Transforming the Data Ecosystem through Machine Learning and Artificial Intelligence: A Systematic Review of Innovative Big Data Frameworks
Abstract
The digital revolution era has created fundamental transformation in data management and utilization, where machine learning and artificial intelligence integration becomes the primary catalyst in optimizing contemporary data ecosystems. Global data volume predicted to reach 181 zettabytes by 2025 demands innovative approaches in big data management, yet 80% of organizations still experience difficulties integrating AI technology with their existing data infrastructure. This research aims to identify and analyze characteristics of innovative frameworks that integrate machine learning and artificial intelligence in data ecosystem transformation, and formulate comprehensive framework recommendations for the future. The research method employs a qualitative approach with Systematic Literature Review (SLR) on 2021-2022 publications via Google Scholar, with thematic analysis using Critical Appraisal Skills Program (CASP) checklist. Research results identify eight major innovative frameworks including AI for Smart Society 5.0, Big Data-AI-IoT Integration, to Digital Responsibility Accounting, with main characteristics of process automation capabilities, service personalization, edge computing for real-time decision making, and blockchain implementation for data security. Implementation challenges include digital infrastructure limitations, human resource skill gaps, data security, and organizational resistance. Transformation impact proves significant in education, governance, and business intelligence sectors. The conclusion shows that comprehensive future frameworks must be adaptive, ethical, and sustainable by integrating technology, human, and environmental dimensions in a balanced manner. A phased implementation approach is recommended with priority on strengthening digital infrastructure and developing human resource competencies through cross-sector collaboration.
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