Reference Architecture for Intelligent Enterprise Solutions

Authors: Shankar Kambhampaty, Harish Rohan Kambhampaty

Abstract:

Data in IT systems in enterprises have been growing at phenomenal pace. This has provided opportunities to run analytics to gather intelligence on key business parameters that enable them to provide better products and services to customers. While there are several Artificial Intelligence/Machine Learning (AI/ML) and Business Intelligence (BI) tools and technologies available in marketplace to run analytics, there is a need for an integrated view when developing intelligent solutions in enterprises. This paper progressively elaborates a reference model for enterprise solutions, builds an integrated view of data, information and intelligence components and presents a reference architecture for intelligent enterprise solutions. Finally, it applies the reference architecture to an insurance organization. The reference architecture is the outcome of experience and insights gathered from developing intelligent solutions for several organizations.

Keywords: Architecture, model, intelligence, artificial intelligence, business intelligence, AI, BI, ML, analytics, enterprise.

References:

[1] S. Kambhampaty, “Reference Architecture for SMAC solutions”, Proceedings of the Sixth International Conference on Computer Science and Information Technology (CCSIT - 2016), 2016. https://arxiv.org/abs/1601.06389
[2] S. Kambhampaty, “It's All About Data”, Forbes article, 2018. https://www.forbes.com/sites/forbestechcouncil/2018/07/23/its-all-about-data/?sh=f41d58e68556
[3] D. Scott, S. Mingay “Scaling Bimodal — Fusing IT With the Business”, A Gartner Trend Insight Report. ID: G00331680, 2017. https://www.gartner.com/en/doc/3772092-scaling-bimodal-fusing-it-with-the-business-a-gartner-trend-insight-report
[4] MPulse, “System of Record vs. System of Engagement, Part 1: What Maintenance Managers Need to Know about ERP Systems”, 2016. https://mpulsesoftware.com/blog/maintenance-management/system-record-vs-system-engagement-part-1/#:~:text=A%20System%20of%20Record%20is,data%20repository%20of%20an%20organization.&text=A%20System%20of%20Engagement%20is,usable%20tool%20for%20capturing%20data
[5] S. Kambhampaty, “'Beam Me Up, Scotty:' The Next-Generation User Experience”, Forbes article, 2019. https://www.forbes.com/sites/forbestechcouncil/2020/04/20/beam-me-up-scotty-the-next-generation-user-experience/?sh=3b7efac41b71
[6] J. Frankenfield, “Data Analytics”, 2020. https://www.investopedia.com/terms/d/data-analytics.asp
[7] E. Wilson, “The differences between descriptive, diagnostic, predictive & Cognitive analytics”, 2020. https://demand-planning.com/2020/01/20/the-differences-between-descriptive-diagnostic-predictive-cognitive-analytics
[8] D. Gupta, Data Analytics and Its Application in Various Industries, Apress, Berkeley, CA, ISBN: 978-1-4842-3524-9, 2018. https://link.springer.com/chapter/10.1007/978-1-4842-3525-6_1
[9] R. Shaw, “The 10 Best Machine Learning Algorithms for Data Science Beginners”, Dataquest, 2019. https://www.dataquest.io/blog/top-10-machine-learning-algorithms-for-beginners/
[10] D. Fumo, “Types of Machine Learning Algorithms You Should Know”, towards data science, 2017. https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861
[11] S. Yegulalp, “14 open source tools to make the most of machine learning”, InfoWorld, 2020. https://www.infoworld.com/article/3575420/14-open-source-tools-to-make-the-most-of-machine-learning.html
[12] delaware. “Intelligent apps: the next generation of applications”, 2020. https://www.delawareconsulting.com/en-us/solutions/intelligent-apps#:~:text=Intelligent%20apps%20are%20applications%20that,personalized%20and%20adaptive%20user%20experiences