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Auflistung nach Autor:in "Stein,Hannah"

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  • Textdokument
    Approaches for Automated Data Quality Analysis: Syntactic and Semantic Assessment
    (INFORMATIK 2022, 2022) Ahiagble,Agbodzea Pascal; Stein,Hannah
    Data quality significantly influences data usability and plays an important role in data trading. This paper presents a data quality analysis (DQA) of data tables on two levels. The first, the so-called syntactic level, concerns the structure of the elements within the database and the second, the so-called semantic level, concerns the relationship between the elements in the database and the "real world". Based on a literature review the most relevant data quality criteria and corresponding metrics were derived. Subsequently, based on heuristics, a data-centric approach and an unsupervised machine learning clustering algorithm DBSCAN, a service for automated DQA, is designed and implemented (syntactic DQA). In the next step, an automated semantic DQA service as well. The approach is used to examine data tables for example for missing relevant columns (i.e., semantic completeness). A data quality index represents the services’ output, which is derived from the automated analysis of various data quality criteria. This enables the assessment of data quality, as well as the detection of potentials for improving quality and thus increasing the value of tradeable data.
  • Textdokument
    Towards a data quality index for data valuation in the data economy
    (INFORMATIK 2022, 2022) Dokic,Dusan; Stein,Hannah
    Data represent a key resource for firm success, being used for strategic decision making and increasing business process efficiency. Despite the large potential of data sharing within data ecosystems or markets, firms are reluctant to do this, due to fear of losing competitive edges, lack of trust and ambiguity regarding data value. According to prior research, data value vastly depends on usage and quality. This paper focuses on data quality, as the lack of methods for quantifying data quality is one main reason for missing comprehensible data valuation approaches. We analyze 15 existing data quality indices (DQI) from theory and practice, identify relevant data quality dimensions and discuss metrics for applicability in data valuation approaches for data ecosystems and markets. Based on a quantitative study, we propose a DQI concept for developing transparent, objective data valuation methods, while providing a better understanding of inter- and intra-organizational data value.