Data Mining in Business Research Term Paper

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The
ability to parse through the many records of transactions, customer
contacts, and many other items stored electronically creates the foundation
for data mining's definition. Data mining specifically is defined as the
process of data selection, exploration and building models using vast data
stores to uncover previously unknown patterns, insights, and observations
that lead to strategies for effective differentiation and growth.
Central to the development of data modeling is the creation of data and
prediction models based on data collected from a variety of sources,
including corporate transactions, customer histories, and demographics,
even external sources such as credit bureaus and services organizations
that sell content (Westphal, C., Blaxton, T., 34). Companies
accomplishing best practices in data mining then use the many data and
prediction models to produce patterns in the information that can support
decision making and predict new business opportunities. What's unique
about data mining is the ability to quickly create entire snapshots and
background statistical and content-specific data quickly using seemingly
disparate and unrelated information (Kay, 44).
As a result, data mining's reach is extending across many industries. The
following are examples of where data mining is being used. In
telecommunications, companies are using data mining to analyze and predict
stock market performance and fluctuations, define credit card and insurance
limits and strategies for delivering better customer service performance to
clients. In the medical industry, companies are increasingly using data
mining to predict the effectiveness of surgical procedures, medical, tests,
medications, and also predict the impact of healthcare strategies and
policies on populations of patients and those for whom treatment is
targeted. Retailers are perhaps the most prodigious users of data mining
today, as their focus turns from pure cost cutting to managing pricing and
promotional discounts for the most profitable sales possible.

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In retailing
there is also the fact that RFID (Radio Frequency Identification) has
become a primary market driver that is creating ever-larger data warehouses
(Romanow, 1 & 2). In the specific case of RFID-generated warehouses, there
is a dearth of data warehouse and data mart application software vendors
who are offering solutions in this area today, which translates into a
burgeoning area of data mining in retail for years to come. RFID is going
to revolutionize the supply chains of many of the world's largest retailers
including Wal-Mart and Target, and data mining will be turn be used to
define which specific products are ordered at what time, for what price.
Data mining is impacting the price anyone pays at Wal-Mart or Target for
the smallest to the very largest product.

Exploring the principles of Data Mining in Business Research
At the center of the principles of data mining is the extraction of useful
data from vast warehouses of information. The basis for this extraction,
translation, and loading (ETL) which has taken on a completely separate
role in the data mining and data interpolation processes in many companies,
specifically those in manufacturing, where ERP and MRP systems often
capture millions of transactions and data points, many of which never get
used for creating strategies. Data mining relies on constraint and rules
engines, machine learning, statistical and visualization techniques to
define the current and future state of a series of variables under analysis
(Han, J., Kamber, M., 16, 17). Data mining techniques rely heavily on
predictive and descriptive statistics (Hastie, T., Tibshirani, R., &
Friedman, J. H., 38). Forward-thinking of software application.....

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