University : Kaplan Business School UniLearnO is not sponsored or endorsed by this college or university.
Subject Code : DATA4000
Country : Australia
Assignment Task

 

Part A: Case Study Analysis
Instructions
: Read the following two case studies. For each case study, briefly describe:
a) The industry to which analytics has been applied
b) A potential and meaningful business problem to be solved
c) The type of analytics used, and how it was used to address that potential and meaningful business problem
d) The main challenge(s) of using this type of analytics to achieve your business objective (from part b)
e) Recommendations regarding how to be assist stakeholders with adapting these applications for their business.
1. Artificial Intelligence in Germany: Reinventing Engines of Growth
 

Part B: The Role of Analytics in Solving Business Problems 

Instructions: Describe two different types of analytics (from Workshop 1) and evaluate how each could be used as part of a solution to a business problem with reference to ONE real-world case study of your own choosing. You will need to conduct independent research and consult resources provided in the subject.

Part C: Developing and Sourcing Analytics Capabilities
Instructions: You are the Chief Analytics Officer for a large multinational corporation in the communications sector with operations that span South East Asia and Latin America. The organization is undergoing significant transformations; it is scaling back operations in existing low revenue segments and ramping up investments in next generation products and services - 5G, cloud computing and Software as a Service (SaaS). The business is keen to develop its data and analytics capabilities. This includes using technology for product innovation and for developing a large contingent of knowledge workers. To prepare management for these changes, you have been asked review Accenture’s report (see link below) and publish a short report of your own that addresses the following key points:
1. How do we best ingrain analytics into our decision-making processes?
2. How do we organize and coordinate analytics capabilities across the organization?
3. How should we source, train and deploy analytics talent?

 

 

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