The challenges outlined highlight the need to introduce a new technological and organizational layer into the enterprise architecture. The Data Product Marketplace is not just a tool. It is an innovative component designed to simplify and enhance the experience of anyone looking to search, explore, publish, or use data.
Managing data as a product requires structures that, while locally complex, aim to reduce the overall complexity of the system. The Data Product Marketplace seeks to build trust among the various actors in the ecosystem by relying on two key components: the transactional engine and the learning engine.
The first consists of the set of services and experiences the platform offers to facilitate relationships and value exchanges among ecosystem participants. The second focuses on simplifying the effort required to understand and solve problems, by promoting knowledge sharing. Together, they enable a distributed process of value creation and guide key interactions within the data ecosystem.
Within this ecosystem, different actors can be identified, each with distinct roles and responsibilities in data management. Two primary figures stand out:
- Data Producer: defines the product’s value (description, schema, quality KPIs), maintains the product, publishes data contracts, and manages access and SLAs.
- Data Consumer: acts as the actual consumer of the data published by Data Producers, enabling business use cases and supporting various objectives.
The mindset shift encouraged by Data Mesh and the data-as-a-product approach promotes a participatory and sustainable operating model, where these roles collaborate within a complex ecosystem to solve business problems through the use of data.
The platform designed to support the development and management of Data Products throughout their entire lifecycle is called XOps Platform. In Quantyca’s vision, an effective XOps Platform is structured into three distinct planes: the Experience Plane, the Control Plane, and the Utility Plane, as shown in the figure below. The consumers of the services exposed by each plane differ: the Experience Plane primarily serves business users (Data Consumers), while the Control and Utility Planes are leveraged by technical users (Data Producers).
The Data Marketplace is positioned at the highest level of the platform, enabling highly intuitive, self-service consumption experiences while minimizing friction between Data Consumers and Data Producers. A Data Product Marketplace is not just a simple data catalog, now a commodity, but a true exploration and “shopping” platform. Thanks to an intuitive and engaging interface, it delivers a unique user experience. At its core are metadata, a cornerstone of modern Data Strategies, which provide order, coherence, and value to the entire enterprise information ecosystem.
The user experience within a Data Product Marketplace is a critical element. The information available on the marketplace must support the discoverability: browsing features integrated into the interface enable intuitive and streamlined consumption experiences.
Search comes into play when a user has a specific goal and wants to quickly locate the data they need. For search to be truly effective, it is essential to have a complete metadata model that includes accurate descriptions, consistent tags, and alternative terms. In this way, every new contribution progressively enriches the search experience, making it more precise and accessible across the organization. The search approach is inherently exploratory: users can navigate through categories, domains, and hierarchies to discover datasets or products of interest.
Once a Data Product of interest is identified, the Data Marketplace allows users to explore the product in detail. They can view:
- General information: high-level summaries such as domain, purpose, and ownership
- Interface information: details about the physical assets linked to the product’s input and output ports
- Semantic metadata: semantics related to the assets managed by the product, referencing concepts defined in the enterprise ontology
- Data flow: end-to-end data flow specifications, including upstream and downstream Data Products
- Policy and quality: governance information to evaluate the intrinsic quality of the data exposed by the product
- Documentation, feedback, and reviews: a set of complementary information enabling further evaluation based on other users’ consumption experiences
Finally, the actual “purchase” experience takes place. The Data Product Marketplace, supported by the XOps Platform, aims to make this experience fully self-service, straightforward, and transparent. Users can submit a data access request specifying what they want access to (which output port) and why (purpose of use). This typically triggers a workflow involving the Data Product Owner (Data Producer), who has the ability to review and approve (or reject) the request.
Modern marketplaces enable a fast and governed access experience: users initiate requests independently and rely on automated workflows, with human intervention only when specific manual actions are needed. In this way, security rules, data masking, and authorization management become an integral part of the experience without obstructing data consumption.
Data Producers and Data Consumers are not the only roles that can benefit from a Data Marketplace. It also serves as a strategical asset for governance teams. Unlike traditional security or ticketing tools, the marketplace enables the collection and maintenance of a verifiable record of how data is actually used. It does not merely track who accessed a dataset but also documents the purpose for which the data is requested and consumed. This level of detail provides valuable metadata on the real usage of Data Products within the organization: which domains are most utilized, which products deliver value, and which require improvements.
The Data Product Marketplace becomes not only an access point for consumers but also an observability tool for the entire data lifecycle, enabling teams to:
- Ensure compliance through transparent and auditable trails
- Monitor adherence to governance and security policies
- Collect feedback to continuously improve the quality and usability of Data Products
- Measure success metrics related to the usage of the data platform
