What is a Data Product and Why Does It Matter to Data Mesh?

[jrineakter]

When a team or individual within your enterprise owns data in the same way a team owns a product, i.e. the set of services that implement a facet of the business they support, and they treat other teams as internal customers of that data, you’re treating data as a product. These teams or individuals act as data product managers, serving as the bridge between data producers and data consumers, and making sure data satisfy the requirements of internal users. But what is a data product as it relates to data mesh?

What are Data Products?
As we’ve mentioned on the data.world blog, a data mesh is an approach that empowers domain experts to own the data they create and make it available to consumers across business lines. It is defined by four pillars: data ownership by domain, data as a product, self-service data, and federated computational governance.

According to Zhamak Deghani — originator of the phrase “data mesh” — the data as a product archetype is an integral component of a data mesh architecture, and it’s essential for making data governance more scalable across broad parts of a company. Treating data as a product means bringing product thinking to data management, already a common practice in software product development. But what does this look like in practice?

A data product is a reusable data asset, built to deliver a trusted dataset, for a specific purpose. It collects data from relevant data sources — including raw data — processes it, ensures usa whatsapp number data data quality, and makes it accessible and understandable to anyone who needs it to meet specific needs. Data products are analyzed by data scientists and analysts to inform predictive analytics, build data models, build new reports, assist in machine learning, and more.

A data product makes a dataset easier to understand, easier to discover, and easier to access as a data asset. It generally corresponds to one or more business entities — customers, orders, etc. — and is made up of metadata and dataset instances.

When building data products, a data product manager gathers requirements and use cases, and learns the specific needs of end users to define a roadmap and plan. The data product team executes on the plan, and tests, releases, and iterates in an agile fashion to improve that product, continually enhancing data assets and data quality with every iteration.

Data consumers can then use these data products to create business intelligence dashboards and reports, and data teams can run artificial intelligence and machine learning models to improve business decision-making and gain a competitive advantage. Data analysts, BI developers, and data scientists are examples of data consumers.