem
  • Put AI and automation first
  • Prioritize data democratization and self-service

To understand more about Forrester’s view of the enterprise data catalog landscape and evaluation criteria, download the full report here.

Meanwhile, if you are evaluating an enterprise data catalog solution for your business, consider Atlan — a leader in EDCs (scoring highest for Current Offering and Strategy).

Atlan enjoys deep integrations and partnerships with best-of-breed solutions across the modern data stack. Plus, Atlan already enjoys the love and confidence of some of the best data teams in the world, including WeWork, Postman, Monster, North American Bancard, and Ralph Lauren.

If you are looking for an enterprise data catalog for your team — Book a demo with Atlan.

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