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Data Mining
CIS-500
August 31, 2013
Data Mining is a concept that companies use to acquire new customers or clients
in an effort to make their case and profits increase. The ability to use data mining can result in
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the accrual of new customers by taking the latest information and advertising to customers who
are either not currently utilizing the company’s product or also in winning more customers that
may be purchasing from the competitor. Generally, data are any “facts, numbers, or text that can
be used processed by a computer.” Today, organizations are accumulating vast and growing
amounts of data in different formats and different databases. This includes working or
transactional information such as, sales, cost, inventory, payroll, and accounting. Data mining
also known as “knowledge discovery”, is the process of analyzing data from different
perspectives and summarizing it into useful information-information that can be used to raise
revenue, cuts cost, and pursue the goals outline for the company Data mining consist of five
major elements: “Extract, transform, and local transactions data onto that data warehouse
systems, store and manage the data in a multidimensional database system, provide information
access to business analysis and information technology professionals, analyzes the data by
application software, display the information in a useful format, such as a chart of chart”.
Extracting this information for future use will manage the business growing and adapting as the
consumer preference changes.
In the past, most companies acquired new customers by using direct mail, telemarketing,
or press advertisement. With the use of these avenues for marketing, different demographics for
individual customers are understood and catered to. Before Data Mining, predicting customer
behavior could be difficult due to the amount of data that may have been received for any
particular demographic group, the growing number of customers, their even changing patterns
and the information process not being assuredly accurate. However, by implementing
determining into the business process, it allow as for assistance with processing the larger
amount of information that is collected from making customers demographics. The behavior of
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customers will come to the business in the type of data, so in order for the company to responds
successfully to this information, data mining is going to be the best tool to use in efforts to
improve the facts that have been received on these customers. Predictive review is going to be
given the opportunity to get an true analysis based on a large amount of data that may possibly
be too large for individual human processing, however the information is still somewhat
considered to be predictive data due to the fact that the information will only associate with the
patterns of surveyed customers, who are more than likely existing customers and not potential
customers. Although data mining is relatively new term, the technology is not. Companies have
used powerful computers to sift through volumes of supermarket scanner data and analyze
market research reports for years. Continuous innovations in computer processing power, disk
storage, and statistical software are dramatically increasing the accuracy of research while
driving down the cost.
While many companies are aware of the importance of collecting data which is related to
their customers, using this information to properly interact with the customer can be difficult.
Due to the fierce and vigorous competition that exists in certain industries, there is no guarantee
that customers will stay with a specific company. If customers become annoyed with a particular
product or service, they will certainly find another company to buy from. Because of this,
companies must be able to respond to the needs of their customers as soon as possible. There is a
list of algorithms that may be applied to the data mining technologies; however the most popular
algorithms are “Statistical Algorithms which is an analysis system such as SAS that have been
used by analysts to detect unusual patterns and explain patterns using statistical models such as
linear models. Such systems have their place and will continue to be used. Neural Networks
which mimic the pattern-finding capacity of the human brain and hence some researchers have
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suggested applying Neural Networks algorithms to pattern-mapping. There is also Genetic
Algorithms which optimize techniques that use processes such as genetic combination, mutation,
and natural selection in a design based on the concepts of natural evolution. The Nearest
Neighbor method is a technique that classifies each record in a set of data based on a
combination of the classed of the k record(s) most similar to it in a historical set of data. Rule
induction is also an algorithm that extraction of useful if-then rules from data based on statistical
significance. Another very useful algorithm is Data Visualization which is the usual presentation
of complex relationships in multidimensional data. The most useful of these algorithms is
arguably the Nearest Neighbor algorithm. This algorithm has been listed as one of the top ten
data mining algorithms to use.
While data mining is an effective way to gather information about consumers, privacy
issues are a valid concern for any participant. Customers should be told how the data collected
about them would be used and whether or not it will be disclosed to third parties. This is
important because not all customers desire to provide their information to other vendors that are
not affiliated with the first supplier that holds the customers information. Data mining is a
technology that can easily be abused. When a customer’s information gives advice to a trader it
is assumed that the information will stay in one point when actually the information is right into
a database with the capabilities to be taken and distributed or replicated. This is an moral issued
that can be resolved by stating to the customer what information is needed, will be taken, and
what will be done with this information, customers should be given the right to choose whether
or not they want to have their information placed in a database. Another example is the issue of
categorizing and profiling patient’s based on various factors such as age, gender or disability.
This may also lead to biased and exclusionary effects by just in the HealthCare industries as this
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can relate to insurers, physicians or hospital. Removing personal identifiers such as name, age
and social security number may make it difficult to relate data up to unique individual, but it will
possibly eliminate the risk of discriminatory practices.
Many companies employ data mining to improve their business profit. Facebook can be
considered one of the most beneficiaries of Data Mining. Facebook uses the friend request that
have been accepted by a single user and then suggests the user may identify other friends who
are friends of the person that is now newly befriended. Merck-Medco Manage Care is mail-order
business which sells drugs to the nation’s largest health care providers: Blue Cross and Blue
Shield government organizations, large HMO’s, U.S. Corporations, State Government, etc.
Merck-Medco are data mining helped to uncover hidden links between illnesses and known drug
treatments, and spot trends that help identify which drugs are most effective for what types of
patients. The results are more effective treatments that are also less costly. Merck-Medco’s data
mining project has help customers save an average of 10% to 15% on prescription costs. Another
company taking advantage of data mining is Blockbuster video. Blockbuster Entertainment
mines its video rental history database to recommend rentals to individual customers; this could
benefit the store by giving customer a visual stimulant that may cause them to purchase two-
three rentals instead of just one.
The best way to assist your customers is to have a good idea of what they want before it
becomes obvious that they want it. Data Mining allows companies to adapt to the needs of their
customers within a short period of time. This technology brings a lot of benefits to businesses,
society, government as well as individuals. However privacy, security, and misuse of
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information are issues that can potentially harm business’s profits and results in a loss of
customers.
References
Josh, K. (2012). Analysis of Data Mining Algorithms. Retrieved August 29, 2013 from:
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http://www.desikan/research/datamininggoverview.html
Pupo, E. (2010).HIMSS News: Privacy and Security Concern in Data Mining. Retrieved from:
http://www.himss.org/ASP/ContentRedirector.asp?type=HIMSSNewsitem&ContentId
Stein, J. (2011). Data Mining: How Companies Now Know Everything About You? Retrieved
August 30, 2013 from:
http://www.time.com/time/magazine/article/0,9171,2058205,00.html
Turban/Volonino (2011) Information Technology for Management: Improving Strategic and
Operational Performance, 8th Edition. John Wiley & Sons, Inc.
