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Running head: TECHNOLOGY 1
SIT717 Enterprise Business Intelligence 2019 T2
Student Name
Institute Affiliation
Date

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TECHNOLOGY 2
Abstract
Today’s commercial and industrial scenario is characterized by high levels of competition,
resourcefulness and increasing use of technology. It is extremely necessary that the management
has a firm grip over the company's data and can maintain a healthy relationship with customers.
Such analytics help in smart decision making. Added, it helps in achieving goals and increase
productivity. It significantly contributes to strengthening customer knowledge and increasing
customer base. With an increase in the use of Artificial Intelligence, such analysis is increasingly
done by all corporates around and has become an important aspect of the modern business world.

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Table of Contents
Abstract 2
Introduction 4
Summary - Data 4
Data Mining Techniques 5
Evaluation and Demonstration 8
Conclusion 11
References 12

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SIT717 Enterprise Business Intelligence 2019 T2
Introduction
Data Mining Techniques should be chosen according to the types of business and the type
of problem the business faces. A generalized approach can increase the effectiveness and
efficiency in business by the techniques that are used in data mining, the following are a few
techniques that are used in data mining: Clustering, Statistical, Visualization, Classification,
Neural Networks, Rules, and Decision Tree. Consumers are the epitome of profit and loss. If the
consumers want, they can make the company the best from scratch and if they want, they can
destroy the company from the top to nothing. Thus, it is important for a company to work on the
consumers who will take in the products to provide the company with profits. Big Data helps in
the accumulation of the consumer’s needs. It helps to create a picture of what the consumers need
and how the company can play a part. If the Big Data is not involved in this then the consumer’s
needs will never be out to the companies because all they can do is work on one or two consumers
but cannot generalize them. If the generalization of the consumers has to be done, then it has to be
kept in mind that the statistics have to be taken from a large group. It can only be achieved by the
big data system. Consumers create the structure where all of the data rests and its influence on all
the production is vivid yet striking. If the data about the consumers are indifferent, then the
company needs to rethink their products. The sales are also affected by this.
Summary - Data
One of the central issues in an account is to comprehend why firms money themselves as
they do. This issue has turned out to be progressively significant in light of the fact that how firms
are financed impacts their exhibition and worth. Since the 1950s, the capital structure writing has
tended to this major issue by concentrating on a company's blend of obligation and value. Be that
as it may, firms frequently utilize more than one sort of obligation guarantee. Moreover, a few
firms utilize particular sorts of obligation guarantee that others don't utilize. The financing
decisions of different types of obligation guarantee and various measures of obligation issued lead
to monetary information with numerous constant extents and a large number suggested by
obligation structures.

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The investigator ought to have a sound learning in bookkeeping, its standards, ideas, shows.
Else he won't most likely examine the budget summaries in subtleties. Besides, down to earth use
of bookkeeping information is completely required so as to achieve his targets. The expert must
be clear about the target or reason for the investigation. As a rule, he is depended to do as such for
his customers. Normally, he should know his customer and his prerequisites. As needs are, he will
examine the fiscal summaries for gathering such data that are wanted by his customers (Bertoni
and Larsson, 2017). The expert must choose the suitable procedures with the end goal of
investigation. He may apply a specific system in one spot though an alternate method in different
spots. The expert must revamp or regroup the essential information gathered by him from budget
reports with the end goal of his needs and employments of fiscal summaries. For instance, in the
event that he needs to know the working capital position, he should know the absolute current
resources and all-out current liabilities position from the information contained in fiscal reports.
The examiner must decide first the degree of his investigation which will encourage him to make
arrangements for his work and furthermore to set up a calendar of work for the examination.
The expert ought to be all around familiar with the outside just as the inner condition which
is looked by the organizations; for example contenders, disposition of the leasers and account
holders and so forth. So also, he should ponder the inward condition of the organization likewise,
for example, basic changes, representative resolve and so forth which will basically assist him with
studying and investigate the financial reports and to set up a report. The investigator must look to
his discoveries in a clear style in a basic structure which is effectively reasonable by the regular
clients of fiscal summaries.
Data Mining Techniques
Data Mining Statistical Technique is a technique that signifies are considered as a branch
Mathematics that signifies the clustering of data and its description does not consider as a data
mining technique by the data analysts. It helps in discovering the various forms of a pattern.
Nowadays people actually have to deal with big data which can be further derived with important
patterns. This technique definitely helps you in data mining with massive data itself.

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Statistical Technique helps in identifying the data in various forms which also includes the
statistical form of data. This helps the organization in saving time and many of the data more
relevant.
It includes a number of methods which verifies the numerical data. Statistical Technique
helps in finding out the patterns in the database, probability of the event that will occur, patterns
useful to the business, summary that gives you a detailed review of the database. Through these
reports that are made through statistical techniques will help people can make smart decisions.
There are a number of statistics but the one which is considered as useful and important in
collecting and counting of data (Kahane et al., 2007). There are different ways by which data can
be collected, some of them are given below:
Mean, Median, Histogram, Max, Min, Linear Regression, Variance.
Statistics is the said to be the component of data mining that provides the tools and the
techniques which deal with a large amount of data. Statistics give a proper review and reports of
the big data without wasting any time. It is the science of learning data which includes everything
starting from collecting to organizing and presenting the type of data for business. There are
basically two types of statistical data which are descriptive and inferential. Descriptive one
organizes and summarizes the data. The descriptive data when draws to the conclusion it is said to
be an inferential data. This analysis and presentation of big data, it is considered to be a core for
data mining along with machine learning. It provides the analytical technique and tool which is
helpful for large sets of data volume. Statistical Techniques is considered to be amongst the top
technique as compared to other as many of large numbers of data are handled by it and the reviews
for it are extremely good and sensible. They make the possibility of making the information easy.
Big Data Intelligence helps in strengthening customer knowledge. The demands could be
better understood along with a more accurate understanding of the target market. It improves a
firm's capacity to reach out to prospective customers. Business Intelligence reports are
fundamental to understanding or predicting consumer behavior and take decisions accordingly.
The demographics of the consumer set can also be of great importance. Enterprise Intelligence
helps in the evaluation of Return on Investment (Kovalerchuk and Vityaev, 2009). A higher return

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is the basic goal of any organization. Examining information through Data Analytics even helps
in determining return per department/divisions. This makes it easier to enhance business
productivity.
Data Mining Techniques help in resolving the problems with big data that are actually
trouble for many organizations and businesses, there are many techniques that will help in
extracting, collecting, and clustering the data. Data Mining Techniques will help you in collecting
the data and making them readable in an easier way, the only thing that organizations have to do
is choosing the perfect techniques which suit them better. Data Mining has many advantages which
include the Analysis of Big Data, Improvised Predictions, exploring the discoveries with hidden
patterns, the models that can be made available to understand the complex data easily.
Implementation of a new system with existing platforms, helps in identifying the various aspects
related to criminal suspects, they are actually cost-effective and efficient for data separation, it also
helps the e-commerce website to cluster the data. The biggest disadvantage of data mining is the
privacy issue, the companies can sell important information to different companies or customers.
Data Mining tools work in different manner which is due to a different algorithm which itself are
employed in the design, therefore, one should choose the correct data mining technique for the
work. Some of the data mining analytics software is a bit difficult to obtain which requires
knowledge and training. The information through data mining can be misused.
A direct model can be utilized to check whether one classification or thing is identified
with another. A case of a direct model is straight relapse: information focuses are plotted on a
diagram to check whether they have a direct relationship; at the end of the day, can a straight line
be utilized to speak to the information. In the event that a straight line can be drawn, this
demonstrates there is a connection between the two classifications. A straight model can be utilized
to discover data about how age, sexual orientation, pay, and different qualities identify with case
size.
A period arrangement model is a place a statistician takes a gander at how a specific thing
performs after some time. For instance, they may see how policyholders' cases history changes
after some time to decide the amount to charge for explicit policyholder attributes or they may
think about the exhibition of ventures over some undefined time frame to decide rates to charge

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TECHNOLOGY 8
for entire life coverage arrangements. Quite a long while of quickening interest in information and
information examination are changing the protection business.
To be precise obviously, information examination is one of the verifiable mainstays of
protection. Statisticians have utilized numerical models to anticipate property misfortune and harm
for quite a long time (Kovalerchuk and Vityaev, 2009). When they sell approaches, safety net
providers gather enormous informational collections about their clients that are refreshed when
those clients make a case.
As of late, as safety net providers have tried to turn out to be progressively significant to
their clients and increasingly proficient, they have understood the key significance of their
information ventures. They need to tackle information investigation to improve client experience
altogether while cutting cases taking care of time and costs, and disposing of extortion. Physically
spotting irksome cases early is testing; working out procedures to alleviate the hazard once
recognized is even harder. The data should be conveyed in an opportune manner (ideally
promptly), into the characteristic work process of the agent, perhaps with a warning to the boss or
huge misfortune unit. The data conveyed needs to raise an alarm, however, to clarify the traits
which bolster the hazard level and propose an answer or work plan for the agent. This procedure
should rehash itself continuously as basic information changes are made to the case document,
especially for long-tail lines, for example, real damage.
Evaluation and Demonstration
If the reference work is a part of the Business Intelligence, then it is important to refer the
Big Data Analytics in order to get the calculations right. In simpler words, Big Data helps in the
holistic idea to be considered. The idea of Big Data is to collect the macro level of data and furnish
it so that it can be supportive of business management. The complete idea of Enterprise Business
management is to work on the aspect of funds, data support, and the profit section. The production
and the dealing are also handled but the data analytics is one of the most important parts of the
complete structure.
But what if the book tells you that Big Data and Business Intelligence is not related. In the
books, it says that business analytics has nothing to do with the macro-level of data that it

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accumulates in the course. On the other hand, we find the complete system revolving around the
data and the data analysis program. The analytical system of Big Data is one of the major
influences on the Business Intelligence system (Kwak, Eldridge and Shi, n.d.). It is not limited to
the data for the Sales, but it also works on inventory and consumer data. These help in the best
planning and mapping of the system that makes it more proficient for the company to begin with.
The idea of Big Data analytics is to take in the account of the structure in the base of the
macro level. And the Business Intelligence system has to go through all the minute details inside
the big level system. The extraction of the data is the primary focus of the Business Intelligence,
but Big Data works on the accumulation and creation of the stats. Information is collected in Big
Data whereas Business Intelligence works on the extraction of information to find out the key
points of the complete structure.
More importantly, it takes time for Business Intelligence to work without the idea of
analytics. The analytics of the information is important for the decision-making purpose. If there
is no data, stats or information to be worked on then there cannot be any decision to be made. It is
one of the key features that connect the dots of these two components of the business. It is true that
both of these are different ideas, but it has to be kept in mind that others are incomplete without
the one. Thus, it has to be understood that you cannot neglect one for the complete focus on the
other structure. Big Data helps in the improvement in the consumers whereas the Business
Intelligence helps in the increment of the revenues. As the idea of operations is taken into the
account of the business the operations have to be increased and the efficiency of the operation can
be increased by the Business Intelligence. If the Big Data plays its role then the operations don’t
come into their liability, for them, it is the strategies that count more for the company.
Where all the discussion end then is the real question for many of the business companies.
The discussion about the Big Data for Business Intelligence is one of the major parts of the
company’s development. And to achieve the success that a company requires can only be done
when both ideas work in sync. The statistics and the reports should be the best that Big Data can
do and the management of the data for the use of the company is the work of Business Intelligence.
Thus, in order to get the best results for the company it has to be kept in mind, that the
accountability of both parties is different yet they complete the system for better efficiency (Mak,

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Ho and Ting, 2011). Business Intelligence has to be very accurate with the data that it handles
from the Big Data because it is based on the macro-level understanding and statistics.
The big data will help in the production of the company as well as support the sales
department in order to gain the best output form the company. Sales make the company’s profits
and losses; thus, the company has to work on sales as a different entity. If the sales are not taken
seriously then it will bring about damage to the company’s market value. The sales department is
based on the inventory and its production unit. The macro idea about all the entities of the
company comes through the channel of big data which in turn deals with the inventory’s
analytics. There are no substitutes to the raw as well as built product, thus, the inventory has to
be kept intact with all the data about it. The complete system surrounds the company’s
manufacturing of the products or services. The inventory can be looked upon by the batch testing
or the data based on the testing and reviewing. If Big Data loses the information on this account,
then the company will be seriously affected.
Business intelligence is the collection of systems and products that have been
implemented in business, but not the information derived from the systems and products i.e. big
data. But, both big data and Business Intelligence helps to analyze the huge data sets to expand
the business and optimize the cost. This data analysis also involves an active part in the
development of strategies and methods that make sure the success of organizations. Business
Intelligence helps in handling a large amount of structured, unstructured or semi-structured big
data to help in developing business strategies. In simpler words, big data makes Business
Intelligence a more valuable and useful tool for most businesses. Previously, many decisions
were based on historical data, but now big data analytics helps that happen at greater speeds.
This is accomplished through the integration of open source technologies or the sources of big
data platforms. In the present world, the importance of data in business is enormous as the most
effective decisions can be made only by analyzing the data which helps to grow the business
further (Smith, Willis and Brooks, 2000). Both big data and Business Intelligence helps in
analyzing the data and to get further insights. Rapid and smart decision-making requires a
holistic view of any business. Ideally, it is needed to be able to collect, analyze and act on
information with rapid and smart decision-making. Therefore although big data and Business

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Intelligence are not the same things, they both need to be synchronized to achieve the required
goal in business.
Conclusion
Big data Intelligence refers to an analysis of available data of huge scale to explore
hidden patterns, establish relationships, correlations and other useful insights. It is concerned
with the structure of data and data processing to add value to the organization. The concept
extends to the use of internal data for better workforce performance. Business Intelligence is a
collection of system, software and products which can be used to re-organize data into a
meaningful form for better decision making the process and improved business performance. It is
all about the application of Business Intelligence tools to formulate coherent data and present it
into a visually understandable form.

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References
Bertoni, A. and Larsson, T. (2017). Data Mining in Product Service Systems Design: Literature
Review and Research Questions. Procedia CIRP, 64, pp.306-311.
Kahane, Y., Levin, N., Meiri, R. and Zahavi, J. (2007). Applying Data Mining Technology for
Insurance Rate Making: An Example of Automobile Insurance. Asia-Pacific Journal of Risk
and Insurance, 2(1).
Kovalerchuk, B. and Vityaev, E. (2009). Data Mining for Financial Applications. Data Mining
and Knowledge Discovery Handbook, pp.1153-1169.
Kovalerchuk, B. and Vityaev, E. (2009). Data Mining for Financial Applications. Data Mining
and Knowledge Discovery Handbook, pp.1203-1224.
Kwak, W., Eldridge, S. and Shi, Y. (n.d.). Data Mining Applications in Accounting and Finance.
Encyclopedia of Business Analytics and Optimization, pp.609-617.
Mak, M., Ho, G. and Ting, S. (2011). A Financial Data Mining Model for Extracting Customer
Behavior. International Journal of Engineering Business Management, 3, p.16.
Smith, K., Willis, R. and Brooks, M. (2000). An analysis of customer retention and insurance
claim patterns using data mining: a case study. Journal of the Operational Research Society,
51(5), pp.532-541.