We start data mining definition with an example: In recent years, some marketing research in American stores has shown that customers who buy milk buy bread as well. Following these results, many stores’ managers decided to separate the bread and milk and put low-consumption goods between the two.
In this way, sales of recently mentioned goods increased, and a lot of profits went to business owners. The primary data in this research was the people buying, and the knowledge that makes this data usable was data mining. In this article, we want to introduce you to this magical science.
What is data mining?
Today, companies gain a lot of information by providing services and continuous communication with the customer. Business owners will significantly benefit if they know how to use this data. Data mining or knowledge discovery in databases (KDD) is simply a problem-solving method that analyzes large volumes of data and extracts repetitive patterns from them. It then provides solutions to challenges by finding connections between different occurred and these patterns. In fact, data mining discovers valuable results from information that may not be useful and usable.
Today, this science’s importance is so well known that huge companies first obtain public data before deciding and planning to run specialized campaigns or design expensive products.
Read more about Data Mining Algorithms
KDD importance and applications
In a world where most communications are virtual, getting information from unseen customers can be a tremendous blessing for companies. In some of these organizations, data mining is so essential and entrenched that they even launch data collection campaigns.
Recently, a campaign called ”10YearsChallenge” was launched on social networks such as Instagram, Twitter, and Facebook, during which people collage photos taken ten years ago with that of the present. Users very well received this challenge all over the world. Meanwhile, some sources which have not yet been confirmed or rejected, called this challenge Mark Zuckerberg’s new trick to test Facebook’s face recognition algorithm. If this is true, Zuckerberg has probably been able to gather large amounts of diverse and unique data in the best possible way.
In fact, organizations that use data mining to analyze competitors and markets will predict current trends. Therefore, in the company’s plans, they go in the direction of the public demand and attract customers’ attention before other competitors.
In addition to marketing, mining is also used in other fields such as health, economics, and politics. Some areas of this science application are as follows:
- Public health: To spread health culture at the lowest cost in different parts of the world.
- Customer Purchasing Research: This topic, which is a kind of data mining application in management, seeks to identify goods related to the customer’s shopping cart to increase their purchasing rate. (Like the example of milk and bread mentioned earlier)
- Education: To improve the quality of the educational system and guide students properly
- Construction: To facilitate road construction and optimal urban patterns due to population growth.
- Customer Relationship Management (CRM): The goal is to improve customer relationships with companies and increase productivity.
- Prevent e-banking attacks: Used to identify attack algorithms.
- Criminal Investigation and Criminology: Data mining can be used to examine the links between criminal incidents.
The connection between online businesses and knowledge discovery in databases
Suppose a user publishes an image on his/her page on a social network like Instagram; this means creating a new piece of data. Each time other people see the image, like, or make a comment, another new data is generated. Imagine that this simple process is performed daily in many applications and creates several terabytes of data.
As time goes on, the production of data gets accelerated. So the question is how this data can be processed. Traditional methods, such as conventional algorithms, can no longer process this amount of data in a reasonable amount of time.
For example, imagine the same social network Instagram. Suppose we want to identify two people with similar interests out of a few million users and introduce them to each other as a suggestion to follow each other.
Doing this using a standard algorithm will probably take years. But the good news is that new methods have emerged to develop such systems, known as data mining and machine learning techniques. Join us in the next article to discuss data mining techniques.
Given the above, data mining is one of the most important techniques for improving Internet businesses.
