Question 1

Why did I ask you to read the book and/or watch the film Moneyball, and in what ways is the book/film           emblematic of the analytics revolution?  What label would you give to the type of analytics that was used to   develop the model of baseball players’ salaries?  What defines this approach to analytics?  (Can you think of another example – like Moneyball – that is illustrate of the potential impact on a business of predictive           analytics?)

Question 2

Compare and contrast the “stages model” and “born analytical” 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 factors do you think influences the choices of firms in pursuing the stages versus the born analytical            pathways?)

Question 3

What is algorithmic trading and what are the main components of algorithm trading systems?  Can you offer an example of a firm that uses an algorithm trading system?  Longer-term, what are some of the potential    challenges for firms implementing algorithmic trading systems?  (What is the alternative to using algorithmic trading, how is it changing the practice of trading financial securities?)

Question 4

What is personalisation?  What are the potential benefits and costs to consumers and to firms of                    personalisation?  Can you explain how personalisation could be used in one of the following four industries:  agriculture, education, medicine, and restaurants.  Can you offer an example of a firm that is known for         personalisation?  (Does personalisation require firms – think Netflix – to be innovate in relation to the content they serve consumers?)

Question 5

What is the basic role and responsibilities of the Chief Analytics Officer?  Do you see the role of

Chief Analytics Office as a complement or a substitute for existing C-suite roles, and why?  Outline the key  progressions a business analyst might make from the point of entry into the workforce as a graduate analyst through to the role of Chief Analytics Officer?  (What expectations do you think firms have for graduate         analysts?)

Question 6

Briefly describe 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 important is creativity in the             business analytics process – where can creativity play a role?)

Question 7

What is “big data” and what are its defining characteristics?  Give an example of business information that is quantitative and structured and an example of business information that is qualitative and unstructured.  Tom Davenport says we should “stop using the term big data” – what does he mean by that statement?  (Can       structured, quantitative data and unstructured qualitative be combined?)

Question 8

What are the basic expectations of business analytics and data scientists?  How are the roles of business   analyst and data scientist similar and different?  How would you expect a business analyst and a data         scientist to allocate their time among the following core activities: project conceptualisation and establishing business metrics, data preparation and manipulation, data analysis and model validation, reporting and       communication, implementation of recommendations/model deployment?  (What is needed for business     analysts and data scientists to work together effectively?)