Market Perspectives

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 Products: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research. 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. 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. 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. 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. This research-based index evaluates the full business and information technology value of data products 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 products 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 Products in 2024 finds Microsoft first on the list, followed by Informatica 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: Microsoft. Informatica. SAP. The Leaders in Customer Experience are: Databricks. Microsoft. SAP. The Leaders across any of the seven categories are: Informatica, which has achieved this rating in five of the seven categories. Microsoft in four categories. Databricks and SAP in three categories. Actian, Alation, AWS, DataOps.live, Denodo, One Data and Qlik 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 products, 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 products 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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