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Daten zugänglich zu machen, zu nutzen und zu teilen wird heute ganz allgemein als wichtiger Baustein für nachhaltiges Wachstum und Wohlstand anerkannt. Entsprechend arbeiten Akteure in verschiedenen gesellschaftlichen Bereichen an ihren je eigenen Digitalisierungs-, Daten- und Smart City-Strategien. Data Governance wird dabei als elementare Voraussetzung erkannt. Geht man ins Detail, findet man sich jedoch schnell mit einer Wolke aus Begriffen wie Data Pools and Data Lakes, Intermediären, Plattformen und Datentreuhändern konfrontiert. Selten bzw. nur unter Schwierigkeiten findet man heraus, wie die dahinter stehenden Data Governance-Strukturen konkret beschaffen sind. Doch erst wenn wir diese Strukturen anhand eines Frameworks konsistent beschreiben können, sind wir in der Lage, unterschiedliche Data Governance-Modelle zu vergleichen und auf die Frage hin bewerten, welche Modelle in welchen Kontexten „erfolgreich” sind. Hierfür muss man zunächst klären, was Data Governance „erfolgreich“ macht und was die besonderen Herausforderungen dabei sind. Das vorliegende Discussion Paper fasst die Ergebnisse der bisherigen HIIG-Forschungsprojekte “Data Governance: Definition einer gemeinsamen Forschungsgrundlage” sowie “Data Governance: Reconciling conflicting interests in data - a research framework” zusammen.
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