A few years ago, I boarded a Southwest Airlines flight from Chicago to Kansas. The flight was full and delayed due to passengers who were left behind. The airline provided live entertainment with a humorous special program, enacted by the air hostesses, thus drawing our attention away from the frustrating delay we were experiencing.
After about 20 minutes, a woman accompanied by her four year-old son boarded the aircraft appearing tired and weary. The pilot came on the PA system and announced that although our flight was delayed, we should be able to arrive in Kansas City on time.
“McKinsey & Company cites the example of an airline deciding whether or not to hold a plane for 15 minutes to await the arrival of connecting passengers. In reality, it will take 32 minutes for the passengers and their bags to make it onto the plane because of the specific locations of the gates and airport traffic conditions. If the plane waits, the airplane will forfeit its take-off slot and eight other passengers will be in danger of missing their connections at the destination airport.”
Gregory says that without analytics, there’s no way to take all these factors into account when making a decision. The airline has to make its best guess and deal with the consequences.
Add analytics to the equation, and the airline can run an algorithm that considers the arriving gate information, walking and baggage transport time between gates, the cost of re-booking passengers and the frequent flier status of the affected passengers. Consequently, the airline can foresee that the 15-minute hold will likely turn into a 30- or 40-minute delay, and it makes a decision accordingly – how’s that for clairvoyance?
Big data isn’t as complex and scary as it sounds. Big data is starting to change how organizations are structured, and airlines may need to rethink how they operate as a result of big data. We are also familiar with disruptive technologies that have an impact on our everyday lives, such as: Uber transportation, commercial drones delivering groceries and food to our doorsteps, 3D printing, smart homes, blockchain technology, advanced virtual reality, medical innovations, Internet of Things, remote security systems for our workplaces and so on.
From using weather patterns to predict sales, to combining data from web search trends, social networks and industry forecasts to predict product trends, along with forecast demand to pinpoint customers and optimize pricing and promotions, big data is used to formulate many types of predictions in the daily economics of life.
However, in the industrial realm, many businesses are still figuring out how to apply big data, artificial intelligence, cloud storage, connectivity, mobility and social media to look beyond their own needs to support their customers.
Airlines are now using big data to enhance the passenger experience and cultivate high-value customers. Big data provides airspace data, operational data and general information messages, and it has built-in support for data analytics. Businesses use data analytics to obtain knowledge in specific areas of interest from a structured and unstructured mass of raw data.
Another key to proper use of big data is automation. With the positioning of accurate data and the right analytical tools, you can create adaptive algorithms to counter competitor pricing. An airline may have extensive data assets about previous customer purchases, frequent flying, discount offers, mobile, social media, website data, loyalty programs and reservations. It could incorporate machine learning to identify numerous patterns of customer behavior that led to a purchase.
