Be the architect of your AI-driven future at our digital event "Blueprints for Generative AI."

NEW Tool:

Use generative AI to learn more about

Product Launch: has officially leveled up its integration with Snowflake’s new data quality capabilities

PRODUCT LAUNCH: enables trusted conversations with your company’s data and knowledge with the AI Context Engine™


Accelerate adoption of AI with the AI Context Engine™️, now generally available

Upcoming Digital Event

Be the architect of your AI-driven future at "Blueprints for Generative AI." 

View all webinars

Top Data Governance Tool Capabilities for 2024

Discover the best data governance tools and how their key capabilities can help you automate your governance workflows and ensure compliance.


Data governance is the process of managing and controlling data collection, storage, usage, and sharing across an organization. In this process, organizations create a data governance strategy where they set policies, procedures, and guidelines to ensure that data is:

  • Accurate

  • Consistent

  • Secure

  • Accessible

Modularized datasets require administrative monitoring and access controls, which cannot be done effectively without advanced tools. That’s why we need the best data governance tools to assist us in data management.

What is a data governance tool?

A data governance tool helps organizations manage and control their data quickly and precisely. It provides a centralized platform to enforce enterprise data catalog policies, standards, and processes. Some of the most important data governance tool capabilities are:

  • Data cataloging

  • Metadata management

  • Data quality monitoring

  • Access control

  • Policy enforcement

  • Collaboration features for data stewards

Want to know more about these key capabilities? Read on.

Top 5 data governance tools - and their unique capabilities

Here are the 5 data governance tools currently providing the best features and capabilities for data management:

1. is the only agile data governance tool built on a knowledge graph architecture based on AI and ML. It models data as interconnected entities and shows how datasets relate to concepts, objects, and sources through its data visualization features. As a result, finding, understanding, and governing data becomes exponentially easier than any other data governance tool.

IT administrators consistently struggle to maintain compliance with their datasets, but this job can become automated with’s compliance features. These AI-driven governance features enable data teams to create flexible workflows and monitor them collaboratively. 

This improves data teams' productivity and makes metadata management, data classification (by levels of sensitivity and value), and collaboration across data stakeholders much easier.

Key capabilities

What sets apart are its core capabilities and here’s a detailed overview of each of these:

  • Provides a chat-like interface for business users to find the information they need from unstructured data

  • Streamlines data governance by automating data-related tasks

  • Uses an AI-based knowledge graph architecture to show datasets, metadata, tables, documents, etc., as objects on a graph—showing how everything is interconnected

  • Improves DataOps by surfacing important data context within different analytics tools using Hoots and Sentry Bots

  • Eradicate data silos with advanced data cataloging capabilities that provide a unified view of data stands out as a top-tier solution because of its AI-integration capabilities. 

Top use cases has more than 2 million + users, and organizations have used it for multiple uses. So here are some of its top use cases that are worth noting:

View data with knowledge graph architecture helped Prologis’s digital transformation journey using its knowledge graph technology. With’s knowledge graph architecture, Prologis now efficiently organizes, clarifies, and makes its historical data warehouse accessible for self-service in the cloud. By doing so, they now make better decisions through insights obtained from data architecture.

Bridge relevance and reusability gaps curates your data from different sources and prepares them for analysis according to your relevant query. For example, The Associated Press used to provide self-service data to over 300 customers. With’s governance features, they doubled data production and usage.

Organizations can take this approach to highlight and reuse the most valuable data assets effectively.

Increase impact through analysis reproduction and reuse

Organizations need data-driven decision-making to stay ahead of the competition and use predictive analysis techniques to forecast outcomes accurately. makes it possible by providing a unified system where different operations teams can collaborate in an agile and iterative manner.

Aceable (a tech startup) used to eliminate bottlenecks in revenue recognition reporting, which helped them make strategic decisions. pros & cons

Here's what’s customers think of its platform:


  • Gives a single platform to access multiple datasets and makes data management easier

  • Has tools for automated data governance that provide efficient regulatory compliance

  • Provides a secure data catalog within a cloud-native SaaS platform so you don’t bear the costs of an on-premise system

  • The platform's architecture and design are user-friendly

  • Knowledge Graph architecture provides context and improves Large Language Models (LLMs) outputs


  • Need more flexible plans that allow unlimited users to facilitate broader organizational adoption

  • Graphs and charts could be improved for better data visualization

Ready to get these benefits? Book a demo with today.

2. Atlan

Atlan is a third-generation data governance solution that addresses the challenges faced by data teams. It's an open-source, API-based architecture that provides quick and agile solutions for businesses and data teams.

Another major benefit of Atlan is that it unifies metadata collected from different sources, such as Databricks, Snowflake, Tableau, Postgres, and Looker, into a single source. With this feature, organizations can easily review their whole data ecosystem through a single glance.

Key capabilities

  • Atlan’s data discovery and cataloging features make data more accessible and actionable for all stakeholders

  • Gives personalized access policies based on user roles, such as analysts, engineers, and consultants, so every user has the appropriate access and permissions

  • Easy integration with different BI tools shows data lineage at a granular level which makes governing easier

  • Data asset 360° feature connects resources such as Slack discussions, GitHub links, and Confluence documents related to a data asset in a single place

Top use cases

Here’s how organizations can use Atlan for their data problems:

Data mesh strategy

Organizations can use Atlan's data mesh strategy to implement a decentralized data architecture and management approach. Your team can also own and govern data products within the business domains and publish them to a centralized catalog for discoverability and self-service access.

Data stack optimization

Recruitment companies can improve the usability of their modern data stack with Atlan’s automated column-level lineage. Its popularity metrics will help them assess the utility of their data assets. This will decrease unused data assets, streamline data infrastructure, and optimize business resource utilization.

Atlan pros & cons

Let’s see what customers think about the pros and cons of Atlan according to their reviews:


  • User interface helps in quick onboarding for new users and simplifies navigation

  • Metadata management capabilities connect to various data lakes and warehouses to fetch information like usage, lineage, and table activities

  • Provides lineage tracking between data platforms, such as drill-down or column-level lineage, which is crucial for investigations

  • With its chrome extension, users can access metadata and other information within their preferred tools without switching contexts


  • Documentation updates are inconsistent

  • Depending on a company's maturity level, too many Atlan features can overwhelm new users

  • Lacks basic text editing functionalities in the Glossary feature, like the "Undo" function

  • Role-based access mechanisms with access to multiple are confusing

3. Microsoft Purview

Microsoft Purview is a data governance solution that provides a centralized interface for data management. It integrates capabilities previously found in Azure Purview and Microsoft 365 compliance solutions. This integration addresses common challenges such as data fragmentation, visibility issues, and the evolving roles within IT management.

Key capabilities

  • Automatically discovers and classifies data across an organization's entire data setup

  • Uses metadata, machine learning, and other advanced technologies to identify sensitive data and assess potential risks

  • Safeguards sensitive data with tools to classify data, encrypt it and apply comprehensive access controls

  • Uses advanced analytics and reporting to provide invaluable insights into data usage patterns 

Top use cases

Organizations can use Microsoft Purview in many different ways and here are some of its most common use cases: 

Compliance and fraud detection in finance

With its advanced data discovery and classification features, Purview can help financial institutions or banks track and manage sensitive customer information across their databases. It also identifies anomalies that indicate fraudulent activities and alerts organizations to take preventive measures. 

Patient data management in healthcare

The healthcare industry can also use Purview to manage and secure vast patient data, such as electronic health records (EHRs) and medical imaging files. Purview will catalog these diverse data sources using its data management and cataloging features to facilitate better clinical decision-making and support data-driven research. 

Purview pros and cons

Here are some pros and cons of Purview according to reviews from Purview customers


  • Provides a single pane of glass management for visibility and governance of scattered digital data

  • Monitors stored data in multiple platforms from a single interface

  • Delivers data protection across multi-cloud and multi-platform environments

  • Provides pre-made templates and flexibility in creating Data Loss Protection Policies


  • Primarily suitable for MS applications

  • Continuous scanning and classification of data can impose performance overhead on scanned systems

  • The API needs improvement when connecting to non-Microsoft API sources

4. Alation

Alation is a data catalog and intelligence platform that allows organizations to drive data discovery, governance, and analytics. It automatically extracts metadata, profiles, and insights about enterprises' data assets across databases using machine learning and natural language processing. 

This metadata enriches the data catalog and empowers users to find relevant, trusted data sources, critical context around their relationships, and lineage.

Key capabilities

  • Alation's data catalog system increases team contribution to turn high-quality data into useful insights

  • Finds data based on specific keywords, business terms, or natural language queries without requiring any technical knowledge

  • Makes selecting appropriate datasets for analysis easier

  • Its behavioral intelligence learns from user interactions to recommend best practices and signal data quality concerns

Top use cases 

Here are some of the most widely known use cases of Alation:

Data discovery and collaboration

In industries like e-commerce, big data is spread across different systems, which makes it difficult to search for relevant data and extract information. To address this, e-commerce brands can use Alation’s data discoverability feature to search for data using its Behavioral Analysis Engine. Doing so will quickly locate relevant datasets, understand their usage, and make informed decisions.

Streamlining data governance and stewardship

Alation simplifies compliance reporting and audit trails for regulated industries like finance and healthcare. Its centralized governance documents data lineage, certification status, and access controls across the data lifecycle. This way, these organizations can confidently demonstrate adherence to external governance policies like GDPR and HIPAA.

Alation pros & cons

We analyzed reviews from G2 to see what customers think about Alation:


  • Powerful search features enable users to locate relevant information easily

  • Enables knowledge sharing among team members to create a collaborative working environment

  • Helps organizations catalog and manage customer and partner data

  • Provides a unified source of truth for all data references


  • Customizing data governance policies or integrating with external tools is complex

  • Extensive documentation and support resources make finding specific information or troubleshooting issues difficult

  • Cost per data steward and additional charges for enabling column-level data lineage is high 

5. Collibra

Collibra is another data governance tool that simplifies data discovery for users across various sectors. It uses artificial intelligence to assess data for accuracy and completeness and identify issues that impact data reliability.

Key capabilities

  • Provides a centralized policy management system to create, update, and review data policies regularly

  • Identifies missing records or broken relationships in vast datasets with reporting and analysis

  • Improves collaboration among cross-functional teams by providing clear definitions of regulations and compliance standards

See how stacks up against Collibra

Top use cases

Here are Collibra’s three common use cases:

Data lake management

Data lakes are reservoirs of raw and unstructured data. Organizations can use Collibra to prevent these lakes from becoming murky swamps in their data systems. Its AI-powered governance and cataloging features ensure the data stored is relevant, compliant, and aligned with business glossaries.

Report certification

Organizations can use Collibra for report certification to reduce redundant and incorrect reports. In this process, an owner is established for critical data assets, and data lineage is documented to trace the assets back to their source. 

Data quality rules are defined as standards and adherence to these standards is measured as data flows through the organization. Through this process, an authoritative body can certify trustworthy reports. 

Collibra pros & cons

Here's what customers had to say on G2 about Collibra's pros and cons:


  • Quick and hassle-free implementation process

  • Solves critical challenges in managing decentralized data assets so that trusted data sources are more discoverable

  • Easily create personalized workflows for users

  • Create a centralized repository for metadata to document, discover, and understand data resources 


  • Improvements are needed in the search functionality 

  • Lack of support when creating custom connections for data lineage

Can a data governance tool automate workflows?

Data governance tools streamline data management by automating data access and stewardship workflows. 

Traditionally, granting access to datasets involved manual work through an administrative interface. However, data governance tools automate this by pre-defining access levels based on the rules set in the data governance framework. This framework outlines how data should be accessed, used, and protected. 

Data governance tools allow you to define these rules and processes within the software. Doing so helps data stewards automate the following tasks:

  • Route data requests to the appropriate approvers and notify the requester of the decision

  • Allows users to submit and review proposed changes to datasets electronically

  • Maintain accurate and up-to-date metadata to understand and utilize datasets effectively

What are the benefits of rolling out a data governance tool within your organization?

Data has become the lifeblood of modern enterprises, but many organizations struggle with ensuring its trustworthiness. You can increase this trust through a centralized governance tool that establishes proper data management practices, with the following benefits: 

Increased efficiency and productivity

Data governance tools remove manual effort from common administrative tasks by automating these workflows and freeing valuable time for data stewards. However, tasks requiring multi-approval workflows automatically notify everyone involved to eliminate delays and bottlenecks associated with manual approvals.

Better visibility and data control

Data governance tools provide a single pane of glass view into data access and usage across the organization. This way, you can see who is accessing what data and for what purpose. You can configure unique rules based on your organization's specific policies and requirements. 

Simplified decision-making based on data

Data governance tools can generate insights through a knowledge graph by collecting data on data usage. This shifts data governance from a checklist of tasks to a more proactive approach. 

What should you look for in a data governance tool?

Choosing the right data governance tool is tricky—especially when you’ve got so many options. Here are the evaluation criteria to help you find the right data governance tool:

  • Must have the ability to handle complex approval flows 

  • Flexible enough to add different access levels for different types of users in an organization 

  • Gives clear visibility into how data is accessed and used across the organization

  • Store and retrieve insights on access patterns to the data

  • Provides options to build customized dashboards to use data analytics for business decisions

  • Adept at updating metadata so it's always accurate and updated with important details available at a glance

How can you decide whether to buy or build a data governance tool?

Organizations face a critical decision about data governance: should they buy an off-the-shelf solution or build a custom tool in-house? The answer depends on your specific requirements. 

For organizations with standard data compliance requirements like GDPR, a fully featured commercial product may be the best choice because it can handle complex data governance workflows, including multiple approval chains and intricate access controls.

However, building an in-house tool would be better if your organization deals with highly sensitive data, such as health records or personally identifiable information. That’s because custom tools can be tailored to meet your exact data privacy and access requirements.

If your data governance needs are relatively straightforward, with few data sources shared across departments, in such cases, you can build a light custom software with limited functionality.

But if you want to avoid this hassle, book a demo with today and see how it can help you.

chat with archie icon