Executive Summary

Data Products

Data as a product is one of four key principles of data mesh, a cultural and organizational approach, alongside domain-oriented ownership, self-serve data infrastructure and federated governance. However, each of these four principles is gaining momentum outside the context of data mesh. Data as a product is the process of applying product thinking to data initiatives to ensure the outcome—the data product—is designed to be shared and reused for multiple use cases across the business as it enables enterprises to streamline and accelerate the delivery of analytics and data initiatives.

Data as a product is the process of applying product thinking to data initiatives to ensure that the outcome—the data product—is designed to be shared and reused for multiple use cases across the business.

ISG Research defines data products as the outcome of data initiatives developed with product thinking and delivered as reusable assets that can be discovered and consumed by others on a self-service basis, along with associated data contracts and feedback options. ISG asserts that by 2027, more than 6 in 10 enterprises will adopt technologies to facilitate the delivery of data as a product as cultural and organizational approaches to data ownership in the context of data mesh evolve.

A data product can take many forms. In many cases, a data product will be a domain-specific data set—the equivalent of what has traditionally been thought of as a data mart—but it could also be an algorithm, artificial intelligence/machine learning model or a custom-built operational application. Either way, the format is not a defining characteristic of the data product. All of these have traditionally been delivered on a project-by-project basis, often by a centralized IT team, with little or no effort to ensure the data can be easily accessed and used for other purposes without duplication.

The defining characteristic of a data product is the application of product thinking in the development process to ensure that the outcome is designed to be delivered as a reusable asset that can be discovered and consumed by others on a self-service basis. The principle of domain-oriented ownership is also important to the development of data products. Domain-oriented ownership makes business departments responsible for managing the data generated by owned applications and making it available to others using standard and interoperable interfaces. Data as a product is primarily concerned with the sharing of data products within an enterprise via a data marketplace rather than selling data products to partners, suppliers or customers, although this could be achieved by making the data available externally via data as a service.

The application of product thinking ensures that consumers of data products are treated as customers. Data owners must be aware of data requirements from across the enterprise to understand how the resulting data product will be used.

The application of product thinking ensures that consumers of data products are treated as customers. Data owners must be aware of data requirements from across the enterprise to understand how the resulting data product will be used. Product thinking also requires data owners to provide instructions and service-level commitments so data consumers can feel confident that the data product is up-to-date and of sufficient quality to be relied on for business decision-making. This is fulfilled through the development of data contracts, which are created alongside the data product and form the basis of an agreement between the data owner and the data consumer about the nature of the data product and its intended use.

Data contracts should include a description of the data product, defining the structure, format and meaning of the data, as well as licensing terms and usage recommendations. A data contract should also define data quality and service-level key performance indicators and commitments. The metrics generated by data observability form a critical component of the development and sharing of data products, therefore providing the information by which data consumers can gauge if a data product meets their requirements in terms of a variety of attributes, including validity, uniqueness, timeliness, consistency, completeness and accuracy.

Enterprise interest in data as a product has driven the emergence of a new category of software designed to provide an environment for the development, publication and consumption of data products. Key capabilities for these data product platforms include a dedicated interface for the development of data products with versioning, change tracking and data lineage capabilities, as well as templates for the classification of data products and data contracts.

A data product platform also needs to provide a dedicated interface for the self-service discovery and consumption of data products and related data contracts. As with any product, consumers of data products should be able to view and provide feedback, comments and ratings as well as request improvements or new products.

Data owners also require the functionality to view and manage access to data products, as well as requests for data product modifications and the development of new data products. Data owners also need functionality that enables the monitoring of data product usage and performance metrics, as well as identifying and managing the relationships and dependencies between data products.

Some data product platforms also offer functionality to support the sale and licensing of data as a service to external partners or customers, which brings additional requirements in the form of a dedicated interface for the discovery and consumption of data products by external data consumers and functionality to define and identify licensing and pricing options for external usage.

The development of any product relies on a complex supply chain of components, and data products are no exception. As such, data product platforms need to provide native or integrated data operations functionality, including the development and testing of data pipelines, as well as data orchestration and data observability functionality to deliver the all-important information related to the validity, integrity, quality and lineage of the underlying data. Given the complexity involved in developing, accessing and managing data products, data product platforms should also be assessed in terms of support for AI to enhance and automate data product development, data product classification, data product consumption and data product management.

Enterprises adopting data as a product stand to benefit from interoperability and the accelerated delivery of data products that more rapidly provide business stakeholders with high-quality, trusted data.

Making data available as a product on a self-service basis increases the importance of agreed-upon data definitions and entity resolution. Only 16% of participants in ISG’s Data Governance Benchmark Research say data is well-trusted in their organization, while one-half cite agreement on the definitions of data as a primary concern in managing data effectively. To improve trust in data, it is important that data product platforms provide native or integrated functionality for data governance, data cataloging and master data management. Enterprises adopting data as a product stand to benefit from interoperability and the accelerated delivery of data products that more rapidly provide business stakeholders with high-quality, trusted data.

It is recommended that all enterprises evaluate the principle of data as a product and platforms that enable the development and delivery of data products with a view to streamlining and accelerating the delivery of analytics and data initiatives and improving trust in data used to make business decisions. Would-be adopters should pay careful attention to the status and relative maturity of available products.

The data product platforms category is nascent, and many software providers offer functionality that could be used to facilitate the development and consumption of data products but do not address all the requirements listed above. Additionally, some software providers in this space have products currently in the public or private preview stage of development. ISG only assesses generally available functionally as part of the Buyers Guide process. As a result, providers that fall into either of these two categories have been listed as Providers of Promise.

The ISG Buyers Guide™ for Data Products evaluates software providers and products in key areas, including the development, classification, consumption, discovery and management of data products. This research evaluates the following software providers that offer products that address key elements of data products as we define it: Actian, Alation, Alteryx, Amazon Web Services, Atlan, Collibra, Databricks, DataOps.live, Denodo, IBM, Informatica, K2view, Microsoft, Nexla, One Data, Qlik, SAP, Starburst and The Modern Data Company.

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 Products 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 products 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 products 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 products 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 products 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 products 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 Microsoft atop the list, followed by Informatica and SAP. Companies that place in the top three of a category earn the designation of Leader. Informatica has done so in five categories; Microsoft in four; Databricks and SAP in three; and Actian, Alation, AWS, DataOps.live, Denodo, One Data and Qlik 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: Alation, Alteryx, AWS, Databricks, IBM, Informatica, Microsoft 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: Actian and DataOps.live.

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: Collibra and Qlik.

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: Atlan, Denodo, K2view, Nexla, One Data, Starburst and The Modern Data Company (aka Modern).

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 products, 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 (25%), Reliability (15%), Adaptability (15%) and Manageability (15%). This weighting impacted the resulting overall ratings in this research. Microsoft, Informatica and SAP were designated Product Experience Leaders. While not a Leader, Databricks was 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 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.

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 Products in 2024, a software provider must be in good standing financially and ethically, have at least $10 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 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 last 18 months.

The software provider must provide a product or products that support agile and collaborative data operations and are marketed as addressing at least one of the following functional areas, which are mapped into Buyers Guide capability criteria: data product development, data product classification, data product consumption and data product management.

Data as a product is the process of applying product thinking to data initiatives to ensure that the outcome—the data product—is designed to be shared and reused for multiple use cases across the business. Data product platforms provide an environment for the development, publication and consumption of data products. 

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

• Data product development 
• Data product classification 
• Data product consumption 
• Data product 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 product software 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.”