In the process of enterprise data standardization, it is expected to feedback value to business through data standardization management. So, the importance of data quality can not be overemphasized. In this process, the generation of low quality data is inevitable, because the mass data initialization, unprocessed historical data diffusion and emergency businesses will all affect the quality of data standard coding library. Thus, what the enterprises can do is to control the probability of generating low quality data and to discover low quality data in time and deal with it effectively. Therefore, the correct understanding on enterprise data quality management is that it is to reduce and control the production rate and the existence rate of low quality data through scientific, effective and professional management and technical supports, and discover low quality data in time and deal with it effectively, and keep standard code library's high health degree rather than not generating low quality data.
Data quality management platform (DQMP) is the core standard component of SunwayWorld's information standardization and management integration Platform solution (6P+2E+Mobile), ensuring the data quality of the enterprise data standard library and transparent visual analysis of the standard data code library. However, due to factors such as the large amount of data, the complexity of data information and the high professional requirements of the data coding library, manual guarantee of quality is difficult. Thus, the standard data coding library should be tested by professional quality management tools so as to discover incomplete and abnormal (but real) data to be processed, iterant and noise data to be removed. Data health analysis should be provided by a professional data quality management platform in order to steering data cleaning and governance, so as to ensure the uniqueness, integrity,consistency, and improve data quality.
Detect the constraint of data contents related to classification codes and meta data, including measurement units, prefix symbols, suffix symbols, joint symbols, maximum, minimum, associated values and other constraints.
Support to configure soundness analysis parameters, make normal monitoring and analysis on the standard coding library, produce status analysis reports of assorted master data coding libraries based on the soundness parameter model and provide the list of to-be-processed data, thus providing a basis for data cleaning.