Issues of Data Mining
Data in the database has proven to grow rapidly in this digital era. Anything related to technology contributes greatly to the growth of data. As a result, it has called for discussion of topics like data and big data. Data mining is a process used to obtain information from big data to produce an information pattern. Its significance is that it explores large volumes of data, therefore extracting information from several databases, which is useful in running companies. In so doing, classification is achieved, which is the core purpose of data mining. Types of data include nominal attributes, binary attributes, and ordinal attributes (Pradiska & Sugiantoro,2018). However, data mining is of great essence, with several challenges attributed to its usage. It will only become useful when these issues are fully addressed. This article will analyze one of the major issues caused by data mining.
The article review of big data and data mining highlights major issues with data mining. One of the issues addressed is information poorness, where large data and the demand for effective tools for data analysis have been termed a data-rich but information-poor situation (Weber, 2018). It is where there is a deluge of data but only a little information produced from it which is appropriate for a particular period. In other words, data mining harnesses a lot of data from databases, but turning this data into useful information has proven to be an issue. It needs to go through a process to be converted into useful information. Only organizations that have attained success can minimize the information into structured bits of data, formulate algorithms and use this information to their advantage. However, this greatly limits organizations that are still growing.
Another issue associated with data mining in information poorness is that data in repositories form ‘data tombs’ (Wren & Bateman, 2008). Many data may be stored for future purposes, but they may rarely be used. It isn’t easy to estimate how often a database might be used because a few individuals might use a narrow scope, but they may find it beneficial. In contrast, a wide scope might be used by many individuals, but the information may be useless. However, ‘data tombs’ can be identified by checking whether a database is cited, suggesting that it is utilized. Moreover, creating a subsection for authors to describe their user base in detail and make comments about it would help eradicate ‘data tombs.’ It will put to use all the data which has been mined, preventing information poorness.
Lastly, data mining causes a lot of information to pile up. The information which is piled up remains dormant, forming a data archive (Pradiska & Sugiantoro, 2018). Data archives are rarely visited. Bulks of data stored away may become obsolete because of advancements in technology, causing the archive to be useless. This causes poorness in information due to wastage.Moreover, archived data cannot be obtained in a hurry if required because it has to be found, fetched, and put into the computer system, which is time-consuming. It discourages many from using it, making them opt to use readily available sources. Furthermore, in case of calamities like fire, archived data gets lost, leading to loss of information because another copy may not be stored. This and many more are demerits of data mining, even though it may be beneficial.
To sum up, the review on big data and data mining addresses critical issues in data mining that need to be looked into. Data growth is a continuous process that will prompt researchers to keep looking into data and data mining techniques. Those experiencing difficulties with big data will be more overwhelmed due to the increase in complexity of big data. Therefore, this calls for an improvement in data mining algorithms. It will serve not only the successful organizations but will also give growing companies an advantage. It will reduce the complexity of data and the poorness of information too. It is still possible to reduce all these problems associated with data mining because of their effect on society.
Weber, G. (2018). Data Rich and Information Poor: The Adversary of Lethality. https://www.realcleardefense.com/articles/2018/11/06/data_rich_and_information_poor_drip_the_adversary_of_lethality_113943-full.html
Wren, J., Bateman, A. (2008). Databases, data tombs and dust in the wind, Bioinformatics, Volume 24, Issue 19, Pages 2127–2128, https://doi.org/10.1093/bioinformatics/btn464
Prasdika, F., Sugiantoro, B. ( 2018). A Review Paper on Big Data and Data Mining: Concept and Techniques. International Journal on Informatics for Development. Vol 7, Pg 33-35.