What is a data marketplace?

A data marketplace is an online platform designed to facilitate the buying, selling, and sharing of data products.

Data marketplaces connect data providers with data consumers to enable the exchange or commercialization of data assets in a secure and governed way. By reducing the time it takes to access and get value from data, data marketplaces are crucial for organizations aiming to be data-driven. Rather than merely providing access to data tables, the best data marketplaces enable the access, use, and distribution of any type of digital asset via user experiences that support the full range of data consumers. A data marketplace leads to quicker access to data, a reduction in time to value, and accelerated business outcomes.

Public vs private data marketplaces

Data marketplaces can be thought of in two categories: public and private.

Public data marketplaces

Public data marketplaces are open to any user and tend to offer data products from a wide range of providers. Examples include Snowflake Marketplace, AWS Data Exchange, Databricks Marketplace, and Datarade. For data consumers, these platforms can be used to augment internal datasets with external data sources that are widely available. For data producers, public data marketplaces can be an easy route to market, with the expectation that there is an existing market of data consumers actively looking for data products. Due to the commodity nature of the data public data marketplaces, and the need to serve a range of data consumers, they have limited flexibility for data producers.

Private data marketplaces

Private data marketplaces are operated by and for specific organizations or groups and access tends to be invite-only and behind a login screen. They facilitate secure data sharing within an organization across individuals, teams, functions, and divisions. Private data marketplaces can also be used to share data with trusted third parties — including suppliers, partners, and customers — in a way that maintains control over data privacy and security. Private data marketplaces are designed to be far more flexible for both data providers and data consumers and tend to meet specific needs, such as offering customized data products and services.

Learn more about private data marketplaces in this detailed guide.

Access-only vs. end-to-end data marketplaces

Access-only data marketplaces

Access-only data marketplaces focus solely on data discoverability and access control. They enable users to find and access data but offer limited tools for using the data or distributing it to a different location. While useful for providing basic data access — typically at source — they often struggle to scale. By accelerating time-to-access, they also accelerate time-to-failure because data consumers quickly gain access but are often unable to complete their use case. This frequently results in large backlogs of consumer requests for data producers to transform, reformat, and redistribute data, or to integrate it with various tools and applications.

End-to-end data marketplaces

End-to-end data marketplaces also enable discovery and access, but access tends to be via pre-determined user experiences. While this includes directly accessing data at source, more focus is placed on a wide range of use cases for a diverse group of users. These can include:

• Sandboxes and cleanrooms: Secure environments for accessing data without the ability to see the data being queried, or the ability to remove it from the platform. This is particularly useful when there is low/no trust between the producer and the consumer.
• Workbenches and query engines: Tools to structure, query, and transform data using a range of languages including SQL, Python, and R. These tools are typically used by data scientists and data engineers.
• Natural language queries: Interfaces that allow any user to ask questions, and receive answers, in natural language. This often requires integrating a large language model (LLM).
• APIs: Application Programming Interfaces to integrate data with other systems and applications, typically used by application developers.
• Data pipelines: Automated processes for transforming and distributing data across different platforms, endpoints, and environments.

By providing these capabilities in a self-service platform, end-to-end data marketplaces avoid large backlogs, streamline workflows, and enable users to rapidly get to value.

Use cases for data marketplaces

Data commerce

Data marketplaces facilitate data commerce by allowing data providers to monetize their data assets. Providers can offer their data on subscription plans or pay-per-use models, generating new revenue streams or improving the experience for their existing customers. Data providers tend to use a mix of both public and private data marketplaces to offer customer choice, deliver high-value customized experiences, and guard against vendor lock-in.

Enterprise data marketplace

Within the enterprise, a data marketplace serves as a central hub for all data products in the organization. It enables secure data sharing and collaboration across teams, departments, and legal entities, improving operational efficiency and fostering innovation. Crucially, enterprise data marketplaces are also used to facilitate controlled access to data for AI, limiting what data can be exposed to LLMs and maintaining an audit log.

Data mesh

In a data mesh architecture, a data marketplace acts as a ‘self-service data platform’, which is a decentralized platform that supports domain-oriented data management. This allows different business units to share and access data as a product while maintaining control and ownership. This federated approach promotes a more scalable and flexible data infrastructure that avoids dependencies on monoliths — e.g. moving all data assets to a particular cloud storage.

Data distribution

A data marketplace can act as a robust platform for data distribution, ensuring that the right data is delivered to the right users at the right time. In this mode, a data marketplace supports seamless data integration and distribution across the various systems and applications within a data ecosystem. With automated data pipelines and the ability to customize data formats, costs are kept down. Baked-in governance and access control reduces operational risk.

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What is a data marketplace?

A data marketplace is an online platform designed to facilitate the buying, selling, and sharing of data products.

Data marketplaces connect data providers with data consumers to enable the exchange or commercialization of data assets in a secure and governed way. By reducing the time it takes to access and get value from data, data marketplaces are crucial for organizations aiming to be data-driven. Rather than merely providing access to data tables, the best data marketplaces enable the access, use, and distribution of any type of digital asset via user experiences that support the full range of data consumers. A data marketplace leads to quicker access to data, a reduction in time to value, and accelerated business outcomes.

Public vs private data marketplaces

Data marketplaces can be thought of in two categories: public and private.

Public data marketplaces

Public data marketplaces are open to any user and tend to offer data products from a wide range of providers. Examples include Snowflake Marketplace, AWS Data Exchange, Databricks Marketplace, and Datarade. For data consumers, these platforms can be used to augment internal datasets with external data sources that are widely available. For data producers, public data marketplaces can be an easy route to market, with the expectation that there is an existing market of data consumers actively looking for data products. Due to the commodity nature of the data public data marketplaces, and the need to serve a range of data consumers, they have limited flexibility for data producers.

Private data marketplaces

Private data marketplaces are operated by and for specific organizations or groups and access tends to be invite-only and behind a login screen. They facilitate secure data sharing within an organization across individuals, teams, functions, and divisions. Private data marketplaces can also be used to share data with trusted third parties — including suppliers, partners, and customers — in a way that maintains control over data privacy and security. Private data marketplaces are designed to be far more flexible for both data providers and data consumers and tend to meet specific needs, such as offering customized data products and services.

Learn more about private data marketplaces in this detailed guide.

Access-only vs. end-to-end data marketplaces

Access-only data marketplaces

Access-only data marketplaces focus solely on data discoverability and access control. They enable users to find and access data but offer limited tools for using the data or distributing it to a different location. While useful for providing basic data access — typically at source — they often struggle to scale. By accelerating time-to-access, they also accelerate time-to-failure because data consumers quickly gain access but are often unable to complete their use case. This frequently results in large backlogs of consumer requests for data producers to transform, reformat, and redistribute data, or to integrate it with various tools and applications.

End-to-end data marketplaces

End-to-end data marketplaces also enable discovery and access, but access tends to be via pre-determined user experiences. While this includes directly accessing data at source, more focus is placed on a wide range of use cases for a diverse group of users. These can include:

• Sandboxes and cleanrooms: Secure environments for accessing data without the ability to see the data being queried, or the ability to remove it from the platform. This is particularly useful when there is low/no trust between the producer and the consumer.
• Workbenches and query engines: Tools to structure, query, and transform data using a range of languages including SQL, Python, and R. These tools are typically used by data scientists and data engineers.
• Natural language queries: Interfaces that allow any user to ask questions, and receive answers, in natural language. This often requires integrating a large language model (LLM).
• APIs: Application Programming Interfaces to integrate data with other systems and applications, typically used by application developers.
• Data pipelines: Automated processes for transforming and distributing data across different platforms, endpoints, and environments.

By providing these capabilities in a self-service platform, end-to-end data marketplaces avoid large backlogs, streamline workflows, and enable users to rapidly get to value.

Use cases for data marketplaces

Data commerce

Data marketplaces facilitate data commerce by allowing data providers to monetize their data assets. Providers can offer their data on subscription plans or pay-per-use models, generating new revenue streams or improving the experience for their existing customers. Data providers tend to use a mix of both public and private data marketplaces to offer customer choice, deliver high-value customized experiences, and guard against vendor lock-in.

Enterprise data marketplace

Within the enterprise, a data marketplace serves as a central hub for all data products in the organization. It enables secure data sharing and collaboration across teams, departments, and legal entities, improving operational efficiency and fostering innovation. Crucially, enterprise data marketplaces are also used to facilitate controlled access to data for AI, limiting what data can be exposed to LLMs and maintaining an audit log.

Data mesh

In a data mesh architecture, a data marketplace acts as a ‘self-service data platform’, which is a decentralized platform that supports domain-oriented data management. This allows different business units to share and access data as a product while maintaining control and ownership. This federated approach promotes a more scalable and flexible data infrastructure that avoids dependencies on monoliths — e.g. moving all data assets to a particular cloud storage.

Data distribution

A data marketplace can act as a robust platform for data distribution, ensuring that the right data is delivered to the right users at the right time. In this mode, a data marketplace supports seamless data integration and distribution across the various systems and applications within a data ecosystem. With automated data pipelines and the ability to customize data formats, costs are kept down. Baked-in governance and access control reduces operational risk.

Conclusion

Data marketplaces have become an essential tool for data-driven organizations to accelerate time-to-access and time-to-value. They are also critical for achieving data democratization, deploying federated data infrastructures like data mesh, and avoiding costly vendor lock-in.

Increasingly, data marketplaces are becoming part of the modern data stack. Large organizations are engaging with multiple public data marketplaces, while also maintaining a private data marketplace that meets their specific needs and works across their entire data ecosystem.

Data marketplaces provide a streamlined, secure, and scalable solution for buying, selling, and sharing data and data-related services. They offer significant benefits when used for data commerce, as an enterprise data marketplace, part of a data mesh, or as a platform for data distribution.

By incorporating advanced tools and features, data marketplaces help organizations maximize the value of their data assets and ultimately become more data-driven.

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