How to Choose the Right Data Governance Tools

In a data-abundant world, how organizations manage their data is now much more important than the amount of data they have to manage. Similarly, data quality now far surpasses data quantity since data-driven businesses’ needs continue to grow more complex, nuanced, and complicated.

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This is also true because of data governance—namely, how organizations must handle enterprise data to ensure that it meets global, regional, and local regulations. These regulations are as comprehensive as they are essential to stay on the right side of fines, security issues, and reputational damage that could result from falling down on data compliance.

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Thankfully, data governance tools evolve at an ever-increasing pace to help data leaders meet their ongoing compliance conundrums.

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But as more tools with more features hit the market, it becomes increasingly important for data leaders to understand what sets each apart and how best to leverage a tool—or a combination of tools—to meet their organization’s needs.

To make this possible, the below article will cover everything from the role these tools should play internally, some initial tools worth considering, and a simple strategy for making the tool vetting process as productive as possible.

Data governance tools: Key functions to prioritize

Today’s data governance tools must do more than ensure compliance—they also need to help data leaders strike a careful balance between enabling innovation and maintaining control over data access. As organizations become more reliant on data to drive business decisions, the frameworks that support data governance must evolve to keep pace, not only with expanding regulations across industries and regions but also with emerging technologies.

AI and machine learning (ML) add new layers of complexity to this landscape. While the core risks of regulatory noncompliance remain largely unchanged, organizations now face additional pressure to explore and adopt these transformative technologies—or risk falling behind.

This puts data leaders in a challenging position since they must ensure that governance frameworks are robust enough to meet external demands while remaining flexible enough to support internal growth. But choosing the right combination of governance tools can make all the difference here. They offer a strategic edge and, in many cases, preserve an organization’s competitive standing altogether.

Though these tools are becoming more advanced, most still function by supporting data governance strategy in these foundational ways:

Data quality management

Data quality is, overall, the foundation of any business where success or failure rests on data-driven decision-making. Therefore, organizational data needs to be optimally accurate, consistent, and reliable.

A major feature of data governance tools is their ability to automate processes like data validation, cleansing, and monitoring to maintain high data quality throughout its lifecycle. Here are some key practices that these tools typically help with:

• Implementing automated quality checks within data pipelines
• Establishing consistent standards for data validation and cleansing
• Monitoring data quality metrics continuously to identify and resolve issues proactively

Security and privacy

Data security and privacy enforcement determine whether or not an organization stays in compliance within its regulatory environment. But with the help of governance tools, DatOps teams can integrate governance practices like access controls, encryption, and audit trails.

As a result, data consumers in an organization only use the data that they have access to, and data remains encrypted when not in use. Audit trails also help teams demonstrate that sensitive data stays safe from breaches or other nefarious use while adhering to all legal requirements. 

Below are some additional capabilities of governance tools:

• Monitoring and modifying access controls as necessary to limit data access based on roles and permissions
• Embedding privacy-by-design principles into systems and workflows
• Maintaining detailed audit logs for transparency and regulatory compliance

Ownership, accountability, and collaboration

Effective data governance relies as much on consistency as it does on comprehensiveness. Because of this, data governance tools support clear documentation of data ownership and accountability across the organization. 

Modern governance tools should also support governance-specific roles like data stewards (who are responsible for enforcing policies) and data owners (who are accountable for specific datasets). This means that governance tools also contribute to the following:

• A shared responsibility culture for governance and treating data as a product
• Data silo mitigation that promotes collaboration and innovation across functions
• Overall data transparency and an understanding of data lineage through available metadata management tools

While there is certainly more nuance at play, it’s helpful to keep these key aspects of data governance in mind when choosing a governance tool. They remind us that data governance is a strategic framework as much as it is an essential data-related business function. 

Moreover, when vetting tools—especially those with innovative capabilities—you should always prioritize outcomes over features. 

7 top data governance tools to consider in 2025

At the time of writing, there are currently dozens of data governance solutions on the market globally, ranging from niche tools to enterprise-grade platforms.

For data leaders who are wondering which tools they should invest in first (or want help determining how to complement their existing tools), that can make for an intimidating wall of research to begin scaling.

The below seven tools serve as an initial consideration set, with a helpful comparison of pros, cons, and pricing models to create a valuable baseline. That way, you can decide what types of tools are more or less worth learning about in the near term.

Without further ado, let’s jump right in:

1. Gable.ai

Gable.ai, an emerging data contracts platform, is increasingly impacting the data governance space. This one-of-a-kind tool maps data flow from source to consumption and enforces governance policies at the source code level. In doing so, Gable tracks data from its origins to downstream users, ensuring consistency and compliance across organizations of any size.

Pros:

• Integrates with GitHub and GitLab for code-level governance
• Enables automated data lineage and dependency tracking
• Reduces data pipeline breakage via contract enforcement

Cons:

• This is an early-stage vendor, so some features are still in development.
• Its shift-left approach may feel less intuitive for business users.

Pricing model: Customized enterprise pricing

2. Collibra

Collibra is a business-friendly platform that emphasizes metadata management, cataloging, and collaborative governance.

Data leaders can use Collibra to create and enforce their governance policies, maximize data asset visibility, automatically classify sensitive information like PII, and track data flow and transformation across pipelines.

Pros:

• Supports flexible, incremental implementation of data governance processes
• Provides an intuitive interface for non-technical users
• Offers strong regulatory compliance features

Cons:

• The platform may require customization for complex workflows.
• Large-scale deployments require more of an investment compared to other tools.

Pricing model: Tiered subscription pricing

3. Alation

Alation is another metadata-driven platform that emphasizes combining data cataloging with governance workflows. This approach aims to elevate both data stewardship and governance enforcement. As a result, Alation fosters data-driven cultures and clear dataset ownership within organizations.

Pros:

• Specific features that aid those in data stewardship roles
• Robust search and discovery for decentralized teams
• Mature integration with popular business intelligence (BI) tools

Cons:

• This tool has a steeper learning curve for policy configuration compared to other tools.
• It also has limited AI automation compared to other governance tools on the market.

Pricing model: Tiered subscription pricing

4. Ataccama ONE

Ataccama bills itself as an AI-powered platform that focuses on data quality, master data management, and data governance. It supports scalable governance by automating related processes, enabling regulatory compliance, and optimizing high data quality.

Pros:

• Scalable for large enterprises
• AI automation for data profiling and anomaly detection
• Prebuilt connectors for cloud and legacy systems

Cons:

• This tool could be overkill for smaller teams and organizations.
• Its complex setup requires dedicated IT support.

Pricing model: Tiered subscription pricing

5. Velotix

Velotix is an AI-driven policy engine that provides data teams with real-time access control and compliance. As a platform, it focuses on supporting scalable governance by dynamically adapting policies while maintaining data security and regulatory compliance.

Pros:

• Dynamic governance for hybrid cloud environments (like auto-updating policies per GDPR changes)
• Federated permissions across platforms like Snowflake and Kafka
• Simplified cross-regulatory compliance for teams

Cons:

• Velotix is a newer vendor with a growing client base.
• It places less emphasis on data cataloging than other tools.

Pricing model: Customized enterprise pricing

6. Atlan

Atlan’s platform combines AI automation, metadata management, and tools for internal collaboration. It can be ideal for organizations that need to prioritize collaborative governance and automated metadata enrichment since it also specializes in real-policy enforcement, data lineage visualization, and integrations with cloud platforms like Snowflake.

Pros:

• AI-driven metadata tagging and glossary creation
• Seamless bi-directional sync with Snowflake for tag management
• An intuitive interface for both business and technical users

Cons:

• The platform comes at a higher cost if you want advanced AI and ML features.
• There are limited task management tools for DataOps teams.
• Atlan users see a steep learning curve for custom workflow configuration.

Pricing model: Tiered subscription pricing

7. Informatica Axon

As part of Informatica’s larger data management ecosystem, Axon offers organizations a comprehensive, end-to-end governance solution that focuses on automating policy enforcement and aligning business teams with IT. It could be ideal for enterprises that are in need of unified governance across data quality, compliance, and lineage. 

The platform also integrates with related tools like Informatica Cloud Data Integration for seamless workflows.

Pros:

• Centralized policy management with role-based access control
• Automated lineage tracking and incident resolution
• Prebuilt connectors for hybrid cloud environments

Cons:

• This tool could be redundant if you use it with other Informatica tools.
• It requires significant IT resources for setup.

Pricing: Flexible, consumption-based and contract pricing

Key considerations for evaluating data governance tools

The tools above offer a strong contextual starting point for evaluating data governance solutions. To build on that foundation, the following checklist will help you ground your vetting process in your organization’s specific needs and challenges:

Define all primary requirements

• Document existing data governance programs and initiatives (like master data management and self-service analytics).
• Familiarize yourself with specific data policies for your organization (such as access controls and retention rules).
• Ensure that you have a firm understanding of relevant data privacy regulations (GDPR, CCPA) and industry standards that apply to your organization.

Shortlist tools

• Filter candidates by cloud-based vs. on-premise compatibility—this is critical for hybrid infrastructure.
• Prioritize tools with native data integration connectors (like Snowflake and Databricks).

Evaluate scalability

• Confirm that the data governance platform supports multi-cloud policy enforcement.
• Assess how pricing scales with data volume and user growth.

Test user experience

• Validate dashboards for monitoring policy compliance and lineage tracking.
• Ensure alignment with existing data governance processes (like approval workflows).

Assess vendor viability

• Thoroughly review implementation support for hybrid deployments.
• Verify audit logs for data privacy adherence.

Negotiate flexibility

• Request sandbox environments to simulate real-world initiatives.
• Ensure that any SLAs will cover uptime for cloud-based tools.

Choosing data governance tools: Focus on fit, not just features

When it comes to getting data governance right for your organization, it’s far too easy to fall into the “best tool” trap—our too-human tendency to focus on selecting tools with the best ratings or those that offer the most compelling set of features. But you must resist this because it quickly blinds you to how best to solve your governance challenges holistically. 

Instead, focus on workflows and how potential tools will integrate into the day-to-day of your data management teams. To be effective in the long term, data governance tools need to function as an extension of your organization’s data environment, not a dictator of it. Because of this, you need to gather good information so you can make the best possible decisions. 

If you’d like to learn more about how Gable.ai and data contracts can guide your governance, visit Gable.ai and sign up for the product waitlist today.

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