This post is part of Series Business Intelligence – Tools & Theory
Currently running topic for this series is listed as below :
Series Business Intelligence – Tools & Theory
>>Chapter 1 : Business Intelligence an Introduction
>>Chapter 2 : Business Intelligence Essentials
>>Chapter 3 : Business Intelligence Types
>>Chapter 4 : Architecting the Data
>>Chapter 5 : Introduction of Data Mining<You are here>
Continuing from my previous post on this series, If you have missed any link please visit link below
We are going to Cover the Following Points in this article
- Various Risks involved in Data Mining
- Advantages and Disadvantages of Data Mining
Various Risks involved in Data Mining
Business Intelligence (BI) is about discovering what reasonable events might be taken, or conclusion made, at dissimilar times. It is about helping you consider the current and potential risk, cost or advantage of taking one action over another, or making one decision versus another.
Risk Management measures and offer data in terms of experience or opportunity for business performance.
Here are some risk management performs to provide stronger BI analysis:
· Identify and remove risk features and contact points within the organization to generate a strong foundation/base.
· Examine opportunities associated to taking tactical risks within the business.
· Examine the possible risk experiences join to movie ahead with strategic company path and scheme.
Advantages and Disadvantages of Data Mining
There are many advantages and disadvantages of Data Mining. Some of them are mentioned below:
Data mining can help direct marketers by providing them with helpful and precise trends about their customers’ purchasing behavior. Based on these tendencies, marketers can direct their marketing attentions to their customers with more accuracy.
For example, marketers of a software company may promote about their
new software to customers who has a lot of software purchasing history. In addition, data mining may also help marketers to predict which products their customers may buy. Through this forecast, marketers can surprise their customers and make the customer’s shopping experience becomes a pleasant one.
Data mining can help monetary institutions in areas such as credit reporting and loan information.
For example, by investigative previous customers with similar attribute, a bank can analyze the intensity of risk related with each given loan.
· Law enforcement
Data mining can assist law implementers to classify criminal suspects as well as capture these criminals by investigative trends in location, crime type, habit, and other sample of behaviors.
Some of the disadvantages of data mining are:
· Privacy Issues: Individual privacy has been a major worry in this country. In current years, with the extensive use of Internet, the apprehension about privacy has increase drastically, because of privacy concerns, some people avoid shopping on Internet. Security and privacy are the main factors which hamper the expansion of online shopping. They are afraid that somebody may access their personal information and then misuse it.
· Security issues: Although companies have lot of private information about us available online, they do not have adequate safety measures in place to protect that information.
For example, The Ford Motor Credit Company had to inform its more than 10,000 of the customers that their private information including address, account number and payment history were accessed by hackers who hacked into a database belonging to the credit reporting organization. With so much personal information accessible, identity theft could become a real problem.
· Misuse of information/inaccurate information
Trends attained through data mining intended to be used for advertising purpose or for a number of other ethical purposes, may be misused. People may misuse the data obtained through data mining by taking advantage of helpless people. In addition, data mining method is not a 100 percent accurate, thus mistakes may happen which can have serious outcome.
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