Business Intelligence and Data Warehousing

Running head: BUSINESS INTELLIGENCE AND DATA WAREHOUSING
ITECH7406- Business Intelligence and Data Warehousing Research Report
Name of the Student:
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BUSINESS INTELLIGENCE AND DATA WAREHOUSING1
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

BUSINESS INTELLIGENCE AND DATA WAREHOUSING3
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.