An Integrated Modernization Architecture for Data-Intensive Industries
DOI:
https://doi.org/10.5281/zenodo.21509468Keywords:
AI-Driven Data Modernization, Cloud-Native Data Architecture, Intelligent Data Ecosystems, Data Product Engineering, AI-Ready Data Platforms, Scalable Data Pipelines, Data Governance Frameworks, SaaS Data Delivery, Cross-Domain Data Integration, Hyperscaler Cloud Systems, Data Quality Assurance, Low-Latency Data Analytics, Automated Data Workflows, Data Lifecycle Management, Enterprise Data Transformation, Data Reliability Engineering, Adaptive Data Infrastructure, AI Data Readiness, Data Maturity Models, Organizational Readiness Analysis.Abstract
An AI-driven architectural framework for cloud-enabled data modernization in business domain was proposed. The framework helps organizations to efficiently endure the cross-sector convergence of cloud computing and Artificial Intelligence by addressing Data modernization on-demand and ensuring high-quality data for Artificial Intelligence algorithms. Data modernization leverages cloud computing in the Software as a Service delivery model, achieves Data product reliability and trustworthiness through Data governance, and meets usage requirements through scalability Data -including Supply, Processing, Storage, and Consumption layers- and Automation. Cross-sectoral deployment was validated by the foundation of the framework in three business domains (Retail, Healthcare, and Finance) and a detailed analysis of requirements. In parallel, the phased adoption of Data modernization was assessed through a maturity model and an Organizational and Team Readiness analysis.
Organizations from all business domains are converging towards the cloud, and so are several processes of Artificial Intelligence Data products. These two trends are fundamentally transforming the Data surrounding businesses by fueling Data modernization. Hyperscalers leverage their global network of Data centers to provide on-demand, scalable, and pay-per-use enablement of Data product development and operation. This unprecedented operational agility represents an attractive opportunity for organizations seeking fast response time and cost competitiveness. However, organizations often struggle in Data modernization, and Data products can suffer from inaccurate, incomplete, inconsistent, or outdated Data, thus risking unexpected behavior, poor prediction performance, or wrong decisions. AI-ready Data ecosystems –encompassing Data Supply, Processing, Storage, and Consumption– that ensure quality Data should be made available for low-latency AI-enabled analytics.
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