Data Mining and Knowledge Discovery Techniques

This assignment delves into the world of data mining and knowledge discovery. It examines various data mining techniques used to extract valuable insights from large datasets. The discussion encompasses diverse applications of these techniques across fields like medicine, business intelligence, and industrial microgrids. Furthermore, the assignment sheds light on the challenges inherent in knowledge discovery and future trends shaping this dynamic domain.

Running head: OVERVIEW OF THE BUSINESS OPERATIONS
Overview of the business operations
Name of the Student
Name of the University
Author Note

2OVERVIEW OF THE BUSINESS OPERATIONS
(A) Summary Report
This section of the assignment gives a summary report of the business operations that
are the Business Intelligence (BI), Data Mining (DM) and Knowledge Discovery in
Databases (KDD).
Business Intelligence (BI)
Business Intelligence or BI is one of the renowned operations to be used in the
business considering the modern day situations of the commercial market. The other name by
which Business Intelligence is known in the market of business and commerce is Descriptive
Analysis. It is named so because it describes the past or the current state of the running
business operations (Sauter, 2014). It is that wing of the business that is confined to
describing what were the conditions of the business and what is the present conditions of the
business. Technically speaking it can be said that business intelligence is a customary of
some specific processes, some critical architectures and some of the profound technologies
that is confined to converting raw data into meaningful information as a result of which it is
used to drive some of the profitable business actions. It is a package that is formed with the
combination of the software and the services targeted to transform the normal data into a
directorial path of knowledge and critical insights.
As mentioned above the objectives of business intelligence in are straightforward.
Besides analysing what has been done and what needs to be done, there are more areas where
the grip of Business Intelligence is substantial (Abelló et al., 2013). The impact of business
intelligence in an organisation is direct and to be confined to be benefitted with strategic,
tactical and operational decisions of business. It hugely supports resource based decision
making using with the use of historical data and not just by relying in the assumptions and the
predictions (Debortoli, Müller & vom Brocke, 2014). The tools used in the business

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intelligence systems are used to perform certain set of operations. This set of the operations
mainly includes the creation of the reports, brief ad concise summaries, dedicated
dashboards, operational maps, statistical maps and insightful charts along with the
amalgamation of the detailed intelligence that is based as per the nature of the business
operations.
Business Intelligence is very much dutiful in leading out the key business operations.
It is used for the creation of the key performance indicators based on the data collected in the
past. It is used for the identification and setting up of the benchmarks of several unique
processes (Chang, 2014). The business intelligence systems helps out the business
organisations to detect out the trends in the market and also find out the business problems
that needs the critical attention from the managers. Another functionality of the business
intelligence is that it helps them on visualizing the data that helps them in enhancing the
quality of the data and thereby helping them to make quality decision making. The flexibility
of the business intelligence not only assists the large enterprises but also allows an assistance
to the small and the medium scale enterprises. With the help of these kind of the modules, the
business intelligence proves to be fruitful to the business organisations.
Data Mining
Data Mining is one of the many operations involved in the business processes that is
similar to that of the business intelligence operations. The operations are almost similar
because it is also used by the business organisations to convert raw data into valuable set of
information. Dedicated software is used in data mining to drive out useful insights about the
company (Lin, Yao & Zadeh, 2013). By using a special type of software to look for the
patterns in some of the sections of data, business operations will be able to learn more about

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the insightful information about their customers involved to enhance and frame out some of
the effective marketing strategies, reduce the total costs and increase the number of sales for
the company. The main outlines of the Data Minig activities involves the processes of
effective collection of raw data, resourceful warehousing and the processes related to that
computing.
Warehousing is a process when the companies are involved towards centralizing their
data into a single database or a single program. The use of the data warehouse in data mining
is huge in implementing the effective processes in the organisation. It may be used by the
large enterprises to chalk out the segments of the data that are required to perform critical
analysis and use them accordingly in some of the specific situations (Larose & Larose, 2014).
Although, in some of the other cases analysts may entail on the process of selecting data
according to their choices and begin with the process of data warehousing based on those set
of requirements and the specifications (Freitas, 2013). It is regardless of the fact that Data
Mining is used by most of the organisations to carry out operations that are involved with the
processes of tactical decision making among the management of the concerned business
organisation. The programs that are based on the data mining processes are often used for
analysing the relationships and the patterns that are specifically based on the user requests of
a particular area.
One of the instances of the use of data mining processes in the organisations states
that the data mining process can be used by the organisations to create separate classes of
information. As a generic example, the online stores of the internet world can be taken up. It
is these online stores that collects the information of their valuable customers online and
separate them into unique classes where they are served with special deals according to their
likings and the requirement pattern. The insights of the customers are analysed and stored in
special directories with the involvement of the dedicated data mining software. These insights

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are valuable for the organisations to gain profitability as a result of which they are facilitated
to foster in the commercial and global market (Braha, 2013). This approach is fast as the
mining process gets completed within a very short span of time as the insights are reused over
and over again to reach to specific point in the business.
Knowledge Discovery in Databases
Knowledge Discovery in Databases or KDD is another business operation that is often
implemented by the large organisations along with the small and medium scale organisations
to facilitate their business process through which they are able to gain a huge profitability that
further aides them to foster in the international market of commerce. The knowledge
discovery in databases is one of such business operations that is almost similar to that of the
business intelligence and the data mining (Hilderman & Hamilton, 2013). Although, it is one
of the processes that somewhat stands apart from the rest of the business operations such as
the process of business intelligence and the data mining process.
Technically stated, it can be said that the Knowledge Discovery in Databases is one
such process used up by the business operations that is used for discovering useful
information or rather useful knowledge from a cluster or a collection of data. It is the
enhanced version of the data mining process and it is widely used by the business
organisations. Knowledge Discovery in Databases is a data mining process that includes the
involvement of preparation of data, selection of data, cleansing of the data and incorporating
of the prior knowledge into the specific data sets (Ester & Sander, 2013). The next step of the
very process involves the accurate interpretation of the solutions from the results that are
observed from a business point of view.

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One of the major applications of the Knowledge Discovery in Databases is that it is
used in the application of the areas in the marketing, detection of the fraudulent activities
enhancing telecommunication processes and manufacturing of the specific set of products. In
the past both the processes of the data mining and the knowledge discovery was performed
with the help of manual processes. The process of both of the business operations has evolved
much in the modern days. In modern day, situations the amount of data has grew larger which
is generally measured in the size of some terabytes and has proved to be beyond the control
of the human activities (Esfandiari et al., 2014). In addition to that, for the successful
processes involved in the business there is a great part played by the Knowledge Discovery in
Databases to drive out valuable information about the customers and the insights related to
them. It is used for the discovery of the underlying patterns in the collected data, as it is very
much essential to formulate the business processes.
It is due to this reason, dedicated software tools were discovered and developed to
mine out effective information that is mostly hidden in the approaches. It is also used to make
valuable assumption and tactful predictions that is comprised of the processes involved in
artificial intelligence (Dhar, 2013). The positive impact of the process of Knowledge Data in
Discovery of business operations has been huge for over the last several years. This business
operation is involved in the housing of several dedicated approaches to discovery of the
valuable information about the requirement of the customers.
(B) Research on application in BI, DM and KDD
Application of Business Intelligence in Business
Business intelligence focuses on certain software and services which helps in
transforming data into proper action. It is provided in such a way that it can deal with the
organization strategy and business decision (Witten et al., 2016). Business intelligence tools

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can be used for accessing and analyzing various data which are present in analytical findings
in any report. It helps the user by providing user with detail intelligence regarding the
business state. Business intelligence software system helps in providing current, historic view
of business operation. It is mainly done by making use of data which has been gathered in
data warehouse. It also worked occasionally from various operational data.
Business intelligence tools are mainly considered to be an important method for data-
driven DSS. In some of the cases, BI can be used in briefing books, reporting tools and lastly
executive system (Wu et al., 2014). By the help of these tools, people can easily analyze data
rather than waiting for IT to run for some complex system. The gathered information can help
the users to easily back up business decision with hard number. Various application of
Business intelligence is considered to be important for tackling data from various domains
like sales, production, finance and other sources.
Sisense is a well-known business platform which helps the user to join, analyze and
picture out the information which is required for making better and intelligent decision. Apart
from this, it also helps in crafting out work plan and different strategies. It is considered one
of the best business intelligent application (Zheng, 2015). Business intelligence software can
be considered to be suitable for creating insight with respect to business value from complex
data. By the help of this application, user can unify various kind of data which is required by
them. It is considered to be a dashboard which is needed for drag and drop interface.
Application of Data mining in Business
Data mining is considered to be an important process for sorting large set of data and
identification of pattern (Torgo, 2016). It helps in overcoming large number of problems by
proper analysis of data. Tools related to data mining is very much helpful in understanding
future trends. Grocery stores are considered to be well-known user of data mining techniques.

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Most of the supermarket tend to offer loyalty card to large number of customers. It gave them
access to reduce the price which is not available to users. Card makes it very much easy for
various stores to easily track the buyer and when the thing is bought. After the proper
analysis of data, stores can easily make use of data which is offered to customer coupons. It is
mainly used for targeting the buying habits and deciding the item which needs to be put in.
Data mining can be considered to be a cause of concern when a particular firm sells
out selected information. It does not represent all over the sample group which is needed for
providing a proper kind of hypothesis. Data mining comes up in mainly five stages. The first
stage is all about collecting and loading it into the data warehouse (Sangar et al., 2015). They
can easily store and manage data either in their house server or even in cloud. A number of
business analyst, management teams and lastly IT professional can easily access the required
data. It mainly helps in analyzing how they are organized. This particular application
software can be used for sorting data which is totally based on user result. It mainly helps in
analyzing data in easy format like graph or even table.
Data mining programs are considered to be helpful in analyzing relationship and data
based on user request. An organization can make use of data mining software for classifying
various classes of information (Wixom et al., 2014). For example, data mining can be used by
restaurant can easily make use of data mining for analyzing the things which should be
offered to certain specials. It helps in analyzing information which has been collected and
aims in building classes for creating a customer base and list of thing which is ordered by
them. Data miners look for large of information which is based on various logical
relationship or even proper association.

9OVERVIEW OF THE BUSINESS OPERATIONS
Application of Knowledge Discovery Database
Knowledge Discovery in Database (KDD) can be sated a method of discovery some
useful knowledge which is needed for data collection (Gamarra, Guerrero & Montero, 2016).
It is mainly used for data mining technique process which includes data preparation and its
selection, cleaning of data. It mainly aims in incorporation of knowledge on data sets and
analyzing proper solution from the given result. KDD is used in large number of fields like
marketing, manufacturing, telecommunication and last detection of any fraud activities.
In the earlier days, both data mining and KDD was performed in manual way. But
with the passing time the amount of data in the system become large to around terabytes size.
It cannot be maintained in manual way. The successful existence of any business and
discovery of underlying patterns in data is considered to be important (Ristoski & Paulheim,
2016). There is large number of software tools which has been discovered for analyzing
hidden data and making certain number of assumption. It has formed as an important part of
artificial intelligence. It now considered many aspects of discovery of data which is based on
learning and knowledge acquisition for different system. The main role is all about extracting
high-level knowledge from various low-level data.
KDD encompasses certain number of data storage and access algorithm which is
required for massive datasets and analyzing the output (Wixom et al., 2014). Data cleansing
and its access are mainly included in data warehouse which is needed for facilitating the
overall process of KDD.

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