1. Define & Paradigm Shift
Moving from "Data as Asset" to "Data as Product".
Core Characteristics (DATSIS)
2. Build Lifecycle & Team
The rigorous engineering process and the squad required to execute it.
The Data Product Squad
A successful data product requires a cross-functional team. Compare the skill distributions below.
Role Focus:
Select a role to see details.
3. Evaluate Fit & Utility
Before scaling, evaluate the product against Signal Validity and User Utility.
Criterion 1: Signal Validity
Does the algorithm extract a real signal from the noise? Adjust efficacy to simulate.
Criterion 2: User Utility
Does it solve a real problem? Select utility drivers.
Product Fit Assessment
Based on the Signal and Utility scores above, here is the recommendation.
ADJUST INPUTS
Interact with the tools above to get a strategic recommendation.
4. Validate & Readiness
Ensuring the product is Trustworthy, Consumable, and Discoverable before launch.
Trustworthy
Data must be accurate, consistent, and reliable. Validated via SLAs and automated quality tests.
Consumable
Products must have clear contracts, schemas, and documentation. Validated via access tests.
Discoverable
Metadata and cataloging make the product easy to find. Validated via catalog registration.