Design Principles for Quality Scoring—Coping with Information Asymmetry of Data Products

Abstract

Data products are a hot topic within companies since more and more organizations are implementing data meshes and data product management. Product management is closely related to product quality. The concept of data quality is long established, and there are several notions to measure it. However, these approaches are less practical when data is shared between different domains and organizations with undefined contexts. Such as physical products, data products need a clear definition of what is inside and of what quality they are. With this article, we summarize existing approaches to data quality regarding data products, discuss how a lack of information provision restrains efficient data markets, and provide prescriptive knowledge. To do so, we identify meta-requirements from literature and derive design principles for data scoring systems that cope with information asymmetry for data products to enable efficient data markets.