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http://dx.doi.org/10.18807/jsrs.2020.10.2.001

Case Study on Big Data Sampling Population Collection Method Errors in Service Business  

Ahn, Jinho (Idinnolab Inc.)
Lee, Jeungsun (Eulji University, College of Health Industry, Mortuary Science Department)
Publication Information
Journal of Service Research and Studies / v.10, no.2, 2020 , pp. 1-15 More about this Journal
Abstract
As big data become more important socially and economically in recent years, many problems have been derived from the indiscriminate application of big data. Big data are valuable because it can figure out the meaning of informative information hidden within the data. In particular, to predict customer behavior patterns and experiences, structured data that were extracted from Customer Relationship Management (CRM) or unstructured data that were extracted from Social Network Service(SNS) can be defined as a population to interpret the data, during which many errors can occur. However, those errors are usually overlooked. In addition to data analysis techniques, some data, which should be considered in the analysis, are not included in the population and thus do not show any meaningful patterns. Therefore, this study presents the measurement and interpretation of the data generated when the cause of error in the population setting is strong relationship and interaction between people or a person and an object. In other words, it will be shown that if the relationship and interaction are strong, it is important to include data collected from the perspective of user experience and ethnography in the population by comparing various cases of big data application, through which the meaning will be derived and the best direction will be suggested.
Keywords
Big Data; Service Business; thick data; user experience; ethnography;
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