Low-quality data can cause a variety of property and business losses. Data quality management aims to improve data availability by discovering data semantics rules, automatically locating and fixing data errors. We develop a data availability platform based on the original data quality theory. It features automatic management, forms a positive cycle of interaction between data and the platform, promotes high-quality applications of data production factors in various industries, and leads to a usable development of global data.

Aiming at the five key data quality dimensions-consistency, accuracy, completeness, timeliness and identity of entities-the research works on:
1) a presentation model and description language of data rules;
2) data rule auto-mining algorithm;
3) reasoning system and rules correctness detection;
4) data error auto-detection and positioning;
5) data restoration theory and other key technologies.