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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 Intelligence: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.
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. 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.
This research-based index evaluates the full business and information technology value of data intelligence 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 intelligence 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 Intelligence in 2025 finds Informatica first on the list, followed by Microsoft and IBM.
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.
- Microsoft.
- Actian.
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.
- Actian and Google Cloud in two categories.
- Alation 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 intelligence, 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 intelligence 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.
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