Journal Entry 1: Prepare A One- To Two-Paragraph Journal

Journal Entry 1 Prepare A One To Two 1 2 Paragraph Journal Entry Th

Journal Entry 1: Prepare a one to two (1-2) paragraph journal entry that examines your learning experiences with ORION in Week 1 of this course, that addresses the following: Determine the primary manner in which ORION has increased your business knowledge in the related subject area. Discuss specific challenges that you may have experienced with any of the subject matter presented, and point out the areas for which you would like more information. Suggest at least two (2) possible applications of this week’s material to the company that you currently work for or hope to work for in the future. * Note: To access ORION please see instructions in the Week 1 area.

Paper For Above instruction

My initial engagement with ORION during Week 1 of this course significantly expanded my understanding of how advanced data management and analytics tools can be harnessed to drive strategic decision-making in modern business environments. The platform's capabilities in integrating diverse datasets and presenting comprehensive insights have deepened my appreciation for data-driven strategies, crucial for achieving competitive advantage in today's fast-paced marketplace. By working through ORION’s functionalities, I realized how essential it is to develop proficiency in interpreting complex data visualizations and reports, which are vital skills for any future leadership role in business.

Despite the valuable insights gained, I encountered some challenges when navigating ORION’s user interface and understanding certain analytical features. Initially, some aspects of data input and interpretation appeared complex, requiring additional time and effort to master. These difficulties highlighted my need for further clarification on specific functions, such as customizing reports and applying predictive analytics. For future learning, I am keen to explore more about how to leverage predictive modeling within ORION to forecast market trends and customer behaviors more accurately. Such expertise would be immensely beneficial in crafting proactive business strategies.

Applying the knowledge acquired from this week's material can have practical implications in a corporate setting. First, I envisage utilizing ORION’s data analytics to optimize supply chain operations by identifying inefficiencies and predicting demand fluctuations. This application could significantly reduce costs and improve service delivery. Second, insights from ORION could be used to enhance customer segmentation and targeted marketing efforts. Understanding customer preferences through detailed data analysis enables companies to personalize marketing campaigns, increasing engagement and sales. In my current or future roles, mastering these applications would contribute to more informed decision-making, ultimately leading to better business outcomes.

References

  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.
  • Sharma, S., & Li, R. (2020). Data analytics in supply chain management. Supply Chain Management Review, 24(3), 45-53.
  • 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.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
  • Lausen, T. (2014). Implementing business intelligence in modern organizations: Challenges and opportunities. Information Systems Management, 31(4), 292-308.
  • McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.
  • Riggins, F. J., & Wamba, S. F. (2015). Research directions on the adoption, usage, and impact of the Internet of Things through the use of big data analytics. Proceedings of the 48th Hawaii International Conference on System Sciences.
  • Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media.
  • Sarabi, M. M., & Shams, A. B. (2018). Enhancing decision-making via big data analytics: An overview. Journal of Business Analytics, 1(1), 23-30.

Related Assignments