Data mining is a process used to discover and convert raw data into useful information. Data mining is a scientific discipline in the fields of statistics and computer science whose main objectives are to explore, extract and analyse information from an extensive data set and convert this information into forms and structures that can be easy to understand and used for future references. The goal of data mining is not entirely to the extraction of data but also to establish patterns and knowledge from large amounts of data. The information extracted and knowledge found is useful in activities like credit risk management, database marketing, and even fraud detection (Hofmann & Klinkenberg 2016). The data mining process involves five systematic steps that first start with the collection of data and submitting data warehouses, then of this data in the servers. The next step that follows involves the analysis and organization of this data by experts on information technology. The data is then sorted out using the most applicable software, and then the final step requires data presentation in an understandable and format that can be easy to share.
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Data mining strategies are the consequence of a long procedure of research and item advancement. This development started when business information was first put away on PCs, proceeded with upgrades in information access, and all the more as of late, created advances that permit clients to explore through their information continuously. Data mining takes this developmental procedure past review information access and route to planned and proactive data conveyance. Information digging is prepared for application in the business network since it is bolstered by three advancements that are presently adequately developed: Massive information assortment Powerful multiprocessor PCs Data mining calculations Commercial databases are developing at extraordinary rates. An ongoing META Group review of information distribution centre tasks found that 19% of respondents are past the 50-gigabyte level, while 59% hope to be there by second quarter of 1996.1 In certain ventures, for example, retail, these numbers can be a lot bigger. The going with requirement for improved computational motors would now be able to be met in a savvy way with equal multiprocessor PC innovation. Data mining calculations typify procedures that have existed for at any rate 10 years, however, have as of late been actualized as develop, dependable, justifiable instruments that reliably outflank more seasoned factual strategies.
In the evolution from business data to business information, each new step has built upon the previous one. Data mining derives its name from the similarities between searching for valuable business information in a large database. Foundations of data mining can be classified as philosophical dimensions, technical, and social aspects. The philosophical aspects of data mining mainly cover the nature and scope of mining data (Roiger 2017).
On the other hand, the technical dimension is about the methods and techniques used in mining data. Finally, the social aspect deals with the impacts and consequences that data mining has on the lives and human relations. Foundation of data mining is a field of its own and grounded on the inbuilt and inherent data structures. It covers the systematic view of various aspects that constitute the natural hierarchical structures beginning with primary concepts to the theoretical methods and algorithms used to extract information and knowledge from data as well as analysis, evaluation, and interpretation of results from data mining.
The system of data mining can be broadly viewed as an intermediate link between data and how it is an application in changing the data into useful knowledge. The database is like primary storage where data can be manipulated and retrieved at syntactical and symbolic levels. Knowledge, on the other hand, is what is embedded in the data at the levels of semantics. To understand the foundations of data mining, it is essential to realize that the knowledge within any data set is not dependent on whether there exists an algorithm of extraction. Therefore, in foundations of data mining, the existence of knowledge is what is essential rather than the algorithm of extraction.
The process of mining through data to explore and discover and extract information for present and future predictions can be traced back to the year 1990s. The foundations of data mining, however, consists of three correlated scientific disciplines, namely, statistics, artificial intelligence, and machine learning. These three disciplines, which form the foundations of data mining, have continuously evolved and improved over time due to advancements in computing technology (Dua & Du 2016). Over the past years, with the improvement in speed and processing power of computers, data mining has moved several steps away from the manual, tedious, and time-consuming methods to faster and automated means of data mining. Numerous data mining techniques have been discovered for activities like programming, networking, and extraction of associations. Today, the more complex collected data sets become, the more useful it is because there is a potential to explore and uncover more patterns and information.
Data mining has applications in various fields, including business medicine defence and many others. Business organizations, manufacturers, and even media platforms are using data mining to discover the relationships that influence their daily activities like risk assessment, price optimizations, and other factors affecting their business operations, models, revenues, and Consumer relationships. Data mining also covers classifying, assorting, and making correlations and anomalies in data. Generally, data mining can be used to extract and identify relationships between individuals, geographic locations, and even words.
Data Mining vs Traditional Business Reporting
Most business organizations and industries today have realized that using data mining gives them a more significant competitive advantage over traditional business reporting. Due to the increased growth in the amount of data that can be collected as a result of advanced technology and machine learning, most companies and business organizations are using data mining techniques to ease decision-making processes that are continuously changing and becoming complex. This is opposed to traditional reporting that was used by organizations to collect aggregate financial and other business information. This was a tedious and time-consuming process.
Data mining uses techniques that can make predations and the probability of future trends in business. This is called predictive data mining, which can indicate the conditions under which the outcomes will occur (Rimmel 2019). A good example is the use of machine learning to explore the database of customer transactions when giving loans to customers to help predict the possibility of customers transacting in the future and the ability to repay the loan. In the case of traditional business reporting, the use excels spreadsheet, PowerPoint, and PDF reports were fixed and gave no room for prediction because of bulk and required one to be an expert to analyse and arrive at predictive outcomes.
In traditional reporting, it was generally difficult to manipulate data because most data were static, hard-coded, and document-driven. It takes human intervention and a lot of expertise to discover any patterns within these documents. Most of the trends were presented dashboards or graphs that were ambiguous to interpret. Data mining gives presentations in pixels and printed reports that are easy to understand. Data mining is also helpful because of its automatic nature in finding out patterns and establishing even the tiniest relationships that exist within a data set. The algorithms are programmed in such a way that each can identify a specific hidden trend within a data set without human intervention.
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Using data mining techniques and tools in production processes and management of a business has helped many organizations attain an exponential growth in production and quality work output (Bae et al., 2017). The advancement in information technology with a revolution in manufacturing and the interactive market has driven many organizations to maximize their profits. Data mining tools are crucial in analysing the current market trends as well as future trends. With traditional business reporting techniques, most companies had to take a lot of risks when it came to marketing because almost everything was done through manual observation. Data mining is useful in marketing campaigns, especially in determining how the consumers respond to a given product in the market. This is also useful in making an informed decision on the customer needs and preferences as opposed to traditional business reporting where the customer responses could be seen through suggestions.
Roiger, R. J. (2017). Data mining: a tutorial-based primer. Chapman and Hall/CRC.
Hofmann, M., & Klinkenberg, R. (Eds.). (2016). RapidMiner: Data mining use cases and business analytics applications. CRC Press.
Dua, S., & Du, X. (2016). Data mining and machine learning in cybersecurity. Auerbach Publications.
Rimmel, G. (2019). Human Capital Disclosures in Swedish State-Owned Enterprises—A Comparison of Integrated Reporting Versus Traditional Reporting. In Challenges in Managing Sustainable Business (pp. 55-75). Palgrave Macmillan, Cham.
Bae, Y., Kim, Y. S., Rhee, F. C. H., Kim, Y. T., & Tao, C. W. (2017). Editorial Message: Special Issue on Fuzzy System in Data Mining and Knowledge Discovery: Modelling and Application. International Journal of Fuzzy Systems, 19(4), 1157-1157.
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