Data mining is a technique to turn raw data collected by a company, enterprise, or organization over years into useful information. Just as we mine gold from sand or rocks in a similar way, we mine information from the collected data that can be used for the benefit of the company.
What is Data Mining?
Data mining is defined as a process of extracting useful information from the large data set of an enterprise. Hence, we refer to data mining as knowledge discovery data i.e., KDD. Data mining has evolved hand in hand with the evolution of information technology.
The evolution of information storage has led to the evolution of data warehouses which leverage the growth of big data. Eventually, this accelerates the need for data mining over the last few decades.
Though the technology to handle data at a large scale is growing continuously but there are still some difficulties in scaling and automation of the data.
The data mining technique is an iterative process that has to repeat the following sequence of activities:
- Data Cleaning
As the name suggests, in the data cleaning steps the data is analyzed to identify and eliminate the irregularities and inconsistencies that are present in the data.
The data cleaning stage is important as the irregular or inconsistent data will only lead to confusion and deteriorate the quality of information being mined.
- Data Integration
In the data integration process, the data that has been collected from various heterogeneous sources is integrated. While integrating data from various resources the issues that we have to face are data redundancy, data duplicity, inconsistencies and there are several other problems that we have to overcome.
The data integration step is important as it unifies the data from several sources so that meaningful information can be extracted from the overall data.
- Data Reduction
In the data reduction process, the large-scale data is represented into a smaller volume. While reducing the large volume of data to the smaller volume we have to pay more attention that the integrity of the data is not disturbed.
- Data Transformation
In the data transformation stage, the data is processed, restructured, and organized in such as way that it becomes easy to extract meaningful information from the collected data.
- Pattern Evaluation
The data scientist analyzes the data to identify some interesting patterns. These interesting patterns represent some knowledge that is based on the given measure.
Data Mining Technique
To mine the useful information from the collected data we must implement an appropriate technique such as at first we must set an objective, then we must proceed by gathering the data, then we must apply the data mining algorithm on the gathered data and then evaluate the useful information out of it.
1. Set an Objective
Before we start mining the data, we must set a certain business objective. Though the most organizations do not pay attention to this step. The data scientist or the stakeholder must first ask the question what is the purpose behind mining the data.
2. Collect Data
Once the objective behind mining the data is obtained then the relevant data is collected. The data is cleaned, the redundancy is eliminated, inconsistency is removed and the data is prepared to apply a data mining algorithm on it.
3. Apply Data Mining Algorithm
The useful data is mined by identifying the frequent patterns on the prepared data. The algorithms are applied to classify the data or form a data set from the available data. The data with high-frequency patterns have broader applications.
4. Evaluate Information
Applications based on data mining technique are generally used for business intelligence as it improves the decision-making of an enterprise or organization. We can apply data mining techniques in the following cases.
Data collected by the educational institution has much information that can be used by the institution for understanding the interest of students in a particular course.
2. Sales and Marketing
The customer’s data collected by the organization can help them in identifying the customer’s behavior which can help them in improving their marketing strategy.
3. Fraud Detection
Data collected by the banks have some frequently occurring patterns. By analyzing these frequent occurring patterns any anomaly occurring in the pattern helps the bank in identifying any fraud.
Thus, data mining is the process of extracting important information from the large-scale data that is collected over years. The data is restructured, reorganized, and processed to retrieve the user information.