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Airline Applications of Business Intelligence Systems

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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 carrying on. In this context, business intelligence tools are valuable instruments that can optimally process airline related data so that the activities that are conducted can be optimized to maximize profits, while meeting customer requirements. An airline company that has access to large volumes of data (stored into conventional or big data repositories) has two options to extract useful decision support information: processing data by using general-purpose business intelligence systems or processing data by using industry specific business intelligence systems. Each of these two options has both advantages and disadvantages for the airline companies that intend to use them. The present paper presents a comparative study of a number of general-purpose and airline industry specific business intelligence systems, together with their main advantages and disadvantages.
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INCAS BULLETIN, Volume 7, Issue 3/ 2015, pp. 153 160 ISSN 2066 8201
Airline Applications of Business Intelligence Systems
Mihai ANDRONIE*
*Corresponding author
Spiru Haret University
Str. Ion Ghica 13, Bucharest 030045, Romania
mihai_a380@yahoo.com
DOI: 10.13111/2066-8201.2015.7.3.14
Received: 20 July 2015 / Accepted: 30 July 2015
Copyright©2015 Published by INCAS. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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 carrying on. In this context, business
intelligence tools are valuable instruments that can optimally process airline related data so that the
activities that are conducted can be optimized to maximize profits, while meeting customer
requirements. An airline company that has access to large volumes of data (stored into conventional
or big data repositories) has two options to extract useful decision support information: processing
data by using general-purpose business intelligence systems or processing data by using industry
specific business intelligence systems. Each of these two options has both advantages and
disadvantages for the airline companies that intend to use them. The present paper presents a
comparative study of a number of general-purpose and airline industry specific business intelligence
systems, together with their main advantages and disadvantages.
Key Words: business intelligence, airline industry, big data, information technology, airline data
analysis.
1. BIG DATA IN THE AIRLINE INDUSTRY
Big data is a term that appeared to indicate large collections of data that emerged in the last
years, as a consequence of the unprecedented development of the information and
communication technologies.
Big data was defined by Gartner, one of the companies acting at a global level on the
information technologies area, as high-volume, high-velocity and high-variety information
assets that demand cost-effective, innovative forms of information processing for enhanced
insight and decision making [9]. Big data is considered to have three properties (Figure 1),
also called the 3Vs of big data or the 3V model [10]:
Volume big data is stored in large quantities, making it impossible to analyze or
process it without dedicated computer software and high computational power; big
data also requires large storage devices, its quantity being only limited by the
capacity of storage on these devices;
Variety big data is found in a wide variety of formats; the variety of the big data
can pose an important problem for those who develop algorithms to explore or
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process it; its variety also makes big data exploration much more challenging than
conventional data sources such as structured data warehouses or relational databases.
Velocity big data is generated almost continuously from different technological or
economic processes, such as those found in the airline industry: data related to air
traffic, data coming from sensors in airplanes etc.
Additionally to the three previously mentioned properties of big data, there is a fourth
one that can be mentioned: veracity [8]. Veracity refers to the poor quality of bug data. Large
quantities of data come from uncertain sources and is unverified (for example data from the
internet, like webpages, news etc.). Because of the veracity of data, contradictory results can
be obtained during the analysis process with negative consequences regarding perceived
reliability of big data analysis products. According to the IBM Big Data & Analytics Hub
[8], one out of three business leaders doesn’t trust the information used to make decisions
and over 3 trillion dollars per year are spent due to inaccurate data. In this context,
leveraging unstructured data for enterprise analytics is seen as an important factor [2].
Airline industry, as other similar industries, generates huge quantities of data (that can
be considered big data, having all the properties previously described), this fact being both
an opportunity and, in the same time, a challenge for the companies doing business in this
area.
The accumulation of large data volumes in the aviation industry, as in other fields of
activity, can be seen as an opportunity to exploit these data through specialized tools,
obtaining valuable information that can be used by managers and other people in charge to
develop and improve the processes that are carried on by the aviation companies. According
to a paper published in 2013, Airlines, airports, aircraft manufacturers, suppliers,
governments and others in the global aviation space depend on data for operational planning
and execution. Complex and concurrent data sets create immense technical and human
challenges in collecting, sorting, and mining aviation databases. Aviation data sets exceed
the capabilities of desktop computing [1].
Aviation data come in large volumes, having varied formats and continuously, having
all the characteristics of big data. Big data with provenance from the airline industry,
according to the paper Cross-Platform Aviation Analytics Using Big-Data Methods can have
multiple sources: flight tracking data, passenger information, airport operations, aircraft
information, weather data, airline information, market information and air safety reports [1].
It has to be noted the fact that, even if the eight types of information previously
mentioned are in some way interconnected, none of them can be used independently to form
a global image about the airline industry domain. For this reason, we have to use the
available information in correlation to obtain reports that are valid and useful for the airline
Figure 1. The three properties of big data
Volume:
Data in large
quantities
BIG DATA
Variety:
Data in different
formats
Velocity:
Data changing
frequently
Airline Applications of Business Intelligence Systems
INCAS BULLETIN, Volume 7, Issue 3/ 2015
companies’ managers. In this context, integration of available data appears to be the best
action before data processing.
In Figure 2 is presented the proposed data circuit, from the provenance of data to their
integration and processing through business intelligence systems.
As it can be observed from the previous figure, data coming from various sources in the
aviation industry are integrated into a common big data repository before they can be
analyzed using specialized software.
To explore big data and offer decision support at all levels in a company special
software systems have to be used, also known as business intelligence/ business intelligence
type systems.
2. BUSINESS INTELLIGENCE SYSTEMS, INSTRUMENTS TO EXPLORE
BIG DATA FROM THE AIRLINE INDUSTRY
Business intelligence systems received many definitions, some of which are outlined in the
present section. As will be seen, business intelligence instruments can be applied to extract
useful business information from the big data repositories associated with the airline
industry. According to the book Business Intelligence Success Factors: Tools for Your
Business in the Global Aligning Economy published in 2009, business intelligence (BI) is a
set of methodologies, theories, architectures and technologies that are used to transform raw
data into useful information necessary for organizations to improve the economic activities
that they are carrying [5].
Chaudhuri and Narasayya define business intelligence as a collection of decision
support technologies for the enterprise aimed at enabling knowledge workers such as
executives, managers and analysts to make better and faster decisions” [4].
Business intelligence is defined as providing accurate information to the right people at
the right time. The term means also the capability to transform existing data into information
Figure 2. Aviation industry - data sources and processing
Flight tracking data:
- Flight program data
- Radar data (airplane tracks)
Aviation
Big Data
repository
Airport operations data:
- Airport location
- Airport layout (gates, capacity)
Passenger information:
- Identity
- Nationality
- Usual destinations
Meteorological data:
- Wind, precipitation, temperature
distribution in space/ time
Air safety reports:
- Events happened to aircraft and
contributing conditions (weather
conditions, technical issues etc.)
Airline information:
- Company information
- Employees
Aircraft information:
- Aircraft types information
- Service history
- Other aircraft information
Economic information:
- Information related to the
economic development of
different travel areas
Big data
processing
Reports
Data
selection
Specialized
data analysis
instruments
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that everyone in the organization can trust and which they can use to adopt effective
decisions [6].
Howson Cindi, in Successful Business Intelligence published in 2013, gives another
definition to the concept of business intelligence. According to the author, business
intelligence is a technology that allows people from all levels of an organization to access,
interact and process data for an enterprise’s management, to improve its performance, to
discover new opportunities and to work more efficiently [7].
Techopedia [11] presents the main characteristics of business intelligence systems,
defined as software used to collect data from separate data warehouses or data collections,
which are in the same time connected in a stack type architecture with the purpose of
processing and using them for solving different business problems. According to
Techopedia, the essential functionalities of business intelligence systems are:
Business processes performance and objectives achievement measurement (having
benchmarking purposes) for example measuring the process performances of an
airline company related to the others, with the opportunity to improve one’s
processes in accordance to the best practices available at the moment;
Quantitative analysis by predictive analytics, predictive modeling, business process
modeling, statistical analysis for example, by making predictions related to the
passengers habits of travel, an airline company can optimize the schedule of aircraft,
service intervals, stocks etc.;
Reporting at department level or enterprise level through various techniques an
essential feature for any company, regardless of its domain of activity;
Ability to use different tools to enable both entities inside and outside of the
company to work through electronic data interchange (EDI) or by data sharing;
Using knowledge management software to identify information within the company
and make them available to those interested mainly the managers of the company
who can adopt informed decisions according to the results offered by the system;
Using specific methodologies and procedures for implementing interactive
information gathering techniques.
Big data poses new requirements to business intelligence tools. In this context,
traditional techniques, models, and methods must be redefined to provide decision makers
with service of data analysis through the cloud and from big data” [3].
Taking into account the facts previously presented, it can be concluded that business
intelligence systems for the airline companies are designed to optimally process airline
related data so that the activities that are conducted can be optimized to maximize profits,
while meeting customer requirements.
3. COMPARATIVE STUDY OF BUSINESS INTELLIGENCE SYSTEMS
USED BY THE AIRLINE INDUSTRY
A company that has access to large volumes of data (stored into big data repositories) has
two options to extract useful decision support information:
Processing data by using general purpose business intelligence systems;
Processing data by using industry specific business intelligence systems.
The general purpose business intelligence systems have the advantage that they are
usually the most advanced systems on the market, with functionalities that make them
suitable for analyzing big airline related data in a timely manner, having interactive
Airline Applications of Business Intelligence Systems
INCAS BULLETIN, Volume 7, Issue 3/ 2015
interfaces for presenting the analysis results. These types of business intelligence systems are
the most flexible on the market, their functionalities being designed to fit a wide variety of
companies. However, the specialized business intelligence systems may need to be
customized on the data available to the airline companies and to their individual needs.
On the other hand, the specialized business intelligence systems designed for companies
carrying on activities in the airline industry are designed around the necessities of airline
companies. They have functionalities that enable them to offer answers to specific problems,
being more specialized tools.
General-purpose business intelligence tools
Some 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.
Table 1. General-purpose business intelligence products
No.
Business
intelligence
software
product
Advantages
Disadvantages
1.
IBM Cognos
Business
Intelligence
Software
Multiple analysis tools:
o What if analysis;
o Trend analysis;
o Advanced analysis;
o Analytical reporting;
Self-service functionality enables
working offline or on mobile devices;
Interactive dashboard, friendly user
interfaces;
Interactive operation for multiple
users;
Integration with other APIs;
Relatively high entry cost
compared to other similar
software products;
Not designed for beginner
or inexperienced users;
Difficult error tracking as
error messages contain little
useful information;
Large installer compared to
other similar software
products;
2.
Birst Software
Compatible with a wide array of data
sources;
Refines multiple heterogeneous data
into a uniform business data tier;
Assures accuracy and consistency of
data;
Interactive dashboard, friendly user
interfaces;
Works on both cloud and local data;
Easy to integrate with other products;
Administration operations
are more difficult than on
other similar software
products;
Needs good system
resources to offer results in
a timely manner;
Some users complain about
the user interface which
they say is a bit confusing;
High cost for small
companies;
3.
SAP Business
Objects
business
Multiple analysis tools:
o Ad-hoc analysis;
o OLAP;
The software interface is not
very user friendly according
to some users;
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intelligence
o Predictive analysis;
o Trend indicators;
Works on both on premises and cloud
data;
Users can easily retrieve their own data
without technical support;
Mobile device compatible;
SAP offers training for the users;
Users need to have minimal
tech knowledge to use this
software;
Confusing licensing for the
customers;
Maintenance costs have to
be considered;
4.
Oracle
Business
Intelligence
Foundation
Suite 11g
Multiple analysis tools:
o Ad-hoc analysis;
o OLAP;
o Predictive analysis;
o Profit analysis;
Ideal to both small and large users;
Works on both on premises and cloud
data;
Works on both traditional data sources
and big data;
Users can easily retrieve their own
data without technical support;
Accessible cost for small companies;
Includes eight dedicated platforms;
Difficult and costly
customization for a
company’s needs;
Upgrade a little difficult as
reported by a number of
users;
Complex interface for the
users;
5.
Tableau
business
intelligence
Multiple analysis tools:
o Ad-hoc analysis;
o OLAP;
o Predictive analysis;
o Trend indicators;
Can handle local or cloud data but
intended mainly for cloud data;
Simple to use intuitive interface;
Fast operating platform;
Mobile devices compatibility;
Costly for small to medium
businesses;
Some users complain about
OLAP functionalities being
relatively slow;
The great complexity of the
product makes it confusing
to novice users;
6.
Microsoft
SharePoint
business
intelligence
Microsoft Office compatibility (MS
Excel, MS PowerPoint etc.);
Web based application easily
accessible to different people;
Multiple analysis tools:
o Ad-hoc analysis;
o Predictive analysis;
Does not work on all mobile
devices;
Requires some tech
knowledge from users;
May require additional
technical support inside the
company.
Airline companies can use with success any of the business intelligence software
products described in the previous table. As airlines are usually large companies, they have
to choose business intelligence software that best suits their needs, offering business
solutions at all company levels.
Airline specific business intelligence products
Over the last years, some business intelligence systems specific to the airline industry
were developed. These systems are optimized to operate on data specific to the airline
industry as presented in the first part of the present paper.
Two of the most used airline industry specific business intelligence products are
presented in Table 2, with their main characteristics as advertised on the official websites.
Airline Applications of Business Intelligence Systems
INCAS BULLETIN, Volume 7, Issue 3/ 2015
Table 2. Airline industry specific business intelligence products
No.
Business intelligence software
product
Main characteristics
1.
Teradata Airline Decisions
Passenger management functionalities that helps
airlines to maximize airplane usage while avoiding
overbooking;
Shows passenger trends/ behavior according to the
available data;
Offers insight into the causes of trends that are
observed;
Multiple analysis tools:
o Ad-hoc analysis;
o Exception alerts;
2.
IATA Business Intelligence &
Statistics Services
Multiple tools designed for the airline industry;
Is different from other business intelligence software
providers through the fact that it also offers access to
industry specific data;
Access to airline specific databases (AirportIS
database);
Benchmarking tools related to customer satisfaction
(AirSAT);
Different reports related to the airline industry (cargo,
market analysis etc.);
Is useful not only to airline companies, but also to other
agents operating in the transportation domain;
Offered at a global level, with globally gathered data;
It can be observed that airline industry specific business intelligence tools come with
some facilities that are essential for the companies acting in this field of activity. The
availability of airline specific data is a big advantage for such companies, a known fact being
that the results obtained through such software products cannot be better than the available
data.
4. CONCLUSIONS
Airline industry, as other similar industries, generates huge quantities of data (that can be
considered big data, having properties like high volume, velocity and variety), this fact being
both an opportunity and, in the same time, a challenge for the companies doing business in
this area. The accumulation of large data volumes in the aviation industry, as in other fields
of activity, can be seen as an opportunity to exploit these data through specialized tools,
obtaining valuable information that can be used by managers and other people in charge to
develop and improve the processes that are carried on by aviation companies.
In the presented context, data coming from various sources in the aviation industry has
to be integrated into a common big data repository before being analyzed by means of
specialized software. To explore big data and offer decision support at all levels in a
company, special software systems have to be used, also known as business intelligence/
business intelligence type systems.
Business intelligence systems for the airline companies are designed to optimally
process airline related data so that the activities that are conducted can be optimized to
maximize profits, while meeting customer requirements.
Mihai ANDRONIE
160
INCAS BULLETIN, Volume 7, Issue 3/ 2015
Analyzing the different business intelligence tools available on the market, it was
concluded that a company that has access to large volumes of data has two options to extract
useful decision support information: processing data by using general purpose business
intelligence systems or processing data by using industry specific business intelligence
systems. After presenting both the general-purpose and airline industry specific business
intelligence tools, it was concluded that the former offer to companies more flexibility and a
wider range of instruments, but, in the same time, they are not as adapted to the needs of
airline companies as the latter. On the other hand, dedicated airline industry business
intelligence systems offer solutions to specific problems that airline companies are facing,
offering even access to specific data interesting to such companies. Dedicated airline
industry business intelligence systems are not only useful to airline companies, but also to
others involved in related businesses.
Further research can be conducted regarding the possibility to integrate into a common
platform the advantages of both general purpose and dedicated business intelligence systems
(flexibility, performance, multiple functionalities, access to industry specific data).
ACKNOWLEDGEMENTS
This work was financially supported through the project Routes of academic excellence in
doctoral and post-doctoral research READ co-financed through the European Social
Fund, by Sectoral Operational Programme Human Resources Development 2007-2013,
contract no POSDRU/159/1.5/S/137926.
REFERENCES
[1] T. Larsen, Cross-Platform Aviation Analytics Using Big-Data Methods, Integrated Communications
Navigation and Surveillance (ICNS) Conference, April, 2013, ISI Web of Science Proceedings Paper,
ISBN: 978-1-4673-6253-5.
[2] C. Surajit, Big Data and Enterprise Analytics, CONCEPTUAL MODELING, ER 2013, Book Series: Lecture
Notes in Computer Science, Volume: 8217, ISI Web of Science, ISSN: 0302-9743.
[3] S. O. Mwilu, P. Nicolas, C.-W. Isabelle, Business Intelligence and Big Data in the Cloud: Opportunities for
Design-Science Researchers, ADVANCES IN CONCEPTUAL MODELING, Book Series: Lecture
Notes in Computer Science, Volume: 8823, Pages: 75-84, Published: 2014, ISI Web of Science, ISSN:
0302-9743.
[4] C. Surajit, N. Vivek, New Frontiers in Business Intelligence, Proceedings of the VLDB Endowment, Volume
4, No. 12, August 2011.
[5] O. Parr Rud, Business Intelligence Success Factors - Tools for Aligning Your Business in the Global
Economy, John Wiley & Sons, 2009.
[6] G. J. Miller, D. Bräutigam, St. Gerlach, Business Intelligence Competency Centers: A Team Approach to
Maximizing Competitive Advantage, John Wiley & Sons, 2006.
[7] C. Howson, Successful Business Intelligence, McGraw-Hill/Osborne, 2013.
[8] * * * IBM Big Data & Analitics Hub http://www.ibmbigdatahub.com/infographic/four-vs-big-data.
[9] * * * http://www.gartner.com/it-glossary/big-data.
[10] * * * http://whatis.techtarget.com/definition/3Vs.
[11] * * * http://www.techopedia.com/definition/345/business-intelligence-bi.
... 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. ...
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  • P Nicolas
  • C.-W Isabelle
S. O. Mwilu, P. Nicolas, C.-W. Isabelle, Business Intelligence and Big Data in the Cloud: Opportunities for Design-Science Researchers, ADVANCES IN CONCEPTUAL MODELING, Book Series: Lecture Notes in Computer Science, Volume: 8823, Pages: 75-84, Published: 2014, ISI Web of Science, ISSN: 0302-9743.