Problem statement
AirQo, a leading player in air quality monitoring, faced a critical challenge in its hardware approval process. The existing process relied heavily on manual reports generated by data scientists, resulting in considerable delays and inaccuracies in approving devices for deployment on the AirQo network. On average, it took approximately one week to create and review these reports, impeding the timely and accurate approval of devices.
Challenge
The primary challenge lay in the hardware team's inability to effectively analyze the machine learning (ML) datasets required for device approval without the direct involvement of data scientists. This dependency on data scientists significantly slowed down the device approval and deployment processes, impacting AirQo's ability to scale its operations efficiently.
Solution
As a lead designer, I proposed and implemented an innovative solution to address these challenges. Leveraging the power of AI/ML, I designed a web application that empowered the hardware team with the autonomy to test and approve devices for deployment.
Success Metrics
1. Time on Report and Approval
2. Hardware Team Autonomy
3. Accurate Device Approval
Impact
In just over a month, the hardware team conducted over 43 device tests, meticulously identifying 82 errors and approving only 2 devices. This rigorous testing, facilitated by the web app, not only elevated device quality but also introduced a heightened level of accountability into the approval process.
We streamlined the report review and approval timeline, slashing it from 7 days to an impressive 1 day. This empowered the hardware team to independently conduct tests, eliminating the dependence on data scientists, enabling AirQo to establish its presence in 8 countries across Africa.