Sample Paper on Significance of Writer’s Interpretation of Big Data
“Huge volumes of data may be compelling at first glance, but without an interpretative structure, they are meaningless.” Tom Boellstorff. First, I strongly support Tom’s argument because it is not obvious that a writer or a researcher would extract the desirable information from huge volumes of data. As a result, it calls for a careful and proper analysis in order to locate the most important data. As an entrepreneur, I believe that within large data lies smart data. It is data that can be interpreted into actionable and useful information to resolve various challenges effectively. In my research project, the objective was to design a pay performance system that would motivate employees. It is a form of compensation that would reward workers fairly based on their input on the job performance. Therefore, I uncovered big data on a variety of existing pay systems from (Koulayev et al., 293-325) that served as a guide to selecting a suitable compensation method.
Access to large data on different pay systems enabled me to conduct a meaningful comparative analysis and choose the most preferred mode of payment to use. It involved reviewing of advantages, cost-benefit, and conditions under which a given pay system would work conveniently. In addition, I got huge data on various theories that highlighted how a specific method of reward encourages employees in the workplace. After the analyzing the data, it was reasonable to incorporate a variable pay performance system. It did not only motivate employees but also reduced the high rate of employee turnover. Therefore, it is important noting that the writers’ focus should be based on how to obtain smart data that he or she anticipates from huge volumes of data available. It requires an interpreter’s great intelligence and insight to obtain valuable data that would effectively apply to a given research problem.
Koulayev, Sergei, et al. “Adoption and use of payment instruments by US consumers.” The RAND Journal of Economics 47.2 (2016): 293-325.