Data science is a relatively new field; you can see more interest in it daily. It is also growing rapidly as more and more companies, big or small, recognize the importance of using this discipline in their business practice. The aviation industry is one of the most interesting spheres to study with respect to data science, both because of its specifics regarding technical challenges and because it has specific data collection and usage characteristics.
The Big Data Analytics market size in the global aviation industry is anticipated to reach $7178 million by 2023 and increase at a CAGR of 17.5%.
The global aviation industry has to focus on both improving the safety of flights and reducing the cost of the ticket. Data science can be crucial in solving these serious problems and offering solutions for new needs and challenges. Data science is constantly growing today, from building a better statistical model to predicting the future demand for aircraft by analyzing big data.
This article will discuss how airlines can use data science to improve their operations and increase profitability.
Overview of Data Science
The aviation industry is a complex network of systems, each with its own unique set of data. In order to optimize these different systems, the data must be analyzed and distilled into useful insights. This process is called "data science."
Data science is the process of applying advanced statistical and mathematical methods to data to extract actionable insight. Data science is typically used in academia, business, and government to improve decision-making through analytics and optimization. Since it's relatively new as a field of study, no standardized textbook focuses on data science training. However, many training institutes provide AI and data science courses in Bangalore for working professionals.
The industry is experiencing a tremendous increase in demand for data scientists. The reason behind this is the increasing number of drones and UAVs used in modern aviation and other industries. Many airlines deploy them on routine flights to monitor air space and weather conditions to runway lengths, passenger loads, and even airline performance metrics such as ground delays.
The Potential of Data Science in Aviation
Aviation is a field that requires the use of data science on a daily basis. The aviation industry is one of the most challenging industries, but it also has an extremely high job growth rate. Aviation professionals are needed in every field: maintenance, IT, operations, finance, sales and marketing, management, and legal departments.
In the aviation industry, data science is a big deal. With an increasing number of planes and passengers on the planet, airlines must have access to the most accurate information about their fleets, customers, and the world around them.
In the aviation industry, data science is used to help airlines make better decisions about maximizing revenue and minimizing costs.
For example, a data scientist might analyze airline records to determine which planes are most likely to be overbooked, which are least likely to lose luggage, or which routes are most profitable.
The need for data science in aviation has grown so much that it's now an official part of the industry's professional certification process. In fact, there are now universities that offer degrees in "Data Science," which means you can get a degree by studying how to analyze data in your field!
Ways Data Science is Used in the Aviation Industry
Here are some of the big data use cases in the airline industry and how it helps the industry grow.
Ticket Pricing
Pricing for airlines is influenced by supply and demand. Pricing is affected by various elements, including routes, weekends, and holidays. Additionally, it is reliant on flight schedules.
Flight prices for the evening and early morning are different from those for the afternoon and late at night. In order to draw customers, pricing must, however, constantly be attractive. Airlines may automate their pricing processes using analytics-driven charges, which will also help them maximize capacity utilization and increase their revenue.
Air Crew management
There are many different aspects to managing a crew. Working hours, vacation days, language proficiency, and data science can all be used to automate crew schedules and provide insights into people management, crew fitness, and regulatory compliance issues.
Consider a department in charge of scheduling that has to assign crews to each of the thousands of flights flown daily. That takes a lot of effort. To establish conflict-free schedules for pilots and flight attendants, experts consider various criteria, including flight routes, crew member licensing, certification, aircraft type and fuel usage, work laws, vacations, and days off. Besides, government laws should be considered, like aircraft maintenance schedules and training requirements such as pairing senior crew members with junior ones.
Chatbot automation
Travelers become worried when a disruption like a flight delay or a luggage loss happens. Customers will probably not choose this airline for their subsequent journey if they don't receive a response or explanation of an issue from an airline representative in a timely manner. Responding to consumer inquiries quickly is important, even more so than the actual procedures to resolve a problem.
From leveraging algorithms for interpreting natural language or unstructured text, artificial intelligence (AI) software, like Closer by Arbor Solutions, accelerates and streamlines the operations of customer support representatives.
Developing AI chatbots is another method to automate and enhance customer care. Today, several airlines use AI-powered chatbots to improve customer service by assisting travelers with flight booking and management, luggage tracking, queries, and other needs. To know how chatbots are developed, refer to the data science certification course in Bangalore now.
Customer Feedback Analysis
Customer feedback is obtained from various sources in the digital age, including tweeters, photos, calls, videos, etc. The customer support team can use the structured and unstructured data stored in the data science application to listen to customers and rapidly address their requirements.
Fleet Management
Every cancellation impacts sales and brand perception—late maintenance results in delays. Predictive maintenance can assist airlines in maintaining their fleet in good condition as they work to enhance revenues through efficient fleet optimization. Real-time data collection and airplane analysis can help the maintenance team prevent technical difficulties and plan their uncertain maintenance schedule.
Fuel Optimization
Nearly 2% of atmospheric carbon dioxide (CO2) emissions are caused by aircraft worldwide. Because of this, carriers and aircraft manufacturers work to increase fuel efficiency. The players in the aviation sector are motivated to employ technology to minimize carbon emissions for a variety of reasons, including both ecological and economic ones. According to Investopedia, airlines spend 10-12% of their operating expenses on fuel.
Airlines collect and analyze flight data, including information about each route's distance and altitude, the kind and weight of the aircraft, the weather, etc., using AI systems with built-in machine learning algorithms. Systems determine the optimum fuel requirement for a flight based on data discoveries.
These are a few requirements for applying data science in the airline sector. Airlines are an example of a customer-facing, technology-driven industry that must effectively use data to develop. That's what will make the future competitive.
The aviation industry is at a crossroads where technology changes faster than ever. The demand for skilled data scientists will only grow as companies leverage machine learning and big data solutions to improve their efficiency and provide better customer experiences; if you are considering a career change to data science and AI, join an IBM-accredited data science course in Bangalore led by MNC tech leaders.
Bottom Line
It's clear that airlines are using several data science tools to boost their performance and improve customer experience. As both sides adopt more advanced data science tools, I'm sure that the benefits will grow. However, the most important aspect is that as data is being generated exponentially, it'd be impossible to reimplement any of this manually. Automation is the key here.
After all, with the complexity of modern data science projects, it's almost impossible to outsource everything. So what's more important than ever is know-how applied to data science principles and business objectives.