Re-discover Values of Data Using Data Jackets by Combining Cluster with Text Analysis

Since the development of Big Data, we have been doing various researches on data utilization, also through data mining, the importance of data value in terms of degree of contribution to business decision-making and planning has been discussed in previous studies. However not enough attention has been paid to the value of the data itself. Therefore, there is a high risk that the data will not be useful to the user even if developed or mined by massive steps. Moreover, once the value of data cannot be fully demonstrated, it will also cause a great loss in terms of business. In this paper, we focus on the present condition that there is no fixed standard in the data value evaluation, the model is structured to evaluate the data from different fields, and the value of each data has been relatively accurate assessed. In order to build such a model, first of all focus on how to determine the value of Data Jackets. Are the values of the Data Jackets fully assessed in the context of the data market? What is the difference between the value evaluated from the virtual data market and the reality of itself? This paper focuses on the gap, and provides a new idea and new method for the re-discovery of the value of Data Jackets.