Big Data in Airline Industry – Fifteen years ago, it would have been acceptable for airlines to focus purely on flying planes in the sky. Today, they also generate tera bytes of data. The core competency of airlines has been extended to shifting and sorting data from different constellations of data sources. Big data in the aviation industry changes everything.
Imagine if you run a large airline, and there’s a storm passing through one of your airline’s flight destinations, which means several flights will be delayed. Every flight has passengers with their own loyalty and profit profiles, and many will need to make connecting flights with your airline.
The airline must determine its next course of action. Whether to hold each connecting flight long enough to accommodate late passengers or not.
Airlines also have to consider the timing of baggage transfers, the number of passengers transferring, which flight they are from, and the length of time it takes to arrive from one gate to another, along with many other variables regarding the profitability of each outbound flight. Complicated isn’t it?
This is one example of how big data in aviation can help make a business more efficient. These transportation companies live and die by data, they generate more data every day in every part of their operations.
The Boeing 787, for example, generates an average of half a terabyte of data for each flight it makes. Combined with weather information, contact center interactions by consumers, ticket information, and airport performance times, this overflow of data can offer significant business insights for companies engaged in highly competitive industries.
The Benefits of Big Data in Airline Industry
Here’s some benefits of Big Data in Airline Industry:
Take fuel usage, for example. Taking up about 17 percent of operating costs, fuel is the second highest expense at an airline after labor. This makes fuel efficiency an important metric. Airlines are now using big data to help inject efficiency into their fuel use.
Computing power has grown to the point where airlines can collect and process the vast amounts of data they need to analyze fuel usage per flight. One carrier, Southwestern Airlines has collected data directly from sensors embedded in its planes, including information about wind speed, ambient temperature, aircraft weight and thrust.
All of those details are absorbed by the analytics engine and combined with operational data on fuel, passengers, cargo load, along with weather data, to look for patterns in trip profitability.
The airline hopes that the data mining that is being carried out will produce something actionable for decision making such as adding or reducing flights to various routes, adjusting the fuel load for each aircraft, and selling additional passenger tickets.
They can also provide this information to pilots in the air. If turbulence creates the need to adjust the plane’s height in flight, big data can now provide Southwestern pilots with detailed analysis of the extra fuel used associated with specific altitudes and associated costs.
Data from aircraft sensors can also provide insight beyond fuel efficiency. Boeing uses analytics to view the condition of 2 million of its 4000 aircraft each day as part of its Airplane Health Management (AHM) system.
This data, which includes in-flight measurements, mechanical report writing, and other findings, helps companies to plan equipment maintenance with minimal disruption to flights.
For example, data analytics predicts integrated generator drive failures, enabling it to investigate and fix issues before they become problems, saving $300,000 in service delays and repair costs.
By drawing on data from aviation accidents, regulators are also hoping to improve safety across the industry. The European Aviation Safety Agency (EASA) launched Data4Safety, a data collection and analysis program to detect risks using a combination of safety reports, in-flight telemetry data, air traffic control information and weather data.
This program enables regulators to identify the greatest security risks and determine whether industry stakeholders are taking appropriate actions to minimize them. By combing through terabytes of data, it is hoped that he will be able to find weak points in the flight chain.
While much of the data airlines collect focuses on what goes on in and around planes, there is great potential on the other side. United Airlines uses big data to transform its customer profiles into more focused individual profiles.
Rather than just identifying its most successful products, the airline uses big data to explore every consumer’s buying habits. By analyzing more than 150 variables about each customer, including past purchases and flight destinations, it can predict the most likely action and dynamically generate personalized offers.
Using big data like this increases revenue from sources other than tickets, such as baggage fees, in-cabin meals and services by as much as 15 percent.
The airline industry in Indonesia is often marked by delays and losses. The largest state-owned airline in Indonesia, Garuda Indonesia, even recorded a loss of 2.88 trillion rupiah throughout 2017. This is quite unfortunate considering the potential of the Indonesian aviation industry which will reach $ 2 billion in the next 4 years.
To minimize the losses incurred, big data analytics can be one way out. Is Paques, big data analytics created by an Indonesian company that is ready to be used for various industries. With the concept of a ‘data lake’, Paques has the ability to process various types of data without having to convert it into a certain format, this is of course crucial because it can save time and costs. The existence of a self-service analytics feature also makes it easier for anyone who works in the aviation industry to be able to operate Paques easily.
Cases like the ones in this paper show how airlines are not just a transportation company, now airlines are also data producing companies and cannot be separated from information technology. Those are the benefits of Big Data in Airline Industry.
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