The term “data product” originated from data science. At least, I first heard this term from data scientists. My latest investigations have demonstrated that no aligned definition of this term exists.
According to the Forbes Council, a data product is “a self-contained data “container” that directly solves a business problem or is monetized.”
This definition assumes that a data product can be of any type, digital or non-digital. A report, dashboard, and data set are examples of a data product in this context. The level of a data model of the data set is not defined. I’ve seen in practice that some companies define a data product at the logical, application-agnostic level of a data model. Others do it at the application physical level.
Gartner defines a product in the digital business context as “a named collection of business capabilities valuable to a defined customer segment. A product may be just software and data. Alternatively, it may comprise any combination of software, hardware, facilities, and services as required to deliver the entire product experience. A product may be a repeatable service (for example, a subscription service), or it may be a platform (one-sided or multisided).”
So, this definition extends sufficiently the components of a data product by including software, hardware, etc.
Zhamak Dehghani, in her book about data mesh, also promotes the extended scope of a data product: data and metadata, codes for pipelines and APIs, and infrastructure.
To summarize all discussed above, a data product is an output of a data-related process that can include data, metadata, software, application, database, and service. Infrastructure (hardware and networks) are optional components.
So, any organization has many options to define the data product to fit its needs and understanding of data management.
How Do YOU define a data product in your company?
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