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Aviation Data Intelligence For Efficient Revenue Management Modeling And Passenger Experiences

Constant changes and shifting opportunities have necessitated the streamlining of aviation data by a...
Home Blog Aviation Data Intelligence For Efficient Revenue Management Modeling And Passenger Experiences
Categories : Big Data & Analytics
Ankur 19 Nov 2020

Constant changes and shifting opportunities have necessitated the streamlining of aviation data by airlines to optimize their decision-making. Airlines are volume-driven and small variations in passenger, operational or maintenance data can multiply into major effects. They also suffer substantial difficulties in data integration and access.

Airline industry, especially the commercial aviation sector, is striving to improve how they operate and serve their customers. They need data and verification models to control costs arising from various operational activities.

Airline data can be integrated and analyzed to develop effective forecasting models to assess the impact of various parameters, such as introducing new routes and adjusting fares. Big data analytics can help in automating airline data and activity reports such as estimates for performance management and revenue generated for specific routes or sectors.  Artificial Intelligence (AI) has made it possible to enhance customer experiences with automation, optimization of employee workflow and self-service solutions.

Airlines leveraging big data and artificial intelligence

Leading airlines, airports and travel tech innovators are using big data to deliver seamless customer experience. United Airlines uses a system that analyses 150 variables in a customer’s profile including their previous purchases to provide customized offerings. E-tourism market player FoxTripper is using software to predict the best places to be in for the passengers based on a moving map that collects data about the passengers’ travel behavior. British low-cost carrier EasyJet is leveraging AI to determine seat pricing in real-time, depending upon the demand. Delta Airlines has developed a novel application for baggage tracking predictive aircraft maintenance.

Dynamic pricing for value-added products

Willingness to Pay (WTP) reveals when a customer is likely to pay a maximum price for a service or product. Most passengers are willing to pay a higher price for tickets on the day before departure (DBD). The concept of WTIP is connected to dynamic pricing – the practice of pricing a value-added data product based on the customer’s WTP.

Critically interrelated datasets

Data Management and Interface Management are critical components that need to support and interact with each other. The interface provides data from legacy systems to which business rules can be applied to curate and authenticate data products for analytical use. That way, business rules management, interface management and metadata management are critically interrelated and need to be efficiently integrated for insightful analysis.

Data analytics helps the industry to understand customers’ preferences and other operational issues. Analysis of ticket booking allows to target customers with personalized offers while optimizing price in real-time utilizing predictive analysis techniques.

Data architecture and storage solutions

Below six types of reference data architectures are currently employed by developers that provide a variety of storage solutions to the airline and airport industry:

Relational storage: It uses structured query language (SQL) and is optimized for non-hierarchical data that does not rely on the large collections of binary objects. Relational design is ill-suited for rapidly changing data sources due to the effort required to revise the schema.

Graph Storage: It’s a specialized type of storage for reading and writing graph structures at a high speed. It does not readily support the indexing and retrieval of documents.

Document storage: This commonly used noSQL data store keeps records as discrete documents rather than rows in a table. It tends to be more flexible than relational store as it is optimized to store document collections but it does not support the transactional system well.

Hybrid storage: The data storage architecture combines the features of one or more of other data store types and tends to scale well. However, as it is not optimized to a specific data type, hybrid store will not maximize performance in any given category.

Key-value storage: The data type supports the native storage of documents, records, and large binary objects. It is designed to optimize extremely large datasets but lacks many basic functions and ACID compliance that are standard in the document and relational stores.

Geospatial storage: The data storage architecture allows airlines to utilize geospatial functions in document and relational stores. It combines features to replace master content stores and reduces major costs of deployment and data maintenance by eliminating duplication of efforts.

Additionally, airlines can also utilize Microsoft’s SQL Server Reporting Services or SSRS to prepare and deliver a variety of interactive and printed reports. SSRS provides an interface into Microsoft Visual Studio for SQL administrators and developers to connect to the SQL database and use SSRS reports in a variety of ways. SSRS flexibly delivers the right information to the right user.

SQL Server Data Tools for Business Intelligence (SSDT)

SQL Server Data Tools for Business Intelligence (SSDT) reduce the report definition language component or RDL in a graphical user interface so that instead of writing code, the developer can drag-and-drop graphic icons into an SSRS report format. RDL (Report Definition Language) reports can be viewed with the help of Microsoft SQL Server or ASP.NET ReportViewer web control.

Optimizing the airspace use

With the airport traffic increasing day-by-day, big data analysis enables airlines to optimize airspace use and develop an efficient revenue management model. Sentiment analysis and travel journey analysis can be used to keep the customers updated in real-time, promoting special offers based on their preferences. Big data analytics provides some key benefits to the airlines, such as lower operational costs, market-leading competitiveness, stakeholder value and increased shareholder value.

Data subscribing to SVAULT standards

Flexsin has worked on airline projects in which the client’s datasets were residing in legacy systems with brittle and antiquated architecture that restricted access due to which the client was facing a lot of issues related to insights of their passengers’ information.  Our developers worked on this requirement of the client to make the data Secure, Visible, Accessible, Understandable, Linked and Trusted (SVAULT). Our developers transformed, harmonized and provisioned the data in a way that enabled the extraction of the maximum value for the client.

Driving value for your aviation business

When your business planning pivots around passenger priorities and schedules, you need access to integrated airline data that is accurate and available to you at a frequency you require. There are many areas in airline the industry that can be tapped by big data and AI solutions to understand their customers individually, and also predict the requests that might come up.

With dynamic data feed and access to new market insights, Flexsin Technologies, a business intelligence development company can help businesses with airline-specific insights for global ticketing and sales. Our airline data integration solutions can help you see a more accurate view of your marketing landscape to increase efficiencies.  Let’s help you too drive value for your aviation business with our capability to curate, clean and seamlessly integrate data.

Ankur
He has over 10 years of experience in mobile app development and iPhone app development. Working on multiple challenging assignments, he has developed an acumen and expertise in this domain. The skills gained allowed him to identify specific mobile app development issues and devise strategies to resolve them.
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