Sometimes, in order to address a major risk identified in discovery, we need to be able to collect some actual usage data. But we need to collect this evidence while in discovery, well before taking the time and expense of building an actual scalable and shippable product.
Some of my favorite examples of this are when applying game dynamics, search result relevance, many social features, and product funnel work.
This is the purpose of a live‐data prototype.
A live‐data prototype is a very limited implementation. It typically has none of the productization that's normally required, such as the full set of use cases, automated tests, full analytics instrumentation, internationalization and localization, performance and scalability, SEO work, and so forth.
The live‐data prototype is substantially smaller than the eventual product, and the bar is dramatically lower in terms of quality, performance, and functionality. It needs to run well enough to collect data for some very specific use cases, and that's about it.
When creating a live‐data prototype, our engineers don't handle all the use cases. They don't address internationalization and localization work, they don't tackle performance or scalability, they don't create the automated tests, and they only include instrumentation for the specific use cases ...