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Data Product Design aims to provide structure and usability to data-driven solutions. It involves techniques for capturing, analyzing, and presenting data to maximize benefits for end users and support data-driven decision-making.
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Executable approach: can be applied and produces an outcome.
What organizes, connects, or makes decisions possible.
Data product design shapes data, metadata, interfaces, and ownership into a usable product for defined consumers.
The approach grew from data mesh and data product discussions that aimed to bring data closer to its domain experts and users. Dehghani popularized Data Mesh in 2019/2020 with the principle “data as a product”; research frames it as a sociotechnical design approach.
A data product is a package with content, contract, documentation, quality promises, and stewardship. Design starts with a recurring user task and ends with dependable use.
A person, team, or system using the data product for a task.
An agreement on schema, meaning, quality, and access method.
Accountability for a data product's value, operation, and evolution.
Good design improves discoverability, trust, and reuse. It can encourage local optimization when domain ownership, shared standards, and real consumers are absent.
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