Data Mining Applications for Business Intelligence and Analytics

Running Head: DATA MINING APPLICATIONS FOR BUSINESS INTELLIGENCE AND
ANALYTICS 1
BIS551 Data Mining Applications for Business Intelligence and Analytics: A Case Study of
South West Airlines
By (Name/Group of Student)
(Institutional Affiliation)
(Date of Submission)

DATA MINING APPLICATIONS FOR BUSINESS INTELLIGENCE AND ANALYTICS 2
Table of Contents
Abstract............................................................................................................................................2
Introduction......................................................................................................................................3
Background and Literature Review.................................................................................................4
Methodology....................................................................................................................................4
Data Analysis and Results...............................................................................................................6
Conclusion.....................................................................................................................................16
References......................................................................................................................................17

DATA MINING APPLICATIONS FOR BUSINESS INTELLIGENCE AND ANALYTICS 3
Abstract
Business Intelligence is appreciated as a result of intense research from statistical through data
mining application tools such as MIS (Management Information System) and OLAP (Online
Analytical Processing). Old style techniques of decision making through expertise, sometimes
guess work and knowledge were very ineffective in the area of Business Intelligence. This paper
aims to apply data mining techniques and the analytical approaches at South West Airlines in
order to determine the application of data mining and business intelligence within the company.
The application of data mining in business intelligence has been known to add values through the
implementation of data mining technique which increases profit margin by larger percentages.
Companies such Amazon, Shell Services International, Google, etc., have gained functional
efficiency via Business Intelligence and Analytics applications through implementation. In this
paper, we aim to analyze the applications of the data mining in business intelligence within
Southwest Airlines which is known to be applying data mining techniques in most of its business
operations. This paper also presents some the analytics of the achievements and weakness that
have been encountered by Southwest Airline through the application of data mining in business
intelligence.

DATA MINING APPLICATIONS FOR BUSINESS INTELLIGENCE AND ANALYTICS 4
Introduction
Data mining can be defined as the practice of data collection from diverse sources with
many methods such the use of artificial intelligence techniques and get the results which are
important for discovering the rules that are hidden in the data and help in improving the business
itself. Data mining applies techniques such as neutral networks, advanced statistical tools and
artificial intelligence to determine the staff skills, prices and also analyzing their competitors
According to Brijs, Business Intelligence refers to a technology driven process for collecting,
analyzing data and presenting processed information to assist organization management team,
end users and business managers make well-informed business decisions (Brijs, 2016).
Data mining and business intelligence comprises of various tools methodologies and
applications that help institutions to collect data from external sources and internal systems,
prepare it for analysis, create and execute queries against the data, and develop reports and data
visualizations to make the analytical results available to organization decision makers and
functional employees. It provides important information to organization management
concerning customers, employees, business associates, and suppliers, which is used in efficient
decision making.
Notwithstanding, the expansion in consumer items defaults and rivalry in the business
market, the majority of the organizations are hesitant to utilize Business Intelligence (BI)
innovations in their basic leadership schedules (Kin, 2010); Yan and Xiangjun, 2010). For the
most part, an organization's workers depend on data mining techniques to direct them in
assessing the value of strategic plan within the firm. Furthermore, strategic agreement
assessments depend on a representative abstract appraisal. Such judgment is wasteful,
conflicting, and non-uniform; this will prompt increment the expense of activities, hazard, and

DATA MINING APPLICATIONS FOR BUSINESS INTELLIGENCE AND ANALYTICS 5
potential misfortune to the organizations. A lot of business decision-making system has been in
existence since the 1980s. In 1989, Howard Dresner introduced the term Business Intelligence,
which was the umbrella for such systems (Sherman, 2015).
Background and Literature Review
Data mining as a strategic business tool has long been proposed in an effort to increase a
company's competitiveness (Porter, 2010). It is an important aspect of strategic management
because it serves as the first link in the chain of perceptions and actions that permit an
organization to adapt to its environment. Being a relatively new management tool in the business
world, Business intelligence plays an important role to support managers today for better
decision making and strategic planning.
Hannula and Pirttimaki (2013) argue that a competitive edge is gained through the ability
to anticipate information, turn it into knowledge, craft it into intelligence relevant to the business
environment, and actually use the knowledge gained from it (Calof and Wright, 2018). The intent
of any business intelligence System is simply to provide a system for developing or improving
processes through a structured approach, effective deployment and better control.
An organisation which does not rigorously monitor and analyze key competitors is poorly
equipped to compose and deploy effective competitive strategy and this approach leaves the firm
and its markets vulnerable to attack and its performance decline (Prescott and Bharwajh, 2015).
Failure to collect, analyze and act upon competitive information in an organized fashion can lead
to the failure of the firm itself (Marceaus and Sawka, 2011).
Business Intelligence and Data Mining
Application of data mining for Business Intelligence (BI) enables the business to make
intelligent, fact-based decisions. The most cogent argument for establishing a new roadmap to

DATA MINING APPLICATIONS FOR BUSINESS INTELLIGENCE AND ANALYTICS 6
business Intelligence (BI) excellence is to rid the organization of the technology scramble and
cobbled together solutions that Information Technology (IT) has had to deal with as it struggled
to meet business requirements.
According to Ranjan (2009) a Business Intelligence (BI) organization fully exploits data
at every phase of the Business Intelligence (BI) architecture as it progresses through various
levels of informational metamorphosis. Data is first collected including metadata, such as the
creator or creating system, the time of creation, the channel on which it was delivered, sentiment
contained in plain text, and so on. According to Olszak and Ziemba (2016) metadata facilitate
the process of extracting, transforming and loading data through presenting sources of data in the
layout of data warehouses. Metadata are also used to automate summary data creation and
queries management.
Challenges of using Data Mining Applications for Business Intelligence
According to (Chuah and Wong, 2013) data mining applications have appeared the top
spending priority for many Chief Information Officers (CIO) and it remain the most important
technologies to be purchased for past five years (Gartner Research 2017; 2018; 2009). Although
there has been a growing interest in Business Intelligence (BI) area, success for implementing
Business Intelligence (BI) is still questionable (Ang and Teo 2010; Lupu et.al., 2017;
Computerworld, 2013). Lupuet.al. (2017) reported that about sixty percent of business
intelligence applications fail due to the technology, organizational, cultural and infrastructure
issues. Furthermore, EMC Corporation argued that many Business Intelligence (BI) initiatives
have failed because tools were not accessible through to end users and the result of not meeting
the end users‟ need effectively.