Updated September 27th, 2024

Data Governance Tools Comparison: How to Select the Best in 2024?

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Gartner predicts that by the end of 2024, 75% of the global population will have privacy regulations covering its personal data. This along with factors such as data security, quality, and increasing data quantity have spawned a growing need for data governance tools.

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Here’s how to make sense of the market and whether you’re using the right tools for your needs.

Table of contents #

  1. The foundation of a good data governance tools comparison
  2. Challenges in data governance tools comparison
  3. How to make a data governance tools comparison
  4. Conclusion
  5. Related reads

The foundation of a good data governance tools comparison #

Without adequate data governance, an organization is limited in what it can do with its data.

As data regulations expand, compliance poses a serious challenge to businesses. According to Flexera’s 2023 state of the cloud report, 70% of all businesses rate compliance as a top concern with moving their workloads to the cloud.

Data quality is also a point of concern. Without consistent quality standards, data ecosystems can become too complex, and analyses can become inaccurate.According to Gartner, poor data quality costs organizations $12.9M annually.

With data governance tools, an organization can handle these challenges, and more. Here are the major features of good data governance:

Compliance #


Regulations like the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) mean data privacy and compliance are more important than ever. Good data governance tools automate compliance mechanisms ensuring that new data and users adhere to regulations, thus avoiding expensive fines.

Data quality #


All data systems need to focus on data quality to ensure accurate analysis. Good data governance tools continuously and automatically check data quality - checking for accuracy, timeliness, consistency, etc. - so that new and existing data are correct, consistent, and useful.

Data management #


Having a single source of truth is crucial for organizations to avoid fragmented perspectives, data silos, and redundant storage. Good data governance manages these conflicts, ensuring that data is consistent, reliable, and up-to-date.

Data cataloging #


Cataloging data enhances an organization’s ability to find, understand, and utilize data assets across the company, regardless of where they’re stored… Good data governance tools ensure accurate and consistent metadata, increasing the analytic leverage that data provides.

Data security #


Data breaches are a significant risk to any organization, potentially resulting in direct losses, fines, and brand damage. Good data governance comes with strong security features that protect your data, minimizing this risk. In particular, they ensure that access to data is regulated with role-based access controls and that sensitive customer data is hidden from unauthorized users.

Challenges in data governance tools comparison #

Data governance tools, then, are critical for an organization’s modern data system. But how can we perform a data governance tools comparison? This is a complicated question for a few reasons.

Firstly, “data governance” is not just one thing. As we have seen, it spans a large array of features from compliance to discoverability to quality. Evaluating a particular tool means taking into account all these dimensions.

Furthermore, each organization will have different needs and priorities regarding its data. One size doesn’t fit all. Your organization will need to think through its data governance strategy, prioritize use cases, and then map them to the features that matter most.

There will also be a growing number of choices at different stages of maturity. A business may add specific tools to their existing framework, or leverage features from a suite that doesn’t cover all use cases. Evaluating these options again and again can be a chore.

Finally, evaluating the budget for data governance poses its own challenges. Data governance extends beyond just the purchase of the tools themselves, it also includes setup time, training, integration effort, and maintenance. Comparing these aspects requires careful consideration.

How to make a data governance tools comparison #

In other words, a data governance tools comparison can be complicated. Breaking the comparison into categories helps focus your investigation.

Here is a list of important data governance tools comparison categories:

  • Data governance and metadata management
  • Automated data quality checks and statistics
  • Automated data lineage
  • Sensitive data & policy management
  • Collaboration & workflow management
  • Driving innovation

Let’s look in detail at each category, its use cases, and the features to look for during a data governance tools comparison that deliver on it.

Data governance and metadata management #


Effective data governance tools need to support manual and automatic importing of metadata. They also need to automatically enforce data policies.

Features to look for:


Adding sensitivity classifications to protect Personally Identifiable Information

Adding business context to improve understanding of data

Creating automated tasks that identify and classify sensitive data with little code

Dashboards to find, review, classify, and edit metadata

Intelligent automation that identifies data based on criteria and applies transformations

Data policy definition tools that allow authoring, testing, and deploying data rules

Automated data quality checks and statistics #


Data governance tools must measure data quality to ensure that data is correct, complete, and usable for decision-making.

Features to look for:


Out-of-the-box checks that cover common data quality criteria

Customizing data quality rules via intuitive tooling, SQL statements, or other scripting (e.g. Python or Javascript)

Calculating an overall quality score for data quality based on custom rules

Creating data quality dashboards to track data quality

Scheduling automatic data quality reports

Automated alerts when there are data quality issues

Automated data lineage #


Data governance tools need to track data lineage so that you can tell where data comes from, who owns it, and when it was last updated.

Features to look for:


The ability to schedule scans of lineage data and metadata

Automated and manual data lineage tracking that supports manual uploads (e.g. Excel or CSV files)

A zoomable visual data lineage map that shows relationships between data producers and consumers

The ability to see data relationships in graphical and textual representations.

Column-level data lineage that shows how column structure and data change over time

Impact analysis integration with tools like GitHub to catch any lineage issues with a transformation

Policy & sensitive data management #


A data governance system needs to manage data access to ensure users can only access data they have the right to access.

Features to look for:

Role-based access control (RBAC) to control data access

Masking sensitive information from authorized individuals

Defining, previewing, rolling out, and monitoring data policies and access rules

The ability to define different policies per rule (restrict, redact, obfuscate, substitute)

Policy-enforced persona-based rules that control data access based on how it is used

Role-by-role rule definition that automatically propagates across the system following data lineage

Collaboration & workflow management #


Data governance should enable users to find data and work together, integrating with the collaboration tools they already use.

Features to look for:

Data workflow creation

Monitoring and measuring the quality of data workflows

Displaying who is using what data in the system

Finding data in current productivity tools (e.g. Slack or Teams)

Embedded collaboration tools for discussing and reporting issues

Metrics dashboard that displays data usage and governance platform adoption rates

Driving innovation #


Ultimately, the point of a data governance tool is to allow for higher-quality data products to be created in less time.

Features to look for:


An AI recommendation engine that suggests metadata, documentation, data policy, and data rules.

AI assistants that help create SQL queries

AI that mines data governance structure for business insights

Automated updates and tracking of data lineage and metadata as data changes

Support for active metadata to automatically update and optimize the data system

An open API structure that allows the governance platform to be extended, enhanced, and integrated with internal systems

Conclusion #

A data governance tools comparison is useful whether you’re selecting a tool for the first time or evaluating your existing options.

You may have at least some of the data governance tooling listed above already in place. If so, use our data governance tools comparison to review what you have as well as what you’re missing. Chances are that you can drive even greater value by taking a more comprehensive, automated approach to data governance.

See something on this list that your current data governance software doesn’t cover? Or looking for data governance tools for the first time? If so, consider Atlan. With Atlan, you can implement a data governance system with features that handle data policies and rules, strengthen data security, automate data lineage tracking and data quality checks, and more.

Interested? Contact Atlan today to discuss your needs.

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