Intelligent Data Management Frameworks for Digital Transformation

Francisco A. Gómez Vela

Intelligent Data Management Frameworks for Digital Transformation

Author(s)	Francisco A. Gómez Vela
Country	Spain
Abstract	Digital transformation has fundamentally changed the manner in which organizations collect, manage, analyze, share, and utilize information. The increasing adoption of cloud computing, artificial intelligence, Internet of Things technologies, enterprise platforms, mobile applications, and distributed digital infrastructures has resulted in rapidly expanding data volumes and increasingly complex information environments. Traditional data management practices are often insufficient for addressing requirements associated with real-time processing, heterogeneous data integration, scalability, security, governance, and intelligent decision-making. Intelligent data management frameworks provide an integrated approach for addressing these requirements by combining automated data processing, artificial intelligence, machine learning, metadata management, data quality mechanisms, cloud technologies, knowledge management, and advanced analytics. This study examines the role of intelligent data management frameworks in supporting digital transformation and evaluates how intelligent data practices can improve information accessibility, operational efficiency, decision-making, scalability, innovation, and organizational adaptability. A qualitative descriptive research design based on secondary academic literature was adopted. The analysis focused on data acquisition, integration, quality management, intelligent processing, governance, analytics, and continuous monitoring as major components of an intelligent data management architecture. Findings indicate that intelligent data management can strengthen digital transformation by improving data consistency, automating repetitive management tasks, enabling predictive analysis, and supporting real-time access to trusted information. Artificial intelligence can assist with anomaly detection, metadata classification, data quality assessment, and automated decision support, while cloud and distributed technologies provide flexible infrastructure for large-scale information processing. Nevertheless, implementation is constrained by data silos, legacy infrastructure, interoperability problems, privacy concerns, cybersecurity threats, inadequate data quality, skill shortages, and governance complexity. The study concludes that intelligent data management should be treated as a strategic organizational capability rather than merely a technical infrastructure. Successful digital transformation requires integrated data architectures that combine automation, analytics, governance, security, interoperability, and human oversight. Future frameworks should emphasize adaptive data governance, privacy-preserving technologies, intelligent metadata, real-time data management, edge-cloud integration, and explainable artificial intelligence to establish sustainable and trustworthy digital ecosystems.
Keywords	Intelligent Data Management, Digital Transformation, Data Governance, Artificial Intelligence, Machine Learning, Big Data, Cloud Computing, Data Quality, Information Systems, Digital Innovation.
Field	Engineering
Published In	Volume 2, Issue 3, May-June 2020
Published On	2020-05-01

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Intelligent Data Management Frameworks for Digital Transformation

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