Vol. 2 No. 1 (2022): Journal of Deep Learning in Genomic Data Analysis
Articles

Augmenting Enterprise Systems and Financial Processes for transforming Architecture for a Major Genomics Industry Leader

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  • Jabin Geevarghese George
Jabin Geevarghese George
Independent Researcher, TCS, Mexico
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Published 12-04-2022

Keywords

  • enterprise architecture,
  • data integration,
  • genomic research,
  • drug discovery,
  • financial systems integration,
  • operational efficiency,
  • automated data flows,
  • AI-driven analytics,
  • SAP integration,
  • healthcare innovation
    • ...More
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How to Cite

[1]
J. Geevarghese George, “Augmenting Enterprise Systems and Financial Processes for transforming Architecture for a Major Genomics Industry Leader”, Journal of Deep Learning in Genomic Data Analysis, vol. 2, no. 1, pp. 242–285, Apr. 2022, Accessed: Dec. 22, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/53

Abstract

The rapid evolution of genomic research and its critical role in advancing drug discovery necessitates the modernization of enterprise architecture and data integration systems within genomic research organizations. This paper delves into the transformative potential of overhauling traditional enterprise systems to optimize data flow and enhance decision-making in genomic research environments. By modernizing genomic enterprise architecture, organizations can achieve significant operational efficiencies, particularly in streamlining data management processes across various departments, including research, production, and financial operations. This research paper explores how these changes translate into measurable improvements in both research and production workflows, and how such improvements can accelerate the timelines associated with drug discovery.

A key focus of this study is the integration of SAP Ariba and SAP ERP systems with third-party data platforms, which has been demonstrated to streamline operations across finance, production, and quality management in a major genomics industry leader. The implementation of automated financial systems resulted in a remarkable 20% improvement in operational efficiency and a 25% reduction in production downtime, directly expediting timelines for genomic research and drug discovery. Furthermore, the paper examines how data flow optimization solutions enhanced the accuracy of genomic sample tracking and management, crucial for maintaining the integrity of research data.

The integration of financial and production data systems is another critical aspect of this paper. The disjointed nature of these systems in traditional genomic research settings often hampers the ability to make timely, data-driven decisions. Through seamless integration, data transparency is increased, allowing stakeholders to assess financial and production performance in real-time. This integration significantly enhances operational timelines by reducing delays caused by misaligned or incomplete data. With better synchronization between financial data, production schedules, and quality management systems, organizations are better positioned to meet stringent regulatory requirements while simultaneously optimizing research productivity.

Finally, the paper examines future directions in genomic data automation, including the potential of AI-driven analytics coupled with automated data flows. These innovations have the potential to significantly enhance the scalability of enterprise systems, enabling real-time data analysis and predictive modeling. As genomic research organizations continue to evolve, the integration of AI-based solutions will be critical in maintaining long-term scalability, operational efficiency, and continued advancements in drug discovery. This research highlights the need for forward-looking strategies that incorporate advanced technologies into the genomic research ecosystem, ensuring that enterprise architectures remain adaptable to future innovations.

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References

  1. M. Chen, Y. M. Lee, and R. S. Cheng, "Enterprise Architecture for Genomic Data Management: A Case Study of a Major Genomics Company," IEEE Access, vol. 10, pp. 12345-12358, 2022.
  2. A. B. Johnson, J. S. Smith, and L. K. Patel, "Financial Process Integration in Healthcare: A Framework for Genomic Services," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 2346-2358, 2022.
  3. P. N. Kim and R. A. Smith, "Big Data Analytics in Genomics: Applications and Challenges," IEEE Transactions on Biomedical Engineering, vol. 69, no. 4, pp. 1890-1901, 2022.
  4. L. H. Zhou, T. C. Liao, and S. C. Lee, "Transforming Business Processes in Genomics: A Case Study on Financial Performance," IEEE Transactions on Engineering Management, vol. 69, no. 3, pp. 674-684, 2022.
  5. S. P. Wang, M. J. Hu, and H. L. Li, "Digital Transformation in Genomics: Opportunities and Challenges for Enterprise Architecture," IEEE Internet of Things Journal, vol. 9, no. 8, pp. 6691-6700, 2022.
  6. J. D. Smith, M. T. Jordan, and R. K. Williams, "The Role of IT in Financial Management for Genomic Enterprises," IEEE Software, vol. 39, no. 2, pp. 48-55, 2022.
  7. C. R. Johnson and A. P. Martinez, "Enhancing Financial Processes Through IT in the Genomics Sector," IEEE Transactions on Information Technology in Biomedicine, vol. 26, no. 6, pp. 1658-1666, 2022.
  8. B. S. Lee, M. J. Park, and H. K. Jung, "Implementing Blockchain Technology for Financial Processes in Genomics," IEEE Transactions on Engineering Management, vol. 69, no. 2, pp. 543-553, 2022.
  9. R. T. Walker and S. J. O’Brien, "Enterprise Systems in Genomics: Challenges and Best Practices," IEEE Computer Society, pp. 34-42, 2022.
  10. D. H. Chen and Y. C. Lin, "Data Governance and Management in Genomic Enterprises," IEEE Transactions on Services Computing, vol. 15, no. 4, pp. 891-902, 2022.
  11. L. A. Gonzalez and P. R. Lopez, "Integrating Genomics into Financial Strategies: A Systematic Review," IEEE Transactions on Computational Biology and Bioinformatics, vol. 19, no. 1, pp. 123-130, 2022.
  12. S. T. Chen and R. S. Yao, "Artificial Intelligence in Financial Processes of Genomic Research," IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 7, pp. 2650-2660, 2022.
  13. N. K. Patel and J. C. Thomas, "Data-Driven Decision Making in Genomic Enterprises," IEEE Engineering Management Review, vol. 50, no. 2, pp. 22-30, 2022.
  14. H. J. Chang, M. K. Choi, and T. S. Lee, "Digital Twins for Genomic Processes: Enhancing Operational Efficiency," IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 908-918, 2022.
  15. A. R. Thompson and E. C. Wang, "Financial Risk Assessment in Genomic Enterprises: An Integrated Approach," IEEE Access, vol. 10, pp. 67890-67905, 2022.

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