Product Eyes – Seeing Data In A New Light To Build Valuable Products
summary
Frames data product work as a factory: traceable lineage and labeling from source to derived outputs, pipeline choices for batch or streaming processing, and explicit gating for PII and regional use. Mandates version control and downstream lineage, catalog metadata, quality and usage scores, and multiple consumption endpoints or APIs. Claims productization needs merchandising and iteration not just analytics. Mentions Databricks Marketplace and rolling clean rooms as concrete infrastructure for sharing models, dashboards and datasets.
AI-written summary · about
chapters (13)
- 0:00Intro
- 0:15Data Maturity
- 1:07Derived Data Products
- 2:06Popcorn
- 2:28Building a Product
- 3:32Product Ingredients
- 5:42Labels
- 7:14Process
- 8:38Merchandising Marketing
- 10:41Over Time
- 12:15Consumer Choice
- 13:56Recap
- 14:33Databricks
related talks
22mData as a Product: Transforming Analytics for Business Success
13mData for product
17mSpeeding Data Product Delivery from Model to Marketplace
39mData Mesh: Why, What and How
49mPanel discussion - Building Data Products
30mData Products 101: From Data Chaos to Clarity
29mHow to treat your data as a product
40mData Products and AI: A Conversation on Success Factors and Hurdles