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The 10 Best Data Governance Tools

Considering a data governance tool for your business? Dive into the best data governance tools on the market, to find the best one to elevate your data management strategy. 


Data governance refers to the processes and controls that ensure an organization's data assets are properly managed and utilized across the enterprise. 

It matters for all organizations because effective data governance maintains data quality by ensuring it complies with rules and regulations. By establishing proper data governance, companies can maximize the value derived from their data assets.

In this article, we’ll explore the top 10 data governance tools.

What is data governance?

Data governance is a framework that checks your data’s accuracy by setting procedures for managing it throughout its lifecycle. With proper governance, businesses can remain compliant and make better decisions as they become increasingly data-driven.

10 best tools for data governance

Since agile data governance tools can improve your governance strategy, let’s look at some of the best tools in the market. is a one-stop solution for data governance. It uses knowledge graph architecture for data cataloging and discovery. Users can view all objects—metadata, tables, documents, and more—as interconnected entities on a graph with contextual results.

Its AI-based architecture allows companies to find and leverage relationships within data 10 times faster than manual work. With, you can automate time-consuming data governance tasks by creating flexible multi-step workflows and allowing everyone to search through data.

Simply put, makes sure that organizations deploy groundbreaking AI-powered data governance solutions to accelerate innovation for future success in a data-driven world.

Read more about the role of data governance tools in this growing data-driven world in the Dresner Advisory Services report.

Key Features

Here are the best features of that govern and provide full-proof security to your organization’s sensitive data:

  • Knowledge graph architecture: Shows all data objects as interconnected entities for deeper insights and relationships

  • Agile data governance: Reduces manual effort with streamlined and automated governance activities

  • Flexible workflows: Creates adaptable and multi-step workflows to meet evolving business needs

  • AI-ready data: Gives access to ready-to-use data for AI deployment with advanced AI-powered tools

  • Sensitive data discovery: Provides tools to identify and manage sensitive data to ensure compliance and security

  • Comprehensive data catalog: Gives centralized and organized catalog to manage all data assets effectively


We analyzed G2 reviews to determine how customers feel about’s capabilities, and whether they are satisfied with its data governance. One user noted that “was very helpful in data analysis and governance, data ops, and creation of any knowledge-related graphs.” Others believe that’s catalog “creates a unified body of knowledge” that anyone in the team can use. pros & cons


  • Can integrate data from multiple sources into a single platform

  • Uses a knowledge graph to provide actionable insights and data governance

  • Efficient team collaboration and metadata usage 

  • Supports third-party tool integration like Tableau and BI

  • Provides a single place to access diverse data types

  • Shares actionable insights for better data utilization


  • May be improved by additional pricing options with more flexible tiersdata

Pricing’s pricing is simple and customizable. Plans range from Essentials, to Standard, to Enterprise, to Enterprise+. Users can contact the team to customize a package specifically for their needs. 

Book a demo today to see what can do for your data governance needs. 


Informatica's data governance and cataloging solutions provide complete capabilities for managing and using data assets. They give you access to multiple features, like automated metadata management and intelligent data discovery. It also has a detailed data lineage tracking feature that makes it much easier to find the right data for analysis.

Key features

  • AI-powered data automation: Uses the CLAIRE AI and machine learning engine to automate data management tasks and scale operations

  • API lifecycle management: Applies privacy policies to APIs to ensure data protection and compliance with regulations

  • Data governance and privacy: Delivers data intelligence and reliable insights to maintain data consistency and protection

  • Unified data platform: Combines data governance and cataloging into a single tool to give automated insights


Saudi Airlines praised Informatica for its comprehensive AI-powered data governance and management capabilities. According to them, “Informatica Intelligent Data Management Cloud is the bridge” for their initiative of incorporating ChatGPT4-based personal assistants in the airlines globally.

On a less positive note, an Informatica user on G2 said, “It’s challenging to extract insights from data directly within the platform.”

Informatica pros & cons


  • Data replication and migration across many data sources and formats for data management flexibility

  • Includes data profiling and cleaning tools that provide data accuracy and consistency

  • Provides comprehensive governance tools for policy management to help organizations maintain compliance

  • Suitable for organizations that have already invested in the Informatica ecosystem


  • Requires hardware and infrastructure, which increases overhead costs

  • Lacks powerful data collaboration and discovery capabilities, which makes it challenging to extract insights directly within the platform


Informatica claims the following benefits within its pricing model:

  • Volume-based pricing

  • Pay for what you need

  • AI-powered optimization

  • Lower cost of ownership

However, they don’t have transparent pricing on their platform. Contact their Sales team for specific pricing details. 


Collibra is another data governance tool that uses AI to simplify data discovery and accessibility. It provides different features covering data accuracy and consistency. It also focuses on identifying anomalies that could impact data reliability.

Key features

  • Centralized policy management: Allows users to create and review data governance policies 

  • Data lake management: Data lake governance and cataloging to prevent disorganization

  • Report certification: Data asset ownership allows you to document data lineage for trustworthy reports

  • Embedded privacy: Incorporates privacy into all data activities to reduce compliance costs


We analyzed G2 reviews for IBM Cloud Pak, which contains IBM’s data governance features. 

Here’s what one of IBM’s customers said: “I just really love the way how IBM Cp4D has a bunch of features in one tool. It does data integration/data ingestion, data virtualization, data fabric, and dealing with ML aLgos, etc. It's really flexible to use for data integration connecting to a different number of data sources. It's way advanced compared to the tools in the market at the moment and very much user friendly.

On the other hand, one user wrote, “It’s a bit complex since it's heavily loaded with so many features. It will take time for the user to understand and implement solutions.”

Collibra pros & cons


  • Simplifies data governance with centralized catalogs and metadata management

  • Improves data quality and accuracy through profiling and quality assessment

  • Offers customizable workflows and data dictionaries that fit specific organizational needs

  • Provides advanced cataloging and quality assessment features


  • Complex UI/UX with a steep learning curve since there is a huge range of tools available

  • Requires extra resources for implementation and maintenance, which can be expensive for startups


Collibra’s official pricing is not on their platform. However, data from the AWS marketplace shows that Collibra's Data Intelligence Cloud platform subscription costs are as follows:

  • $170,000 for 12 months

  • $340,000 for 24 months

  • $510,000 for 36 months

IBM data governance

IBM Data Governance provides data discovery and lineage tracking tools to ensure that an organization’s data remains accurate and trustworthy. A key component of this platform is the Watson Knowledge Catalog, which helps organize and access data assets whenever needed. It uses AI to automate data governance processes and simplify data management.

Key features

  • Data governance automation: Streamlines data governance processes and reduces manual efforts of the team members

  • Data privacy and compliance: Identifies sensitive data and enforces data protection rules to protect it from compliance breaches and violations of role-based access

  • Data products management: Simplifies data stewardship and metadata management with self-service data access and simpler product lifecycles

  • Data quality management: Monitors and maintains data quality to support reliable analytics and insights


We analyzed G2 reviews for IBM Cloud Pak, which contains IBM’s data governance features. 

Here’s what one of IBM’s customers said: “I just really love the way how IBM Cp4D has a bunch of features in one tool. It does data integration/data ingestion, data virtualization, data fabric, and dealing with ML aLgos, etc. It's really flexible to use for data integration connecting to a different number of data sources. It's way advanced compared to the tools in the market at the moment and very much user friendly.

On the other hand, one user wrote, “It’s a bit complex since it's heavily loaded with so many features. It will take time for the user to understand and implement solutions.”

Pros and cons


  • Automates onboarding, offboarding, and mid-lifecycle changes for organizations which reduces the workload for IT teams

  • Automates security policies in a multi-SaaS environment to stay compliant with regulations

  • Integrates with various applications, including Google Workspace, Microsoft 365, Slack, and more

  • Unifies different SaaS environments to simplify performing actions on data for data teams


  • Requires significant setup and configuration efforts, which poses a steep learning curve for new users

  • Expensive for smaller organizations, with additional costs for customization and maintenance


IBM Cloud Pak offers a free version in a few regions. They also provide three general pricing tiers: 

  • Pay as you go

  • Subscription

  • PayGo Commit 

They don't publish the quantitative details of these pricing tiers. Contact their sales team to get the specifics of how they price their data governance features.

Microsoft Purview

Microsoft Purview is a unified data governance service that helps organizations gain visibility and control over their data estate across on-premises and SaaS environments. With Purview, organizations can automate the cataloging of data assets and gain insights into how data is used and accessed across the organization.

Key features

  • Unified data map: Centralizes data assets and their relationships for effective governance

  • Automated metadata management: Manages metadata from hybrid sources automatically

  • Data catalog: Makes data discoverable using business and technical search terms

  • Sensitive data insights: Provides metrics and status updates on data management activities


Here’s what one of Purview’s customers said on G2: "Azure Purview is the best application that automatically discovers and classifies data without moving to other tools. All the metadata can be accessed, which is the best thing; it increases the efficiency of employees.”

However, another user also pointed out its limited connectivity and costly pricing, saying “The API needs some improvement when connecting to non-Microsoft API sources.”

Microsoft Purview pros and cons


  • Integrates with Microsoft services like M365 and Azure

  • Has a user-friendly interface that is easy to implement

  • Provides data loss prevention features and pre-made templates for data protection policies

  • Provides a single pane of glass management for scattered data


  • Limited connectivity with non-Microsoft API sources

  • Continuous scanning can impose performance overhead


Purview offers a free version that you can try to understand its features. However, you must contact them to receive an exact quote based on your consumption and region.


OvalEdge is a data management platform that provides several data management tools, including governance and lineage tracking with role-based access controls. Within the tool, you can build data glossary where data stewards manage an org-wide business vocabulary. 

Key features

  • Automated metadata discovery: Automatically scans and indexes metadata from various data sources

  • Search and discovery: Allows users to search and discover data assets using business and technical terms

  • Data lineage: Tracks and visualizes data flow from source to destination to give transparency in data usage

  • Role-based access control: Gives access to only authorized users to protect sensitive data


One user on G2 wrote, “Earlier I spent 70% of my time looking for data and now with OvalEdge, I spend only 5% of my time in data search!” On a less positive note, another user pointed out the drawback that “it’s difficult to find PII in unstructured data using OvalEdge.” 

OvalEdge pros & cons


  • Centralizes all company data into a single repository to ease up data management

  • Automatically catalogs enterprise data assets to make data asset discovery easy

  • Provides a self-service collaborative experience for business teams so they can find and share data assets quickly 

  • Defines and applies data controls to enforce governance policies and verify compliance


  • The implementation can be complex 

  • Users face a steep learning curve, particularly those unfamiliar with data governance and cataloging tools


OvalEdge provides three pricing plans:

  • Essential plan: Starts at $15,600/year

  • Professional plan: Contact for pricing

  • Enterprise plan: Contact for pricing


Erwin's data governance solution makes data governance a shared responsibility across an organization, not just an IT function. It provides advanced AI-driven asset discovery and classification capabilities for business glossary management and data stewardship automation to assign business meaning to data. Erwin promotes a data-driven culture through self-service data discovery and collaboration capabilities. 

Key features

  • Data discovery and cataloging: Facilitates the cataloging of data elements to search and understand data easily

  • Quality and accessibility: Ensures that high-quality data is accessible to the right people at the right time, regardless of where it is stored or its format

  • Enterprise collaboration: Promotes strategic ongoing efforts and collaboration to operationalize data governance

  • Risk management: Helps limit risks by ensuring proper governance and understanding of data usage


An Erwin’s review on G2 noted, “I found Erwin Evolve to integrate well with other Erwin data modeler tools like data governance, data catalog, data lineage and data intelligence to provide us with a unified view of all our data assets, process and systems.” Another user specified minor issues, saying: “For large-scale databases it’s difficult to import diagrams to a single page and format them.”

Erwin pros and cons


  • Supports maintenance of entity relationships through simple drag-and-drop functionality

  • Has a vast number of connectors, enhancing its compatibility with various data sources and services

  • Automates repetitive and manual tasks to reduce errors and increase efficiency

  • Available as both a cloud-based and on-premise solution with flexibility based on organizational needs


  • Lacks a workflow management system for data model approval at different stages, which could streamline processes further

  • Requires added investment in IT resources for proper implementation and maintenance


Erwin’s features are available on a free trial basis. However, you need to request a full quote from the Sales team to fully understand pricing options based on your data governance needs.


Atlan is a third-generation data governance tool that addresses the modern challenges of data teams. Its open-source API-based architecture provides quick and agile data governance solutions to help organizations manage their data ecosystem efficiently.

Key features

  • Data discovery and cataloging: Centralizes metadata from various sources into a unified view for better accessibility

  • Personalized access policies: Allows role-based access controls tailored to user roles like analysts and engineers

  • Data mesh: Supports decentralized data architecture with centralized cataloging for discoverability

  • Data stack optimization: Provides automated column-level lineage and popularity metrics to optimize data asset utility


One Atlan customer on G2 said, “I am most impressed by Atlan's ability to demystify complex data sets and provide actionable insights. It was very easy to set up, and we quickly found it useful on a daily basis.”

On the other hand, another customer said: “As the product is complex and dynamic (improvements keep on coming), there were a few cases where it did not work perfectly.”

Atlan pros and cons


  • Provides metadata insights on data usage, lineage, and table activities

  • Allows users to access metadata within their preferred tools without switching contexts

  • Provides customizable features for policies and reporting

  • Centralizes data documentation and definitions to make data easily discoverable


  • Requires IT resources for optimal use

  • Access mechanisms can be confusing, especially with multiple roles


Atlan keeps its pricing information private. However, multiple sources report that they generally provide three pricing plans:

  • Starter

  • Premier

  • Enterprise

This information subject to change, and you may have to contact their team to get the pricing option that would suit your business needs. 


Alation is a widely-used data governance platform that streamlines and automates data management across complex enterprises. It allows organizations to manage all their data policies in one place—making it easier to ensure everyone follows the same rules and works securely with data.

Key features

  • ALLIE AI: Uses generative AI to automate data governance tasks

  • Automated data governance: Automatically documents new data assets during ingestion and defines governance policies for incoming data

  • Chrome extension: Allows access to metadata within preferred tools without switching contexts

  • Metadata management: Manages metadata with context-specific and compliant results to safeguard data privacy


A user on G2 reviews noted that “Alation is extremely flexible and able to fit small to large organizations. They tread the perfect line between analytics and governance.”

However, another user pointed out Alation’s costly pricing, saying: “The cost per data steward is a bit high is one thing. Another thing is having to pay to enable column-level data lineage. Sure you can get by with just table level, but column level lineage is key for impact analysis.”

Pros and cons


  • Documents new data assets and defines governance policies during ingestion

  • Uses natural language understanding and vector search for improved data access and search results

  • Maintains a balance between automation and human oversight to ensure accuracy and relevance

  • Has a modern and simple UI that simplifies new user onboarding


  • Pricing is not suitable for smaller organizations or those with limited budgets

  • Lacks connectors for some tools so as a result, it requires workarounds or additional engineering resources


According to data from Amazon Marketplace, Alation’s data catalog subscription begins at $60,000 for 12 months. Additional taxes and fees may apply. You can get more information by contacting Alation’s team and sharing details about your data governance requirements.


SAP's Master Data Governance platform is an all-in-one solution designed as a centralized hub for managing and improving the quality of important business data. It supports all kinds of data domains and implementation styles, whether on-premise, cloud, or private. 

Another great thing about SAP is that it gives you pre-built data models and business rules to manage and integrate data from SAP and third-party systems. So, transferring your data into SAP is a hassle-free process.

Key features

  • Data modeling and governance: Customizable data models to fit specific business requirements with defined governance processes

  • Data consolidation: Provides tools to consolidate data from different sources into a single unified view which eliminates duplicate records.

  • Compliance and risk management: Ensures compliance with regulatory requirements by maintaining accurate and auditable data records

  • Centralized master data management: Gives access to a central repository for managing master data across the enterprise


A previous SAP customer on G2 said, “It solves data inconsistency and data governance issues. It benefits us by providing reliable data that helps us make decisions and streamline workflows.”

However, users have also spotted many issues with SAP. One customer said, "The implementation process can be time-consuming and require significant resources. Additionally, licensing and customization costs can add up.”

Pros and cons


  • Helps users pull together master data and manage it centrally using the SAP Business Technology Platform

  • Eliminates duplication and reduces the need for manual data entry

  • Provides timely and accurate reports to administrators

  • Makes searching through documents easy


  • Does not support mobile platforms 

  • Pricing based on active records in the database makes it very expensive


SAP’s data governance service costs $1,044 for 5000 objects per year with a minimum contract of 3 months.

The 4 pillars of data governance

Data governance relies on four key pillars that provide the foundation for an organization to manage its data assets correctly and derive maximum value from them. Let’s take a look them: 

  • Data quality: Provides automated features to keep huge chunks of data accurate and reliable without missing its context so that it’s always available for intended use

  • Data stewardship: Assigns responsibility for managing and safeguarding data assets to designated individuals or teams

  • Data compliance: Ensures a high level of adherence to legal and regulatory compliances according to relevant data policies 

  • Data management: Provides an automated process to store your data while ensuring its security and sharing it only with users who have access

Why is data governance important?

As organizations rely more on data for competitive advantage, it has become important to make sure their data is accurate and complies with fundamental security principles. This can be done using a strong data governance strategy as it benefits any business—e-commerce, B2B, or B2C—to manage vast data stores.

Key benefits of data governance 

Here are the key benefits that data governance offers to organizations:

  • Keeps your data united in a single place to help you make quick decisions for your operational efficiency 

  • Minimizes inconsistencies and inaccuracies to enhance the trustworthiness of data

  • Facilitates the management of diverse data types for seamless integration and usability

  • Helps organizations adhere to legal and regulatory standards to avoid penalties and establish trust with stakeholders

  • Protects sensitive information from breaches and unauthorized access to maintain data integrity and confidentiality

  • Promotes data sharing across departments to have a more collaborative and cohesive data strategy and break silos

  • Improves the ability to analyze and interpret data to provide valuable insights for strategic advantage

Common data governance challenges

While data governance tools help to streamline, there are a few common challenges that companies may face when they're new to data governance:

  • User adoption: It can get tough for employees to embrace and adhere to data governance policies

  • Additional training: Implementing data governance requires substantial training, which can be time-consuming and resource-intensive

  • Complex processes: Without an agile tool, complex data governance processes can overwhelm staff and delay processes

Power your data governance with

An AI-powered agile data governance solution like gives you all you need to manage and secure data effectively. It’s driven by a knowledge graph, which means companies can see the relationships between different data assets and understand how different datasets are related.

Want to learn more about’s data governance capabilities? Book a demo today.

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