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Insbesondere die zunehmende Nutzung unternehmensexterner und offener Datens\u00e4tze (Open Data) f\u00f6rdert die M\u00f6glichkeiten evidenzbasierter Entscheidungen. Dabei basieren evidenzbasierte Entscheidungen mit diesen Datens\u00e4tzen immer h\u00e4ufiger auf Analysen, welche mittels maschineller Lernverfahren bzw. Machine Learning (ML) vorbereitet oder durchgef\u00fchrt werden. Weil der Inhalt und die Qualit\u00e4t und damit der Nutzen eines Datensatzes f\u00fcr solche Analyseverfahren im Vorfeld ungewiss ist, stellt die Auswahl und die Beschaffung von geeigneten Daten unabh\u00e4ngig vom ML-Verfahren eine Kernherausforderung dar. Dieser Beitrag stellt deshalb zum Zwecke der Effizienz ein hierarchisches Vorgehen vor. Mit diesem k\u00f6nnen schemabasierte Datens\u00e4tze strukturiert und effektiv dahingehend \u00fcberpr\u00fcft werden, ob deren Qualit\u00e4t und inhaltliche Fit f\u00fcr einen bestimmten Anwendungsfall (z.\u202fB. eine wiederkehrende Entscheidungssituation) ausreichend ist. 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