Business Intelligence and Data Mining: Industry Applications Report

This report delves into the applications of business intelligence (BI) analytics and data mining techniques across three key industries: banking, healthcare, and education. It explores how these techniques, including customer relationship management (CRM), predictive analytics, and data mining, are used to improve decision-making, streamline operations, and enhance business value. The report highlights specific applications such as risk modeling in banking, personalized treatments in healthcare, and student performance analysis in education. It also examines the added business value in each sector, including improved cash flow monitoring, patient care, and academic progress prediction. Finally, it addresses the challenges associated with implementing these techniques, such as legacy system limitations, privacy concerns, and the complexity of data analytics, providing a comprehensive overview of the benefits and obstacles of BI and data mining.

BUSINESS
INTELLIGENCE AND
DATA WAREHOUSING

INTRODUCTION
 Business intelligence analytics is a process to extract,
transform, manage as well as analyse the business data to
support the decision making processes (Llave, Hustad &
Olsen , 2018).
 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 (Akter et al., 2016).

BUSINESS INTELLIGENCE ANALYTICS AND DATA
MINING TECHNIQUE FOR BANKING INDUSTRY
 Implementation in chose industry is customer relationship
management (CRM), analysis of credit card, customer
segmentation
 CRM helps the industry to build a strong relationships with
the customers so that it leads to increase in revenues as
well as profits (Dincer et al., 2016).
 It is used to model the risks from various loan applications
without required to have lot of resources (Jain & Bhatnagar,
2016).

BUSINESS INTELLIGENCE ANALYTICS AND
DATA MINING TECHNIQUE FOR
HEALTHCARE 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).
 Predictive analytics is ensured that the healthcare information
is reached right people at right time period.

BUSINESS INTELLIGENCE ANALYTICS AND
DATA MINING TECHNIQUE FOR EDUCATION
INDUSTRY
 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.

APPLICATIONS OF BUSINESS INTELLIGENCE
ANALYTICS AND DATA MINING TECHNIQUE
IN BANKING SECTOR
 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).

APPLICATIONS OF BUSINESS INTELLIGENCE
ANALYTICS AND DATA MINING TECHNIQUE
IN HEALTHCARE SECTOR
 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).

APPLICATIONS OF BUSINESS INTELLIGENCE
ANALYTICS AND DATA MINING TECHNIQUE
IN EDUCATION SECTOR
 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
 Business intelligence education is a way to search array of the case studies, interactive to
the business intelligence as well as education focused solutions.
 Performance as well as analysis evaluation is required in all levels of the educations such as
students, teachers, alumni, legislators, administrators and others (Secundo et al., 2016).

BUSINESS INTELLIGENCE ANALYTICS AND
DATA MINING TECHNIQUE ADDED
BUSINESS VALUE TO BANKING SECTOR
 Monitor the cash flow
 Streamline the banking operations
 Unify the data
 Monitor the business profitability
 Reporting
 Security

BUSINESS INTELLIGENCE ANALYTICS AND
DATA MINING TECHNIQUE ADDED
BUSINESS VALUE TO HEALTHCARE SECTOR
 Patient care and satisfaction: The business intelligence can
provide huge amount of data in order to aid in improvement over
the patient’s outcomes.
 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).
 Prevention: Genetic markers will provide the physicians to prevent
from the diseases and reduction of the impact of disease on the
patients.

BUSINESS INTELLIGENCE ANALYTICS AND
DATA MINING TECHNIQUE ADDED
BUSINESS VALUE TO EDUCATION SECTOR
 Data analytics for teachers: The teachers can get the individual feedback based on the performance of each
student and entire class.
 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.
 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.

CHALLENGES ASSOCIATED WITH APPLICATION
OF BUSINESS INTELLIGENCE ANALYTICS AND
DATA MINING TECHNIQUE IN BANKING SECTOR
 Legacy system can struggle to keep up: The banking sector is always
slow to be innovative.
 Risk of safety of transactions: Into the banking sector where there is
data, there are risks taking in account of the legacy systems.

CHALLENGES ASSOCIATED WITH APPLICATION OF
BUSINESS INTELLIGENCE ANALYTICS AND DATA
MINING TECHNIQUE IN HEALTHCARE SECTOR
 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.
 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).

CHALLENGES ASSOCIATED WITH APPLICATION OF
BUSINESS INTELLIGENCE ANALYTICS AND DATA
MINING TECHNIQUE IN EDUCATION SECTOR
 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.
 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).

CONCLUSION
 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 provides personalized services in easier way.
 It makes an improvement over the operational business efficiency so that it can
lead to business profitability.

REFERENCES
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Organizational and End User Computing (JOEUC), 28(3), 1-9.
 Choi, T. M., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for business operations and risk
management. IEEE transactions on cybernetics, 47(1), 81-92.
 Dincer, H., Hacioglu, U., Tatoglu, E., & Delen, D. (2016). A fuzzy-hybrid analytic model to assess investors' perceptions for
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 Haupt, R., Scholtz, B., & Calitz, A. (2015, September). Using business intelligence to support strategic sustainability information
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Information Technologists (p. 20). ACM.
 Jacobsen, O., & Van Vugt, M. (2017). The Role of Business Intelligence in the Internationalisation process of SMEs.
 Jain, A., & Bhatnagar, V. (2016). Analysis of grievances in the banking sector through big data. International Journal of Service
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 Kao, H. Y., Yu, M. C., Masud, M., Wu, W. H., Chen, L. J., & Wu, Y. C. J. (2016). Design and evaluation of hospital-based business
intelligence system (HBIS): A foundation for design science research methodology. Computers in Human Behavior, 62, 495-505.

REFERENCES
 Llave, M. R., Hustad, E., & Olsen, D. H. (2018). Creating Value from Business Intelligence and Analytics in SMEs: Insights from
Experts.
 Mathew, P. S., & Pillai, A. S. (2015, March). Big Data solutions in Healthcare: Problems and perspectives. In 2015
International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-6). IEEE.
 Owusu, A., Agbemabiasie, G. C., Abdurrahaman, D. T., & Soladoye, B. A. (2017). Determinants of business intelligence
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 Secundo, G., Dumay, J., Schiuma, G., & Passiante, G. (2016). Managing intellectual capital through a collective intelligence
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 Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for
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