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Top1. Introduction
Computer and Internet-based technology has evolved steadily in recent decades and has changed work life positively (e.g., flexibly, remotely, virtually) and negatively (e.g., 24 hours a year). These technology developments are a source of rapid growth in the amount and availability of data worldwide (McAfee & Brynjolfsson 2012). Capture larger-scale data more easily than ever, allowing for insights in the form of new information processing or decision-making tools through analytical formulas and rule-development possibilities (data-processing algorithms) to solve problems. (Dormehl 2014).
Most recently, the dissemination of intelligent machine learning algorithms in the field of computer science led to the development of powerful quantitative methods to derive insights from industrial data. Supervised machine running methods (learned from a large number of pasts, labeled data set analyses on computers) include biological and medical science (Bakry et al., 2016), transportation (Mathisa & Ragusa, 2016), political science (Durant & Smith, 2006), and many other areas. Under the development of information technology, researchers studied numerous machine running approaches to improve HR (human resources) management outcomes (Li et al., 2011).
Big data has become a popular label for many data analytics efforts. The original term Big Data emerged to define a technological revolution that enabled massive data collection (Jacobs 2009). Since then, the term has moved to a different domain to represent different aspects of analysis depending on the circumstances in which big data has been mentioned. The term is now used to represent data processing functions and data characteristics and includes both technical and commercial aspects of data collection activities (Nunan & Di Dominico 2017). Mayer-Schönberger and Cukier (2013) regard big data as a new ability to collect vast amounts of information and analyze it immediately (Kitchin 2014). In a similar vein, Boyd and Crawford (2012) suggests that big data does not necessarily have to be a statement describing the size of the data, but instead a term that indicates the ability to search, aggregate and cross-reference large data sets.
There's a huge amount of data in human resources. The system includes data that is structured by default, such as staff, information, participation scores, and performance records. There are places where all the details about an individual or an organization, all aspects of which all transactions are performed or can be documented, are lost immediately after use. As a result, organizations lose the ability to extract valuable information, perform detailed analysis, and provide new opportunities and benefits, as well as knowledge. Everything that's available in the customer's name and address, the purchase, and the reach of the staff has become very important in our daily lives. Therefore, data is a fundamental element of the organization's success Scope, transformations, and rapid changes in this type of data require new types of big data analytics and different methods of analysis and storage. You need to analyze this absolute amount of big data correctly and remove the relevant information. Using data analytics, HR departments have begun to take advantage of huge offensive benefits by identifying the best performance they have ever employed, improving withholding rates, and making everyone happy to participate. HR experts quickly began to embrace data analytics. Now we think about the data and the proliferation of information available today through the evolution of technology and the Internet. As storage capabilities and data classification methods grow, vast amounts of data are available. More and more data is being generated every second and needs to be stored and analyzed to extract value. In addition, organizations must make the most of the vast amount of stored data because of the lower cost of storing data.