MITS5509 - Research Report: Business Intelligence Using Data Mining

This report examines the intersection of data mining and business intelligence, focusing on how data mining techniques are utilized to enhance business decision-making. The report begins by defining data mining as the analysis of patterns and relationships within data, contrasting it with traditional statistical methods. It then delves into a chosen academic paper that explores the effects of data mining in business intelligence, highlighting the paper's intentions and content. The report also addresses key issues raised in the paper, such as the challenges of managing large volumes of data, the emergence of varied data types, and the need for real-time data analysis. Furthermore, it discusses the results and conclusions drawn from the article, emphasizing the benefits of data mining for uncovering hidden patterns and supporting strategic business decisions. The report concludes by summarizing the key findings, emphasizing the importance of data mining and business intelligence in the current business landscape.

Running head: BUSINESS INTELLIGENCE USING DATA MINING
Business Intelligence using Data Mining
Name of the student:
Name of the university:
Author Note

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Executive summary
The data mining is seen as the information analysis of relationships and patterns. For understanding
business intelligence through data mining is investigated. In the following the article at fist the
intentions and contents of the articles are chosen to be discussed. Apart from this, the concerns that
are shown by the authors are been assessed in the report. Again the outcomes that are been
demonstrated in the chosen article is been highlighted here with the results that are seen from the
articles and the method it has been related to the topic analyzed.

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Table of Contents
Introduction:..........................................................................................................................................3
Description of the intention and content of the article chosen:.............................................................3
Issues highlighted in the article:............................................................................................................4
Discussion on results and conclusions that can be drawn from the article:...........................................5
Conclusion:............................................................................................................................................7
References:............................................................................................................................................8

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Introduction:
The data mining is defined as the assessment of data for the patterns and relationships. These
are never been found out through mathematical and statistical measures. The business intelligence
helps in demonstrating methods and processes to systematically gather, store, analyze and deliver
access to data in helping the enterprises for undertaking better operative with strategic business. In
the following study the business by utilizing the techniques of data mining with business analytics is
evaluated. Here, at first the content and intention of the article chosen are discussed. Next, the issues
highlighted by the authors are investigated here. Further, the results discussed are reported with the
findings drawn from the articles and the way it is related to the topic is investigated.
Chosen Artcile:
Name of the article: Business Intelligence using Data Mining Techniques and Business Analytics
Authors: Brojo Kishore Mishra, Deepannita Hazra, Kahkashan Tarannum and Manas Kumar
Description of the intention and content of the article chosen:
The aim of the paper is to demonstrate the effects of DM or Data Mining under the field of
Business Intelligence or BI. Further, the article has also highlighted different elements of data
mining. Apart from this, BI is a popular area of research that every sector is finding. Business
intelligence and data mining have been working together for the process and assessing the
information for lightening the workload for the organization and users. Thus it is also helpful to
analyze the materials discovered. This is also useful to demonstrate the BA or Business analytics as
an element of BI that has been relying on BI. Hence, there are different areas of business where BA
is seen to an effective tool to retrieve the efficient outcomes. The content of the research includes the

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measures that are used and designed for exploring information that is known as data mining. The
overall method is same as the actual measures to mine out gold out of the land surface. Particularly,
the article compares it with the retrieve of nontrivial type of nuggets from high amount of data
available. Moreover, the authors have provided a view about how the process of data mining and
help BI to seek the patterns and achieve the knowledge from the data present there. Apart from this,
it is also highlighted from the research is that there is intense level of competitions that the business
can compel to seek the innovative ideas regarding how they can capture and level the market shared.
This can done through the costs also. Moreover, the deployment of techniques of data assessment is
helpful for business to seek solutions such as finding the unexpected patterns from huge amounts of
information in the data warehouse or database. Besides, the patterns are able to deliver the data that
can be helpful to predict the further results.
Issues highlighted in the article:
In order to undertake decisions further it is seen that most of the executives have been
comparing not to have real time data. It includes in such a way that the decisions can be taken. Here,
the data requires to be controlled and then kept under organized manner such that it is easy and
quickly referred to take the effective decisions. Next, there are limited insights because of huge
volumes of data. Apart from this, there are six out of ten respondents agreeing to those statistics that
have every business that has more amount of information that they can control and use that
efficiently. As the businesses are unable to control that data, the procedures of working and the
insights are restricted and can function in effective way. Besides, there are issues of emerging
varieties. For instance he images, documents, videos, audio and emails are liable for giving rise to
most of the data. Because of this. The data that is newly generated results into another issue of
storages. Here, the data must be stored in such a manner that this can be determined and then

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segregated at any time. Furthermore, there are overflowing of the volume. Here, the amount of data
has been rising with the pace of 44x. For the upcoming decades this is seen to be ranging from
SOOK petabytes towards 35 zettabytes. Hence, it is that because it is vital to gather the information
from the starting such that the efforts and confusions can get avoided. Further, huge amounts of
volumes are there and here the information can be categorized into various classifications. Besides,
there are challenges with attitudinal data. This involves the desires, needs, preferences and options.
Further, there are the interaction data that involves the notes of call center, various in-person
dialogues, web clickstreams and email chat transcripts. Next, there is the behavioral data that
involves the orders, payments histories, usage histories and transactions. Further, there are
descriptive data that consist of demographics, self-centred info, attributes and characteristics.
Discussion on results and conclusions that can be drawn from the article:
The article has spread the understanding that current day business community has been
suffering from an overload of data and analysis of business source. From a prolonged time through
statistical techniques are used. However, now to make the activities simpler, various developed
techniques such as data mining are utilized. It is method of discovering knowledge within the
database utilized for making decisions. This has been a quick type of expanding and dynamic area
using machine learning, artificial intelligence, database systems and statistics that are applicable to
the advanced techniques of the innovation of data analysis. The article demonstrates the updated
impact of techniques of data mining in the field of business intelligence. Further, couple of powerful
tools are useful to find the rise in the sector of business. Again, the fundamental data mining can be
utilized for dealing with huge quantity of data to yield helpful results. On the other hand, secondary
business intelligence has helped to make the business-related decisions. Furthermore, the article
highlights the business analytic having broad domain of applications in all the sectors. Here, the data

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is developed and this is reason the data mining is one of the vital outworks in information systems
and databases and the business intelligence in the interface of the business.
Conclusion:
In the above report the impacts of DM within the technology of the business intelligence is
determined. Besides, various elements of data mining are tried to be highlighted. It can be concluded
by saying that data mining and business intelligence has been together to lighten the burden of users
and business. It is useful to assess the materials found out. Moreover, the study shows business
analytics as the element of business intelligence that has been depending on business intelligence. As
a result of this various sectors of business there where business analysis has been a smart way of
getting the results. Apart from this, the article discusses the upgraded effect of measures of data
mining in the area of business intelligence. Furthermore, there are some effective tools to seek a rise
in business sectors. Further, the main sector of data mining is used to deal with large amount of data
for gaining useful outcomes. Again, the study analyses the secondary business is helping to make the
decisions that are business-related. Apart from this, the study has shown that business analytics
comprises of wide domain of implementations at al type of areas. Furthermore, the data is created
and it is the cause that data mining can be seen as the important outwork under the information
systems. Here, business intelligence and databases are there under the business interface. Again, the
implementation of data analysis is helpful to determine the unexpected patterns. In this way the
authors have analysed the various sites like deployments, identifying of patterns and explorations.

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References:
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data mining techniques and business analytics. In 2016 International Conference System Modeling
& Advancement in Research Trends (SMART) ,2016, (pp. 84-89). IEEE.
[2] C, Kimble and G, Milolidakis. Big data and business intelligence: Debunking the myths. Global
Business and Organizational Excellence, 35(1), 2015, pp.23-34.
[3] D, Larson and V, Chang. A review and future direction of agile, business intelligence, analytics
and data science. International Journal of Information Management, 36(5), 2016, pp.700-710.
[4] G, Neelima and S, Rodda. Predicting user behavior through sessions using the web log mining.
In 2016 International Conference on Advances in Human Machine Interaction (HMI) , 2016, March
(pp. 1-5). IEEE.
[5] K, Kasemsap. Multifaceted applications of data mining, business intelligence, and knowledge
management. In Intelligent Systems: Concepts, Methodologies, Tools, and Applications (pp. 810-
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[6] M, Injadat, F, Salo and A.B, Nassif, 2016. Data mining techniques in social media: A survey.
Neurocomputing, 214, 2016, pp.654-670.
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and knowledge discovery. In International Conference on Enterprise Resource Planning Systems,
2016, November (pp. 114-122). Springer, Cham.
[8] S, Fan, R.Y., Lau, R.Y. and J.L, Zhao, J.L. Demystifying big data analytics for business
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[9] S, Moro, P, Cortez and P, Rita Business intelligence in banking: A literature analysis from 2002
to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3),
2015, pp.1314-1324.
[10] V.H., Trieu. Getting value from Business Intelligence systems: A review and research agenda.
Decision Support Systems, 93, 2017, pp.111-124.
[11] Z, Sun, L, Sun and K, Strang. Big data analytics services for enhancing business intelligence.
Journal of Computer Information Systems, 58(2), 2018, pp.162-169.