Data Mining as a Business Intelligence Tool: Applications, Pros and Cons
https://ilokabenneth.blogspot.com/2016/10/data-mining-as-business-intelligence.html
Published by: Iloka Benneth Chiemelie
Published on: 8th October 2016
Published on: 8th October 2016
Introduction
Studies
indicate that there have been a tremendous increase in the business technology
landscape and this shift has been very fast that it is now possible for one to
take a little step out, just to come back and witness a different thing. Mainly,
this shift have been influenced by the increasing volume of innovation and
creativity, which is further aided by the increase in advanced technologies
across the globe, which has made consumers demand more complex and easily
customizable (Computer Weekly, n.d.). As a result of
that, businesses are doing whatever is necessary enhance their market shares by
understanding the individual needs of consumers and customizing products that
meet these needs.
What is business intelligence?
Business
intelligence is very board when viewed as a business process and it encompasses
all features of understanding the needs of consumers and utilizing such
understanding to manufacture products and services that excite them. Business
intelligence is considered a decision making process that is data driven and it
covers all aspects of analyzing, aggregating and visualizing the data that has
been gathered in order to create information and aid the business management
process and also enhance overall strategic decision making (Expert Systems, 2016). From a generalized
view, BI is used to reference some feature of information that are being
gathered and compressed, but that is not all as it also involves what the
business does with the information that have been gathered – as it reflects in
their decision making process.
The
importance of business intelligence in the present economic setting is that
businesses are internationalizing, and the global economy is increasing
industrializing – leading to a higher level of competition. Thus, the only way
for business to ensure sustainability is to understand the independent needs of
consumers as a group and service these needs individually (customized). Thus,
gathering information and data from the consumers as well as compressing them
to generate real meaning for decision making is a necessity in modern
businesses. This is based on the understanding that it allows companies to
create products and services that better serve the needs of the consumers –
increasing their loyalty – and enhancing the performance of the company in the
process. In essence, business intelligence is correlated with the performance
of a company.
Data mining as a business
intelligence tool
Data
mining involves finding answers to the business process that were formerly
unknown. Due to the vast volume of information that can be obtained through
such analysis, it has been noted that managers are never certain as to whether
or not they are overlooking vital information in their business management
process. As a process, data mining is the practice of siting through all
generate data in course understanding patterns that were previously
unrecognizable. This has become so important in modern business that some
companies are now hiring data scientists, statistical analyst, and computer
scientist that are vastly equipped with the necessary competence for finding
the hidden signs within a sounded business process. (Junk, 2015) To a greater extent,
data mining does fit well within analytics category, but majority of the analysis
are based on data from system that has been set up to tract known KPIs – thus,
it can be considered more of measuring than mining.
Application of data mining in
business
As
the importance of data analytics grow continually, corporations are finding new
ways to apply data mining and business intelligence. There are numerous ways it
can be applied but some of the major ways are as discussed below.
Service provider:
the first example is form services providers in the utilities and mobile phone
industries. They utilize data mining for the purpose of predicting churn, a terms used when a company
departs a given company for a competitor in search of service offered by the
company. The service providers collate billing information, interactions with
customer services, visits to websites and other metrics to attach probability
score to each customers, and then target offers and incentives to the customers
which they consider to be highest level of risk churning (Matillion,
n.d.).
Retail:
the retail industry is another example of corporations that use data mining for
business intelligence. They separate the consumers based on Recency, Frequency,
and Monetary (RFM) groups and target marketing related promotions to these
different groups. A consumers that spend less time is normally be handled in a
different way from those that spend more time with the company. A higher volume
of loyalty, upsell and cross-sell offers will be accorded to those that send
less time with the company, while those that spend more time with the company
will be given more of a win-back deal (Matillion, n.d.).
E-commerce: one
of the most commonly known and utilized form of business intelligence in the
case of data mining is in e-commerce sales. Many of the e-commerce companies
use data mining and business intelligence to provide cross-sells and up-sells
via their corporate website. Some of the most famous companies in the area
include Amazon and eBay that utilize sophisticated data mining approaches to
drive enhance the product availability of people who viewed and liked their
products. For instance, if a consumers wants to make purchase, other related
products will be shown to the person for comparison and more purchase in the
process (Matillion, n.d.).
Supermarkets:
another good example of business process that involves data mining is the
supermarket. The famous supermarket loyalty card programmes are usually driven
(if not solely) by the decisions of the supermarket to gather details
information about their consumers which will be later used for data mining. A
good and recent example is the Target store, which recently developed rules
that the company will use to predict if their consumers are more likely to be
pregnant by taking a look into the content of their consumers’ shopping baskets
and spot those that they are more likely to be expecting babies and initiate
targeted marketing to these consumers. The programme was so popular that target
actually started sending out promotional coupons to families that did not know
or are yet to realize that they could be pregnan (Matillion,
n.d.)t.
Crime agencies:
the sue of data mining and business intelligence is not something that is
solely reserved for the business world as they are increasingly being applied
in the crime and prevention agencies where data analytics and mining are used
to spot new trends. They utilize data mining to determine where to deploy
police manpower and to search at bother crossing in order to determine recent
activities (Matillion, n.d.).
Overall,
data mining is increasingly becoming an integral aspect of the business process
based on the understanding that it can easily be used to predict changes
occurring in the business world. However, data mining is not all about the
flare as it does come with a number of issues. The major issue when it comes to
data mining is the issue of data quality.
Normally, the mining process are based on vast combination and process of big
data warehouse (which can normally contain outdated or incomplete data) (Rouse, n.d.). In essence,
processing these data might not necessary guarantee success with the outcome
based on the fact that findings might not be a clear reflection of the present
needs.
Secondary,
the issue of validity and reliability
has also been stressed with data mining based on the understanding that it can
sometimes contain information that are coned for the purpose of delivering set
objectives (Rouse, n.d.). In unethical
research process it is possible for data analysts to just feed huge volume of
data into the data warehouse in order deliver their desired outcome – making
the finding invalid and unreliable. As such, it is important to consider the
issues with data mining and adopt necessary measures to eliminate these issues
in order to ensure that the process is effective and efficient, and capable of
delivering quality data.
Conclusion
In
the present business setting, it is increasingly hard to find any business that
does not use data in decision making. Every aspect of a company’s decision
making process is based on a varied volume of data gathered, analyzed and used
to generate important information. For instance, data mining is sued to
generate new understanding about consumer trends in relation to specific
products, and the factors pushing or limiting these trends. These understanding
are then used to target certain products to the segmented units of consumers.
Secondly,
data mining is used to target specific promotions to certain consumers by offering
them products that are related to the ones they presently use. Outside of the
business world, data mining is also used in security agencies for the purpose
of detecting trends in crime and where to shift manpower. In essence, data
mining is an integral and important aspects of the daily business process.
Essentially, understanding the trends and application of data mining is
increasingly becoming important in the present world.
In
any case, this research does show that data mining is not all about the flares
are there numerous issues that need to be considered in the course of
performing successful data mining. They include quality, reliability and
validity of data being mined. However, under ceteris paribus¸ data mining is an important business process that
can be used to deliver products that offer better value to the consumers.
References
Computer Weekly. (n.d.). Master data management
gains ground in UK public sector. Retrieved from Computer Weekly: http://www.computerweekly.com/feature/Master-data-management-gains-ground-in-UK-public-sector
Expert Systems. (2016, 9 15). Text Mining vs Data
Mining: Which came first? Retrieved from Expert Systems:
http://www.expertsystem.com/text-mining-vs-data-mining-differences/
Junk, D. (2015, 4 15). Business Intelligence vs
Analytics vs Big Data vs Data Mining. Retrieved from Aptera Blog:
http://blog.apterainc.com/business-intelligence/business-intelligence-vs-analytics-vs-big-data-vs-data-mining
Matillion. (n.d.). 5 real life applications of
Data Mining and Business Intelligence. Retrieved from Matillion:
https://www.matillion.com/insights/5-real-life-applications-of-data-mining-and-business-intelligence/
Rouse, M. (n.d.). business intelligence (BI).
Retrieved from Tech Target:
http://searchdatamanagement.techtarget.com/definition/business-intelligence