26 Advantages of Data Mining: The data can be found through various digital tools from different sources to get raw data from digital and physical world. There are lots of advantages of Data mining, huge points of these advantages is given below.

26 Advantages of Data Mining

a) Web Mining: While launching new product online across public, market research need to be conducted that drives mining websites for relevant data to simplify the research. Research tools are called like e-commerce stores, online journals and many more.
b) Social channel Data Mining: in today world data mining tools uses social channel to get the required data to drive firm and organizational analysis or services
c) Business data mining: business firms and organizations fetch their data from records on business processes for business research.
d) Document mining: when data is searched from web, the main focus is to extract the relevant pages with the relevant information on the relevant topic.
e) Resume data mining: the curriculum vitae or resume of job applicants can explore everything about human resources, while filling job application form opportunities for public generally. The mining efforts are included into resume research for getting more balanced information about relevant candidate.
f) Music data mining: mining is also used in the area of music for dredging the music, visual mining, sensor mining and many more for specific organizational needs or requirements.
g) Todays era, knowledge is power, and this is exactly what data mining is about. It is about acquisition of relevant knowledge that can allow you to make strategic organizational decisions which will allow your business or organization to succeed.
h) Data mining is not simply limited to businesses also used to learn more about the behavior of customers. It can be used in other areas as well such as, a cricket coach can use data mining tools to analyze the behavior of team and the behavior of all competitors. Furthermore, It can also be used by banks, organizations, and governments. Moreover , data mining is useless if there is no data to analyze.
i) In data mining, When segmentation and clustering are used, user will be able to split the customers into clusters based on the revenue of organization. user can determine how much revenue will bring over time, and also determine the chances of retention based on the shopping behaviors of customer.
j) Data mining can also be used with is demographics that will allow you to target type of advertising that is used with customers. User will use advertising that is directly related to the customer behavior. A bank is a good example of an organization that could combine demographics with data mining.
k) There are a number of reasons why data mining is important. When data is taken from multiple sources and placed in a centralized location than data mining is used to analyse the data. Data mining is connected to data warehouses, neural networks or computer algorithms and more which are responsible for extracting.
l) Other advantage of data mining is that whenever data is extracted from multiple sources, patterns and connections can be examined which would not be found by linear extraction.
m) Another advantage of data mining with warehouse is that they can create such structure which will allow several changes within the stored data which can be further transferred to operational systems.
n) In data mining, data which is stored with dimensional methods will contain information which is related only to one event. The dimensional method is useful for workers who have a limited amount of information technology skills makes easy for them to study and understand.
o) The popularity of data mining and analysis throughout the Medicare is now a part of every Medicare recovery program. Providers should be preparing now for anticipated recovery efforts by the new contractors in market.
p) In area of CMIPof the Medicaid Integrity Program, data mining is also used as a part of the plan to prevent Medical fraud, waste, and abuse in society.
q) To successfully use data mining on the information, there are two terms known as segmentation and clustering plays an important role in marketing and customer interaction. These two terms deal with tracking the purchasing behavior of customers over a long period of time.
r) Most companies differentiate the customers which are high in value and low in risk. Because of the 80/20 principle, aware of the fact that 20% of customers will bring 80% of profits. The customers that with 20 percent are important, and they cannot be loss. On other 80 percent, the goal of company should be to increase the profits.
s) The patterns that are created by data mining tools can give rise to methods that the user never considered with the challenge by taking information that has been predicted to use solving these problems.
t) To deal with data mining tools is simply trust the system you worked. User can score a database of potential customers, and then based on the scores mailing out deals to all target audiences.
u) By using data mining, user should always understand the data that are being processed. Data mining will require user to explore the data in an presentable way.
v) Creativity is combined with logic, will increase the more chances to success. There are simultaneously number of benefits that are being included in data mining tools that makes easier for users to make good corrective decisions.
w) Interaction is important while dealing with data mining program directly,whcih will be able to define powerful relationships and connections.
x) Data mining is helpful when someone has talent to create self programs of data mining to place an emphasis on software to solve particular problems. They can also make the programs easy to understand.
y) When user tried to interact with the data mining tool directly in a manner, than user will be able to take better and smarter marketing decisions for certain organization.
z) Mining can help user to save money by allowing user to reduce the costs involved in marketing , sending out offers while maintaining the market response rate .

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