Bisnis.RumahBudidaya.Co.Id – Big Data in Airline Industry – These days we get lots of data. Many companies are adopting big data and business analytics to analyze available data in order to improve their products and services.
Big data is described as the massive volume of both structured and unstructured data which is difficult to process using common software techniques or by using traditional statistical methods.
The 5 V’s of big data are velocity, volume, value, variety, and veracity are the five main characteristics of big data which allows the data scientists to derive more value from their data.
Big data is being generated through different sources including internet traffic, mobile transactions, user-generated content, and social media also sources of big data such as the content captured through sensor networks, business transactions, and many other domains such as bioinformatics, healthcare, and finance.
The Problems Identified in the airline industry are:
- Costs of fuel are very high for airlines.
- The flight broke downs, and engine fires were due to weather conditions.
- Misplace of baggage in airports.
How to tackle the problems in the airline industry?
1. Smarter maintenance
Big data helps airlines to better maintain their aircraft. Take fuel for example; fuel accounts for 17% of all airline operating costs, making it the most significant overhead after labor.
Therefore, fuel efficiency is a critical metric. With big data, airlines can identify new efficiencies. Greater computational power has allowed airlines to gather and process huge volumes of data that enable them to analyze fuel consumption on a per-trip basis.
For instance, Southwest Airlines collects data from sensors embedded in aircraft that measure wind speed, temperature, and plane weight alongside fuel consumption.
2. Safer flights
By capturing flight incident data, regulators can improve safety across the aviation industry.
Recently, the European Aviation Safety Agency launched the Data4Safety program, which collects and analyzes in-flight telemetry data, air traffic control information, and weather forecasts to detect risk.
The program will allow regulators to determine safety risks and advise stakeholders. By combining big data analytics and computational power, this program aims to strengthen weak links in the aviation chain.
3. Improve service
While there are significant operational gains, big data can also help airlines to enhance customer service. Instead of simply identifying successful products, airlines can use big data to drill down into customers’ buying habits.
By analyzing variables and aggregating historic information, airlines can predict and model customer behavior to generate personalized offers.
This smart approach not only drives ticket sales, but it also enhances opportunities for upselling, such as baggage fees and onboard refreshments.
4. Improving Marketing Efforts
One of the most common ways airlines use big data is to improve their marketing efforts. By collecting detailed data from individual customers, airlines present them with special offers.
As a result, this increases the chances of getting a favorable response which enables airlines to measure how customers think and behave for future marketing activities.
5. Pricing and Network Strategies
Many airlines go further than a basic data collection and analysis. They can analyze big data such as tracking traveler’s purchase activity while tracking travel demand patterns from across the globe. If an airline sees the demand for flights from A to B going up, they can alter prices accordingly.
6. Increasing Customer Satisfaction
- Real-Time Baggage Status: Delta Airlines introduced an application which allows customers to track their bags from their mobile devices. The application is simply using the baggage data that Delta staff uses, however the app has been downloaded by more than 11 million customers.
- Wearable Technology & IoT: Turkish Airlines is also planning to use big data and the Internet of Things technologies operating the service at Istanbul’s new airport in 2018. They will use beacons in the new Istanbul airport which will interact with smartphones to assist customers navigate their way to lounges, food and retail and to the boarding gates. In addition to that, customers will be able to save the location of where they park their car and track their children with smart bracelets even when they are flying alone.
- More Insightful Customer Behaviours: Southwest Airlines uses a speech analytics tool that allows customer service representatives to understand the nuances of every recorded customer interaction. They also analyze data from various online channels like social media to receive more information about customers in real-time. As a result of this, different metrics they collect guides service personnel to the best solution in every scenario.
The various tools that can be used to solve the problems are:
- Apache HBase to store the unstructured data of various flights, statues, fuel history, etc.
- Apache Hive to store the structured data like customer name, flight name, etc.
- Apache Pig to map and reduce the lots of data.
- Apache spark to make analysis and computations on the data and it can be integrated with Apache Hive/Hbase.
Airlines Data Analytics for Aviation Industry Project
Below are a few big data projects to help you understand the implementation of big data analytics in the airline industry.
Airline Dataset Analysis using Hadoop, Hive, Pig, and Impala
For airlines, it is important to keep an eye on the most popular routes so that more airlines can cover them and increase efficiency.
For instance, do the numbers of individuals flying across a specific path vary over a day, week, month, or year, and what causes these changes?
Additionally, it’s important to pay close attention to delays to determine whether older flights are more likely to experience them; what time of day, week, year, or month is ideal for minimizing delays?
Working on this data benefits the airlines and travelers using them. You can partition and cluster the data using Apache Hive or Apache Impala and preprocess data with Apache Pig.
Airline Data Analysis with Python
This big data analytics project uses Python’s sklearn libraries to predict customer satisfaction and aggregate customers.
You will start working on this project by importing the Python libraries Pandas, Numpy, Matplotlib, Seaborn, and SKlearn.
This project involves applying feature selection and one hot encoding to perform dissatisfaction prediction analysis. Additionally, you will use principal component analysis (PCA) for feature transformation and customer clustering.
Airline Revenue Analysis Project
This airlines data analytics for aviation industry GitHub project aims to answer the question- which airline is most successful in terms of revenue?
This project analyzes and compares the two major efficiency measures across the industry: TRESM (Total Revenue per Equivalent Seat Mile) and PRESM (Passenger Revenue per Equivalent Seat Mile).
By dividing operating income by the total number of available seat times the total number of miles flown, one can calculate the Revenue per Available Seat Mile.
All seats in the commercial aircraft are included in the total number of seats available; therefore, maximizing this measure entails optimizing both the number of seats and kilometers flown.
Big Data helps airlines have a better understanding of the individual passenger, identify patterns in his/her behavior, determine preferences and foresee future requests.
By leveraging Big Data insights, airlines have the ability to make strategic decisions and differentiate themselves in the extremely competitive market.