Data mining is a critical issue in contemporary society, which has emerged as a result of the increment in the need to capture, store and retrieve information. Moreover, this phenomenon entails procedures that companies utilize in the conversion of raw data into valuable information. According to Twin (2019), industries use software to check on the patterns existing in large batches of information, thus enabling them to develop more efficient marketing strategies, improve their sales quantities and eliminate unnecessary costs. However, data mining depends on the effectiveness of information collection methods, storage, computer processing power, and data analysts’ ability to scrutinize the information to determine its usability. This procedure also incorporates the exploration and analysis of huge blocks of information to recognize essential trends and patterns such as customers’ credit data, bank statements and companies’ marketing details (Twin, 2019). However, companies face various challenges in implementing this phenomenon, which ranges from financial problems, lack of talented data analysts and inappropriate organizational culture, which is reluctant to shift from the old methods of data storage to modern technological efficient methods. However, according to Martellini & Urošević (2006), organizations tend to fear the uncertainties of the time of exit. This aspect comes as a result of the risks involved in such decisions, including the asset risks and the time through which the transition becomes effective. Therefore, data mining is an essential aspect of an organization even though it faces numerous challenges.
Information mining poses numerous critical challenges to the industry due to the complexities involved in its implementation. Moreover, the major problem facing big data involves information collection, storage, analysis and retrieval. This idea is due to the knowledge required and the machines’ nature necessary for producing effective results of various data processes. Additionally, big data comprises four main problems that entail data volume, variety, velocity and veracity (Microstrategy, n.d). Data capacity contributes to various issues that arise from the storage processes of the voluminous information that organizations collect. However, gathering the appropriate data decelerates the processing speed of the information mining tools. Additionally, variety in big data comprises the various types of information that organizations store. Its failure to analyze the unstructured and structured data adversely impacts the value of the data mining process (Microstrategy, n.d). Velocity also poses challenges in data mining due to the increment in the rate at which individuals generate information from the system. Besides, data veracity contributes to the issues of data quality and quantity since the information is prone to inaccuracy, biasness and inappropriate data collection methods.
Companies that employ data mining encounter numerous disadvantages. Moreover, one of the significant issues that this technological shift possesses is the privacy aspect (Eclature, n.d). This idea is because as companies employ this procedure, they utilize private details of the individuals, which many people are reluctant to disseminate and always question its usage. According to Kade (2019), data security issues and privacy considerations are critical concerns for organizations which adversely affect the process. Moreover, data miners mainly face challenges of accessing diverse forms of information, which at specific points may be unavailable due to corrupt formats or infection by viruses. Besides, data redundancy is a critical issue in the data mining process as it consumes a lot of time when individuals need to access certain information. Finally, the primary and most crucial challenge that companies face in the data mining process is the cost of implementing the method. The shift from the old technological methods of data collection, storage, retrieval and updating is such a costly event that many businesses are unwilling to incur. However, companies that are ready to adopt this phenomenon must be prepared to spend on such a project.
Data accessibility is another critical challenge for organizations implementing data mining. This issue is due to the nature of the data and the complexities involved in its retrieval. However, data availability can be such a big issue when an individual tries to retrieve it, yet it does not exist (Kumar, Tyagi, & Tyagi, 2014). This aspect may occur when a person changes the previous location of the data, or when an individual deletes the data unknowingly. Moreover, this phenomenon may occur if the system data is corrupt through virus infection or the computer system fails to boot as a result of boot error occurrence. However, according to Kumar, Tyagi, & Tyagi (2014), challenges of information accessibility in data mining arises from the miners’ failure to outline the procedures that the system users should use to retrieve the data. However, to curb this challenge, it is always advisable that companies liaise with the data miners to ensure that they match the organizational problems with the information requirements. This aspect is vital as it acts as a blueprint towards the development of a more extensive plan of data gathering and accessibility strategies.
Data mining algorithms poses a critical challenge in the information abstraction. An intricate pattern leads to misinterpretation since several miners are unable to distinguish data casualties and concurrencies (Kumar, Tyagi, & Tyagi, 2014). Moreover, algorithms in data mining are essential aspects that help individuals in knowledge understanding. Moreover, these phenomena help in recognizing patterns between the various forms of data and their interrelations, as well as their correlations. Additionally, the critical problems in tasks and algorithms entail how individuals achieve practical information mining of diverse types of algorithms (2014). Moreover, information may possess inherent inaccuracies resulting from the randomness of data collection procedures and generation methods.
Data security is another key challenge facing data mining processes. This phenomenon is because businesses use this procedure to analyze various transactions to acquire several quantities of information regarding the clients’ purchasing trends. Another issue facing information mining is data integrity. As a result, the process comprises redundant and conflicting information, which originates from diverse sources, such as banks that access the customers’ data from different branches. Additionally, data addresses of individuals with different accounts in the same database pose a vital issue in data mining. However, data miners must be knowledgeable enough to understand the data differences and realize how to differentiate the various forms of similar information.
Companies face challenges of securing data mining analysts due to the complexity of the knowledge and the necessary knowhow suitable for this task. Moreover, this phenomenon arises since data miners are not readily available, thus adversely impacting firms interested in implementing information mining technology. Moreover, data miners must possess a variety of technological skills, which include programming skills. For example, people willing to become data miners must have strong knowledge and understanding of programming languages such as the R, Matlab, Java, C++, SQL, Shell and SAS, among other languages (Octoparse, 2019). Additionally, such individuals must possess a deep comprehension of the big data execution platforms such as the Hadoop, Flink, Storm and Samza, among others (2019). Linux is a major operating system technology that most businesses and government institutions utilize due to its security features and the nature of its stability. Besides, according to Octoparse (2019), data mining processes involve data storage and retrieval, and due to this aspect, information miners must possess immense knowhow of database operations for both the relational and non-relational databases.
Individuals who qualify for data mining must possess numerous technical skills and understanding. Moreover, some of these skills include necessary statistics knowhow. Such phenomena include linear probabilities, regression, probability, stochastic processes and linear algebra (Octoparse, 2019). Additionally, such people must have in-depth knowhow of the data structures and the accompanying algorithms. Moreover, natural language processing is another vital element that data miners must possess to understand the various syntaxes of information interpretation and manipulation (Octoparse, 2019). Besides, machine learning or the deep learning algorithm is another crucial knowledge that data miners need to enable them to develop the appropriate mathematical models of sample information as well as predictions in the execution of the various tasks and processes.
Data mining trends are essential aspects of modern societies. Moreover, these phenomena entail distributed information mining in which data is stored in the different computer systems in an organization. As a result, advanced technological strategies are vital in the extraction of such kinds of information, and thus individuals involved in such activity must provide detailed reports of extraction processes (Shaukat, 2018). Another trend in data mining comprises multimedia information mining, which is common in modern societies. Moreover, this aspect has become necessary due to the increased demand in need to capture data more appropriately in the video, audio, and written formats (Shaukat, 2018). Additionally, sequential and ubiquitous data mining procedures are vital processes where companies utilize the former method to analyze their customers’ behavior through studying the seasonal data trends as well as evaluation of the occurrences which happen instead of the intended events (Shaukat, 2018). On the other hand, the latter entails information mining from a mobile phone, a highly prevalent aspect in the modern digital era. However, this trend faces numerous challenges, including costs, complexity, privacy and efficiency in studying various human-computer connections.
Data protection is a crucial aspect of data mining that organizations cannot easily ignore. This aspect is because the information is the building block of data mining, and its failure can mean the collective catastrophe of a business organization. Additionally, one of the strategies that businesses use to safeguard their data entails information encryption. This aspect entails an improved technology through which individuals secure their data and prevent it from unauthorized access by the unintended users. According to Groot (2019), data backup is an essential practice that organizations should practice to ensure that they produce a copy of the original information. This practice ensures that the organization is safe even in an instance where one data source fails. This phenomenon is because businesses can utilize the second data source and ensure continuity of operations. Cloud computing also provided establishments with solutions to data storage on other preservation sources instead of local storage gadgets. According to Groot, the use of passphrases instead of passwords to secure data and computer systems is an ideal practice comprised of combinations of randomized words (2018). As a result, the more characters in a passphrase the higher the system’s security.
The advancement of technology has led to the invention of systems that offer solutions to the data miners. Moreover, such systems can handle and process massive data, which is both structured and unstructured. Accord to Jaseena & David (2014), one of these technologies includes the Hadoop. This element possesses all the necessary features for a system to store, process and execute big data. Hadoop is a form of an operating system that accommodates scalability features and tolerates faults in the computing field. Moreover, this element is open-source software that individuals can easily acquire at no cost. One of the advantages of this phenomenon is that it runs on standard hardware. Besides, the critical characteristic which promotes its functionality is that it is a fault-tolerance system with high bandwidth capacity with clustered information storage architecture (2014). Additionally, Hadoop is an appropriate technology for business organizations as it helps them handle issues of velocity and information heterogeneity.
MapReduce is an essential element in data mining that helps organizations overcome the challenges that arise as a result of data mining. Moreover, this phenomenon refers to a programming tool that enhances the processing of immense amounts of information sets that have parallel and distributed procedures (Jaseena & David, 2014). Moreover, this software helps data miners develop technological applications that can process numerous data quantities at a faster rate. Besides, the principal constituent of this phenomenon which helps it to achieve the intended goals entails the map () and reduce () (Jaseena & David, 2014). According to Jaseena & David, the mapper is responsible for sorting and filtering data. At the same time, the reducers summarize the results by checking errors and applying the appropriate algorithms to rectify any existing errors. Therefore, MapReduce technology provides a solution to the challenges of data mining.