Business Analytics, Algorithmic Trading System, and Recommendation System

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Question 2
What are the precursors to business analytics; that is, what established fields have informed the practice of business analytics and/or its domain of content? What worldview has had the most influence on the emerging field of business analytics – the data science paradigm or the social science tradition (and why)? (What might these worldviews learn from one another?)
Business analytics evolved from mathematics, business, and data science, and decision support systems. The data science paradigm has had a more influential worldview on business analytics where data is used to assess business performance management and make forecasts. Nowadays, machine learning makes it possible to analyse large data sets and make decisions based on the analysis. The data science paradigm is mainly quantitative, while the social science tradition is mainly qualitative and includes a focus on experience and helps get a deeper understanding of a phenomenon or people.
Question 3
What are the two main pathways to becoming an analytical competitor? What pathways have Amazon and Netflix followed to become analytical competitors, have they followed the same or different pathways and where do they currently sit on their analytical journeys? (What can companies on each pathway possibly learn from companies on the other pathway if anything?)
Pattern finding using data analysis and creating distinctive capabilities from data are useful to become an analytical competitor. Amazon and Netflix have followed different paths to becoming analytical competitors, but they both collected data and information on user preference. Amazon has focused more on pattern-finding with the company using statistical analysis to identify patterns in the recommendation system. Netflix used analytics to improve the company’s competitiveness and distinctive capability, where analytics is helpful to understand customer behaviour and buying pattern.
Other companies can use algorithms and data on user preferences to understand customer preferences and support business decisions to optimize business strategies.
Question 4
What is an algorithmic trading system? What are the main components of algorithm trading systems? Can you offer an example of a firm that uses an algorithm trading system(s); if yes, what is the firm? What is one of the main challenges for firms implementing algorithmic trading systems? (How does the firm you mentioned in your answer address this challenge?)
Algorithmic trading is automated trading using a computer program that follows algorithms, which are defined instructions to make a trade. Algorithmic trading is faster than human traders and more frequent. The main components are the core infrastructure and the algorithmic trading strategy. The trading strategy requires using market data and generating signals from the information where strategies are executed based on the statistical predictive abilities. Goldman Sachs uses an algorithmic trading program (in pricing corporate bonds. One of the challenges with the system is that they require constant monitoring as their predictive ability partly depends on data and information available. Goldman Sachs uses data and information from various sources and integrates aspects of automatic trading and machine learning. 
Question 5
What is a recommendation system? What are the main components of recommendation systems? Can you offer an example of a firm that implements a recommendation system(s); if yes, what is the firm? What is one of the main challenges for firms in implementing recommendation systems? (How does the firm you mentioned in your answer address this challenge?)
A recommendation system is a system and information tool that provides relevant suggestions and searches. The main components of recommendation systems are user data and the recommender function that makes user prediction and rating. The recommender function uses the users’ information in making predictions. Netflix uses a recommendation system that predicts the series that customers are more likely to choose by analysing their customer behaviour, watching history, and buying patterns. The recommendation system relies on data and information available but fails to account for changing user preferences and unpredictable Items. Netflix employs employees with knowledge of data science and algorithms to make better predictions from user-generated data.
Question 6
What is the business analytics process? What are the key steps in the business analytics process, and why are these steps the key steps? What specific activities can business analysts undertake to improve organisational and/or stakeholder “buy-in” to an analytics project? (How should the criteria for evalua...