ISG Buyers Guide for Data Intelligence 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 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 organizations 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 are distributed across a variety of products. ISG Research defines Data Intelligence as the combination of data integration, data catalog, data quality, data lineage, metadata management and master data management to facilitate and understand how, when and why data is produced and consumed across an organization. It also encompasses AnalyticsOps, which is used to deliver agile and collaborative analytics, enabling self-service access to data that is trusted to fulfill operational and analytics initiatives in compliance with data privacy and security policies and regulatory requirements. By 2027, three-quarters of enterprises will be engaged in data intelligence initiatives to increase trust in data by leveraging metadata to understand how, when and where data is used in the organization and by whom.

Although the term data intelligence has been used by multiple software providers across analytics and data for several years, it is not a clearly defined product category. Software providers using the term typically offer unique definitions that make self-serving reference to the functional strengths of products. Over time, it has become clear that there is a common thread to these multiple definitions related to the use of data intelligence as an umbrella term for functionality required to enable enterprises to better facilitate and understand data production and consumption across the organization. It has also become clear that data intelligence is fundamental to strategic data-democratization initiatives to provide data analysts and business users with governed self-service access to data across an enterprise.

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. Only 15% of participants in Ventana Research’s Analytics and Data Benchmark Research are very comfortable allowing business users to work with data that has not been integrated or prepared by IT. Many organizations see data catalogs as the solution to data democratization because they provide a central repository of the data used across an enterprise, along with guided data discovery capabilities and natural language search.

Self-service access to data 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.

Data intelligence represents a layer in the stack above data platforms that combines related functionality, such as:

• Data integration, enabling enterprises to extract data from applications, databases and other sources and combine it for analysis in a data warehouse or data lakehouse with the intention of generating business insights. Without data integration, business data would be trapped in the applications and systems in which it was generated.
• Data governance, enabling enterprises to ensure 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.
• Data quality, as both a discipline and a product category. As a discipline, data quality refers to the processes, methods and tools used to measure the suitability of a dataset for a specific purpose. The precise measure of suitability will depend on the individual use case, but important characteristics include accuracy, completeness, consistency, timeliness and validity.
• Master data management, managing an enterprise’s master data. Master data is the term used for an enterprise’s foundational reference data. It provides an agreed list of entities that can be shared throughout the organization, including categories such as parties (customers or workers), places (addresses or regions) and things (products, assets, financial instruments). It encompasses processes such as data validation, matching and merging duplicate records and enriching data with related information.
• Application integration, involving the enablement and management of direct communication between applications, supporting the fulfilment of business processes and workflows that rely on multiple applications operating in concert. While application integration has traditionally relied on point-to-point integration between individual applications, today’s application integration is increasingly dependent on application programming interfaces and API management.

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 adoption of art.

Data intelligence provides a holistic view of data production and consumption, becoming 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 6 in 10 enterprises will adopt technologies to facilitate the delivery of data as a product while adapting cultural and organizational approaches to data ownership in the context of data mesh.

Our Data Intelligence Buyers Guide is designed to provide 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 suite of products. As such, the Data Intelligence Buyers Guide includes the full breadth of data governance, data quality, master data management, application integration and data integration functionality. Software providers that primarily address one of these aspects are represented in separate Buyers Guide research reports. Our assessment also considered whether the functionality in question was available from a software provider in a single offering or as a suite of products or cloud services.

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 intelligence, data governance, data quality, master data management, application integration or data integration. To be included in this Buyers Guide, products must be marketed as a data intelligence tool or platform or address at least three of data governance, data quality, master data management, application integration, data integration.

To deliver data intelligence, enterprises should look for data integration, application integration, data governance, data quality and master data management products that enable collaborative approaches to data management and governance. Enterprise should also look for capabilities that support the development of a data-driven culture, including data as a product, AnalyticsOps capabilities to deliver agile and collaborative analytics and metrics and key performance indicators that illustrate data usage. 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.

The ISG Buyers Guide™ for Data Intelligence evaluates the following software providers that offer products that address key elements of data intelligence as we define it: Alation, Alibaba Cloud, Amazon Web Services (AWS), Ataccama, Boomi, Cloud Software Group, Collibra, Databricks, Google Cloud, Huawei Cloud, IBM, Informatica, Microsoft, Oracle, Precisely, Qlik, Quest Software, Reltio, Rocket Software, SAP, SAS Institute, Solace and Syniti.

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 2024 finds Informatica first on the list, followed by IBM and SAP.

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.
• Oracle.
• IBM.

The Leaders in Customer Experience are:

• Databricks.
• Microsoft.
• SAP

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.