DATA4000: Introduction to Business Analytics - Assessment Answer

Part A: Case Study Analysis

Netflix

1. Netflix is using predictive analytics. Predictive analytics has the main function of analyzing current and historical data, therefore, using that information to make predictions to the various statistical techniques through data mining, predictive modelling and even machine learning. This helps in the process of branding and understanding their customers. This information is very important when it comes to identifying risks and opportunities, therefore, getting the company in proper decision-making.
2. The company is aiming at solving the issues to do with competition. The company is also using predictive analysis to help in forecasting inventory requirements, therefore, helping the company manage their shipping schedules. In addition, the company is also aiming to use predictive analysis to be able to optimize its product development, advertisement and even distribution while doing market research. The company also aims to use predictive analysis to help attract and retain its most valuable customers.
3. The company is using predictive analysis to be able to help in making reliable and accurate insights. This tool is very important when it comes to predicting consumer behaviour, therefore, enabling the company to be able to predict the purchase patterns of their consumers. Netflix is using predictive analytics to make predictions about future outcomes, therefore, being able to understand the person's current data trends. This is applied to the number of consumers who are using Netflix in terms of ratings and searches. Netflix can analyze this data by looking at their interaction’s app and the response to the number of shows and movies. The company is also using this data about the date time and location of the consumers.
4. One of the major challenges of using predictive analytics is that it requires the use of big data which is usually very difficult to manage and analyze (Ayodeji, Ibitoye, Olufade, & Onifade, 2022). In addition, big data also requires highly developed techniques and skills to be able to utilize this technique. It also requires a lot of time to make forecasting and predictions because a lot of data has to be reviewed back in history and the current data. It also requires personalization of consumer demands to come up with accurate decision-making.
5. It is therefore recommended that the company continue using predictive analytics in their forecasting needs because this will help them make proper business decisions. In addition, the company should also develop their predictive machines in terms of data and even software to be able to reduce cases of churning. Such kind of data is usually vulnerable to abnormal and risky behaviour courtesy of cyber-attacks.

Coca-cola

1. Coca-Cola is applying the use of machine learning and artificial intelligence when it comes to real-time analytics. Through mobile applications, Coca-Cola data scientists can track long-term consumer behaviour.
2. The company is using this information to be able to develop its market and grow its food brands. Through artificial intelligence, they can get into the consumer attachments and analyze millions of menus, items, recipe interactions and how consumers are validating their new products. For example, during the pandemic, the company was able to launch new products which have immunity and wellness versions of water. 
3. Artificial intelligence and machine learning are providing consumer data to Coca-Cola data scientists because they can track long-term consumer behaviour which is very important. This behaviour helps the data analysis to be able to identify consumer demands through social media. Using this information, the company has been able to personalize its marketing campaigns based on consumer experiences.
4. One of the major challenges of using machine learning and artificial intelligence is it is also vulnerable to cyber insecurity. Big data requires a lot of skills and techniques to be handled. In addition, it is very expensive and time-consuming (Reem, Alhabeeb, Alhumud, & Rehan, 2022). Feedback is also given in real-time therefore requiring immediate analysis and feedback to the consumers. The other problem is that this information is also usually vulnerable to attacks by cyber attackers and cyber insecurity and must be handled carefully.
5. It is recommended that the company invent new ways of tracking consumer data and consumer behaviour apart from using machine learning and artificial intelligence. The company can also use data aggregation, data mining, association and sequencing, forecasting text mining and even predictive analytics to be able to make a more important decision regarding their consumer demands in the food and beverage industry.

Part B: The Role of Analytics in Solving Business Problems

The data analytics tools that are used in this case are focused sales and inventory as well as inventory management with restock alerts. Amazon is using predictive analytics as a way to help boost its sales and also manage its consumers. Through analytics, Amazon has been able to analyze its consumer behaviour sales and even how improve data points using this data-driven information to make decisions. The company has been able to use different metrics to improve overall performance. The company is using data analytics to compile consumer behaviour like the type of products they purchase and what they are also saving on their wish list (Meiyu & Cheng, 2022). Using the geo-shopper application, the company has been able to obtain feedback on the products that are not purchased and therefore collect this information and scale it to be able to improve their consumer experience. The advantage of using inventory management as a form of data analytics is that it makes it possible for the sellers to be able to know the type of product that is particularly needed and required at a particular time. This information helps in giving the data scientist information on the conversion rates (Carbonneau, Laframboise, & Vahidov, 2018). This means that the sellers can understand the number of shoppers who clicked on their ads and can follow through by helping them complete their purchases. This has helped in improving advertising campaigns and therefore helping the company in optimizing their product listing consequently increasing their conversion rates. Amazon is also using keyword searches to help them improve their sales. Data analytics remains very important for the company to improve their sales. 

Uber company has also incorporated the use of machine learning algorithms as a form of data analytics. Since the company is having over 8 million users and conducting over 1 billion trips across 449 cities and 66 countries Uber is one of the fastest-growing businesses in the world. Tracking transportation infrastructure and unsatisfactory customer experiences together with late deliveries and poor fulfilment has become very difficult for this company (Munir, 2019). For that reason, the company acquired the use of big data analytics to be able to solve this problem. One of how big data is helping Uber is that it is possible to collect insightful and intelligent decisions which help in proper decision-making. Big data has been used when it comes to pricing, detecting fake rates, fake cards, fake ratings, estimation of fares and even driver ratings. Uber is also using this information to track car algorithms. Drivers can track the nearest customer and link them to them within a 15 seconds window (Kumar, 2016). This means that the Uber store and analysis data is running within every single trip to be able to predict demands for their customers, set fares and even allocate sufficient resources. In addition to this, big data analysis is also helping data scientists to perform in-depth analyses of public transport networks across different cities. This has helped in managing transportation and poor customer service experience.

Part C: Developing and Sourcing Analytics Capabilities

Accenture company in its latest recommendations requires proper capturing of their business values to have proper analytics investments. The company is also struggling with a lot of big data that is spanning from their huge consumer base and therefore processes of data mining methods and even organization of data is becoming a problem. Deploying and generating hindsight and reviews to handle this big data is required. The company has challenges when it comes to data quality which is only 67%. Decision-making is also only taking 57% while data integration methods are only viable at 53%. In addition, the type of metrics and key performance indexes functionality is also not providing enough insight and are only average at 50%. The methods used in gathering and manipulating information are below average at 49%. The company has no proper technology and the methods that are used to analyze data are also very poor at 34%. Organization within the company is also very poor because the company is lacking appropriate talent. Process of investment and analytics are not efficient and the organization is also lacking proper sponsorship.

One of the ways of addressing these challenges is by infusing data analytics with proper decision-making. One of the ways is to construct an appropriate data analytics process that can provide proper insight and deliver proper decision-making. This is a very important element in business position-making across the globe.

Accenture can use business forecasting techniques such as predictive analytics to be able to make pertinent decision-making processes. This will help the company in packaging its goods and services in response to consumer demand (Acar & Gardner, 2012). Through predictive analytics, the company can analyze consumer behaviours, and sales and even improve its data-driven decision-making processes. The company can use proper metrics from the different key performance indexes to improve their performance especially when it comes to its conversion rates in terms of sales and returns on investments. Through the use of data analytics tools, the company should be able to use the tools in making forecast sales and inventory. They should also use such tools to be able to get information on keyword research. Such tools are also very important when it comes to optimizing product listings as well as inventory management with restock alerts. Data analytic tools also help in sourcing the products and also the management of PPC campaigns.

To organize and coordinate analytics capabilities across the organization the company should be able to define the mission and the vision of the organization so that they are in line with the business analytics tools. The second process will be to identify an existing team that is capable of transforming the company into an analytics team. The next process is to define and fill the roles so that a team leader is available to help the rest of the members to coordinate the company in data analytics about the objectives and mission of the company (Ma, Fildes, & Huang, 2016). The definition of the team structure should then follow followed by the definition of the relationships of other team members to their other functions. This will then require the development and recruitment of the needed skills. The model assumed for this team will comprise both centralized and decentralized operation models aligning with the culture of the company and creating multiple models within each group. This should enable the business to have a more centralized management team which is operating in units. This will help in proper decision making which will be executed through the top-down process. The culture of the company will also be encouraged so that employees are trained to think independently. It is important to understand that using a centralized model is very important for data analytics because the marketing and sales (data scientists and engineers) involved with data management have their centralized roles hence easy to manage. This allows for an effective flow of data across a different organization while reducing the risk of data security, therefore, making it easy to apply the analytics initiatives that have been developed.

The Accenture company can organize workshops to train and retain analytics talent. The company can also source this talent by organizing competitions and providing internship training in data analytics for students who are still in college. This will help them in training and coordinating the young talent and absorbing them into the company's culture early enough even before their employment (Hazen, Skipper, Ezell, & Boone, 2016). In addition to this through mergers and acquisitions, it is possible also to source train and deploy data analytics talents. To retain this talent the company should also provide proper motivation and an improved working environment to maintain this talent. The company should also offer the scholarship to data engineering students and develop a strong community sustainable development project to retain and train such talents. 

References

Acar, Y., & Gardner, E. .. (2012). Forecasting method selection in a global supply chain. Int J Forecast., 28(4):842–8. https://doi.org/10.1016/J.IJFORECAST.2011.11.003.

Ayodeji, O., Ibitoye, J., Olufade, F., & Onifade, W. (2022). Social Opinion Network Analytics in Community Based Customer Churn Prediction. Journal on Big Data 2022, , 4(2), 87-95. https://doi.org/10.32604/jbd.2022.024533 .

Carbonneau, R., Laframboise, K., & Vahidov, R. (2018). Application of machine learning techniques for supply chain demand forecasting. Eur J Oper Res, 184(3):1140–54. https://doi.org/10.1016/J.EJOR.2006.12.004.

Hazen, B., Skipper, J., Ezell, J., & Boone, C. (2016). Big data and predictive analytics for supply chain sustainability: a theory-driven research agenda. Comput Ind Eng, 101:592–8. https://doi.org/10.1016/J.CIE.2016.06.030.

Kumar, M. (2016). Applied big data analytics in operations management. Appl Big Data Anal Oper Manage, https://doi.org/10.4018/978-1-5225-0886-1.

Ma, S., Fildes, R., & Huang, T. (2016). Demand forecasting with high dimensional data: the case of SKU retail sales forecasting with intra- and inter-category promotional information. Eur J Oper Res., 249(1):245–57. https://doi.org/10.1016/J.EJOR.2015.08.029.

Meiyu, P., & Cheng, L. (2022). Application of Big Data Information Platform in Medical Equipment. Journal on Big Data, 4(2), 113-123. https://doi.org/10.32604/jbd.2022.028791.

Munir, K. (2019). Cloud computing and big data: technologies, applications and security, vol. 49. Berlin: Springer.

Reem, A., Alhabeeb, S., Alhumud, S., & Rehan, U. K. (2022). A Survey of Machine Learning for Big Data Processing. Journal on Big Data, 4(2), 97-111. https://doi.org/10.32604/jbd.2022.028363.

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