The Role of Data Mining in Solving Business Problems

Posted: January 4th, 2023

The Role of Data Mining in Solving Business Problems

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The Role of Data Mining in Solving Business Problems

Introduction

The growth and advancement in technology provide businesses with many platforms that they can use to make their practices more effective. It shows how the practice developed after several years of using similar concepts to examine business data trends. Data mining offers numerous advantages to businesses in various sectors. Organizational leaders are making a huge investment to facilitate how the firm learns from the information it gathers from various stakeholders. 

Background Information

The physical analysis of patterns and trends from sets of data has happened for many years. The Bayes theory and the regression analysis are some of the old methods that appeared in the 1700s and the 1800s, respectively (Puga, Krzywinski, Altman, 2015). The Bayes rule explains the chances of an incident, depending on the preceding awareness of factors connected to the occurrence. For instance, if the risks of developing high blood pressure increase with body size, Bayes rule permits the risk to a person of large body size to undergo more precise analysis than merely imagining that the person is typical to the entire group (Puga, Krzywinski, Altman, 2015). Regression analysis, on the other hand, measures the connection between dependent variables and one or multiple independent variables. The linear regression is a widely applicable type of regression analysis. The analyst identifies the linear regression with a great relationship with the data based on a particular analytical framework (Fotheringham et al., 2002). The increasing advancement in technology has improved the generation, storage, and use of data (Fotheringham et al., 2002). The volume of data that companies handle has expanded in size and intricacy. Automated analysis has replaced the direct handling and analysis of information, facilitated by new, more effective technology innovations, particularly in the area of machine learning. 

The term database was developed in the 1990s, even though evaluating data to identify the hidden relationship and foretell future patterns has a long history. The origin of data mining comes from three interrelated scientific fields. The first is machine learning, which involves using algorithms that can acquire meaning from data to foretell the future (Olson, 2007). The second field is artificial intelligence, which entails creating and using human-like cleverness shown by machines and software. The third field is statistics, which entails the numeric evaluation of the connection between data (Olson, 2007). The data mining technology keeps transforming to be able to put up with the infinite potentiality of big data and increased computing prowess. Over the last ten years, progress in handling speed and power have allowed various groups to shift from the manual and time-consuming practices to the effective and automated handling of data (Olson, 2007). The more intricate the data sets generated, the higher possibility of learning essential information. The efficiencies of using data mining encourage businesses to incorporate the concept. 

Businesses use data mining to transform raw data into helpful information. Data mining involves using suitable software to examine the patterns in large volumes of data to gain more information about buyers. SAS Institute (2020) describes data mining as identifying irregularities, relationships, and patterns within large quantities of data to foretell the results. SAS Institute (2020) informs that using various approaches and an organization can use the generated data to improve consumer relationships, lower costs of production, and increase revenue generation. An organization can use data mining in many ways, including managing risks, increasing marketing practices, detecting fraud, identifying cyber-attack or threats, and differentiating and understanding buyers’ opinions (SAS Institute, 2020). Data mining fills the gap between AI and data management by examining how data is safeguarded and analyzed in their databases to apply the correct algorithms appropriately and effectively. The information then helps the firm develop more effective marketing approaches, expand sales, and lower production costs. However, data mining only becomes successful if the firm engages in suitable data compilation, storage, and computer analysis (SAS Institute, 2020). Many firms use data mining activities to create machine learning frameworks that are utilized to form machine learning structures that enhance applications, such as website recommendations software and search engine. 

Benefits to Businesses

Various sectors already use data mining, and most of these areas record impressive results from their application. For example, the communication sector gains valuable insight into their practices from data mining. Telecommunication and multimedia corporations use data mining to make sense of large volumes of data about customers, assisting them in foretelling buyer behavior, and providing highly targeted and applicable programs (SAS Institute, 2020). Insurance firms get many benefits from data mining practices because, with analytic proficiency, insurance firms can deal with intricate issues concerning customer needs, management of risks, compliance, and fraud-related issues. Insurance firms have continued to use data mining styles to determine the prices of products and to come up with new ways of offering competitive products to their consumer base (SAS Institute, 2020). Manufacturers get many benefits from data mining, and many firms are striving to align their supply chain plans with demand predictions. Manufacturers also use data mining to predict when their production equipment is likely to require maintenance (SAS Institute, 2020). Businesses continue to explore how they could benefit from data mining, and the impressive results encourage more organizations to utilize the model. 

Data mining helps other sectors improve their practices and is why more firms are embracing the concept. Operators in the banking sector record considerable benefits from the use of data mining. The algorithms help financial institutions know their customer base and make it easy to effectively handle the numerous transactions associated with the sector (SAS Institute, 2020). Data mining makes it possible and easier for financial institutions to acquire a clearer perception of the threats in a market, identify cyber-attacks and fraud, handle regulatory adherence requirements, and get maximum benefits from the various investments (SAS Institute, 2020). The retail sector witnessed considerable gains from the use of data mining. The large and sophisticated consumer databases of many retailers usually carry hidden consumer information and insight that can help the firm advance its relationship with all stakeholders, advance marketing campaigns, and predict sales (SAS Institute, 2020). Retailers can develop and provide more targeted awareness programs through more precise data frameworks and come up with a plan that would have a significant impact on the buyer (SAS Institute, 2020). The many advantages businesses achieve from data mining suggest that the process is effective, and firms should develop mechanisms to use the model. 

Organizations are working hard to incorporate data mining in their practices because of the several other benefits they are likely to get from the innovation. Data mining allows businesses to peruse through all the organizational data mixed up, and offers the chance to know what is applicable and then make appropriate use of the data to achieve the desired outcomes (SAS Institute, 2020). Data mining proves to be an effective business practice because it accelerates the progress of making appropriate choices (SAS Institute, 2020). When businesses use data mining correctly and predictive analytics happen correctly, the evaluations are not an avenue to a predictive outcome. Instead, the predicted outcomes become an avenue to analytical awareness and identification. 

Limitations

Businesses must watch out for some of the limitations that could affect their usage of data mining. One of the major limitations is the practice requires skilled personnel to perform the task, which may require much financial commitment (SAS Institute, 2020). The tools available for data mining require advanced specialists to arrange the information and comprehend the output. The other challenge is the process is susceptible to security breaches because of the large volume of data involved in the process (SAS Institute, 2020). The process is susceptible to privacy concerns because the process entails gathering and arranging personal information. Corporations must watch out for the possible demerits of data mining to know how to implement the process.

Methods of Data Mining

As a broad aspect, data mining represents several techniques utilized in varying analytic capacities that deal with a range of business concerns, ask different forms of queries, and utilize different levels of social regulations to attain the desired goals and objectives. Descriptive modeling is one of these methods, and it includes uncovering the similar features in acquired data to tell the reasons behind good or dissatisfying outcomes (Feldman & Sanger, 2007). For example, the process could entail categorizing consumers based on their sentiments or preferences. The firms that use descriptive modeling may choose to use the clustering method, which involves identical grouping records or anomaly detection, which involves recognizing multidimensional aspects (Feldman & Sanger, 2007). A firm using descriptive modeling may choose to use the principal component evaluation, which entails finding the connection between different variables, or affinity grouping, which entails grouping features with similar desires or aspirations. Alternatively, the company can use predictive modeling, which goes deeper to categorize future events or measure unknown possibilities. Predictive modeling helps acquire vital information regarding reactions to marketing practices and consumer desires, and provides the chance to understand how things may turn out in the future (Liu, 2011). Using predictive modeling, the analysts may use sample techniques such as regression, which estimates the strength of the connection between one dependent variable and several independent variables, or neural networks, which involves computer software that recognizes trends or patterns before becoming possible predictions.  

Conclusion

The study evaluates the impact of data mining on business operations. The process of studying the patterns and trends in data started many years ago, but data mining emerged in the 1990s after considerable growth in technology. Data mining benefits the company in the way it becomes possible to understand consumer behaviors and desires. Also, the process provides the chance to improve other essential operations. However, businesses must watch out for some of the possible limitations such as privacy concerns, security issues, and need for skillful labor. Business leaders should select a data mining method that suits their practices to achieve the best results.  

References

Feldman, R., & Sanger, J. (2007). The text mining handbook. Cambridge: Cambridge University Press.

Fotheringham, A., et al. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Chichester: John Wiley. 

Liu, B. (2011). Web data mining: Exploring hyperlinks, contents and usage data. London: Springer.

Olson, D. (2007). Data mining in business services. Service Business, 1(3), 181-193.

Puga, J. L., Krzywinski, M., & Altman, N. (2015). Bayes theorem. Nature Methods, 12(4), 277-278. 

SAS Institute. (2020). Data mining: What is it and why it matters. Retrieved from https://www.sas.com/en_us/insights/analytics/data-mining.html