MULTI-DIMENSIONAL EVALUATION OF DATA PLATFORM MATURITY IN CLOUD-NATIVE ENTERPRISES

Abstract

Cloud-native enterprises increasingly depend on advanced data platforms to support analytics, artificial intelligence, automation, and strategic decision-making. However, evaluating the maturity of enterprise data platforms remains challenging due to complex architectures, distributed cloud environments, evolving technologies, and diverse organizational requirements. This paper proposes a MultiDimensional Evaluation Framework for Data Platform Maturity in Cloud-Native Enterprises by integrating cloud computing, data engineering, data governance, analytics capabilities, automation, security assessment, scalability evaluation, and operational intelligence. The proposed framework evaluates multiple maturity dimensions including architecture flexibility, data pipeline automation, data quality management, governance practices, security mechanisms, cloud scalability, machine learning readiness, and business intelligence capabilities. Intelligent assessment models analyze enterprise platform characteristics and generate maturity insights for continuous improvement. Experimental evaluation demonstrates improvements in maturity assessment accuracy, capability analysis, operational visibility, governance effectiveness, and strategic planning. The proposed framework provides a comprehensive approach for organizations seeking to evaluate and enhance cloud-native data platform maturity. Keywords— Cloud-Native Data Platforms, Maturity Evaluation, Data Engineering, Cloud Computing, Data Governance, Analytics Platforms, Enterprise Architecture, Digital Transformation.