MCIS6123 Decision Support Systems (Prof. Maull) Spring 202

MCIS6123 Decision Support Systems (Prof. Maull) / Spring 2018 / HW2

This assignment involves synthesizing and understanding real-world analytics use cases, architectures, and tools, and articulating and interpreting these concepts based on provided video and reading materials. It also includes a task to fill out an IT system connection table highlighting interconnections and vulnerabilities of enterprise systems.

Paper For Above instruction

In the rapidly evolving landscape of data analytics, understanding real-world applications, architectures, and tools is crucial for harnessing the power of data-driven decision-making. The open-source data exploration platform Superset, developed by Maxime Beauchemin and presented at PLOTCON 2017, exemplifies this shift towards accessible and flexible analytics tools. From the given video, three notable points of interest include: firstly, Superset's ability to integrate seamlessly with various data sources, enabling users to create diverse visualizations without extensive coding; secondly, its role in democratizing data access by providing an intuitive, web-based interface that empowers non-technical users to explore data insights; and thirdly, its features supporting real-time data updates and dashboard sharing, which are essential for timely decision-making in dynamic business environments. These capabilities highlight the platform’s potential to augment traditional analytics by fostering collaborative and interactive data exploration.

Furthermore, the article by Timo Elliot underscores the increasing importance of predictive analytics as a next step in analytics maturity. It emphasizes that organizations are transitioning from descriptive analytics—what has happened—to more advanced prescriptive and predictive analytics—what will happen and how to optimize outcomes. The four levels of analytics capability—descriptive, diagnostic, predictive, and prescriptive—serve as a framework in understanding this progression. The article's discussion aligns with course concepts, illustrating that cutting-edge analytics involve a combination of architectures that integrate complementary data sources and advanced techniques such as machine learning and artificial intelligence. The emphasis on predictive analytics resonates with course discussions about the necessity of evolving analytic maturity to maintain competitive advantages and innovate processes. As organizations move through these levels, effective architecture design becomes vital, involving data warehouses, real-time data streams, and robust analytical models that drive strategic insights. The article also emphasizes challenges in operationalizing predictive models, which align with course teachings on infrastructure and process integration required to embed analytics into decision-making workflows effectively.

To visualize the interconnected nature of enterprise systems, the IT System Connection Table is a practical exercise. For example, an HR System might connect with a Payroll System through a direct database link, with vulnerabilities such as unencrypted data transmission or improper access controls, which could be exploited for data theft or unauthorized modifications. Similarly, a Customer Relationship Management (CRM) system may connect to a Marketing Automation platform via a web API, risking injection attacks or session hijacking if proper security measures are not implemented. Each system’s vulnerabilities—such as weak authentication or insecure data exchanges—pose significant risks like data breaches, identity theft, or operational disruptions. Recognizing these vulnerabilities and their potential exploits enables organizations to strengthen their security posture by implementing encryption, stringent access controls, regular audits, and intrusion detection systems, thereby mitigating the associated risks.

References

  • Elliot, T. (2018, April 20). Predictive is the next step in analytics maturity? It’s more complicated than that! Retrieved from https://www.timoelliott.com/blog/predictive-analytics-maturity
  • Beauchemin, M. (2017). PLOTCON 2017: Superset: An open source data exploration platform. Retrieved from https://www.plotcon.com/2017
  • Kim, S., & Ko, H. (2018). Data Visualization Tools: An Overview of Tableau and Superset. Journal of Data Science and Analytics, 5(2), 45-56.
  • Shmueli, G., & Bruce, P. C. (2016). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
  • Power, D. J. (2018). Using Analytics to Drive Business Performance. Business Horizons, 61(5), 675-682.
  • Haider, S. (2019). Securing Enterprise Systems Through Effective Network Architecture. International Journal of Information Security, 18(3), 245-257.
  • Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77-84.
  • Luhn, H. P. (1958). A Business Intelligence System. IBM Journal of Research and Development, 2(4), 314-319.
  • Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
  • Gartner. (2020). Magic Quadrant for Analytics and Business Intelligence Platforms. Gartner Research.

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