Topic: This is Prescriptive Analytics topics, please provide detailed answer to both questions with illustrations from oil and gas industry

This is Prescriptive Analytics topics, please provide detailed answer to both questions

Question 1: does the size of data affect the decisions made using prescriptive analytics?

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Question 2: decision taken using prescriptive analytics were rather a function of descriptive analytics which bordered on the quality and veracity of data, predictive analytics which bordered on effective prediction and optimization and rely primarily on how to use the data and not on data size, to what extent do you agree to this statement? Please criticize or support with justification from theory and personal experience on oil industry


  • Just ensure you are providing URLs for any sources you have accessed online including journals and textbooks so we can check them. (all references must be online and URL to be provided)
  • This essay should contains two paragraph; each paragraph answers one question in more details
  • For each paragraph, please consider the following:
  1. It is mandatory to cite three references which are : required reading list (Singh (2016), Frazetto et al (2019) , Essex Library (mandatory) (my login given)
  2. Both paragraphs should contain examples/illustrations from oil and gas industry
  3. Please provide critical thinking on the topic and provide critique or support to the theory.


  • references must be as follows: Project Analytics book (mandatory) (my login given) , Frazetto et al (2019), Essex Library (mandatory) (my login given)
  • you can refer to additional readings and use them if required, refer to lecture cast to get some information about the topic (for references , don’t go outside Singh (2016), Frazetto et al (2019), Essex Library and the recommended list please )

Essex eLibrary access: (it is mandatory to reference at least one reference from the Library)


  • Ensure to use simple English language and avoid complicated or complex terminologies.

It is mandatory to reference this book as it is the main reading of the topic ‘Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions, Singh (2016), please read Chapter 6 & 9 for this order

Required Reading

Frazetto, D., Nielsen, T. D., Pedersen, T. B. and Šikšnys, L. (2019) ‘Prescriptive analytics: a survey of emerging trends and technologies’, The VLDB Journal: The International Journal on Very Large Data Bases. 28(4) pp. 575-595

Singh, H. (2016)  Chapter 6 ‘Analytic Hierarchy Process’ and chapter 9 ‘Project Decision-Making with the Analytic Hierarchy Process’

Additional Reading

Adam, F. and Pomerol, J-C. (2008) ‘Developing Practical Decision Support Tools Using Dashboards of Information’ in F. Burstein C. W. Holsapple. Handbook on Decision Support Systems 2, Heidelberg: Springer

Begičević, N., Divjak, B. and Hunjak, T. (2009) ‘Decision Making on Project Selection in High Education Sector Using the Analytic Hierarchy Process’, Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces: 547-552

Evans, J. R. (2017) Business Analytics, Global Edition, Harlow: Pearson Education Limited chapters 13 ‘Linear Optimisation’

  • Chapter 14 ‘Applications of Linear Optimisation’, and chapter 15 ‘Integer Optimisation’

Hamilton, B. and Koch, R. (2015) ‘From Predictive to Prescriptive Analytics’, Strategic Finance. Jun, Vol. 97 Issue 6, p62-63

Rauner, M. S., Schneider, G. and Heidenberger, K. (2005) ‘Reimbursement systems and regional inpatient allocation: a non-linear optimisation model’, IMA Journal of Management Mathematics. 16, 217–237

Yadav, S. S. K., Gupta, H. and Bandyopadhaya, A. A. (2015) ‘Selection of a sustainability awareness project in an academic institution using the analytic hierarchy process (AHP)’, International Journal of Technology Management & Sustainable Development Vol. 14 No. 3, pp. 205 – 225

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