Data mining is the work of extracting useful information from large-scale data. It can also be defined as the search for correlations that can enable us to make predictions about the future from large data piles using a computer program.
Today, with the development of technology, the time spent on the internet, backed up documents, emails, videos and photos are also increasing and the concept of big data is gaining importance. This means that the number of data is increasing day by day. So, does the fact that there is so much data mean anything without processing this data?
In order to better understand the concept of data mining and have an idea about this subject, first of all, it would be correct to remember the meanings of the words data, information and information and to define data mining within this framework.
Data, in its most general definition, means a raw, unprocessed record. Comments can be made on these records, but it is not possible to reach clear information. For example; Concepts such as person names, phone numbers, grade point average are data on their own.
Information, on the other hand, can be expressed as pieces of information obtained as a result of organizing and classifying data. In other words, it can be said that data is given meaning and information emerges. For example; Concepts such as the names of babies born in the last five years and the grade point average of the last year are information.
The acquisition of meaning as a result of the analysis and synthesis of data that has been converted into information is defined as knowledge. Information is effective in the decision-making process. For example; In the last three years, a statement that babies named Ayşe have doubled compared to previous years is considered information.
What is Data Mining?
Even in just a single day, a lot of data is obtained around the world. While some of these data make sense at the point of transformation into information and knowledge, some of them are dysfunctional. That is, data must be processed in order to make it meaningful and use it. The work of software systems with millions of data to obtain valuable data is called data mining. It is possible to establish a connection between data mining and the data at hand and to make predictions on these data in the following processes.
The main purpose of data mining is to separate the data that can be useful to institutions and individuals and provide an improvement from non-functional data, to process and make usable with certain methods.
How Does the Data Mining Process Work?
Although data mining differs according to the size of the information to be accessed and the processes required for this, it generally takes place as follows:
- First, the data stack is detected and the security of this stack is ensured.
- Useless and meaningless data are cleaned.
- The remaining data is integrated and transformed.
- Data miners group data with methods such as clustering, decision support tree, classification, etc., which are suitable for the data at hand.
- The results obtained are tested and the results are evaluated.
In Which Areas Is It Applied?
Today, with the use of technological infrastructures by almost all sectors, data mining has gained value and its usage areas have developed considerably. In recent years, data mining has been carried out in almost every field and sector both in the world and in our country. We live a life where we are almost always intertwined with our computers, tablets or phones. We often spend time on the internet both at work and in our private life, and we do research with many keywords through search engines. All these searches are analyzed through a data miner. In the next process, sales strategies such as which advertisements will be made, which products will be shown to you or entered into promotions are created thanks to this data that marketing companies examine.
As in every sector, the banking sector also benefits from the power of data. Thanks to data mining; By analyzing the behavior or habits of users, tools can be created where users can make payments more easily and quickly. Bank customers; Quality services such as effective savings methods, faster transactions in less time, and customer relations experience that respond to needs instantly can be offered.
What are the Benefits of Data Mining?
- Transactions made on the Internet, which mean nothing on their own, can be interpreted and turned into valuable information, and products and services that meet the needs of people in many fields in the future can be made.
- It is possible to have an idea about the purchasing habits of internet users, and when a new product or service is created, a prediction is formed about which audience they will appeal to. Thus, when you launch a new product on the market, you analyze the target audience before which you will market this product.
- A better quality and customer-oriented service understanding develops. A sales process can be experienced in which both the seller and the buyer are satisfied.
- Based on the current target audience analysis, sales forecasts can be made. This can reduce the risk.- In the banking sector, customers can be grouped according to their credit card usage habits by examining credit card expenditures.
What are the Features Required for Data Mining?
In order to become a data miner, it is very important to learn the necessary equipment to establish technological infrastructures, rather than to follow the technology or even just use the technology. It is also necessary to have an interest in areas such as software, mathematics, statistics, to think analytically and to have problem solving skills. You can specialize in data mining, one of the rising professions of the future, by improving yourself in these areas.
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