Optimizing Healthcare Analytics Pipelines: A Data Engineering Approach Using ODI and OBIEE

Fred Jane
Ladoke Akintola University of Technology

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Abstract

In summary, data analytics is becoming increasingly important in healthcare to make better choices and improve patient outcomes while smoothing operations. On the other hand, healthcare data is so complicated and scattered that fast, flexible, and useful analyses of healthcare data are not without challenges. This paper reviews an approach to data engineering using Oracle Data Integrator (ODI) and Oracle Business Intelligence Enterprise Edition (OBIEE) that elevates healthcare analytics pipelines. ODI provides ETL capabilities to extract, transform, and load healthcare data from multiple sources while ensuring data accuracy and reliability. Healthcare providers utilize OBIEE to enhance reporting and visualization capabilities for better decision-making. The integration of ODI and OBIEE helps address challenges such as data integration, data quality improvement, and faster decision-making in healthcare analytics pipelines. This paper discusses best practices, challenges, and emerging trends in the field while emphasizing the importance of data engineering in improving healthcare services and operational efficiency through optimized analytics pipelines.

Keywords

Healthcare analytics, Data engineering, Oracle Data Integrator (ODI), Oracle Business Intelligence Enterprise Edition (OBIEE), Data integration, ETL processes, Data quality.

References

[1] Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press.

[2] Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3.

[3] Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13.

[4] Kudyba, S. (2010). Healthcare Informatics: Improving Efficiency through Technology, Analytics, and Management. CRC Press.

[5] Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.

[6] Oracle Corporation. (2019). Oracle Data Integrator 12c: Get Started Guide. Oracle Documentation.

[7] Oracle Corporation. (2020). Oracle Business Intelligence Enterprise Edition (OBIEE) Concepts Guide. Oracle Documentation.

[8] Batini, C., & Scannapieco, M. (2016). Data and Information Quality: Dimensions, Principles and Techniques. Springer.

[9] Mehta, N., Pandit, A., & Shukla, S. (2019). Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study. Journal of Biomedical Informatics, 100, 103311.

[10] Wager, K. A., Lee, F. W., & Glaser, J. P. (2017). Health Care Information Systems: A Practical Approach for Health Care Management (4th ed.). Jossey-Bass.