Develop a Framework for Creating Business Value Using a Cost-Optimized Azure Data Mesh: A Complete Data Management Solution in the Financial Sector - Karthika Puttha, Sai (Digital Repository of Theses, 2026)
The study showed evidence for developing a cost-optimized framework for implementing Azure Data Mesh within financial institutions, aiming to unlock measurable business value through scalable and decentralized data management practices. As the financial sector faces increasing demands for real-time insights, regulatory compliance, and operational efficiency, traditional centralized data architectures often prove inadequate. This study proposes a structured approach to adopting Azure-based data mesh solutions that balance technological innovation with cost governance and business outcomes. A comprehensive mixed-methods methodology was employed. Primary data was collected through surveys targeting industry professionals across various roles, focusing on current data management challenges, technology adoption patterns, and governance practices. In addition, case studies and proof-of-concept (PoC) implementations were conducted to validate the practical applicability of the proposed framework. These methods provide rich empirical evidence, ensuring the framework is both grounded in industry needs and capable of addressing domain-specific challenges inherent to the financial sector. Initial findings indicate that successful data mesh adoption in financial organizations depends on four critical pillars: technological maturity, clear data ownership, proactive cost optimization, and alignment with business domains. Leveraging Azure-native services such as Azure Data Factory, Azure Databricks, Synapse Analytics, and Purview, the proposed framework demonstrates the ability to enhance data democratization, improve governance, and significantly reduce operational costs. Furthermore, the integration of cost-monitoring mechanisms into the architectural blueprint ensures that financial organizations can scale their data platforms sustainably. This research concludes by offering a detailed, actionable roadmap for financial sector organizations seeking to transition toward a modern, domain-driven data architecture. By bridging theoretical insights with practical validation, this work provides a complete guide to establishing a scalable, cost-effective Azure Data Mesh solution that directly contributes to business growth and operational resilience. The findings aim to empower decision-makers, data strategists, and IT leaders with the necessary tools and knowledge to drive successful cloud-based data transformations in the evolving financial domain.