Data mining

Data mining is defined as extracting the information from the huge set of data and using it to make crucial business decisions. In other words we can say that mining is mining the knowledge from data that improves business efficiency.  

Data mining brings a lot of benefits to businesses, society, governments as well as individual. However privacy, security and misuse of information are the big problems if they are not addressed and resolved properly.

Need of Data Mining 

We need data mining mainly due to following reasons:

In field of information technology, we have huge amount of data available information from the huge set of data is called data mining. This information further can be used for various applications such as market analysis, fraud detection, customer retention, production control, science exploration etc.

We are living in extremely competitive world today where once more information can make or break a business. More and more insight is needed to get pass the competitors in any business. Managers and executives of these organisations are always under pressure to obtain and apply more and more effective methods of carrying out their business. This ever increasing need of more and more business intelligence has motivated the development of data mining more than anything.

Advantages of data mining 

Data mining brings a lot of advantages when using in a specific industry. Some of the main advantages are:

1. Data mining predict future trends, customer purchase habits etc. Based on these trends, marketers can direct their marketing attentions to their customers with more accuracy.

2. Data mining helps marketing companies to build models based on historical data to predict who will respond to new marketing campaign such as direct mail, online marketing campaign etc. Through this prediction, marketers can have appropriate approach to sell profitable products to targeted customers with huge satisfaction.

3. In addition, data mining Mahal marketers in protecting which products their customers may be interested in buying. Through this prediction, marketers can surprise their customers and make the customers shopping experience become a pleasant one.

4. Data mining can help banks to detect fake credit card transaction to help credit card's owner prevent their losses. 

5. By applying data mining in operational engineering data, manufacturers can detect faculty equipments and determine optimal control parameters.

6. Data mining gives Financial institutions information about loan information and credit reporting. By building a model from historical customers data, the bank and financial institution can determine good and bad loans.

7. Data mining can assist researchers by speeding up their data analysing process; thus, allowing them more time to work on other projects.

Disadvantages of data mining: 

There are few disadvantages of data mining, which are:

1. Because of privacy issues, people are afraid of their personal information is collected and used in an ethical way that potentially causing them a lot of trouble. Businesses collect information about their customers in many ways for understanding their purchasing behaviours trends. However businesses don't last forever, some days they may be acquired by other or gone. At this time the personal information on probably is sold to other or leak.

2. Businesses on information about the employee and customers including social security number, birthday and payroll etc. There have been a lot of cases that hackers were accesses and stole big data of customers from big Corporation with so much personal and financial information available. So, security is a big issue in data mining.

3. Information collected through data mining intended for marketing of ethical purposes can be misused by unethical people.

4. Data mining technique is not perfectly accurate therefore if inaccurate information is used for decision making will cause serious consequence.

Applications of data mining 

Today, various industries organisations have been adopting data mining to gain competitive advantages and help business grow. Some of the common application area of data mining are:

Data mining in sales/marketing: data mining enables the businesses to understand the patterns hidden inside past purchase transactions, thus helping in plan and launch new marketing campaigns in prompt and cost-effective way. In sales and marketing, data mining is used for market basket analysis to provide insight information on what product combinations were purchased, when they were bought and in what consequence by customers. This information helps businesses to promote their most profitable products to maximize the profit.

Data mining in banking or Finance: data mining is used to identify customer loyalty by analysing the data of customers purchasing activities such as the date of frequency of forces in a period of time, total monetary value of all purchases and when the last purchase was. After analysing those dimensions, the relative measure is generated for each customer. To help Bank to retain credit card customers, data mining is used. By analysing the past data, data mining can help banks to predict customers that likely to change their credit card affiliation so they can plan and launch different special offers to retain those customers.

Data mining in healthcare and insurance: the growth of the insurance industry is entirely depends on the ability of converting data into the knowledge, information on intelligence about customers, competitors and its markets. Data mining is applied in insurance industry lately but brought reminders competitive advantages to the companies who have implemented it successfully. The data mining applications in insurance industry I am listed below:

a) data mining is applied in claims analysis such as identifying which medical procedures are claimed together.

b) data mining enables to forecast which customers will potential purchase new policies.

Data mining in transportation: data mining helps to determine the distribution schedules among warehouses and outlets and analyse loading patterns.

Data mining in fraud detection: data mining is also used in fields of credit card services and telecommunication to detect fraud. Infra telephone call it helps to find destination of call, duration of call, Time of day or week.

Data mining in telecommunication: today the telecommunication industry is one of the most emerging industries providing various services such as facts, pager, cellular phone, internet messenger, images, email, web data transmission etc. Due to the development of new computer and communication technologies,  the telecommunication industry is rapidly expanding. This is the reason why data mining is become very important to help and understand the business.

Data mining in telecommunication industry helps in identifying the telecommunication patterns, catch fake activities, make better use of resource and improve quality of service.