Contextual Data Quality Management within Enterprise Architecture Frameworks

Gestion de la Qualité des Données Contextuelles dans les Cadres d'Architecture d'Entreprise

Yara Amine ()
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Yara Amine: CEDRIC - ISID - CEDRIC. Ingénierie des Systèmes d'Information et de Décision - CEDRIC - Centre d'études et de recherche en informatique et communications - ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise - Cnam - Conservatoire National des Arts et Métiers [Cnam]

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Abstract: In the era where data-driven decision-making is essential, maintaining high data quality and robust data management practices within Enterprise Architecture (EA) is a priority, where EA serves as a blueprint that integrates data quality and data management into organizational processes. In our thesis we aim to improve data quality within EA layers using Machine Learning. Embedding Machine Learning into EA components enhances data quality, automates data management processes, and provides predictive insights, optimizing decision making and aligning data governance with organizational objectives. Our research is focused on a real estate organization where the implementation and maintenance of optimal data quality and effective data management practices present significant challenges.

Keywords: ENTERPRISE ARCHITECTURE; DATA QUALITY; DATA MANAGEMENT; DATA GOVERNANCE; MACHINE LEARNING; Architecture d’lenterprise; Qualité des Données; Gestion des Données; Gouvernance des Données; Apprentissage Automatique (search for similar items in EconPapers)
Date: 2025-06-03
Note: View the original document on HAL open archive server: https://hal.science/hal-05115292v1
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Published in INFORSID (INFormatique des ORganisations et Systèmes d’Information et de Décision ), Jun 2025, Pau (France), France. ⟨10.3166/RCMA.25.1-n⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05115292

DOI: 10.3166/RCMA.25.1-n

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