The significant role of data mining in knowledge management

Over the years, companies are implementing various approaches to retain their customers and win over their competitors. With the development of information system applications, companies have been able to capture a huge amount of data about customers and products through barcode scanning, online shopping, surveys, etc. However, this data can help make informed business decisions, but it remains intact in huge databases for years. But to understand customer behavior, companies need to integrate innovative tools that can uncover valuable insights hidden in a huge repository of data.

In addition, emerging competition and alternatives available to customers have evolved the need for effective customer relationship management. For this reason, owners are employing a knowledge management approach to transform this customer knowledge into informed business decisions. Here, the role of data mining services and techniques come into play to identify new opportunities by turning this hidden customer data into useful information. Knowledge management (KM) is central to this.

Data processing

Data mining is basically a process that uses clever techniques to reveal useful patterns of knowledge in large databases. With the application of various algorithms, you can predict useful information from the stored data, further helping to interact between subsets of data. Data mining tasks involve two aspects: prediction and description. Where prediction predicts unknown values ​​of variables by using some known variables in data sets, description extracts interesting patterns and trends in the data.

administrative knowledge

With knowledge management (KM), we mean turning data into appropriate knowledge. However, defining exactly what KM is could be challenging due to the intangible nature of knowledge; where knowledge is defined as the organization’s ability to share knowledge effectively to gain competitive advantage. Furthermore, KM is considered as one of the crucial business aspects and therefore companies need to know how to acquire, capture and share this knowledge to improve long-term performance.

Data mining and its applications for the knowledge retrieval process

The role that data mining plays in business knowledge management to acquire and extract useful information is discussed below:

Decision making

Data mining applications help an organization make informed decisions. Consequently, the interactions generate Business Intelligence (BI) that help companies to use and convert the information and knowledge available in real time for business development. Additionally, data mining techniques uncover hidden customer/product information for businesses that can provide valuable insights and establish BI. With this, it becomes easy to analyze the sales information of the product which, in turn, helps the marketing department to formulate the strategy for the promotion of the product.

Also, let’s discuss some applications of DM and KM in business domains, which use data mining techniques to find interesting data patterns in the form of knowledge:

Retail Industry: This industry collects huge data on sales, customer purchase history, etc. due to the growing popularity of e-commerce these days. Here, data mining can help generate extensive insights into customer buying behavior and trends. Knowing this, retailers can achieve better customer satisfaction, reduce operating costs, and can extend their brands.

Banking and Financial Sector: The banking and finance sector has huge databases full of critical financial and economic data. Here, DM techniques can provide the benefits of identifying patterns and deviations in trading information and market prices necessary to recognize overall risk and ROI. By helping banks in the areas of risk management, fraud detection, customer relations, etc., it facilitates decision-making and knowledge-sharing processes.

Health Institutions: Mining technique like clustering can help to get demographic data of patients who have serious diseases like cancer, tumors, etc. This knowledge can help clinicians explore symptoms and disease relationships, which, in turn, can improve treatment therapies and operative procedures.

Aviation Industry: This industry can benefit from the association rule or grouping technique to gain customer knowledge which can later be used to offer airline ticket discounts by determining the customer’s flight frequency.

Online business: E-commerce stores can take great advantage of integrating DM tools and techniques to extract the information stored in a customer profile. Once the information is collected, the owner can offer trusted product recommendations to customers based on their interest to drive sales.

Insurance companies: Insurers can sell more policies and improve conversion rates through effective campaigns, execute processes and reduce operational costs after knowing how many customers are interested in buying policies, their requirements and interests.

Manufacturing: Manufacturers could produce products that people are most interested in, after learning about their options through the DM and KM process.