Insights and actions
Azure helped NIA draw meaningful insights after analysis and deploy necessary measures, as discussed in the following sections.
Reducing flight delays by 17% using predictive analytics
Description: While performing initial data discovery and exploration, the NIA business intelligence team found that inefficient gate assignment was a major contributor to flight delays. Flight delays have a snowball effect because a delay in one flight can impact the next flight and the one after that. There is also the negative passenger experience that it produces. Currently, the assignment of gates at NIA is based on the capacity of their waiting area and the maximum capacity of the airplanes. This assumes that all flights are full, which is not necessarily true.
Combining weather data, city traffic data, historical flight delay data, and other sources allowed the business intelligence team to produce a better recommendation engine for gate assignment. The new recommendation...