ITC 516 Assignment 1: Data Mining and Visualisation for Business

This report, prepared by a student, examines the application of data mining and visualisation techniques within the context of business intelligence. It begins by outlining the core uses of data mining in business, emphasizing its role in identifying patterns, predicting trends, and informing strategic decision-making, including the development of marketing campaigns and customer loyalty predictions. The report then explores the ethical and security implications of data mining, addressing concerns related to data privacy, potential discrimination, and the responsible handling of sensitive information. Specific attention is given to the security challenges associated with data mining, such as ensuring data integrity and preventing unauthorized access. The report also discusses how data mining can be used for analysis purposes and the implications in business sectors. The document concludes by highlighting the importance of establishing robust integrity controls and ethical guidelines to maximize the benefits of data mining while mitigating its risks. This report is available on Desklib for students studying business intelligence and data science.

Running head: DATA MINING AND VISUALISATION FOR BUSINESS INTELLIGENCE
Assessment 1: ITC 516
Data Mining and Visualisation for Business Intelligence
Name of the Student
Name of the University

1DATA MINING AND VISUALISATION FOR BUSINESS INTELLIGENCE
Table of Contents
Task 1: Data Mining in Business.........................................................................................2
Why Data Mining is used in business..............................................................................2
Data Mining for Analysis Purposes.................................................................................2
Task 2: Security, Privacy and Ethics...................................................................................5
Ethical Implication in Data Mining.................................................................................5
Security Implications.......................................................................................................6
Implication in Business Sector........................................................................................7
References............................................................................................................................9

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Task 1: Data Mining in Business
Why Data Mining is used in business
The main usage of data mining in businesses is to search for new business patterns and
establishing new business relationships for making better decisions in the business processes. It
helps to develop smart marketing campaigns, identifying different trends with the loyalty
prediction of customers (Shmueli & Lichtendahl Jr, 2017). Data mining searches for information
from the scratch that generally remains unnoticed and finds out patterns that helps to predict the
business. Data mining can also generate new opportunities in business:
 Systematic figure of examples and application: Data mining modernises the route toward
searching insightful information in a huge database. Request that needs extensive hands-
on examination would now have the capacity to be precisely answered from the data. An
ordinary instance of an insightful problem is centred around publicising. Data mining
uses data on past constrained time sends to recognise the destinations well while in transit
to help the level of productivity any further.
 Systematic disclosure of in advance darken designs: Data mining mechanical assemblies
searches in the blocks and perceive officially hid plans. An instance of delineation
disclosure is the examination of local bargains data to recognise apparently pointless
things that are together often obtained.
Data Mining for Analysis Purposes
A group of mineralogists are working with various instruments to examine and study the
relationship between minerals of the earth. Mining and Mineralogy are often considered as a

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single manner (Hooper, 2017). In any case, analysts at the Carnegie Institution for Science
mining an option that is other than what is expected – gigantic data, and it may fundamentally
influence the search and inspection for minerals. There are no under 5200 particular known
minerals (ordinarily happening substance blends not confined by specific sources), which have
been found and ordered by many regions around the globe. This gives mineralogists an
expansive number of data concentrates on working with, yet expelling criticalness from this data
is much of the time troublesome.
According to another paper of American Mineralogist, the gathering of researchers
worked with the framework study to increment current information in the scattering and
modification of chromium and copper stores after some period. This mindset may provoke the
desire of latest exposure of mineral stocks. Framework speculation is a strategy for separating
complex affiliations and collaborations between different things (Shmueli and Lichtendahl Jr,
2017). This system is every now and again in-use to determine the wide presence of infection,
the architecture of the web and regular structures. The Carnegie aggregate worked along these
lines for managing doles out each alluded to mineral like "centre point" and individual range
with two crystals located at a place forms relationship among these centres.
One of the main researchers Dr. Shaunna Morrison said "The mission for new mineral
stores is persevering, yet starting at as of late metal disclosure has been more a matter of fortunes
than coherent desire, All that may change with massive data." It can be also used for predicting
the presence of minerals with this system. Framework speculation helps to study the historical
geography of earth that reshaped after some period, and the way it affects by life forms. The
makers depict the way the movement of minerals containing copper reshaped the atmosphere of

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the earth. It is assumed that equivalent examination can be associated with planets, for instance,
the geographical researches clarifies the natural background of the planet Mars.

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Task 2: Security, Privacy and Ethics
In Data mining, the protection and legitimate issues that may come about are the
fundamental keys to the developing clashes. The courses in which data mining can be utilised is
bringing up issues concerning security. Consistently the administration and corporate elements
assemble large measures of data about clients, putting away it in information distribution centres
(Ryoo, 2017). Some portion of the worry is that once information is gathered and stored in an
information stock room, who will approach this data? Frequently a buyer may not know that data
collected about him/her is not recently imparted to who gathered the data. With the advances that
are accessible today, data mining can be utilised to remove information from the information
stock rooms, finding unique data and connections about clients and making associations given
this extraction, which may put customer's data and protection in danger (Tasioulas, 2017). Data
mining requires information plans that can cover shopper's data, which may trade off
classification and security.
Ethical Implication in Data Mining
The utilisation of data mining, particularly information about individuals, has positive
moral ramifications. Organizations confront a moral quandary when notwithstanding choosing if
the organisation should make a man mindful his/her data is being put away for future data
mining. By giving a person the alternative to quitting information gathering, an organisation can
hurt its upper hand in a commercial centre (Witten et al., 2016). A group must choose if an
absence of moral concern will cause a misfortune in cooperative attitude from purchases and
experience the ill effects of a reaction from the organisation's customers. Organizations who
utilise data-mining procedures must act dependably by monitoring the moral issues that are

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encompassing their particular application; they should likewise consider the intelligence in what
they are doing. For instance, data mining once in a while can be utilised to segregate individuals,
in particular about racial, sexual and religious introductions. The utilisation of data mining along
these lines is viewed as deceptive, as well as illicit. People should be shielded from any
untrustworthy utilisation of their data, and before they settle on any choice to give their
information they have to know how this data will be utilised, why is its use, what parts of the
data will be taken, and what results this activity will have. By doing this, Individuals will be
educated and enlightened direct concerning the reasons and outcomes of utilising their data.
Security Implications
Data mining is the way of arranging right and serious inquiries to remove data from a lot
of information in the database. As we probably are aware, data mining methods can be helpful in
recouping issues in database security. Notwithstanding, with the development of advancement, it
has been a genuine worry that data mining procedures can cause safety issues (Larose, 2014). A
lot of security specialists see data mining as a standout amongst an essential difficulties that
buyers will experience in the following decade. The unmistakable multifaceted nature in data
mining is developing precise models for information examination without giving the privilege to
utilise the data in particular client records, which will secure the database from being utilised the
wrong way. Growing such models can lessen the security issues that customers may confront.
Security issues in data mining are a standout amongst the most well known concerns due to the
way that when utilising data mining people are regularly working with a substantial measure of
data, and they can approach it effortlessly (Larose, 2014). This is hazardous if this information
was not utilised as a part of a safe way. As data mining certifications to open up heaps of new
fields for extricating data from both old databases and future databases that might be created

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with data mining as a help reason, the data mining session in some great organisations propose
that there can be not kidding security issues in data mining.
Information warehousing organisations must screen who approaches the information
inside and what parts of the information stockroom they approach. A case of a body that enables
confined access to their information stock room for data mining reasons for existing is Wal-Mart
(Witten et al., 2016). Wal-Mart has a comprehensive database of all their inventory, stores, and
gathered information. Organizations that have items conveyed by Wal-Mart are permitted into
Wal-Mart's database. This enables these organisations to dig this information for data concerning
the offer of their items.
Implication in Business Sector
Guaranteeing information honesty is a key factor to ensure that data mining instruments
and examination are significant and exact. Information uprightness ensures that information is
predictable all through the database (Freitas, 2013). There are a few business rules that keep up
the precision and uprightness of information put away in the database. Space imperatives
concentrate on what might be relegated to a trait. Upon the production of a database, each quality
must contain space name, information sort, (for example, numeric, character, date, or number),
estimate, and the adequate range or estimation of the information.
Referential trustworthiness expresses that each remote key esteem must be
indistinguishable to an essential key esteem. Assume that a database exists with a database table
titled client. The client will be doled out a customer ID. The customer id will in a perfect world
at that point turn into the essential key (Larose, 2014). Presently, assume the client puts in a
request inside the organisation. The Order table ought to contain the trait client ID also which

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will be recognised as the outside key of the Order table. Utilizing referential uprightness will
ensure that when the client ID is questioned, that one the client who exists with the
predetermined ID appears and what particular request that specific client has put.
Consolidating the area, substance, and referential trustworthiness tenets will reduce the
access of database data and enable the clients to change and erase mistake and irregularities.
Respectability controls are put inside a database to shield the database from unapproved updates
and sources of info (Rokach & Maimon, 2014). Attestations are made so particular decides that
is standing inside a business are executed through the database, (for example, Sarbanes-Oxley
commands). Trigger controls are made so that if an occasion happens, (for example, a late
instalment) a particular activity will occur, (for example, a new fine added to a record). Approval
rules are made to so that there are limitations on who might have the capacity to see the
information, enter the date, change the date, and erase the information. Approval rules are
utilised to shield the information from the shot of a representative to adjust information that their
employment has no approved limit in doing as such (Shmueli & Lichtendahl Jr, 2017). It
additionally secures a man's data, that is contained in a database not to be perused by unapproved
representatives.
It is fundamental that honesty controls and standards are put into databases so the
information may keep up its helpfulness and security assurance. If trustworthiness requirements
were not executed inside a database, any data that could be produced from the database would be
pointless. This thus would be futile for any data mining methods and examination.

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References
Freitas, A. A. (2013). Data mining and knowledge discovery with evolutionary algorithms.
Springer Science & Business Media.
Hooper, J. (2017). Data mining: How digging through big data can turn up new |
Cosmos. Cosmosmagazine.com. Retrieved 12 August 2017, from
https://cosmosmagazine.com/geoscience/data-mining-how-digging-through-big-data-can-
turn-up-new-mineral-deposits
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John
Wiley & Sons.
Rokach, L., & Maimon, O. (2014). Data mining with decision trees: theory and applications.
World scientific.
Ryoo, J. (2017). Big data security problems threaten consumers' privacy. The Conversation.
Retrieved 12 August 2017, from http://theconversation.com/big-data-security-problems-
threaten-consumers-privacy-54798
Shmueli, G., & Lichtendahl Jr, K. C. (2017). Data Mining for Business Analytics: Concepts,
Techniques, and Applications in R. John Wiley & Sons.
Tasioulas, J. (2017). Big Data, Human Rights and the Ethics of Scientific Research – Opinion –
ABC Religion & Ethics (Australian Broadcasting Corporation). Abc.net.au.
Retrieved 12 August 2017, from
http://www.abc.net.au/religion/articles/2016/11/30/4584324.htm

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Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.