Working with big data to provide required climate intelligence

The Problem

Climate intelligence company Cervest was looking for a new risk proposition. They required data on buildings around the world exposed to climate hazards – extreme heat, strong winds and flooding. They wished to identify critical data points for the climate risk proposition.

The Solution

Our product discovery employed the Double Diamond framework, with an outline of organisation objectives, and financial and competitor landscapes. We ran workshops to identify pain points, and talked to stakeholders and prospects to uncover user needs. Identifying primary use cases was crucial, including user story mapping. These enabled a build-out of high-level epics (a large user story that can be broken down into smaller elements) along with the product backlog.

The Results

We created a series of prototypes with Cervest. These included a climate ratings pipeline based on live data covering the built environment with an assets database mapped to financial securities. We worked with data scientists and extreme weather data modellers to support the development of data models for the climate-value-at-risk metric. Data modelling was developed by a multidisciplinary team, which led to the creation of a minimum viable product based on predictive AI, ready for the next step.