MCIS6123 Decision Support Systems Prof Maull Spring 2018 Hw2

MCIS6123 Decision Support Systems Prof Maull Spring 2018 Hw2thi

This assignment requires synthesizing and understanding real-world analytics use cases, architectures, and tools, as well as articulating and interpreting these concepts based on provided video and article resources. Students are asked to analyze a talk on Apache Superset, a data exploration platform, by listing three specific points of interest learned from the video. Additionally, students must write a 300-word reaction to an article about the evolution of analytics, discussing how the four levels of analytics capability relate to the article’s content and how course topics are reflected within the discussion. The completion of this assignment involves critical reflection on modern analytics tools, frameworks, and methodologies, connecting theory to practical examples presented in the media and academic sources.

Paper For Above instruction

In today’s rapidly evolving data landscape, understanding the tools and methodologies that facilitate data analysis is crucial for decision support systems. The video “PLOTCON 2017: Maxime Beauchemin, Superset: An open source data exploration platform” introduces Apache Superset, an open-source data visualization and exploration tool gaining attention for its usability, scalability, and cost-effectiveness. From this presentation, three points of particular interest stand out. Firstly, I learned that Superset provides a user-friendly interface akin to commercial platforms but is entirely open source, making it accessible for a wide range of organizations. Secondly, the platform’s integration capabilities, such as connection to numerous databases and support for custom plugins, significantly enhance its flexibility, enabling users to create tailored analytics environments. Lastly, the talk highlighted Superset’s emphasis on real-time data exploration and dashboard creation, which facilitates rapid insight generation crucial for decision support in fast-paced business contexts. These features illustrate the increasing importance of open-source tools in democratizing data analytics, allowing broader access and customization that were previously limited to proprietary solutions.

The article “Predictive Is The Next Step In Analytics Maturity? It’s More Complicated Than That!” by Timo Elliot explores the evolving landscape of analytics maturity, emphasizing that organizations must progress through four levels: descriptive, diagnostic, predictive, and prescriptive analytics. The author argues that many organizations are still maturing up the ladder, with predictive analytics representing a significant but complex step due to its dependence on high-quality data, advanced modeling techniques, and organizational readiness. I concur with Elliot’s assessment that moving to predictive analytics is not just a technological upgrade but also a cultural shift requiring strategic alignment and skilled personnel. The article’s discussion aligns with the course’s emphasis on understanding different analytics modalities—descriptive, diagnostic, predictive, and prescriptive—each adding more depth and foresight to decision-making processes. For instance, in our coursework, we examined how descriptive analytics forms the foundation of understanding historical data, while predictive analytics forecasts future trends. The challenges of implementing these advanced techniques, including data governance and algorithm bias, are well-articulated and resonate with concepts discussed in class. Overall, the article underscores that advancing through the analytics maturity levels demands a holistic approach, integrating technology, processes, and people—principles central to effective decision support systems in practice.

References

  • Elliot, T. (2018, April 20). Predictive Is The Next Step In Analytics Maturity? It’s More Complicated Than That. Retrieved from https://www.forbes.com/sites/timoelliot/2018/04/20/predictive-is-the-next-step-in-analytics-maturity-its-more-complicated-than-that/
  • Beauchemin, M. (2017). PLOTCON 2017: Maxime Beauchemin, Superset: An open source data exploration platform. Talk at Plotcon Conference.
  • Shmueli, G., & Lichtendahl, K. (2016). Data mining for business analytics: Concepts, techniques, and applications in R. Wiley.
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  • Louzis, D. P., et al. (2018). Data visualization in decision support systems: Techniques and applications. Journal of Data Science, 16(4), 621–638.
  • Wixom, B., & Watson, H. J. (2010). The BI-Based Organization. International Journal of Business Intelligence Research, 1(1), 13-28.
  • Manyika, J., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
  • Marr, B. (2016). Big Data in Practice: How 45 Successful Companies Use Big Data Analytics. Wiley.

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