Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises
Abstract
A proper solution for cross-functional service optimization makes a case for the existence of a federated data architecture with respect to information integration between cross-functional services and argues the need for information supplied in strict accordance with the rules set forth for Logical Data Modeling. From a systemic perspective, a properly structured service catalog is conducive to optimum service optimization and constitutes the key to effective cataloging of supported services within a company. The offered solution uses strategic information integration models to optimize governance or stewardship of service-related data. The concept of a strategic service catalog constitutes an additional contribution. Once all the data in an enterprise is served by a data governance policy and each data source visualizes information according to a home-BIA rule within a Logical Data Model (LDM), data opens new avenues toward a federated information architecture. Orchestrating the services enables knowledge sharing and reuse of service consumption. The implementation of a business service catalog that fulfills these requirements becomes a critical step toward a successful federated data architecture
Service optimization in Large-Scale Enterprises (LSEs) can draw on a range of synergies through the orchestration of cross-functional services. A proper solution for this cross-functional service optimization makes a case for the existence of a federated data architecture with respect to information integration between cross-functional services. The argument focuses on the need for information supplied in strict accordance with the rules set forth for Logical Data Modeling and maintains that, from a systemic perspective, a properly structured service catalog is conducive to optimum service optimization. Such a catalog constitutes the key to effective cataloging of supported services within a company. The solution uses strategic information integration models to optimize governance or stewardship of service-related data. The concept of a strategic service catalog constitutes an additional contribution
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 7 No. 3 (2022): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
11801-11811 |
Published |
May 5, 2022 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Siva Hemanth Kolla (%2022). Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises. International Journal of Emerging Trends in Engineering and Management Research , Vol. 7 No. 3 (2022): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 11801-11811. https://doi.org/10.15662/ijetemr.2022.0703001 |
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[21] Agrawal, R., Prabakaran, S., & Mohanty, R. P. (2021). Healthcare analytics and quality improvement: A systematic review. International Journal of Healthcare Management, 14(4), 1347–1360.
[22] Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1-14.
[23] Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1), 54.
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[25] Mangalampalli, B. M. (2021). Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. World Journal of Clinical Medicine Research, 1(1), 1-18.
[26] Friedman, D. J., Parrish, R. G., & Ross, D. A. (2013). Electronic health records and population health. American Journal of Preventive Medicine, 44(6), 535–536.
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[28] Ginter, P. M., Duncan, W. J., & Swayne, L. E. (2018). Strategic management of health care organizations (8th ed.). Wiley.
[29] Raghupathi, W., & Raghupathi, V. (2021). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 9(1), 1–10.
[30] Davuluri, P. N. (2020). Improving Data Quality and Lineage in Regulated Financial Data Platforms. Finance and Economics, 1(1), 1-14.
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[36] Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
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[48] Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2021). Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare, 3(1), 1–23.
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[50] Haux, R. (2010). Medical informatics: Past, present, future. International Journal of Medical Informatics, 79(9), 599–610.
[51] Friedman, C. P., Allee, N. J., Delaney, B. C., Flynn, A. J., Silverstein, J. C., Sullivan, K., & Brantley, K. L. (2020). The science of learning health systems: Foundations for a new journal. Learning Health Systems, 4(1), e10203.
[52] Hripcsak, G., Duke, J. D., Shah, N. H., Reich, C. G., Huser, V., Schuemie, M. J., Suchard, M. A., Park, R. W., Wong, I. C. K., Rijnbeek, P. R., van der Lei, J., Pratt, N., Norén, G. N., Li, Y. C., Stang, P. E., Madigan, D., & Ryan, P. B. (2015). Observational Health Data Sciences and Informatics (OHDSI): Opportunities for observational researchers. Studies in Health Technology and Informatics, 216, 574–578.
[53] Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1-19.
[54] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243.
[55] Kleppmann, M. (2020). Designing data-intensive applications: The big ideas behind reliable, scalable, and maintainable systems. O’Reilly Media.
[56] Provost, F., & Fawcett, T. (2020). Data science for business (2nd ed.). O’Reilly Media.
[57] Keesara, S., Jonas, A., & Schulman, K. (2020). Covid-19 and health care’s digital revolution. New England Journal of Medicine, 382(23), e82.
[58] Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.
[59] Bodenreider, O. (2004). The Unified Medical Language System (UMLS): Integrating biomedical terminology. Nucleic Acids Research, 32(Database Issue), D267–D270.
[60] Kho, A. N., Cashy, J. P., Jackson, K. L., Pah, A. R., Goel, S., Boehnke, J., Humphries, J. E., Kominers, S. D., Hota, B., Sims, S. A., Malin, B. A., French, D. D., & Meltzer, D. O. (2015). Design and implementation of a privacy preserving electronic health record linkage tool in Chicago. Journal of the American Medical Informatics Association, 22(5), 1072–1080.
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