In straightforward terms, data mining is the process of extracting hidden and potentially significant information from massive sets of statistics. By analyzing and exploring large sets of data (with the use of multiple software), meaningful patterns and rules can be discovered.
Data mining has multiple applications across numerous fields, such as database marketing, credit risk management, sentiment analysis, healthcare bioinformatics, fraud detection, training and support, qualitative data mining, and spam filtering. With the help of data mining, businesses can learn essential details regarding their customers and in turn, develop new and effective strategies. The data mining process includes six steps. These steps are accepted within every process of implementation.
- Understanding Data – The first and foremost step is to establish the goals of a business project. A successful data mining plan is not only detailed but also fulfills business goals.
- Accumulating Data – This step includes collecting all available and appropriate information (using visualization tools) of the company.
- Preparing Data – This preparation step takes up most of the time of the mining process (up to 90%). This step involves the organizing, formatting, and transformation of data. Competent data mining processes contain failsafe measures so that information can be stored permanently.
- Data modeling – This process includes the use of complex mathematical codes to determine data patterns based on the business goals set in step 1 of the process.
- Evaluation – In this step, patterns are evaluated in the context of the business objectives. This stage is an essential step in the process because this is the stage when the decision to move the project forward into the next phase is made.
- Deployment – The final step of the process is to employ all findings into day to day business operations. The report created during the last stage is usually written in non-technical and easy to understand language for all non-technical parties involved.
Data mining can potentially change the entire course of an enterprise. Not only does it help businesses to analyze and automate relevant information (without human error) but it also provides reliable predictions based on past and current trends. Moreover, it ensures a more effective allocation and use of resources within an organization resulting in decisions that can drastically reduce individual costs. By uncovering the key characteristics of customers, data mining helps businesses to understand their customers better. Such information allows enterprises to design strategies to improve customer experiences.
While data mining is a robust process beneficial in any aspect, it is faced with many challenges. Such hindrances can include; the volume and variety of information to be collected, data mining models that explain errors instead of trends, the added cost (to implement patterns within an organization) that comes with a massive amount of information, privacy, and security of the data stored. However, adequate and successful models of data mining can overcome such hindrances and avoid any errors.