Within the world of solar panels, installers deal with the problem of “too much” and “not enough” data. While an odd problem to be presented with, solar panel installation companies currently do not leverage large amounts of geospatial data, but do require a significant amount of analysis on a per-property basis. The result is a much larger overhead for individual customers whose costs are largely spent on analysis of their property as opposed to the costs associated with actual installations.
https://www.prnewswire.com/news-releases/aurora-solar-raises-250-million-to-digitize-solar-installations-301297510.html Aurora Solar, funnily enough, does not install solar panels on rooftops. Using geospatial data, Aurora uses larger amounts of data (focusing on blocks, neighborhoods, regions, etc.) as opposed to individual rooftops, to analyze and publish what types and kinds and shapes of solar panels are required for each individual property. Leveraging a shift towards renewable energy, Aurora wants to empower the individuals working in the US and elsewhere around the world installing, managing, and paying for solar panels on their rooftops.
Aurora is a software platform, offering a SaaS offering to solar panel installing companies. These companies use their data to assess and plan for individual installations. A main, albeit not direct, stakeholder is the renewable/solar energy consuming market. Without this market, there is no market for solar panel installing companies.
While Aurora’s sales pitch is quite strong and unique, the company needs to have a competitive offering with what individual solar panel companies may be doing. For example, if it is still cheaper for companies to conduct their own analysis and design on a per-client basis, Aurora may not be an attractive alternative. Moreover, accuracy needs to be a priority. As a SaaS platform, the quality of the geospatial data being utilized needs to be a central focus, leveraging tech that smaller companies may not have access too and may not know what to do with. As I mentioned in the first post, the problem lies in both too much and too little data. Aurora must digest this swath of information and convey it to differing stakeholders in unique but equally powerful ways.