Insights into commonalities of a sample: A visualization framework to explore unusual subset-dataset relationships
Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive...
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Veröffentlicht in: | Data & knowledge engineering 2024-05, Vol.151, p.102299, Article 102299 |
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Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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Zusammenfassung: | Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary teams. Domain experts and data analysts are both empowered to analyze and discuss results and come to well-founded insights and implications. Inspired by a typical auditing problem, we develop and apply a visualization framework to single out unusual data in general subsets for potential further investigation. Our framework is applicable to both unusual data detected manually by domain experts or by algorithms applied by data analysts. Application examples show typical interaction, collaboration, visualization, and decision support. |
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ISSN: | 0169-023X 1872-6933 |
DOI: | 10.1016/j.datak.2024.102299 |