Key points are not available for this paper at this time.
With high quality of data, enterprises will add value of business. How-ever, poor data has resulted in waste of resource, low service efficiency and high costs in every area. In this paper, we firstly focus on basic issues of data quality, like where the quality problems come from and how to describe it. Then we study some cases of data quality management from a holistic enterprise perspective to the details of perspective, that is, hierarchical management architecture, frameworks, approaches and algorithm. Quality assessment of data is also an important theme, which is used to show whether data is good enough and to help people master credibility of data quality. We study some assessment algorithm and models. When problem occur, what tools should be used in a project is also included in the paper. Finally, we provide some outstanding research topics and unresolved issues for future.
Chen et al. (Mon,) studied this question.