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Position Papers of the 19th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 40

QEDrants—Data Quality Quadrants for Business Users and Decision-Makers

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DOI: http://dx.doi.org/10.15439/2024F0005

Citation: Position Papers of the 19th Conference on Computer Science and Intelligence Systems, M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 40, pages 79–88 ()

Full text

Abstract. The adoption of artificial intelligence (AI) in business is often hindered by the complexity of data quality assessment. This paper introduces the quadrant-based data quality representation framework, which evaluates data assets based on two complementary dimensions: Data Integrity (accuracy and reliability, akin to Gartner's ``Ability to Execute'') and Data Coverage (breadth and comprehensiveness, similar to ``Completeness of Vision''). The framework categorizes data into four groups: \emph{Pure Gold} (AI-ready), \emph{Sleeping Giants} (high integrity, low coverage), \emph{Unpolished Diamonds} (high coverage, low integrity), and \emph{Hitchhikers} (low integrity, low coverage). Each such quadrant provides actionable insights for business users, helping them prioritize data assets for AI readiness, identify data cleaning tasks, balancing costs and value realization by focusing on the right data. Given the roots of this idea in QED Software's technology experiences, we call the proposed quadrants as \emph{QEDrants}.

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