A Case Study On The Problem And Countermeasure Of Sunk Data

Under the new industrial 4.0 age, small and medium manufacturing enterprises are facing the second venture. They implemented from extensive development to intensive development, depending on knowledge instead of capital. So the company’s management decisions are also changing from relying on the extensive experience and intuitive decisions to scientific Decision baseing on data analysis. Under the new economic situation, as the cornerstone of enterprise’s decision-making, data in fact sleep in the database, due to the low efficiency of software connection, the isolated islands of information, a list of data reporting but lacking of scientific analysis, no ability to provide an accurate and efficient support to business management decisions etc. At the same time, statisticians still using the traditional method to do historical and relevant business, just simple data of increasing or decreasing in comparison with traditional data list or only display the data. It can not provide a scientific analysis of historical data,either can’t help predict. Even it help less in correcting the secondary data data errors that resulted in deviation between the processing of the case and the final report.Data mining, decision analysing systems technology facing the barriers of capital and people in small and medium enterprises. So how to make this valuable corporate data capital to play its proper value? What can be done in the process of informatization for SMEs? Facing these practical problems encountered in enterprises during my working led me to quest and explore.This research is based on above background. The main purpose of the research is twofold: Firstly, display the exsiting problem of sinking business data in enterprise, then illustrate the reason of data sinking, that is why the data can not play its value.Secondly, basing on actual case situation, it propose solutions to this problem of data sank, valuable data mining, In order to find ways to make business data play a greater value in the case enterprises. And discuss the probability of setting a Methodology to guide to solve data sinking for the SMEs. And it tries to providing reference frame to solve the problem of data sinking for peer or similar sized printing and packaging manufacturing enterprises. Through that, they would enhance the contribution of business data without adding extra costs in the case, that has significant practical value for enterprises.