Ensuring Data Security and Compliance in ETL Processes for Healthcare and Financial Services
DOI:
https://doi.org/10.47941/ijce.2375Keywords:
ETL Process, Data Privacy, Healthcare Regulation and Financial Regulation, Data Control.Abstract
Purpose: This study explores the critical role of ETL (Extract, Transform, Load) processes in managing data within highly regulated industries such as healthcare and finance. It highlights the challenges posed by stringent legal frameworks like HIPAA in the health sector and GDPR in finance while emphasizing the importance of ETL in ensuring compliance and improving data utility.
Methodology: The research examines the application of ETL processes in consolidating and transforming data for organizational use, with specific examples from healthcare (e.g., Electronic Health Records) and financial sectors (e.g., Basel III reporting). It also reviews current best practices for addressing data-related challenges, including governance, encryption, validation, and containerization.
Findings: Key challenges in ETL processes include data privacy, regulatory compliance, data quality issues, and technical limitations. However, implementing best practices such as robust data governance, advanced encryption methods, intelligent validation mechanisms, and containerized workflows significantly mitigates these risks. These practices ensure secure data handling and enhance organizational compliance with regulatory standards.
Unique Contribution to Theory, Policy and Practice: The study contributes to the theoretical understanding of ETL processes as a linchpin for data management in regulated environments. It offers policy insights into how organizations can meet compliance requirements effectively. Practically, it provides actionable recommendations for organizations to adopt ETL best practices, ensuring secure, efficient, and legally compliant data operations. These advancements strengthen client trust, reduce legal risks, and empower organizations to leverage data for strategic advantage.
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References
A. Krylov, “Data Security in Healthcare: Tips for Cybersecurity,” Mar. 14, 2023. https://kodjin.com/blog/why-healthcare-data-security-solutions-are-important/
K. Hoffmann et al., “Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions,” PLOS Digital Health, vol. 2, no. 5, p. e0000140, May 2023, doi: https://doi.org/10.1371/journal.pdig.0000140.
P. Shojaei, E. V. Gjorgievska, and Y.-W. Chow, “Security and Privacy of Technologies in Health Information Systems: A Systematic Literature Review,” Computers, vol. 13, no. 2, p. 41, Feb. 2024, doi: https://doi.org/10.3390/computers13020041.
T. Ong, R. Pradhananga, E. Holve, Iii, and M. Kahn, “A Framework for Classification of Electronic Health Data Extraction-Transformation-Loading Challenges in Data Network Participation,” 2019. Accessed: Nov. 07, 2024. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC5994935/pdf/egems-5-1-222.pdf
Ehsan Soltanmohammadi and Neset Hikmet, “Optimizing Healthcare Big Data Processing with Containerized PySpark and Parallel Computing: A Study on ETL Pipeline Efficiency,” Journal of Data Analysis and Information Processing, vol. 12, no. 04, pp. 544–565, Jan. 2024, doi: https://doi.org/10.4236/jdaip.2024.124029.
A. Itsekson, “The Importance of ETL in Healthcare: All You Need To Know,” Jelvix, 2023. https://jelvix.com/blog/etl-process-in-healthcare-benefits-challenges-and-best-practices
S. Khanra, A. Dhir, A. K. M. N. Islam, and M. Mäntymäki, “Big data analytics in healthcare: a systematic literature review,” Enterprise Information Systems, vol. 14, no. 7, pp. 878–912, Aug. 2020, doi: https://doi.org/10.1080/17517575.2020.1812005.
V. Ehrenstein, H. Kharrazi, H. Lehmann, and C. O. Taylor, Obtaining Data From Electronic Health Records. Agency for Healthcare Research and Quality (US), 2020. Available: https://www.ncbi.nlm.nih.gov/books/NBK551878/
W. Raghupathi and V. Raghupathi, “Big data analytics in healthcare: promise and potential,” Health Information Science and Systems, vol. 2, no. 1, pp. 1–10, Feb. 2019, doi: https://doi.org/10.1186/2047-2501-2-3.
N. Berros, F. El Mendili, Y. Filaly, and Y. El Bouzekri El Idrissi, “Enhancing Digital Health Services with Big Data Analytics,” Big Data and Cognitive Computing, vol. 7, no. 2, p. 64, Mar. 2023, doi: https://doi.org/10.3390/bdcc7020064.
C. Peng, P. Goswami, and G. Bai, “A literature review of current technologies on health data integration for patient-centered health management,” Health Informatics Journal, vol. 26, no. 3, p. 146045821989238, Dec. 2019, doi: https://doi.org/10.1177/1460458219892387.
B. Ozaydin, F. Zengul, N. Oner, and S. S. Feldman, “Healthcare Research and Analytics Data Infrastructure Solution: A Data Warehouse for Health Services Research,” Journal of Medical Internet Research, vol. 22, no. 6, p. e18579, Jun. 2020, doi: https://doi.org/10.2196/18579.
V. Manickam and M. Rajasekaran Indra, “Dynamic multi-variant relational scheme-based intelligent ETL framework for healthcare management,” Soft Computing, Mar. 2022, doi: https://doi.org/10.1007/s00500-022-06938-8.
R. Raja, I. Mukherjee, and B. K. Sarkar, “A Systematic Review of Healthcare Big Data,” Scientific Programming, vol. 2020, no. 1, pp. 1–15, Jul. 2020, doi: https://doi.org/10.1155/2020/5471849.
A. Almalawi, A. I. Khan, F. Alsolami, Y. B. Abushark, and A. S. Alfakeeh, “Managing Security of Healthcare Data for a Modern Healthcare System,” Sensors, vol. 23, no. 7, p. 3612, Jan. 2023, doi: https://doi.org/10.3390/s23073612.
F. Prasser, H. Spengler, R. Bild, J. Eicher, and K. A. Kuhn, “Privacy-enhancing ETL-processes for biomedical data,” International Journal of Medical Informatics, vol. 126, pp. 72–81, Jun. 2019, doi: https://doi.org/10.1016/j.ijmedinf.2019.03.006.
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Copyright (c) 2024 Santosh Kumar, Singu
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