Executive Summary

Data Intelligence

Today’s enterprises seek to increase data-driven decision-making to gain competitive advantage and improve efficiency. It is ironic, however, that many enterprises lack information about when and how data is used in decision-making processes.

The capabilities that provide enterprises with information about how data is generated and consumed across the organization already exist, but, prior to the emergence of data intelligence software, have typically been distributed across a variety of products. ISG Research defines Data Intelligence as software that provides a holistic view of data production and consumption, enabling data administrators to understand and manage the use of data in business intelligence (BI) and artificial intelligence (AI) initiatives and accelerate strategic data-democratization initiatives to provide data analysts and business users with governed self-service access to data across an enterprise.

The term data intelligence has been used by multiple software providers across analytics and data for several years. Within the past 12 months, it has become a more clearly defined product category. Data intelligence platforms provide a combination of data inventory, data discovery and metadata management functionality, as well as data governance, data quality and data lineage to ensure that business users and data analysts can find and access the data they need, while providing analytics and data leaders with key metrics on data production and consumption, including the value generated by data projects. We assert that through 2027, three-quarters of enterprises will be engaged in data intelligence initiatives to understand how, when and where data is used in their organization, and by whom.

Metadata management has played a role in data governance and analytics for many years. It wasn’t until the emergence of the data catalog as a product category just over a decade ago that enterprises had a platform for metadata-driven data management that could span multiple departments and use cases across an entire enterprise. Data catalog functionality has been incorporated into numerous data management, data governance and data platform products to the extent that enterprises have multiple catalogs of data across numerous domains and repositories, perhaps with a “catalog of catalogs” providing higher-level insight. From our perspective, there are four main types of data catalogs, 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 governance catalogs build on technical and business catalog functionality with dedicated interfaces for data stewards, data quality and data governance professionals focused on ensuring the enterprise fulfills its data governance and regulatory requirements. This functionality is addressed in the associated ISG Data Governance Buyers Guide.

Data intelligence catalogs represent the evolution of business data catalogs, combining technical metadata, business metadata and data governance capabilities with knowledge graph functionality to deliver a holistic, business-level view of data production and consumption. A knowledge graph is a structured representation of information that identifies entities, their attributes and the relationships between them. Knowledge graph capabilities implemented by data catalog providers facilitate search-based data discovery by identifying, classifying and maintaining a map of relationships between data assets to provide additional value. For example, knowledge graph functionality helps identify the dependencies between business intelligence reports and dashboards and the complex web of data pipelines that transform and integrate the data on which they depend. We assert that through 2027, data catalog providers will evolve their products to support data intelligence by prioritizing delivery of knowledge graph and data product platform capabilities, as well as the use of AI.

Knowledge graphs are also critical to data intelligence’s role in the delivery of data as a product by providing a representation of data and metadata usage and the relationships between data elements. In combination with curated semantic data definitions that provide a common understanding of the data, knowledge graphs enable enterprises to understand how data ownership maps to logical business units and organizational structures. This functionality complements the role of data intelligence software in self-service data democratization. Removing barriers that prevent or delay users from gaining access to data enables it to be treated as a product that is generated and consumed—internally by workers or externally by partners and customers. For many enterprises, self-service access to data has long been a goal, but few have achieved it. Such access is only truly valuable if users can trust the data they have access to. Enterprises need to ensure that business users and data analysts can find the data they need, understand what it means and trust that it is valid, current and can be relied upon in business decision-making.

While data democratization facilitates access to data, it is not a free-for-all. In addition to core data and data catalog functionality, data democratization requires data lineage and data quality capabilities as well as contextual understanding of the data, such as its criticality and whether it is subject to regulatory requirements.

Managing data production and consumption are separate disciplines with different roles, responsibilities, skills and tools. And while that is likely to remain the case, connecting the dots between data production and data consumption with data intelligence is essential to delivering on priorities for the use of data and the adoption of AI.

Data intelligence is the connective tissue that brings together investments in data fabric and data mesh. Despite often being used interchangeably, data fabric and data mesh relate to independent but intersecting concepts. Data fabric is differentiated by its focus on how data is produced—specifically, the tools and technologies data management and governance practitioners typically use to deliver agile data integration. Data fabric products are largely indifferent to who owns the data and how it is consumed. In comparison, while data mesh is agnostic to the technology that generates, integrates and manages the data, it focuses on who owns the data and how it is consumed by business users. Domain-oriented data ownership is integral to data mesh, with the business departments or units that generate the data responsible for managing ownership of the data and making it available as a data product to be consumed by others.

Making data available as a product requires that enterprises understand how data ownership maps to logical business units and organizational structure.

Making data available as a product requires that enterprises understand how data ownership maps to logical business units and organizational structure. This is facilitated by curated semantic data definitions enabled by intelligence-driven semantic data modeling. It provides a common understanding of the data and knowledge graphs, highlighting data and metadata usage and reflects the relationships between data elements. By 2027, more than 3 in 5 enterprises will adopt technologies to facilitate the delivery of data as a product as they adapt their cultural and organizational approaches to domain-based data ownership.

To deliver data intelligence, enterprises should look for products that enable collaborative approaches to data management and governance. Capabilities that support the development of a data-driven culture, including data as a product, AnalyticOps capabilities to deliver agile and collaborative analytics and metrics and key performance indicators that illustrate data usage are also critical. Together, these capabilities facilitate self-service access to data that is trusted to fulfill operational and analytics initiatives in compliance with data privacy, security policies and regulatory requirements.

Our Data Intelligence Buyers Guide provides a holistic view of a software provider’s ability to deliver the combination of functionality to provide a complete view of data production and data consumption with either a single data intelligence product or a suite of products. This Data Intelligence Buyers Guide evaluates products including at least one tool or platform for the following functional areas, which are mapped into the Buyers Guide Capability criteria: data culture, data discovery, data inventory, data metrics, AnalyticOps, metadata management, data lineage and data quality profiling. To be included in this Buyers Guide, products must be marketed as a data intelligence tool or platform or provide a combination of data governance and data quality. The evaluation also assessed the use of artificial intelligence to automate and enhance data intelligence.

The ISG Buyers Guide™ for Data Intelligence evaluates the following software providers offering products to address key elements of data intelligence as we define it: Actian, Alation, Alibaba Cloud, Ataccama, AWS, Cloud Software Group, Cloudera, Collibra, Databricks, Experian, Google Cloud, Huawei Cloud, IBM, Informatica, Microsoft, Oracle, Pentaho, Precisely, Qlik, Quest, Rocket Software, SAP, SAS Institute, Securiti, Snowflake, Syniti and Tencent Cloud.

Buyers Guide Overview

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.

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.

The ISG Buyers Guide™ for Data Intelligence 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 intelligence 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 intelligence 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 intelligence 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 intelligence 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 intelligence 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 intelligence is becoming the foundation of today’s data strategies, providing visibility into how data is produced and consumed to power trusted self-service for BI and AI. It combines data inventory, discovery, metadata management, catalogs, lineage, quality and usage metrics with AI and knowledge graphs to map relationships, measure value and govern access. By bridging data fabric and data mesh and supporting “data as a product,” data intelligence accelerates democratization while maintaining control. As AI adoption scales, it ensures reliable, compliant and comprehensible data that drives faster insights, greater efficiency and enterprise-wide confidence.

Software Provider Summary

The research identifies Informatica, Microsoft and IBM as the market leaders, with strengths across multiple categories. Providers such as Actian, Alation and Collibra demonstrated targeted capabilities. Classification placed Informatica, IBM, and Databricks in the Exemplary quadrant alongside providers including Google Cloud, Microsoft, Oracle and SAP. Providers such as Alibaba Cloud, Qlik and Tencent Cloud were categorized as Innovative. Cloudera and Precisely were rated as Assurance, and Ataccama, Experian, Huawei Cloud, Quest, SAS Institute, Snowflake and Syniti were 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, Microsoft and Actian led in delivering breadth and depth across governance and integration, while Databricks and Oracle demonstrated adaptability and targeted strengths but less overall balance. Leaders distinguished themselves with scalability, manageability and strong usability, ensuring 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. Alation and Collibra 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 intelligence platform selection as a strategic decision that balances foundational functions such as data discovery, inventory, and lineage with expanded AI-driven capabilities like semantic modeling, knowledge graphs, and metrics on data use. Buyers should prioritize platforms that provide a holistic view of data production and consumption, simplify access through governed self-service and deliver measurable ROI by enabling “data as a product.” Using the ISG Buyers Guide as a structured framework allows enterprises to evaluate providers against both product and customer experience, ensuring investments strengthen data democratization. 

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 Microsoft and IBM. 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, Actian and Google Cloud in two and Alation 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, Oracle, 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 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: Cloudera and Precisely.

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, Cloud Software Group, Experian, Huawei Cloud, Quest, Rocket Software, SAS Institute, Securiti, 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 intelligence, 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 (12.5%), Capability (30%), Reliability (12.5%), Adaptability (12.5%) and Manageability (12.5%). This weighting impacted the resulting overall ratings in this research. Informatica, Microsoft and Actian were designated Product Experience Leaders. While not Leaders, IBM and AWS 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.

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 Intelligence 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 past 12 months.

Data intelligence provides a holistic view of data production and consumption, enabling data administrators to understand and manage the use of data in BI and AI initiatives. The software accelerates strategic data-democratization initiatives to provide data analysts and business users with governed self-service access to data across an enterprise. Data intelligence platforms provide a combination of data inventory, data discovery and metadata management functionality, as well as data governance, data quality and data lineage to ensure that business users and data analysts can find and access the data they need, while providing analytics and data leaders with key metrics on data production and consumption, including the value generated by data-projects.

To be included in the Data Intelligence Buyers Guide, the product(s) must be marketed as a data intelligence platform or provide a combination of data governance and data quality and address the following functional areas, which are mapped into Buyers Guide capability criteria:

• Data culture
• Data metrics
• AnalyticOps
• Metadata management
• Data lineage
• Data quality
• Data inventory
• Data discovery
• 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 intelligence 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.”