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IDQ Asia Pacific Webinars: with Shazia Sadiq



Topic:

Navigating the Data Quality Landscape

Abstract:

The data (and information) quality domain is supported by several decades of high quality research contributions and commercial innovations.

Research and practice in data and information quality is characterized by methodological as well as topical diversity. The cross-disciplinary nature of data quality problems as well as a strong focus on solutions based on the fitness for use principle has further di-versified the related body of knowledge. Although research pluralism is highly warranted, there is evidence that substantial developments in the past have been isolationist. As data quality increases in importance and complexity, there is a need to motivate exploitation of synergies across diverse research communities.

The above factors warrant a multi-pronged approach to the study of data quality management spanning: organizational aspects, i.e. strategies to establish people, processes, policies, and standards required to manage data quality objectives; architectural aspects, i.e. the technology landscape required to deploy developed processes, standards and policies; and computational aspects which relate to effective and efficient tools and techniques for data quality. Despite a significant body of knowledge on data quality management, the community is lacking a resource that provides a consolidated coverage of data quality over the three different aspects. This gap motivated me to assemble a point of reference that reflects the full scope of data quality research and practice, which has now been published as the “Shazia Sadiq (Ed), Handbook of Data Quality Research and Practice, Springer 2013.”

In this webinar, I will firstly present an analysis of the data quality body of knowledge. I will then present a snapshot of contributions from research and practitioner communities on organisational, architectural and computational aspects. The webinar will provide you with a comprehensive list of resources that you can pursue as relevant for your data quality objectives.

Recording:

 

Speaker:

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