What is Data Mining?

Data mining is an innovative approach used by many companies to turn large amounts of unstructured data into valuable information. By using robust programs to search for common patterns in massive data sets, companies can find out about their most important customers, develop effective advertising campaigns, improve sales and reduce operating costs. In a world where knowledge and data are essential for businesses to stay ahead of competitors, data mining promises to deliver the answers you are looking for in a matter of minutes.

Companies engaged in data mining must be able to analyze the data they receive to make informed decisions about how to improve their business models. It helps them learn more about both their competitors as well as new emerging opportunities. This helps them achieve greater efficiency, save time and money and reduce employee turnover.

Types of Data Mining: 

Online Data Mining:

Online data mining uses online data to aid in efficient search and analysis. The primary advantage is that you can access and collate useful information from the web in the privacy of your home. The secondary advantage is that you can also refine the collected information for improved results. You can use this data to refine search criteria and determine what attributes or features you are looking for in a product. Online data mining helps you cut down on the time and money spent on tedious offline research methods, which is especially valuable for start-ups.


Data mining techniques often combine offline and online techniques to form a consistent and effective data mining strategy. The concept of cluster formation describes the process by which users of a database interact with one another. A cluster consists of multiple users who access the same data at the same time. This enables you to leverage the collective power of the users to accelerate business processes and gather a better understanding of customer behavior. The cluster’s size and inherent structure significantly affect the quality of the information gathered.

Machine Learning:

Similar to cluster formation, machine learning uses pre-existing algorithm and training systems to predict user behavior. A popular technique is to use natural language processing tools to build a model of users, their preferences and purchasing habits. Once the model is built, the data mining algorithm can predict, for example, which products will be successful in local stores based on the existing models and which ones will not be. Machine learning makes it possible to predict user behavior thus reducing the amount of wasted effort and human error, which are common with traditional data mining methods.

Artificial Intelligence (AI):

Data mining is now being used in almost every area of human enterprise. Large companies such as Google, Microsoft and Baidu have already started using artificial intelligence to analyze large sets of unstructured data and make inferences and predictions about future business problems. Although this method has a long way to go, more businesses are turning to it for solutions to their business problem. This also reduced the need for hiring additional staff, as data mining can be easily outsourced to other companies who are capable of carrying out the necessary work.

Clustering Techniques:

Different cluster techniques are being used to extract meaningful information from the massive amount of unstructured data. These techniques include tree-shuffling, greedy clusters, negative binomial, principal component analysis (PCA) and principal component analysis (PCA-based). Each technique has its strengths and weaknesses. As a result, they are suitable for various business problem domains. Some of these techniques are suitable for data mining but not for certain business applications, while some are very good for one type of problem but not for another.


Data mining techniques involve mathematical algorithms that help the computer to process large sets of data and come up with specific patterns or “clustered” data. Examples of such algorithms include neural networks, and the greedy algorithm. When the computer programs are run on labelled data, the results give the clusters that were identified by the program. Thus, the goal of data mining is to identify and extract useful information from raw data. This form of technology allows businesses to deal with large amount of data with greater accuracy and efficiency.

Data mining helps to convert unstructured data into useful data to carry out effective marketing strategies. Thus, contributing to an increase in sales and building a reputation. Moreover, taking the help of a tool for implementing marketing is sure to generate efficient results. ONPASSIVE’s AI and ML-driven products help achieve it more easily.  O-Lead interacts with the customers and tries to know their respective interests, ideas and opinions. With such gathered information, personalized email marketing can be done with a tool such as O-Post.