Data Mining Tools
Data mining tools describes a category of software applications and
methodologies designed to help businesses understand and make sense
of their data. Of the vast options available in data mining software,
all will fall into one of two categories- data mining tools and data
mining applications. Although each set has their own distinctive abilities
and requirements, both data mining tools and data mining applications
are valuable. In fact, many companies are beginning to use data mining
tools and data mining applications in an integrated manner- making
the data mining software more effective and the ultimate results more
valuable.
Data Mining Tools vs. Data Mining Applications
Data mining tools contain numerous methods that can be applied universally
to any basic business problem. Data mining applications however, are
typically more customized, operating on a specific business problem.
In this category of data mining software, the application actually
inserts methodology into an application that is previously manipulated
to address the problem.
Why Choose Data Mining Tools?
Data mining tools provide users with a platform for uncovering, converting
and analyzing private or corporate data. One of the reasons that many
companies choose data mining tools in their data mining software is
because of their flexibility, thorough technique, and large margin
for accuracy. Because of data mining tools’ flexibility, they
can be used on existing platforms or combined with other methods and/or
applications to increase accurate predictions. When used in tandem
with data mining applications, data mining tools will only enhance
the accuracy and ability of data mining applications.
Standards of Data Mining Tools
The downside of the flexibility of data mining tools is that the process
comes without a lot of rules or regulation. Therefore, guidelines and
methodology are starting to become more accepted and implemented within
the industry. One such guideline for ensuring consistent results from
data mining tools is the “Cross-Industry Standard Process for
Data Mining” (CRISP-DM). Regardless of the type of data mining
guideline that is used, all include crucial elements such as checklists,
guidelines, tasks and objectives, that serve to keep the practitioner
informed of every step of the data mining process.
Comparing Data Mining Tools
Because of the number of data mining tools available, most users will
find themselves having to compare different types based on their company’s
needs. When choosing data mining tools, it is important to keep in
mind the following elements: the type of platform that the data mining
tool supports/complements, the algorithms included, any decision trees
and neural networks, data input and model output options, usability
ratings, visualization capabilities, and modeling automation methods.
Data Mining Tools in Practice
The catalog industry is rich in data mining opportunities. With customer
subscriptions and purchases that are archived over several years, data
mining tools are crucial to pinpoint customer buying patterns and measure
the effectiveness of direct mail campaigns. |
