Over the years, businesses are implementing various approaches to retain their clients/customers and win their competitors. With the development of information system applications, companies have become able to capture an enormous amount of data about customers and products through scanning bar codes, online shopping, surveys etc. This data, however, can assist in making informed business decisions, but it is kept untouched in huge databases for years. But, to understand a customer’s behavior, companies require integrating innovative tools which can discover the hidden valuable information in a huge data repository.
Also, the emerging competition and available alternatives for customers, have evolved the necessity of sustaining an effective customer relationship management. For this reason, owners are employing knowledge management approach to transform this customer knowledge into informed business decisions. Here, the role of data mining services and techniques comes into play to identify new opportunities by converting this hidden customer data into useful information. Knowledge Management (KM) is central to this.
Data mining is basically a process which utilizes intelligent techniques to reveal useful patterns of knowledge in large databases. With applying various algorithms, it can predict useful information out of 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 the variables by using some known variables in data sets, description extracts interesting patterns and trends in the data.
With knowledge Management (KM), we mean converting data into an appropriate knowledge. However, defining exactly what KM is could be challenging because of the intangible nature of knowledge; where a knowledge is defined as the organization’s ability to share knowledge effectively to gain competitive advantage. Also, KM is considered as one of the crucial business aspects and therefore, companies should know how to acquire, capture and share this knowledge to enhance performance in long run.
Data Mining and its applications for Knowledge recovery process
The role that data mining plays in business knowledge management for acquiring and extracting useful information is discussed below:
The applications of data mining help an organization to make informed decisions. Consequently, the interactions generate Business Intelligence (BI) which help companies to utilize and convert available information and knowledge in real-time for business development. Also, data mining techniques uncover hidden customer/product information for businesses which can provide valuable knowledge and establish BI. With this, it becomes easy to analyze the product sales information which, in turn, help marketing department to formulate the strategy for product promotion.
Further, let’s discuss some applications of DM and KM in business domains, who utilize data mining techniques to find interesting data patterns in form of knowledge:
Retail Industry: This industry gathers enormous data on sales, customer shopping history, etc. due to the increasing popularity of e-commerce these days. Here data mining can help to build extensive knowledge about customer’s buying behavior and trends. By knowing this, retailers can achieve better customer satisfaction, reduce operational costs, and can extend their brands.
Banking and Finance Sector: Banking and finance sector has huge databases filled with critical financial and economic data. Here, DM techniques can provide the benefits of identifying patterns and deviations in business information and market prices necessary to recognize global risk and ROI. By assisting banks in the areas of risk management, fraud detection, customer relationship etc., it facilitates decision making and knowledge sharing processes.
Health Care Institutions: Mining technique like clustering can help to attain demographics of the patients having serious diseases like cancer, tumor etc. This knowledge can help doctors to explore disease’s symptoms and relationships which, in turn, can improve treatment therapies and operation procedures.
Aviation Industry: This sector can be benefitted by association rule or clustering technique to gain customer’s knowledge which, later on, can be utilized to offer discounts on flight tickets by determining customer’s flying frequency.
Online Business: E-commerce stores can take the huge advantage of integrating DM tools &techniques to extract the information stored in a customer profile. Once, the information is collected, the owner can offer reliable products recommendations to customers based on their interest to boost sales.
Insurance companies: Insurers can sell more policies and enhance conversion rates via running effective campaigns, execute processes and reduce operational costs after knowing how many customers are interested in buying policies, their requirements, and interests.
Manufacturing: Manufacturers would be able to produce products, people are more interested in, after knowing their choices through DM and KM process.
Source by Andrew Hudson