DATA4000 Introduction to Business Analytics Case Study 1 Sample
Your Task
Complete Parts A to C below by the due date.
Consider the rubric at the end of the assignment for guidance on structure and content.
Assessment Description
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•You are to read case studies provided and answer questions in relation to the content, analytics theory and potential analytics professionals required for solving the business problems at hand.
• Learning outcomes 1 and 2 are addressed.
Assessment Instructions
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. Netflix Predictive Analytics: Journey to 220Mn+ subscribers
https://www.engati.com/blog/predictive-analytics
2. Coca-Cola vs. Pepsi: The Sweet Fight For Data-Driven Supremacy
https://www.aidataanalytics.network/data-science-ai/articles/coca-cola-vs-pepsi-the-sweetfight-for-data-driven-supremacy
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 for one type of analytics and a SECOND real-world case study of your choosing for the second type of analytics.
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 India, China, the Philippines and Australia.
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 the organisation’s 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?
To help you draft this report, you should review the following working paper from Accenture:
https://pdfcoffee.com/accenture-building-analytics-driven-organization-pdf-free.html
The report is prepared for senior management and the board of directors. It must reflect the needs of your organization and the sector you operate in (communications).
Solution
Part A: Case Study Analysis
1. Netflix Predictive Analytics: Journey to 220Mn+ subscribers
a) Industry applying the analysis:
Analytics is already widely adopted in the entertainment industry, and Netflix is an excellent example of a company that has leveraged analytics to achieve phenomenal growth. Netflix is a streaming service that offers subscribers a huge library of movies, TV shows, and documentaries.
b) A potential and significant business problem that needs to be addressed:
One of the critical business issues facing Netflix is how to continue to grow its subscriber base in a highly competitive industry. With several other streaming services available, Netflix needs to set itself apart by providing the right content to its subscribers and ensuring that they remain engaged with the platform. (Fouladirad et al., 2018)
c) The type of analysis used and how it is used to solve this important and potential business problem:
Netflix has used various types of analytics, such as predictive analytics and machine learning, to solve its business problem. The company uses subscriber data to understand their viewing habits, interests and behaviour. This information is used to customize the content recommendations presented to each subscriber, thereby increasing engagement and retention. (Lamkhede et al., 2019)
d) Key challenge(s) in using this type of analysis to achieve your business goals (from part b):
The enormous volume of data that has to be analysed is one of the key obstacles to employing analytics in the entertainment sector. Due to its massive content collection and millions of members, Netflix needed to create sophisticated algorithms that could analyse its data and offer insightful analysis. Additionally, data privacy issues have always been a challenge when working with user data, and Netflix has had to be vigilant in protecting user privacy while using its data.
e) Recommendations on how to help stakeholders apply these applications to their business:
Stakeholders looking to apply analytics to their businesses can learn from Netflix's approach. One recommendation is to invest in a robust data infrastructure that can handle the volume and variety of data required for analysis. In addition, companies can use machine learning algorithms to identify patterns and trends in customer's behaviour, thereby making decisions about product development and marketing. (AI, Data & Analytics Network, 2022)
2] Coca-Cola vs. Pepsi: The Sweet Fight For Data-Driven Supremacy
a) The industry to which analytics has applied:
The soft drink industry is highly competitive, and both Coca-Cola and Pepsi have leveraged analytics to gain an advantage in this market. By formulating data on consumer behavior and preferences, both companies have been able to make informed decisions about product development, marketing, and distribution.
b) A potential and meaningful business problem to be solved:
One of the critical business problems that Coca-Cola and Pepsi faced was how to maintain their market share in a highly competitive industry. With changing consumer preferences and the emergence of new competitors, both companies needed to adapt quickly to remain relevant and profitable.
c) The type of analytics used and how it was used to address that potential and meaningful business problem:
Coca-Cola and Pepsi both employed different kinds of analytics to solve their business issues. For instance, both businesses employ predictive analytics to spot recurring patterns and trends in consumer behaviour. The businesses may see new trends and modify their product offers by analysing data from sales, social media, and other sources.
Both businesses also employ data analytics to guide their marketing plans. The businesses may create tailored advertising campaigns that appeal to their target audience by analysing data on customer demographics, purchasing behavior, and preferences. (Štofová et al., 2020)
d) The main challenges of using this type of analytics to achieve your business objective (from part b):
One of the main challenges of using analytics in the soft drink industry is the availability and quality of data. While both Coca-Cola and Pepsi have access to a wealth of data, it can be challenging to extract meaningful insights from this data. Additionally, there are data privacy concerns that must be considered when working with customer data.
Another challenge is ensuring that the insights generated from analytics are translated into actionable decisions. While analytics can provide valuable insights, it is up to the company to use this information effectively to inform business decisions.
e) Recommendations regarding how to assist stakeholders with adapting these applications for their business:
Stakeholders looking to apply analytics to their business can learn from Coca-Cola and Pepsi's approach. One recommendation would be to invest in data infrastructure and tools that can handle the volume and variety of data needed for analytics. (Tang et al., 2022).
Part B: The Role of Analytics in Solving Business Problems
There are different types of analytics, each of which can be used to solve specific business problems. This essay describes two types of analysis, evaluates how each can be used as part of solving a business problem, and uses real case studies of Amazon and Coca Cola to describe the points.
Descriptive analysis:
Descriptive analytics is a type of analytics used to make sense of historical data and provide insight into past events. This type of analysis involves analyzing data to identify patterns and trends to gain a deeper understanding of the underlying data. It is often used to identify areas that need improvement and aid in decision-making.
Case Study: Coca-Cola
Coca-Cola, a multinational beverage company, uses descriptive analytics to better understand its customers and identify opportunities for improvement. By analyzing sales data and consumer behaviour, Coca-Cola can gain insight into which products are most popular and which are selling poorly. This information is used to inform product development and marketing strategies. ( Chua et al., 2020)
For example, Coca-Cola used descriptive analytics to identify declining sales of its flagship product, Coca-Cola Classic. By analyzing sales data, the company found that consumers were moving away from sugary drinks and towards healthier alternatives. As a result, Coca-Cola has launched new beverage lines, such as Diet Coke and Coke Zero, to meet this changing consumer preference. ( Guo et al., 2021)
Normative analysis:
Prescriptive analytics is a type of analytics used to make recommendations about actions to take based on insights gained from descriptive and predictive analytics. This type of analysis involves using data and mathematical models to determine the best course of action and provide a set of constraints and objectives.
Case Study: Amazon
Amazon is an e-commerce giant that uses prescriptive analytics to optimize its supply chain and refine the customer experience. By analyzing data on customer behaviour, inventory levels, and delivery times, Amazon can make recommendations to improve operations and provide a better customer experience. For example, Amazon uses prescriptive analytics to determine the best routes for deliveries based on traffic patterns and weather conditions. This information is used to optimize the shipping process and ensure that your package is delivered on time. In addition, Amazon uses prescriptive analytics to provide product recommendations to customers based on their browsing and purchase history. (Mohd Satar et al., 2019)
Evaluation:
Descriptive and prescriptive analytics are valuable tools for organizations looking to gain operational insights and make informed decisions. While descriptive analytics help identifies trends and patterns in data, prescriptive analytics goes a step further and provides recommendations on what actions to take based on those insights. Both Coca-Cola and Amazon show how these types of analytics can be used to solve real business problems. Using descriptive analytics, Coca-Cola was able to recognize changing consumer preferences and adjust its products accordingly. Meanwhile, Amazon used prescriptive analytics to optimize its supply chain and deliver a better customer experience.
However, there are some challenges in using these types of analyses. For example, descriptive analytics may be limited by data availability and quality. Additionally, prescriptive analytics requires sophisticated mathematical models and algorithms that are difficult to develop and implement.
In summary, both descriptive and prescriptive analytics are valuable tools for organizations looking to gain operational insights and make informed decisions. Using these types of analytics, businesses can identify areas for improvement, streamline operations, and deliver a better customer experience. (ETUKUDO et al., 2023).
Part C: Developing and Sourcing Analytics Capabilities
As the Communications industry continues to grow rapidly, businesses must adapt to changing customer needs and new technologies. To stay competitive in this landscape, companies must leverage data and analytics to make any informed decisions and drive innovation. The rise of 5G, cloud computing, and software as a service (SaaS) has made it clear that data and analytics are essential components of a successful strategy.
As the Chief Analyst of a large multinational media company, it is important to develop an analytics and data strategy that incorporates best practices for integrating analytics into the decision making process as well as Organize and coordinate analytical functions throughout the organization. This report will explore these two key points in depth and provide actionable insights for sourcing, training, and deploying analytical talent. By adopting these practices, organizations can unlock the potential of data and analytics to drive growth, improve customer experience, and stay ahead of the competition.
The rapidly evolving Communications industry requires companies to adapt to changing customer requirements and new technologies. With the move to 5G, cloud computing and SaaS, businesses need to leverage data and analytics to stay competitive. This report explores his two key points in developing an effective data and analytics strategy. (Rao et al., 2018)
Incorporate analytics into decision-making processes and organize and coordinate analytics functions across your organization.
1] Ingraining Analytics into Decision-Making Processes
The first step to a successful data and analytics strategy is to ensure that analytics are integrated into the business decision-making process. This requires a cultural shift towards a data-driven decision-making approach. To achieve this, organizations should focus on:
Top-down approach:
Senior management must be committed to using data and analytics to make decisions. This requires setting a clear vision, goals and objectives, and a roadmap for achieving them. Executives should also be actively involved in data analysis and use data insights to make informed decisions.
Adjusting incentives:
Incentives must be balanced with data-driven decision-making. This means employees need to be rewarded for using data insights to make decisions rather than relying on gut feeling and intuition. Training and development:
To make sure that staff have the necessary skills and expertise to utilise data and analytical tools successfully, organisations should engage in training and development programmes. This covers statistical analysis, data visualization, and data literacy. (Morgado et al., 2018)
2] Organizing and coordinating analytic functions
The second key to developing an effective data and analytics strategy is organizing and coordinating analytics functions across your organization. This requires a well-defined governance structure and clear roles and responsibilities. To achieve this, organizations should focus on:
Centralized or distributed analytics:
Enterprises must decide whether to centralize or decentralize their analytics functions. Centralization increases control and standardization, while decentralization increases agility and flexibility.
Governance structure:
Regardless of the approach, organizations should establish clear governance structures to ensure that data and analytics are used consistently across the enterprise. This includes defining roles and responsibilities, setting data quality standards, and ensuring compliance with data protection regulations.
Cross-functional collaboration:
Effective analytics require cross-functional collaboration between business units, IT, and analytics teams. Companies need to put processes and frameworks in place to foster collaboration and ensure that insights are shared and acted upon. (Boehmke et al., 2020)
3] Finding, training, and deploying the right people is critical to developing robust data and analytics capabilities.
Here are some important considerations:
Scout:
Organizations can find analytical talent through a variety of channels, including universities, job boards, and professional networks. It's important to look for candidates with a background in data analysis, statistical modelling , and machine learning. In addition, candidates should have experience using tools and technologies used in data analysis such as SQL, Python, R and Tableau.
Training Talent:
Once you've found your analytics talent, it's important to invest in training and development. This can be achieved through on-the-job training, mentoring, and formal training programs. To keep its employees abreast of the most recent trends and innovations, organisations can also provide access to online courses, workshops, and conferences.
Using talents:
Organizations need to deploy analytical talent across the enterprise in a way that aligns with strategic objectives. This includes assigning talent to specific business areas, such as marketing and finance, and creating cross-functional teams to work on specific projects. Additionally, creating a career path for analytical talent, including promotion and professional development opportunities, is critical.
In summary, when developing an effective data and analytics strategy, organizations should focus on his two key points:
Organize and manage analytics functions throughout your business, and incorporate analytics into decision-making processes. This requires a cultural shift towards data-driven decision-making, incentive alignment, and investment in training and development programs. It also requires a well-defined governance structure, clear roles and responsibilities, and cross-departmental collaboration. By practically focusing on these key takeaways, organizations can leverage data and analytics capabilities to drive innovation, improve customer experience, and stay competitive in the rapidly evolving telecommunications industry.
References
AI, Data & Analytics Network. (2022, November 29). Coca-Cola vs. Pepsi: The Sweet Fight For Data-Driven Supremacy. AI, Data & Analytics Network. https://www.aidataanalytics.network/data-science-ai/articles/coca-cola-vs-pepsi-the-sweet
Boehmke, B., Hazen, B., Boone, C. A., & Robinson, J. L. (2020). A data science and open source software approach to analytics for strategic sourcing. International Journal of Information Management, 54, 102167.
Chua, J. Y., Kee, D. M. H., Alhamlan, H. A., Lim, P. Y., Lim, Q. Y., Lim, X. Y., & Singh, N. (2020). Challenges and solutions: A case study of Coca-Cola company. Journal of the Community Development in Asia (JCDA), 3(2), 43-54.
ETUKUDO, E. H., & UKPABIO, I. (2023). AI AND ITS IMPACT ON GLOBAL BUSINESS OPERATIONS: A CASE STUDY OF AMAZON.
Fouladirad, M., Neal, J., Ituarte, J. V., Alexander, J., & Ghareeb, A. (2018). Entertaining data: business analytics and Netflix. Int J Data Anal Inf Syst, 10(1), 13-22.
Guo, X., & Wen, M. (2021, December). Research on Competitive Strategy of Coca-Cola Company. In 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021) (pp. 2879-2885). Atlantis Press.
Lamkhede, S., & Das, S. (2019, July). Challenges in search on streaming services: netflix case study. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1371-1374).
Mohd Satar, N. S., Dastane, D. O., & Ma’arif, M. Y. (2019). Customer value proposition for E-Commerce: A case study approach. International Journal of Advanced Computer Science and Applications (IJACSA), 10(2), 454-458.
Morgado, A., Huq, K. M. S., Mumtaz, S., & Rodriguez, J. (2018). A survey of 5G technologies: regulatory, standardization and industrial perspectives. Digital Communications and Networks, 4(2), 87-97.
Rao, S. K., & Prasad, R. (2018). Impact of 5G technologies on industry 4.0. Wireless personal communications, 100, 145-159.
Štofová, L., & Kop?áková, J. (2020). The Competition Strategy between Coca-Cola vs. Pepsi Company. Calitatea, 21(179), 40-46.
Tang, Z., Xu, X., Song, Y., & Yang, H. (2022, September). Data Analytics Applications in the Soda Industry. In Proceedings of the 2022 International Conference on Business and Policy Studies (pp. 677-688). Singapore: Springer Nature Singapore.
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