Quaid - a platform for improving data quality

Information holds the key to success. The accuracy of data determines operational performance, regulatory compliance and the effectiveness of business strategy. Organisations in every industry worldwide have an increased awareness of the costs and risks caused by data that is inconsistent, inaccurate, stale or deliberately falsified. The Data Warehousing Institute estimates that poor quality data costs US businesses $600 billion annually. Many companies are investing in data quality solutions that help increase transparency and productivity and, as a result, the data quality market is experiencing rapid growth. Whilst these companies are making progress on internal clean up and consolidation tasks, such activities require large amounts of manual effort. A new breakthrough which provides theoretical background and practical algorithms for data quality management has been pioneered at the School of Informatics. This approach, based on a novel extension of classical dependency theory, increases the level of automation and improves accuracy in the data quality process. In 2008, Prof. Wenfei Fan was awarded with the British Computer Society Roger Needham award along with the Chinese Yangtze River Scholar award for his research in this area. Building on the output of this award-winning research, we aim to deliver a concept system, Quaid, which scales to real commercial datasets and addresses the needs of industrial customers. Through Quaid, we envisage new products and services will be generated from existing digital data sources.