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Informative Article | Information Technology | United States of America | Volume 13 Issue 10, October 2024 | Popularity: 5.3 / 10

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Leveraging Dimensional Modeling for Optimized Healthcare Data Warehouse Cloud Migration: Data Masking and Tokenization

Jaishankar Inukonda

Abstract: As Healthcare organizations increasingly migrate their on - prem data warehouse to the cloud, security, scalability and analytics becomes critical. Dimensional modeling, which structures the data into facts and dimensions, offers a powerful approach to enhance the efficiency of cloud - based data warehouses. This paper explores how leveraging dimensional modeling during healthcare cloud migration streamlines data integration, improves performance, and simplifies business analytic reporting. Key strategies include database schema redesign, ETL/ELT process adoption, and ensuring data quality at every stage of data migration. Through real - world implementation, we demonstrate how dimensional modeling not only reduces cloud storage costs but also enhances agility in data analytics without compromising on data security which is very critical for healthcare data. Data masking has emerged as a critical requirement for protecting healthcare data during cloud migration. The use of synthetic data in healthcare is gaining lot of traction as a solution to address privacy/security concerns and regulatory challenges while enabling the continued advancement of data - driven innovations. Synthetic data, generated from real datasets but free from personally identifiable information (PII) and personal healthcare information (PHI), provides a viable alternative for use in cloud data warehousing, AI model training, and software testing without compromising sensitive data. This paper explores data masking strategies, tokenization and usage of synthetic data in cloud. We demonstrate how effective the data masking techniques can reduce the risk of data breaches, ensure patient confidentiality, and enable secure data sharing for business analytics. This paper provides a roadmap for organizations aiming to future - proof their data warehouse by adopting dimensional modeling in cloud migration, ensuring sustainable growth and adaptability in an evolving data landscape.

Keywords: Dimensional model, Cloud data warehousing, Cloud migration, Healthcare data, data masking, Tokenization, Synthetic data, Healthcare data security, PHI data, PII data, Fact and dimension tables

Edition: Volume 13 Issue 10, October 2024

Pages: 437 - 441

DOI: https://www.doi.org/10.21275/SR241004233606

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