Data Products | A Guide to Data Products

Practical guide to data products: definition, rulebook, business canvas, MVDP, sharing, quality/SLO, change/deprecation and measurable value.

Many organisations do not have “too little data”. They have too little predictability: You find something in the catalogue (if you have one), but you do not know whether it is recommended to build on, how it changes, or who actually responds when something breaks.

Data products are an attempt to make a few important deliverables more manageable over time — without turning the entire data platform into a governance project.

Who is this guide for?

For anyone working with analytical data: data engineers, analytics engineers, data scientists, BI/analytics, data governance — and those who lead data teams.

The ambition is that you should be able to use the text to make concrete decisions:

• What is a data product — and what is it not?
• What minimum requirements are reasonable?
• How do we prevent “data product” from becoming a word we write but never act on?
• How do we build data products that withstand change without people creating copies “just in case”?

Chapters

1. What is a data product?
2. Rulebook and portfolio: product vs component, ownership and domains
3. Business canvas and MVDP: from “good idea” to something people dare to use
4. Value: why this is actually worth the effort
5. Interfaces first: sharing and data contracts
6. In practice: product page, catalogue, components and lightweight governance
7. Quality and reliability: tiers, SLOs and response patterns
8. Change, versioning and deprecation practices
9. Product forum, 100 days as a data product owner, and Learn more