Gooddata-Data_Products__What_They_Really_Are__And_How_to_Build_Them_Right_.pdf

Transcript

1. What are data products and how to do them right! 12 Sept 2025
2. Ryan Dolley VP of Product Strategy at GoodData Host of Super Data Brothers show superdatablog.substack.com Today’s Host Connect with me on LinkedIn!
3. GoodData: The analytics product platform Flexibility Embed data anywhere Simplicity Custom experience for every user Productivity Accomplish more with code Scalability Scale to your organization's needs
4. When Data is a Product Design principles Common mistakes Agenda
5. What is a data product?
6. According to Jean-Georges Perrin A data product is a reusable, active, and standardized data asset designed to deliver measurable value to its users — whether internal or external — by applying the rigorous principles of product thinking and management. It comprises one or more data artifacts (e.g., datasets, models, pipelines) and is enriched with metadata, including governance policies, data quality rules, data contracts, and, where applicable, a Software Bill of Materials (SBOM) to document its dependencies and components. Ownership of a data product is aligned to a specific domain or use case, ensuring accountability, stewardship, and its continuous evolution throughout its lifecycle. Adhering to the FAIR principles — Findable, Accessible, Interoperable, and Reusable — a data product is designed to be discoverable, scalable, reusable, and aligned with both business and regulatory standards, driving innovation and efficiency in modern data ecosystems. JGP, Sr. Product Manager at Actian & Technical Chair of BITOL project
7. According to Anna Bergevin Anna Bergevin, Lead Product Manager at Resmed A product has product thinking. A product has clear lines of ownership, particularly if not all components are owned by the same team A product has robust engineering under it - quality tests, observability, version control, etc A product has strong product market fit and must be under continuous monitoring for usefulness and evolution.
8. According to Anna Bergevin Anna Bergevin, Lead Product Manager at Resmed
9. Do you currently build ‘data products’ and if so, what are they? How did you decide to take this approach? Is it working or not? And why? What do you think?
10. Design Principles
11. 1. Start with the job, not the data 2. Design the interface first 3. Ship and MVP and iterate fast 4. Engineer like software, not BI 5. Performance is a feature 6. Ship to where work is done Design principles
12. Common Mistakes
13. 1. Ignoring product principles 2. Just renaming your existing assets to ‘data products’ 3. Starting with tools and tech instead of user experience 4. ‘Self-service’ as an excuse for skipping design/front end 5. Building a giant monolith 6. Fear of deprecation 7. Rigid thinking and the need for definitions Common mistakes
14. Thank you!