Executive Summary

Data Governance

Data governance is integral to an overall data management strategy. Good data governance provides guardrails that enable enterprises to act quickly while protecting the business from risks related to regulatory requirements, data-quality issues and data-reliability concerns. It accelerates the delivery of analytics projects, providing the confidence required to make agile business decisions.

ISG Research defines data governance as software that enables enterprises to ensure data is cataloged, trusted and protected, improving business processes that accelerate analytics initiatives while supporting compliance with data privacy, security policies and regulatory requirements. While not all data governance initiatives are driven by regulatory compliance, the risk of falling afoul of privacy (and human rights) laws ensures that regulatory compliance influences data-processing requirements and all data governance projects. Improved operational efficiency is another major benefit of data governance, along with reducing the cost of existing IT support.

The importance of data governance is well recognized and understood. Almost 9 in 10 participants in our Data Governance Benchmark Research identified data governance as important or very important to the organization. The data catalog has become an integral component of enterprise data strategies over the past decade, serving as a conduit for good data governance and facilitating self-service analytics initiatives. The data catalog has become so important that it is easy to forget that, just 10 years ago, it did not exist as a standalone product category. Metadata-based data management functionality has played a role in products for data governance and business intelligence for much longer. However, the emergence of the data catalog as a product category provided a platform for metadata-based data inventory and discovery that spans an entire organization, serving multiple departments, use cases and initiatives.

The concept of the data catalog has become so prevalent that there are now a variety of data catalog products available, including technical data catalogs, business data catalogs, data intelligence catalogs and data governance catalogs.

Technical data catalogs represent the fundamental functionality of a metadata repository that scans the enterprise’s data estate and extracts technical metadata to provide an inventory of the data’s location, structure and schema. While there are standalone technical data catalog products, this technology also forms the base layer of functionality used by other types of data catalogs.

Business data catalogs expand on technical data catalog capabilities with an additional layer of functionality that provides business metadata related to the context, meaning and relevance of the data to business domains and applications. This business context is critical to enabling self-service discovery and access to data by business users and data analysts using natural language search.

Data intelligence catalogs have emerged in recent years as the evolution of business data catalogs, combining technical metadata, business metadata and data governance capabilities. With additional knowledge graph, AnalyticOps and data metrics functionality, these catalogs deliver a holistic, business-level view of data production and consumption. This functionality is addressed in the associated ISG Data Intelligence Buyers Guide.

AI and data governance are symbiotic. Data governance processes and products can help improve AI, but AI also plays a role in data governance by automating and accelerating previously manual processes.

More relevant to this Data Governance Buyers Guide are data governance catalogs, which build on technical and business catalog functionality with dedicated interfaces for data stewards, data quality and data governance professionals focused on ensuring the enterprise fulfils its data governance and regulatory requirements. In addition to the data usage, data lineage, data quality, data security and access control capabilities of the underlying technical catalog, these users require additional functionality to define and manage data usage policies, view and manage data profiles, determine and administer data quality rules and define and administer data models and master data definitions.

Traditionally, many enterprise data governance initiatives were driven by manual processes reactive to changing data privacy, security policies and regulatory requirements, limiting access to data to ensure compliance with these guidelines. This approach poses challenges for enterprises trying to respond quickly to evolving security threats, competitive concerns and regulations, as well as new opportunities to deliver enhanced efficiency and new business opportunities with the development of artificial intelligence (AI)-driven applications.

AI and data governance are symbiotic. Data governance processes and products can help improve AI, but AI also plays a role in data governance by automating and accelerating previously manual processes. For example, AI can automatically recognize personally identifiable information and other forms of sensitive data, flagging potentially inappropriate use. AI is increasingly incorporated into data quality software to automate and enhance data quality checks, supporting automation of data classification, metadata management and data lineage.

AI is heavily dependent on data, so data governance and privacy issues that impact data and analytics also impact agentic and generative AI (GenAI). Additionally, agentic and GenAI systems can exacerbate governance risks. GenAI systems can inadvertently generate harmful or offensive content, so enterprises must guard against toxicity to prevent unintended consequences. GenAI models also learn from historical data, which may contain biases. Enterprises need robust mechanisms to detect and rectify bias during model training and deployment. Plus, enterprises should create and enforce policies that govern the use of sensitive data by GenAI applications, including strong privacy controls and best practices to safeguard against breaches. Failure to address these governance challenges could severely impact an enterprise in terms of damaging its reputation and customer relationships, as well as falling afoul of emerging regulations, such as the European Union Artificial Intelligence Act.

Rather than limiting the use of data, the implementation of well-defined data governance policies and procedures provides a framework that expands access to data, enabling enterprises to make faster decisions by providing a platform for self-service data discovery and analysis with AI. Enterprises implementing GenAI find that governance is an essential enabler of success, with better coordination and governance rated as the number one factor that could improve the value, or time to value, for participants in ISG’s GenAI Market Lens research.

Our research also illustrates a gap between awareness of the need for governance in AI initiatives and policies to govern AI and machine learning models. More than three-quarters (78%) of participants in our Data and AI Programs Study agreed that centralized data governance enables the business to deliver more efficient data insights. Despite that, only 43% agreed that they have successfully created a consistent data architecture across the entire business, and more than one-third agreed that the cost of harmonizing data across the business likely outweighed the likely benefits.

Multinational enterprises must be aware of the wide variety of regional data security, sovereignty and privacy requirements. This includes the European Union’s General Data Protection Regulation and similar regulations like the California Consumer Privacy Act. In addition to differing regulatory requirements, our research illustrates varying attitudes and approaches to data governance on either side of the Atlantic: Better regulatory compliance is seen as a benefit of investing in data governance by almost three-quarters of European enterprises, compared to 57% of North American organizations. We assert that, through 2027, regional regulations and cultural attitudes will continue to shape organizational priorities toward the adoption of data governance technologies and processes.

Our Data Governance Buyers Guide provides a holistic view of a software provider’s ability to deliver the combination of functionality that provides a complete view of data governance with either a single product or a suite of products. As such, the Data Governance Buyers Guide includes the full breadth of data governance functionality. Our assessment also considered whether the functionality in question was available in a single offering or as a suite of products or cloud services.

The ISG Buyers Guide™ for Data Governance evaluates products based on the governance of real-time data in motion and data at rest, as well as the use of AI to automate and enhance data governance. To be considered for this Buyers Guide, products must include at least one of the following functional areas, which are mapped into the Buyers Guide Capability criteria: metadata management, data lineage and data stewardship. The evaluation also assessed the use of AI to automate and enhance data governance.

This research evaluates the following software providers that offer products that address key elements of data governance as we define it: Actian, Alation, Alibaba Cloud, Alteryx, Ataccama, AWS, Cloudera, Collibra, Confluent, Databricks, Experian, Google Cloud, Huawei Cloud, IBM, Informatica, Microsoft, Oracle, Pentaho, Precisely, Qlik, Quest, Rocket Software, SAP, SAS Institute, Securiti, Snowflake, Solace, Syniti and Tencent Cloud.

Buyers Guide Overview

ISG Research has designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of business requirements in any enterprise.

For over two decades, ISG Research has conducted market research in a spectrum of areas across business applications, tools and technologies. We have designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of the business requirements in any enterprise. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select software providers and products. The findings of this research undertaking contribute to our comprehensive approach to rating software providers in a manner that is based on the assessments completed by an enterprise.

The ISG Buyers Guide™ for Data Governance is the distillation of over a year of market and product research efforts. It is an assessment of how well software providers’ offerings address enterprises’ requirements for data governance software. The index is structured to support a request for information (RFI) that could be used in the request for proposal (RFP) process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with software providers. An effective product and customer experience with a provider can ensure the best long-term relationship and value achieved from a resource and financial investment.

In this Buyers Guide, ISG Research evaluates the software in seven key categories that are weighted to reflect buyers’ needs based on our expertise and research. Five are product-experience related: Adaptability, Capability, Manageability, Reliability, and Usability. In addition, we consider two customer-experience categories: Validation, and Total Cost of Ownership/Return on Investment (TCO/ROI). To assess functionality, one of the components of Capability, we applied the ISG Research Value Index methodology and blueprint, which links the personas and processes for data governance to an enterprise’s requirements.

The structure of the research reflects our understanding that the effective evaluation of software providers and products involves far more than just examining product features, potential revenue or customers generated from a provider’s marketing and sales efforts. We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data governance technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on a short list of software providers that does not represent a best fit for your enterprise.

ISG Research believes that an objective review of software providers and products is a critical business strategy for the adoption and implementation of data governance software and applications. An enterprise’s review should include a thorough analysis of both what is possible and what is relevant. We urge enterprises to do a thorough job of evaluating data governance systems and tools and offer this Buyers Guide as both the results of our in-depth analysis of these providers and as an evaluation methodology.

Key Takeaways

Data governance is emerging as a cornerstone of modern data strategies, providing guardrails that balance rapid access with regulatory, quality and reliability requirements. Once driven largely by compliance, governance now underpins analytics and AI by enabling trusted, cataloged and protected data for self-service use. Modern platforms integrate catalogs, lineage and stewardship with AI to automate classification, policy enforcement and risk detection, addressing challenges such as bias, privacy and GenAI toxicity. As AI adoption accelerates, effective governance is essential to ensure compliant, trustworthy and accessible data that delivers faster insights, operational efficiency and enterprise-wide confidence.

Software Provider Summary
The research identifies Informatica, IBM and Databricks as the market leaders, with strengths across multiple categories, while providers such as Actian, Collibra and Alation demonstrated targeted capabilities. Classification placed Informatica, IBM and Databricks in the Exemplary quadrant alongside providers including Microsoft, Google Cloud and SAP. Providers such as Alibaba Cloud, Qlik, Rocket Software, Securiti and Tencent Cloud were categorized as Innovative; Alteryx, Precisely Oracle, and Solace as Assurance; and Ataccama, Cloudera, Confluent, Experian, Huawei Cloud, Quest, SAS Institute, Snowflake and Syniti in the Merit quadrant.

Product Experience Insights
Product Experience accounted for 80% of the overall rating, with emphasis on capability, usability, reliability, adaptability and manageability. Informatica, IBM and Microsoft led in delivering breadth and depth across governance and integration, while Actian and Databricks demonstrated adaptability and targeted strengths but less overall balance. Leaders distinguished themselves with scalability, manageability and strong usability, ensuring their platforms can operate while supporting AI-driven governance innovations.

Customer Experience Value
Customer Experience represented 20% of the evaluation, focused on validation and TCO/ROI. Databricks, Oracle and Informatica led in this category by demonstrating strong customer commitment, transparent ROI frameworks and consistent lifecycle support. Collibra and Alation also performed well, though short of leadership. Lower-performing providers often lacked sufficient clarity in CX, making it harder for buyers to justify long-term investments.

Strategic Recommendations
Enterprises should treat data governance platform selection as a strategic decision that balances foundational functions such as capability, adaptability and manageability with expanded AI-driven usability and reliability requirements. Buyers should prioritize platforms that ensure interoperability, simplify administration and deliver measurable ROI through transparent TCO frameworks. Using the ISG Buyers Guide as a structured framework enables enterprises to evaluate providers against both product and customer experience, ensuring investments improve governance outcomes and align with evolving enterprise data strategies.

How To Use This Buyers Guide

Evaluating Software Providers: The Process

We recommend using the Buyers Guide to assess and evaluate new or existing software providers for your enterprise. The market research can be used as an evaluation framework to establish a formal request for information from providers on products and customer experience and will shorten the cycle time when creating an RFI. The steps listed below provide a process that can facilitate best possible outcomes.

1. Define the business case and goals.
   Define the mission and business case for investment and the expected outcomes from your organizational and technology efforts. 
2. Specify the business needs.
   Defining the business requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
3. Assess the required roles and responsibilities.
   Identify the individuals required for success at every level of the organization from executives to front line workers and determine the needs of each. 
4. Outline the project’s critical path.

   What needs to be done, in what order and who will do it? This outline should make clear the prior dependencies at each step of the project plan. 
5. Ascertain the technology approach.

   Determine the business and technology approach that most closely aligns to your organization’s requirements. 
6. Establish technology vendor evaluation criteria.
   Utilize the product experience: Adaptability, Capability, Manageability, Reliability and Usability, and the customer experience in TCO/ROI and Validation. 
7. Evaluate and select the technology properly.

   Weight the categories in the technology evaluation criteria to reflect your organization’s priorities to determine the short list of vendors and products.
8. Establish the business initiative team to start the project.
   Identify who will lead the project and the members of the team needed to plan and execute it with timelines, priorities and resources. 

The Findings

All of the products we evaluated are feature-rich, but not all the capabilities offered by a software provider are equally valuable to types of workers or support everything needed to manage products on a continuous basis. Moreover, the existence of too many capabilities may be a negative factor for an enterprise if it introduces unnecessary complexity. Nonetheless, you may decide that a larger number of features in the product is a plus, especially if some of them match your enterprise’s established practices or support an initiative that is driving the purchase of new software.

Factors beyond features and functions or software provider assessments may become a deciding factor. For example, an enterprise may face budget constraints such that the TCO evaluation can tip the balance to one provider or another. This is where the Value Index methodology and the appropriate category weighting can be applied to determine the best fit of software providers and products to your specific needs.

Overall Scoring of Software Providers Across Categories

The research finds Informatica atop the list, followed by IBM and Databricks. Providers that place in the top three of a category earn the designation of Leader. Oracle has done so in six categories; Databricks and Informatica in five; Google Cloud in two; and Actian, IBM and Rocket Software in one category.

The overall representation of the research below places the rating of the Product Experience and Customer Experience on the x and y axes, respectively, to provide a visual representation and classification of the software providers. Those providers whose Product Experience have a higher weighted performance to the axis in aggregate of the five product categories place farther to the right, while the performance and weighting for the two Customer Experience categories determines placement on the vertical axis. In short, software providers that place closer to the upper-right on this chart performed better than those closer to the lower-left.

The research places software providers into one of four overall categories: Assurance, Exemplary, Merit or Innovative. This representation classifies providers’ overall weighted performance.

Exemplary: The categorization and placement of software providers in Exemplary (upper right) represent those that performed the best in meeting the overall Product and Customer Experience requirements. The providers rated Exemplary are: Actian, Alation, AWS, Collibra, Databricks, Google Cloud, IBM, Informatica, Microsoft, Pentaho and SAP.

Innovative: The categorization and placement of software providers in Innovative (lower right) represent those that performed the best in meeting the overall Product Experience requirements but did not achieve the highest levels of requirements in Customer Experience. The providers rated Innovative are: Alibaba Cloud, Qlik, Rocket Software, Securiti and Tencent Cloud.

Assurance: The categorization and placement of software providers in Assurance (upper left) represent those that achieved the highest levels in the overall Customer Experience requirements but did not achieve the highest levels of Product Experience. The providers rated Assurance are: Alteryx, Oracle, Precisely and Solace.

Merit: The categorization of software providers in Merit (lower left) represents those that did not surpass the thresholds for the Assurance, Exemplary or Innovative categories in Customer or Product Experience. The providers rated Merit are: Ataccama, Cloudera, Confluent, Experian, Huawei Cloud, Quest, SAS Institute, Snowflake and Syniti.

We warn that close provider placement proximity should not be taken to imply that the packages evaluated are functionally identical or equally well suited for use by every enterprise or for a specific process. Although there is a high degree of commonality in how enterprises handle data governance, there are many idiosyncrasies and differences in how they do these functions that can make one software provider’s offering a better fit than another’s for a particular enterprise’s needs.

We advise enterprises to assess and evaluate software providers based on organizational requirements and use this research as a supplement to internal evaluation of a provider and products.

Product Experience

The process of researching products to address an enterprise’s needs should be comprehensive. Our Value Index methodology examines Product Experience and how it aligns with an enterprise’s lifecycle of onboarding, configuration, operations, usage and maintenance. Too often, software providers are not evaluated for the entirety of the product; instead, they are evaluated on market execution and vision of the future, which are flawed since they do not represent an enterprise’s requirements but how the provider operates. As more software providers orient to a complete product experience, evaluations will be more robust.

The research results in Product Experience are ranked at 80%, or four-fifths, of the overall rating using the specific underlying weighted category performance. Importance was placed on the categories as follows: Usability (7.5%), Capability (35%), Reliability (10%), Adaptability (15%) and Manageability (12.5%). This weighting impacted the resulting overall ratings in this research. Informatica, IBM and Microsoft were designated Product Experience Leaders. While not Leaders, Actian and Databricks were also found to meet a broad range of enterprise product experience requirements.

Customer Experience

The importance of a customer relationship with a software provider is essential to the actual success of the products and technology. The advancement of the Customer Experience and the entire lifecycle an enterprise has with its software provider is critical for ensuring satisfaction in working with that provider. Technology providers that have chief customer officers are more likely to have greater investments in the customer relationship and focus more on their success. These leaders also need to take responsibility for ensuring this commitment is made abundantly clear on the website and in the buying process and customer journey.

The research results in Customer Experience are ranked at 20%, or one-fifth, using the specific underlying weighted category performance as it relates to the framework of commitment and value to the software provider-customer relationship. The two evaluation categories are Validation (10%) and TCO/ROI (10%), which are weighted to represent their importance to the overall research.

The software providers that evaluated the highest overall in the aggregated and weighted Customer Experience categories are Databricks, Oracle and Informatica. These category Leaders best communicate commitment and dedication to customer needs. While not a Leader, Collibra was also found to meet a broad range of enterprise customer experience requirements.

Software providers that did not perform well in this category were unable to provide sufficient customer case studies to demonstrate success or articulate their commitment to customer experience and an enterprise’s journey. The selection of a software provider means a continuous investment by the enterprise, so a holistic evaluation must include examination of how they support their customer experience.

Appendix: Software Provider Inclusion

For inclusion in the ISG Buyers Guide™ for Data Governance in 2025, a software provider must be in good standing financially and ethically, have at least $75 million in annual or projected revenue verified using independent sources, sell products and provide support on at least two continents, and have at least 75 employees. The principal source of the relevant business unit’s revenue must be software-related, and there must have been at least one major software release in the last 12 months.

Data Governance software enables organizations to ensure their data is cataloged, trusted and protected, improving business processes to accelerate analytics initiatives while supporting compliance with data privacy and security policies as well as regulatory requirements.

To be included in the Data Governance Buyers Guide requires functionality that addresses the following sections of the capabilities document:

• Metadata management
• Data lineage
• Data stewardship
• AI

The research is designed to be independent of the specifics of software provider packaging and pricing. To represent the real-world environment in which businesses operate, we include providers that offer suites or packages of products that may include relevant individual modules or applications. If a software provider is actively marketing, selling and developing a product for the general market and it is reflected on the provider’s website that the product is within the scope of the research, that provider is automatically evaluated for inclusion.

All software providers that offer relevant data governance products and meet the inclusion requirements were invited to participate in the evaluation process at no cost to them.

Software providers that meet our inclusion criteria but did not completely participate in our Buyers Guide were assessed solely on publicly available information. As this could have a significant impact on classification and ratings, we recommend additional scrutiny when evaluating those providers.

Products Evaluated

Providers of Promise

We did not include software providers that, as a result of our research and analysis, did not satisfy the criteria for inclusion in this Buyers Guide. These are listed below as “Providers of Promise.”