The important role of data mining in knowledge management

Over the years, companies are implementing various approaches to retain their customers / clients and win over their competitors. With the development of information systems applications, companies have managed to capture an enormous amount of data about customers and products through barcode scanning, online shopping, surveys, etc. However, this data can help you 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 the valuable information hidden in a huge data repository.

In addition, emerging competition and the 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 essential for this.

Data processing

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

Administrative knowledge

By Knowledge Management (KM), we mean turning data into proper 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 obtain a competitive advantage. Furthermore, KM is seen as one of the crucial business aspects and therefore companies must 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 data mining plays in managing business knowledge 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 helps companies 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 product sales information which, in turn, helps the marketing department formulate the product promotion strategy.

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

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

Banking and financial sector: The banking and financial 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 assisting 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 pooling can help to obtain demographic data of patients who have serious diseases like cancer, tumors, etc. This knowledge can help clinicians explore the symptoms and relationships of the disease, which, in turn, can improve treatment therapies and operating procedures.

Aviation Industry: This industry can benefit from an association rule or pooling technique to gain customer insight that can later be used to offer discounts on airline tickets by determining the customer’s flight frequency.

Online business: E-commerce stores can take advantage of the 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 reliable product recommendations to customers based on their interest in driving sales.

Insurance companies: Insurers can sell more policies and improve conversion rates by running effective campaigns, running processes, and reducing operating 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.