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Data marketplaces and exchanges provide infrastructure, transactional capabilities and services for consumers and providers of data assets. Marketplaces prioritize data monetization via one-time or recurring subscription transactions, while exchanges prioritize sharing. Internal data exchanges facilitate enterprise data sharing and remove silos to cross-organization data product provision and access. AI’s need for large, varied and specialized datasets to train models has increased the demand for greater convenience in data sharing, purchase and consumption. Although adoption remains in the early phases, they provide liquidity to the data products space, enabling the sale, purchase or exchange of data products with relative ease. They enable secure multi‑party collaboration across partners, suppliers, and regulators, supported by strong data governance frameworks that ensure lineage tracking, policy enforcement, and stewardship consistency. Data Marketplaces and Data Exchanges are typically used by data scientists, analysts, product strategists, and business teams who need high‑quality internal or external datasets for analytics, AI modeling, and decision‑making

• Huwise
  4.5

  2 Reviews

A data and analytics governance platform is a set of integrated business and technology capabilities that help business leaders and users develop and manage a diverse set of governance policies and enforce those policies across business and data management systems. These platforms are unique from data management in that data management focuses on policy execution, whereas D&A platforms are used primarily by business roles — not only or even specifically IT roles — for policy management. Data and analytics (D&A) leaders who are investing in operationalizing and automating the work of D&A governance should evaluate this market. The work of D&A governance primarily includes policy setting and policy enforcement, and collaborates with data management (policy execution). Use cases are employed across numerous governance policy categories and multiple business scenarios and asset types (data, KPIs, analytics models). The intersection of use-case/business scenarios, policy categories and assets to be governed is then used to identify the technology capability. These capabilities may share similar names across policy categories, but may not mean the same thing, or may be used differently by various governance personas. For example, data classification in a data security implementation would be quite different from a data classification effort for creating trust models, which would be based on lineage and curation.

• Huwise
  4.8

  18 Reviews