With the bang of the era of information technology, we have
entered into an ocean of information. This information blast is strongly
based on the internet; which has become one of the universal
infrastructures of information. We can not deny the fact that, with
every passing day, the web based information contents are increasing by
leaps and bounds and as such, it is becoming more and more difficult to
get the desired information which we are actually looking for. Web
mining is a tool, which can be used in customizing the websites on the
basis of its contents and also on the basis of the user interface. Web
mining normally comprises of usage mining, content mining and structure
mining.
Data mining, text mining and web mining, engages various
techniques and procedures to take out appropriate information from the
huge database; so that companies can take better business decisions with
precision, hence, data mining, text mining and web mining helps a lot
in the promotion of the 'customer relationship management' goals; whose
primary objective is to kick off, expand, and personalize a customer
relationship by profiling and categorizing customers.
However,
there are numbers of matters that must be addressed while dealing with
the process of web mining. Data privacy can be said to be the
trigger-button issue. Recently, privacy violation complaints and
concerns have escalated significantly, as traders, companies, and
governments continue to gather and warehouse huge amount of private
information. There are concerns, not only about the collection and
compilation of private information, but also the analysis and use of
such data. Fueled by the public's concern about the increasing volume of
composed statistics and effective technologies; conflict between data
privacy and mining is likely to root higher levels of inspection in the
coming years. Legal conflicts are also pretty likely in this regard.
There
are also other issues facing data mining. 'Erroneousness of
Information' can lead us to vague analysis and incorrect results and
recommendations. Customers' submission of incorrect data or false
information during the data importation procedure creates a real hazard
for the web mining's efficiency and effectiveness. Another risk in data
mining is that the mining might get confused with data warehousing.
Companies developing information warehouses without employing the proper
mining software are less likely to reach to the level of accuracy and
efficiency and also they are less likely to receive the full benefit
from there. Likewise, cross-selling may pose a difficulty if it breaks
the customers' privacy, breach their faith or annoys them with
unnecessary solicitations. Web mining can be of great help to improve
and line-up the marketing programs, which targets customers' interests
and needs.
In spite of potential hurdles and impediments, the
market for web mining is predicted to grow by several billion dollars in
the coming years. Mining helps to identify and target the potential
customers, whose information are "buried" in massive databases and to
strengthen the customer relationships. Data mining tools can predict the
future market trends and consumer behaviors, which can potentially help
businesses to take proactive and knowledge-based resolutions. This is
one of the causes why data mining is also termed as 'Knowledge
Discovery'. It can be said to be the process of analyzing data from
different points of view and sorting and grouping the identified data
and finally to set up a useful information database, which can further
be analyzed and exploited by companies to increase and generate revenue
and cut costs. With the use of data mining, business organizations are
finding it easier to answer queries relating to business aptitude and
intelligence, which were very much complicated and intricate to analyze
and determine earlier.
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