Importance of data mining to a business organization
Data mining is a procedure that includes exploring, gathering, filtering, and evaluating data. Different methods of data collecting have different advantages to an organization. For instance, there are several benefits of data mining when an organization employs predictive analytics to understand the behavior of customers. It makes an organization learn from experience by obtaining relevant information from data which can then be used to envisage future buying trends of the customers. Predicting future buying trends is important as the company will know the amount of products to distribute to the market. Additionally, it will also be easy to make wise decisions by taking considerate actions. Predictive analysis improves fraud discovery by scoring and grading transactions using a predictive model (Siegel, 2013). An organization can detect viruses, hacking burglars, and other illegal activities.
When employing association discovery in products sold to the customers, an organization can determine irregularities that might occur between various products. For instance, irregularities can be detected by analyzing data stored in supermarket’s point-of-sale machines. Such data can be used to make considerable decisions on the type of marketing activities to carry out. Web mining can also be used by business organizations to discover their intelligence from web customers. It is important as it provides elaborate information on the type of products customers search for on the internet. Moreover, it shows internet usage patterns hence an organization can establish the reliability of selling their product through the internet. Information obtained through web mining can be used to establish the type of products required by the customers. Consequently, providing products according to customer’s demands will improve the relationship between an organization and its customers. Clustering can also be used by companies to establish their customer’s buying trends.
Reliability of data mining algorithms
All the data mining algorithms are considered to be reliable because they can produce almost similar type of predictions. Regardless of the type of data mining algorithm used, it is possible to come up with similar types of patterns hence this validates the notion that data mining algorithms are reliable. Its reliability is also supported by the fact that several metrics can be employed to establish whether a certain model provides accurate and important information. For instance, most organizations perform cross-authentication of their data sets to ensure that the information obtained is accurate and reliable.
Metrics employed in data mining algorithms give objective measurement organizations can apply to evaluate the dependability of their predictive analytics data. Metrics can also be used to establish the most appropriate algorithm to use in the development process (Triantaphyllou, 2010). Even though data mining algorithms can be trusted, it is also important for an organization to consider that the algorithms can produce errors. For instance, the data used can be outdated hence data obtained will not be reliable. Additionally, sampling errors can also be realized thus providing inaccurate data.
Customers’ security concerns
Most of the collected personal information for mining has exposed various concerns raised by consumers. A number of consumers have raised security concerns. Products bought through the internet require consumers to reveal certain private identities, like bank account details, which can be a target to internet burglars. Customers are also concerned with the quality of products. They will always complain when they are provided with low-quality products hence it is important for an organization to consider quality improvement during their data mining processes. Product safety is also another concern raised by the collection of personal data for mining purposes (Howard & Prince, 2011). Security concerns raised by customers are valid considering that there have been a number of burglary cases. Providing private information puts customers at risk of losing their money or properties. To effectively solve this problem, many companies have adopted the use of codes to identify their customers.
Quality concerns are also considered to be valid. Customers use the money to buy products of their choice hence they expect the products to have high quality. This explains why the sales turnout of high-quality products is always higher than that of low-quality products. To solve this problem, organizations produce their products according to the demands of their customers. Companies also produce products of higher quality compared to that of their competitors. Product safety is a security concern is also valid considering that food poison cases have always been reported before. Consequently, customers would always want to know the safety of the products they are buying. Many companies solve the problem by testing their products before distributing them to the market.
Examples where businesses have used predictive analysis to gain a competitive advantage
Organizations with strong customer focus have used predictive analysis to establish connections among domestic factors like price and competition. Therefore, they have slightly lowered the price of their products to effectively compete in the market. Predictive analysis has also been used to determine customer demographics. Most organizations use information from various customer sources to determine the demographics. Therefore, many organizations have gained a competitive advantage by learning from their customers. Most banks currently apply predictive analytics to control their relationship with the clients. Consequently, they have been able to establish a long-term customer loyalty.
Howard, D., & Prince, K. (2011). Security 2020: Reduce security risks this decade. Indianapolis, Ind: Wiley Pub.
Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die. Hoboken, N.J: Wiley.
Triantaphyllou, E. (2010). Data mining and knowledge discovery via logic-based methods: Theory, algorithms, and applications. New York: Springer.