Table 1 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
Source publication
Airline industry is characterized by large quantities of complex, unstructured and rapid changing data that can be categorized as big data, requiring specialized analysis tools to explore it with the purpose of obtaining useful knowledge as decision support for companies that need to fundament their activities and improve the processes they are car...
Context in source publication
Context 1
... of the best known business intelligence software products will be analyzed in the present section with the purpose of making a comparison between them and outlining the advantages and disadvantages of using such tools in the airline industry. Table 1 presents some general-purpose business intelligence tools, together with their advantages and disadvantages. The advantages and disadvantages presented are synthetized after consulting several dedicated websites with users' opinions. ...
Citations
... The terminal or leaf nodes, which do not further split, are created when the root node, which represents the most significant predictor, divides into decision nodes, which are sub-nodes as shown in Fig. 21 [7]. 3) Support vector machine (SVM): The objective of the Support Vector Machine technique is to locate, in a space of N dimensions (where N represents the number of features), a hyperplane that categorizes the data points in a way that is unambiguous [8]. The two distinct groups of data points may be partitioned using any one of several hyperplanes that are available to choose from [9]. ...
... To convert the enormous data into meaningful information, almost all industries and sectors have adapted BI tools or systems that help bridge this gap by giving organizations access to only relevant information that would have a positive effect on organizational strategies and decision-making processes (Andronie, 2015;Chen et al., 2012). However, even though the use of BI tools might vary from industry to industry, the result that is expected from BI tools/systems remains the same that is, easy access to relevant information to sustain the optimal running of activities to maximize profits and gain competitive competency (Andronie, 2015). ...
... To convert the enormous data into meaningful information, almost all industries and sectors have adapted BI tools or systems that help bridge this gap by giving organizations access to only relevant information that would have a positive effect on organizational strategies and decision-making processes (Andronie, 2015;Chen et al., 2012). However, even though the use of BI tools might vary from industry to industry, the result that is expected from BI tools/systems remains the same that is, easy access to relevant information to sustain the optimal running of activities to maximize profits and gain competitive competency (Andronie, 2015). ...
... First, the airline industry uses BI tools, as they enable the efficient processing of large volumes of process-related data, such as flight tracking, airport operations, airline information, economic information, passenger information, and aircraft information. The aim behind using BI tools is to ensure smooth operations to maximize profits while fulfilling customer requirements (Andronie, 2015). Second, the health-care industry uses BI, as it requires easy access to clinical and administrative information because it is essential to fulfill legal and customercentric requirements, which are, in turn, vital to enhancing the quality of services and diminishing risks (Mettler & Vimarlund, 2009). ...
... The technology was adopted concurrently with the early adaptors in similar industries particularly for the purpose of customer oriented marketing. Aviation data analytics also considers a similar motive in different aspects [11] in establishing a collaborative platform for sustainable air operations specifically oriented at overcoming operational limitations of an airline. ...
Airline industry has provided a significantly
conventional, faster and reliable mode of transportation for
passengers and freight over the decades in which the industry
has been in service despite the pressure being applied especially
in maintaining operational affordability. The study critically
reviews the techniques and tools, infrastructure and general
application architecture for discussing the applicability of data
analytics based on both batch processing and real time stream
data in general aviation for health monitoring and predictive
analysis in order to predict maintenance and optimize the
performance of aircrafts. In this respect, the study further
evaluates the significant capability in addressing contemporary
problems which are uniquely addressed by data analytics
system.
The aim of this project is to analyze the relationship between airlines based on
their total number of passengers, number of employees, passenger handling rights, service quality and revenue. It places strong focus on analyzing the customer service
quality of the largest airlines in the world, based on their number of employees.
Furthermore it hopes to help airline companies use their data in a more resourceful
way. It also further emphasizes the need for customer service quality in airline
companies, proving that quality is better than quantity. This means that an airline
with more resources might not necessarily render better services but if it does not
use its resources in the right way.
Oil prices, political instabilities, travel legislations, and many other competitive factors make it essential for any international airline with the instinct to survive to be on constant watch in such a fiercely competitive environment. To meet this need, it is vital for international airline companies to integrate competitive intelligence (CI) into their strategy building process. In this study, we create for the first time a typology of competitive intelligence practices of an international airline company (in Turkey), based on the model developed by (Wright et al. Journal of Strategic Marketing, 20(1), 19–33, 2012), and it is one of the very first to investigate Competitive intelligence in this sector. Furthermore, we made a two-step cluster analysis to uncover hidden clusters that change the way of thinking within the company. Our findings show where the company would need to make improvements on the 6 strands of the model which are attitude, gathering, use, location, technological support, and IT support. Yet, that could lead towards stronger business performance. It might also inspire other companies of the airline sector and beyond.
It is very important that the airlines that carry out operations in a dynamic and complex environment struggle to make the right decision despite many limitations. Today, a wide range of data and a large amount of data generated by airline companies and their ability of data evaluation will determine the effectiveness of the decisions. For this reason, in this study, it has been tried to determine the applications of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in airline processes of by examining previous literature. The results show that there has been an increase in the application ML algorithms in "dispatch reliability", "flight safety", "yield management/pricing" and "customer behavior" issues especially in recent years.
Governmental and corporate surveillance make extensive use of the data infrastructure, combined with individualised targeting, real-time experimentation, behavioural science, modelling, and control of the data and media environments. However, these bodies also use data for less sinister purposes, such as providing better services and products. This chapter looks briefly at the many social uses of the data infrastructure by different actors, including governments, corporations and intergovernmental agencies, as well as researchers and journalists. The idea is to frame and contextualise proactive data activism.
Combining communication and data infrastructures with interactive mapping and data crowdsourcing capabilities, Ushahidi was launched in Kenya in 2008. It filled a gap in the mediascape, mapping electoral turmoil and violence when there was no information available. The initial Ushahidi team expanded, requested the help of techies around the world to improve the platform and in 2010, with the Haiti deployment, Ushahidi became known as a global emergency facility, revolutionising humanitarianism. Since then, it has been employed in major crises and disasters globally. This chapter applies the classification of proactive data activism and concepts described in Chapter 3 to examine Ushahidi. It also scrutinises the organisation’s failures and lessons learnt, to help develop a model for effective proactive data activism that can be applied to other initiatives.
