Research Report: Business Intelligence and Data Mining ITECH7406

This research report, prepared for the ITECH7406 course, provides a comprehensive analysis of Business Intelligence (BI) analytics and Data Mining techniques across the banking, healthcare, and education sectors. The report begins by defining BI and Data Mining, then explores their applications, including Customer Relationship Management (CRM), fraud detection, and predictive analytics. It examines how these techniques add value by improving efficiency, customer service, and decision-making. The report also addresses the challenges associated with implementing BI and Data Mining, such as data integration and security. The report concludes with a discussion of the future trends and the overall impact of these technologies on the chosen industries. The report provides insights into how these technologies revolutionize businesses today.

Running head: BUSINESS INTELLIGENCE AND DATA WAREHOUSING
ITECH7406- Business Intelligence and Data Warehousing Research Report
Name of the Student:
Name of the University:

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Table of Contents
Introduction................................................................................................................................2
1. Business intelligence analytics and data mining technique for chosen domain.................2
2. Applications of Business intelligence analytics and data mining technique in chosen
domains......................................................................................................................................4
3. Business intelligence analytics and data mining technique added business value to
chosen domain............................................................................................................................6
4. Challenges associated with application of Business intelligence analytics and data
mining technique in chosen domains.........................................................................................9
Conclusion................................................................................................................................10
References................................................................................................................................12

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Introduction
Llave, Hustad and Olsen (2018) stated that business intelligence analytics is a process
to extract, transform, manage as well as analyse the business data to support the decision
making processes. The process is involved data obtained from the data warehouse. Akter et
al., (2016) discussed that data mining technique is a process to analyse larger database such
as data warehouse and internet, used to discover new information along with hidden patterns.
The selected industries for this report are banking industry, healthcare industry and education
industry.
The report summarizes the business intelligence analytics as well as data mining
techniques for the chosen domains. It also discusses applications of business intelligence
analytics and data mining technique. There is discussion of challenges which are associated
with application of business intelligence analytics and data mining technique in chosen
industries.
1. Business intelligence analytics and data mining technique for chosen domain
Implementation of business intelligence analytics into the banking sector is a key way
to get success for making the business more effective. Examples of this implementation in
chose industry is customer relationship management (CRM), analysis of credit card, customer
segmentation and others. The technological innovations enabled the banking industry to open
an effective delivery channels (Dincer et al., 2016). CRM helps the industry to build a strong
relationships with the customers so that it leads to increase in revenues as well as profits. In
the banking industry, fraud instances are occurred, therefore in this case data mining
technique is used to build a predictive models as well as visualize the report to information to
users. The customer credit analysis is done to implement the customer credit scoring. It is the
most vital activity to evaluate the loan application of customers (Jain & Bhatnagar, 2016). It

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is used to model the risks from various loan applications without required to have lot of
resources. It can lead to reduce the operational cost as well as reduce reasons in decision
making. With competition into the banking sector, it is required to develop improved strategy
by means to customer credit scoring models. Data mining techniques help the banking
industry to gain new customers as well as help them to achieve existing customers (Olszak,
2015). The customer acquisition in addition to retention is required concerns for the banking
industry.
The healthcare data management is a process to analyse data collected from various
sources. Data analytics will help the healthcare industry to treat the patients properly and
secure the patient’s data, enhance the healthcare related outcomes as well as offer
personalized treatments to the patients (Wang, Kung, & Byrd, 2018). The business
intelligence analytics will help the chosen industry to regulate the existing data in order to
make improvement over clinical as well as business operations. It helps to individualize the
services into the existing business communities. Predictive analytics is ensured that the
healthcare information is reached right people at right time period. It helps the healthcare
sector to monitor the healthcare performance better, detect the trends along with deliver
patient’s care properly. The software can help the industry to identify and reduce unforeseen
changes into volumes, contracts plus quality measures (Brandão et al., 2016). It is easier to
identify the methods which will improve over the clinical outcomes of the patients involved
into the healthcare industry. For the industry, it is required to address operational as well as
patient care, clinical practices so that they can implement business intelligence platforms so
that it allows to make analytical abilities. The application of business intelligence is that it
provides better patient care, improve the personnel distributions, decrease readmission as
well as manage expenses (Choi, Chan, & Yue, 2017). The main aim of the data mining
technique is used to provide better patient care based on well-organized healthcare data.

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Implementation of business intelligence analytics into the educational sector is lower
as compared to the banking sector and healthcare sector. Implementation of this into the
education sector is done in process of college, admission as well as teaching management.
Competition is increasing for admission into the college is increasing day-by-day, with most
of the college students are receiving application of admissions and selective in its acceptance
(Haupt, Scholtz, & Calitz, 2015). The acceptance level is well known into the university as it
can reach 10% as well as uncertainty causes the talented students to apply to school on next
layers. Challenge into the admission process is identification of best applicant based on the
selected parameters. In this application of process to admit the students, data mining
technique is used to support the teaching management. Each of the university is managing
marks of students from various faculties (Kasemsap, 2016). With application of data mining,
the faculty manager can able to exploit various hidden information in addition to analysis.
The faculty manager can use to make improvement over the quality of teaching plus
knowledge. Data mining is used to solve the problem behind selecting proper and talented
applications of their university (Kasemsap, 2017). It is used to expand model which can
predict quality of the applicants by means of student performance data dependent on past
performance of the students.
2. Applications of Business intelligence analytics and data mining technique in
chosen domains
The banking industry has undergone changes in the way the business is being
conducted now-days. By means of electronic banking, there is easier to capture the
transactional data and volume of data is also grown. There are huge amount of data that the
banking sector is collecting to provide influence on success of the data mining. By means of
business intelligence analytics as well as data mining techniques, it analyzes the patterns as
well as trends with increasing in data accuracy (Owusu et al., 2017). By means of data

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mining tools, the bank can offer products as well as services to the customers. The bank
analyst can able to analyse the market trends as well as determine present market demand to
forecast the customer behaviour. It helps to provide more business opportunities and
profitable. By means of data mining applications, the banking sector can offer the products as
well as services to the customers. There are various areas where the data mining applications
are used in chosen sector such as profitability, segmentation of customers, prediction of
payment defaults, scoring of credit as well as detection of fraud transactions and forecasting
of banking operations (Trieu, 2017). Therefore, in the banking sector as they are providing
transactions, therefore it is required to secure the payments of the customers as well as reduce
occurrence of frauds.
A real time healthcare analytics solution is an application in the healthcare industry to
provide better patient’s care. This solution can save time as well as it is easily customizable
for the users. Patient segmentation helps the healthcare payers to analyse the patient’s
population for determining potential candidates for the disease management programs (Kao
et al., 2016). The health plan analytics can support the healthcare payers to analyse the health
related programs. Resource planning application helps the healthcare providers to schedule
the outpatient appointment and inpatient survey plans based on various factors such as
availability of doctors (Mathew & Pillai, 2015). Treatment outcome analysis help the
healthcare providers for analysing the patient treatment outcomes. The cost is calculated by
means of total staying the hospital, services provided, revenues as well as claims. The
applications of healthcare data analytics are required data transformation that can relay back
to the end users. Into the healthcare sector, there is adoption of electronic health record makes
the application of the analytical tools more efficient. In selected sector, it is required to
translate electronic bits into the healthcare data (Alansari et al., 2018). Therefore, predictive
analytics is helping to enable the healthcare companies to make proper predictions on the

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patient outcomes, diagnosis along with costs. The software can provide reasons for
predictions which is required to make for providing better patient care.
With increase in demands for the accountability as well as performance into the
educational industry, there is better decision making as well as accurate tracking is required.
In order to select best and talented students among many students, there is high usage of
business intelligence analytics as well as data mining. There is also new collection of online
resources prepared by the industry (Yeoh & Popovic, 2016). Business intelligence education
is a way to search array of the case studies, interactive to the business intelligence as well as
education focused solutions. The new medical schools are enjoying stronger as well as faster
start by means of business intelligence. They are using server based technology for keeping
the cost low. The education management software can bring a cost effective educational
solutions to the educators so that it can grow faster to meet with the students as well as
universities requirements (Secundo et al., 2016). Performance as well as analysis evaluation
is required in all levels of the educations such as students, teachers, alumni, legislators,
administrators and others. Each of them are playing a key role in success as well as growth of
the educational enterprise. If the applications can meet with requirements of educational
sector, it leads to improve the student, administrator as well as teacher satisfaction among the
education stakeholders.
3. Business intelligence analytics and data mining technique added business value
to chosen domain
Business intelligence analytics as well as data mining techniques can add value to the
banking sector by following roles they are playing in selected industry such as:
Monitor the cash flow: The data related to movement of the money is monitored easier
by tracking the total income as well as expenditures (Yeoh & Popovič, 2016).

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Streamline the banking operations: It can point out the data redundancy, boost the
business efficiency along with effectiveness of the business operations of banks.
Unify the data: It can integrate the data from the disparate sources which can able to
create data warehouse. It is a centralized repository where the data are being accessed across
the enterprises (Dincer et al., 2016).
Monitor the business profitability: By achieving the visibility across various
departments and keeping close eye on the cash flow, the bank can able to tell which of the
accounts are used most of the times.
Reporting: The business intelligence can analyse as well as compile into the reports
which are easier to understand as well as read (Llave, Hustad, & Olsen, 2018). It is updated
on regular basis. The business intelligence tool can combine the databases from various
sources.
Security: The dashboards as well as reports are to be generated which are based on
the security measures to make sure that the banking data are being used by authorized users
only (Akter et al., 2016).
The healthcare providers can get insights which are required to reduce the project cost,
increase in business revenue as well as improve the patient’s safety with the regulations by
means of business intelligence analytics. Business intelligence analytics as well as data
mining techniques can add value to the healthcare sector by following roles they are playing
in selected industry such as:
Patient care and satisfaction: The business intelligence can provide huge amount of
data in order to aid in improvement over the patient’s outcomes. The physicians are being
provided with the information they are required to monitor as well as predict, patient
diagnosis. Business intelligence can merge with the data mining and provide claims as well

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as physicians are able to access to them via means of electronic healthcare record (Shollo &
Galliers, 2016). The physician is recorded the patient data by EHR so that data are gathered
in secured place. The physician can see each patient’s past diseases, test results and treatment
that they are currently received. Elimination of repeat tests are helping to save the cost as
well as satisfy the patients those are not repeating the same test due to missing of data. In this
way, the physician can make their customers happy by providing better care of the patients.
Personalized medication: The data of patients become accessible as well as data
analysing is easier by means of business intelligence analytics and data mining (Jacobsen &
Van Vugt, 2017). The treatment regimens are moving from one size fits with the category to
the treatment based on the medical history of each patients as well as current medical
problems.
Prevention: Genetic markers will provide the physicians to prevent from the diseases
and reduction of the impact of disease on the patients. By means of big data, the physicians
can able to establish and better insight in the patterns of determinants which can increase
risks of disease of patients (Wang, Kung, & Byrd, 2018). The information is allowed the
physicians to recommend the medications and advise the patients related to make changes in
lifestyle for reduction of risks of disease among the patients.
Business intelligence analytics as well as data mining techniques can add value to the
education sector by following roles they are playing in selected industry such as:
Data analytics for teachers: The education system which is being powered by the
business intelligence analytics will help the teachers or mentors to devise as well as craft
scholastic experiences along with study curriculum based on the individual’s ability and
learning approaches (Hashem et al., 2016). The teachers can get the individual feedback
based on the performance of each student and entire class.

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Data analytics for students: It is used to collect as well as analyse larger amount of
data gathered from the student to track as well as assess their individual learning processes. It
can use to predict the academic career of each individual student and work on academic
problems (Moscoso-Zea et al., 2016). The intelligent curriculum is built with the data
analytics for modifying as well as adapting the requirements for each student.
Prediction of academic future: The educational programs can fuelled by means of
business intelligence analytics assist the education organization, teachers to gain in-depth
insights to the academic progress of the students. It is used to pinpoint each student who are
facing risk of failure as well as guide path to pursue their educational career based on the
performance statistics obtained (Laudon & Laudon, 2016).
4. Challenges associated with application of Business intelligence analytics and data
mining technique in chosen domains
Following are the challenges of application of business intelligence analytics and data
mining technique in the banking sector such as:
Legacy system can struggle to keep up: The banking sector is always slow to be
innovative. Most of the legacy system is not coping up with growing in workload. In order to
collect, store as well as analyse the banking data by means of outdated system can put the
entire system at risks (Akter et al., 2016).
Risk of safety of transactions: Into the banking sector where there is data, there are risks
taking in account of the legacy systems. It is required that the banking sector can make sure
that the data can process safely at all the times (Jain & Bhatnagar, 2016). There is risk over
the privacy of the data where cybersecurity is the main issue into the banking sector.
Following are the challenges of application of business intelligence analytics and data
mining technique in the healthcare sector such as:

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Privacy: One of the challenge related to the big data is lack of privacy, especially when it
comes to privacy of confidential medical records. Advancement over the technology can
access to the individual’s privacy. It allows the doctor to monitor the health of patient from
anywhere, it is not provide patient freedom (Olszak, 2015). The healthcare providers believe
that privacy regulations are required to protect the patient’s data.
Replacement of doctors: While there is benefit of business intelligence analytics for
predicting the medical issues, there are also challenge in case of replacing doctors. It lacks
personal touch of the human doctor (Choi, Chan, & Yue, 2017). Growth of the business
intelligence can lead to undermine the doctors as well as leave the patients turning to the
technology instead of using licensed doctors.
Following are the challenges of application of business intelligence analytics and data
mining technique in the education sector such as:
Cost: The business intelligence analytics as well as data mining is little too much for
the smaller sized business as it is expensive to track the performance of the students. In most
of the schools as well as universities, this system is not used due to high price of the business
intelligence system (Yeoh, W., & Popovic, 2016).
Complexity: The analytics is complex into the implementation of data. It is a complex
technique to deal with the educational data of the students (Haupt et al., 2015). In order to
track the data of the students, the teachers are sometimes find it difficult to track the talented
students based on their past performance.
Conclusion
It is concluded that usage of the business intelligence analytics can understand as well
as bring the customers effectively. It can drive the performance of the selected industry such
as healthcare, banking as well as education sector. It identifies the sales trends as well as

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provides personalized services in easier way. It makes an improvement over the operational
business efficiency so that it can lead to business profitability. The customer relationship
management can encourage the business to construct a solid associations with the clients so it
prompts increment in incomes just as benefits. In the financial business, misrepresentation are
happened, along these lines for this situation data mining method is utilized to manufacture a
revelatory models just as predict the report to data to clients. The benefit of business
intelligence is that it can bring stronger relations with the customers. The physicians are
being provided with the information they are required to monitor as well as predict using
electronic health record in the healthcare sector. In the education sector, the education
management software can carry a cost effective educational explanations to the educators so
that it can produce faster to encounter with the students as well as universities necessities.