Bus 721 V8vision Framework Worksheet

Bus721 V8vision Framework Worksheet

Bus721 V8vision Framework Worksheet

Part A: Write a 175- to 260-word response to each prompt below. Ensure each response is thorough and complete, supported with rationale, carefully edited for grammar, punctuation, and spelling errors, formatted according to course-level APA guidelines, and correctly cited where applicable.

Prompt 1 –Exploring the Vision

Identify up to three possible optimization opportunities in data analytics relevant to your chosen organization. Describe the optimization issues clearly. Explain the proposed opportunity or remedy for each issue. Provide sufficient context for each issue and opportunity.

Prompt 2 –Developing the Vision

For each opportunity you identified, make connections to Figure 1.1, “The Work of Leaders Overview,” in Chapter 1 of The Work of Leaders. Use the questions below to guide your response: How will you demonstrate open-mindedness as you develop your vision? How will you prioritize the “Big Picture” of your vision? How will your solutions embody boldness?

Prompt 3 – Desired Outcomes

Describe the intended result of implementing your vision for the optimization of operations. Be sure to include legal and ethical considerations. Provide rationale for your response.

Prompt 4 – Leadership Competencies

Describe the leadership competencies necessary to align and execute this vision. Specify any particular milestones necessary for the plan.

Prompt 5 – Theoretical/Conceptual Framework

Identify at least one relevant theory or concept that supports your vision. Be specific in your application of the theories/concepts. Remember to cite sources as needed.

Sample Paper For Above instruction

Implementing data analytics optimization within an organization demands a strategic vision grounded in leadership and theoretical support. First, one opportunity for optimization involves enhancing predictive analytics to improve customer segmentation. Currently, the organization may rely on basic demographic data, which limits targeting efficiency. By adopting advanced machine learning models that analyze behavioral patterns and purchasing trends, the organization can predict customer needs more accurately, leading to targeted marketing campaigns that increase engagement and sales. Second, operational efficiency can be optimized by streamlining supply chain data analysis. Currently, siloed data sources cause delays in decision-making. Integrating real-time data streams and employing analytics dashboards can provide supply chain managers with immediate insights to optimize inventory levels and reduce costs. Third, another opportunity exists in enhancing data governance to ensure compliance with legal and ethical standards. Implementing automated data quality checks and access controls can prevent misuse or breaches of sensitive information, maintaining organizational integrity and customer trust. As these opportunities are developed, demonstrating open-mindedness involves actively seeking diverse perspectives and innovative solutions beyond traditional approaches. Prioritizing the “Big Picture” requires focusing on strategic alignment with organizational goals and long-term sustainability. Boldness can be embodied by embracing cutting-edge technologies and pursuing transformative changes that challenge existing paradigms. Overall, these analytics optimizations aim to improve customer engagement, operational efficiency, and compliance, fostering sustainable organizational growth.

References

  • Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
  • George, G., Haas, M. R., & Pentland, A. (2014). Big Data and Management. Academy of Management Journal, 57(2), 321–326.
  • Kaplan, R. S., & Norton, D. P. (2004). Strategy Maps: Converting Intangible Assets into Tangible Outcomes. Harvard Business Review Press.
  • McKinsey Global Institute. (2016). The age of analytics: Competing in a data-driven world. McKinsey & Company.
  • Simon, H. A. (1997). Administrative Behavior: A Study of Decision-making Processes in Administrative Organizations. Free Press.
  • Shmueli, G., & Koppius, O. R. (2011). Predictive Analytics in Information Systems Research. MIS Quarterly, 35(3), 553–572.
  • Teece, D. J. (2010). Business Model Innovation and the Role of Dynamic Capabilities. Long Range Planning, 43(2-3), 179-190.
  • Vanderbilt, T. (2011). Infrastructure: The Social Value of Shared Resources. Harvard University Press.
  • 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.
  • Zikopoulos, P. C., et al. (2012). Harnessing Big Data: Capabilities, Characteristics, and Opportunities. McGraw-Hill.

Related Assignments