ISG Buyers Guide for Data Governance in 2025 Classifies and Rates Software Providers

ISG Research is happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The Data Governance: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.

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.

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.

This research-based index evaluates the full business and information technology value of data governance software offerings. We encourage you to learn more about our Buyers Guide and its effectiveness as a provider selection and RFI/RFP tool.

We urge organizations to do a thorough job of evaluating data governance offerings in this Buyers Guide as both the results of our in-depth analysis of these software providers and as an evaluation methodology. The Buyers Guide can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP and the definition of an RFI.

The Buyers Guide for Data Governance in 2025 finds Informatica first on the list, followed by IBM and Databricks.

Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader.

The Leaders in Product Experience are:

• Informatica.
• IBM.
• Microsoft.
  
  

The Leaders in Customer Experience are:

• Databricks.
• Oracle.
• Informatica.
  
  

The Leaders across any of the seven categories are:

• Oracle, which has achieved this rating in six of the seven categories.
• Databricks and Informatica in five categories.
• Google Cloud in two categories.
• Actian, IBM and Rocket Software in one category.

The overall performance chart provides a visual representation of how providers rate across product and customer experience. Software providers with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, providers that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as providers of “merit.” Software providers that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating.

Note that close provider scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every enterprise or process. Although there is a high degree of commonality in how organizations handle data governance, there are many idiosyncrasies and differences that can make one provider’s offering a better fit than another.

ISG Research has made every effort to encompass in this Buyers Guide the overall product and customer experience from our data governance blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which software provider and products best fit an enterprise’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects.

You can find more details on our community as well as on our expertise in the research for this Buyers Guide.