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 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.

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.

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.

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 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.

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.

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.

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 SAP. Companies that place in the top three of a category earn the designation of Leader.

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: Alation, AWS, Databricks, Google Cloud, IBM, Informatica, Microsoft, Oracle, Qlik, SAP and Solace.

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 provider rated Innovative is: Huawei 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 provider rated Assurance is: Collibra.

Merit: The categorization of software providers in Merit (lower left) represents those that did not exceed the median of performance in Customer or Product Experience or surpass the threshold for the other three categories. The providers rated Merit are: Alibaba Cloud, Ataccama, Boomi, Cloud Software Group, Precisely, Quest Software, Reltio, Rocket Software, SAS Institute 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 life cycle 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 (10%), Capability (20%), Reliability (15%), Adaptability (20%) and Manageability (15%). This weighting impacted the resulting overall ratings in this research. Informatica, Oracle and IBM were designated Product Experience Leaders.

Many enterprises will only evaluate capabilities for workers in IT or administration, but the research identified the criticality of adaptability (20% weighting) in data intelligence to enable responsiveness to changing business 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 life cycle 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, Microsoft and SAP. These category leaders best communicate commitment and dedication to customer needs.

Some software providers we evaluated did not have sufficient information available through their website and presentations. While many have customer case studies to promote success, some lack depth in articulating their commitment to customer experience and an enterprise’s data intelligence journey. As the commitment to a software provider is a continuous investment, the importance of supporting customer experience in a holistic evaluation should be included and not underestimated.

Appendix: Software Provider Inclusion

For inclusion in the ISG Buyers Guide™ for Data Intelligence in 2024, a software provider must be in good standing financially and ethically, have at least $50 million in annual or projected revenue verified using independent sources, sell products and provide support on at least two continents, and have at least 50 customers. 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.

The software provider must provide a product marketed as a data intelligence tool or platform, or products addressing at least three of the following functional areas, which are mapped into this Buyers Guide’s Capability criteria: data governance, data quality, master data management, application integration, data integration.

The 2024 Data Intelligence Buyers Guide consists of six parallel evaluations focused on data governance, data quality, master data management, application integration and data integration, as well as an overall evaluation related to data intelligence. 

Data Intelligence:

Data intelligence is the combination of data integration, data catalog, data quality, data lineage, metadata management and master data management to facilitate the understanding of how, when and why data is produced and consumed across an enterprise. Data intelligence also encompasses AnalyticsOps, which is used to deliver agile and collaborative analytics, facilitating 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.

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

• Data culture
• Data as a product
• AnalyticsOps
• Metrics and KPIs
• A holistic view of data production and data consumption
• And a product marketed as a data intelligence tool or platform, or products addressing at least three of data integration, application integration, data governance, data quality and master data management.

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.”