> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/data/engineering-intro/data-product.md). # Data Product A platform that nobody adopts is not a product; the work is to treat data as something with users and owners. The page starts with a definition checklist and a framework for a data product, then moves to treating the data platform itself as a product. A product needs a contract with its consumers and a platform to run on. The same notes are in [Data Contract](/data/engineering-intro/data-contract.md) and [Data Platforms](/data/engineering-intro/data-platforms.md). The definition comes as a checklist: the [Definition, phdata.io](https://offers.phdata.io/hubfs/Checklists/Data%20Product%20Definition%20Checklist_D2.pdf) starts by asking whether there is a general description of what you want to do with the data and what the user is looking for. A checklist says what to ask; a framework says who answers. The (good) [A framework for a data product](https://www.datasciencecentral.com/data-product-framework/) is good on accountability, boundaries, contracts, expectations, consumers, and knowledge. The same thinking applies one level up, to the platform that serves those products. [A meta discussion on a data platform as a product](https://medium.com/stuart-engineering/how-were-building-our-data-platform-as-a-product-f89142b6547f) is one team's account of building their data platform as a product. [Build a data platform as a product](http://web.archive.org/web/20220707112131/https://productcoalition.com/how-to-design-and-build-a-data-platform-as-a-product-d22329ff5a3f), read through its archived copy, is Filipe Rigueiro's breakdown of the challenges, teachable moments, frameworks, and recommendations for delivering a data platform successfully. ## Deprecated links {% hint style="warning" %} These links and images no longer work. The original wording is kept here. A same-resource copy, when one was checked, is used above. {% endhint %} * build a data platform as a product. This address no longer opens: --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/data/engineering-intro/data-product.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.