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Unit information: Big Data in Marketing Intelligence in 2018/19

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Unit name Big Data in Marketing Intelligence
Unit code EFIMM0059
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Pantano
Open unit status Not open




School/department School of Management
Faculty Faculty of Social Sciences and Law


This unit aims to introduce students to the purpose, application and value of market data to an organisation and particularly to those working in marketing. It will explore the different forms that data takes and the relative use it has within different contexts. A distinction will be made between large datasets that can be contained within conventional analytical frameworks and ‘big data’ (i.e. Google, Facebook) whose volume, velocity and variety means that it presents data management challenges and cannot be contained within an easily formatted structure. It reviews the range of ways in which data might be identified and harvested and explores ways of filtering data to optimise the quality of the final dataset. Methods of combining data sets will be considered as well as ways of enhancing them to facilitate evaluation and analysis. Various approaches to data modelling will be considered and their application in a range of marketing contexts will be critically assessed.

Intended learning outcomes

On completion of this unit, students will be able to:

LO 1: Distinguish between data contained in large data sets and ‘big data’ and reflect upon the practical, legal and ethical challenges associated with the collection, management and analysis of each.

LO 2: Given a set of market insight objectives, compose a strategy for identifying and harvesting appropriate data.

LO 3: Distinguish between data that has value and relevance to a given context and that which has not, and synthesise data from multiple sources into a single database.

LO 4: Consider various methods of data presentation, analyse the data and present the results in a form that is appropriate and comprehensible to a given set of stakeholders.

Teaching details

The flipped learning style adopted by this unit requires students to engage with a range of sources prior to taught sessions. These include, but are not limited to, databases, short videos outlining threshold concepts, contextual video content (Youtube, TED talks), academic papers, case study material, market reports and news reports. These resources will be delivered through Blackboard and will be supported by existing reading list software.

The unit structure offers 30 contact hours in total. The remaining 170 learning hours will be spent in independent study and in the preparation of assessment.

Assessment Details

Summative assessment on this unit is comprised of two elements: Data Analysis Exercise (50%) and 2 Hour Open Book Case Study Exam (50%)

Data Analysis Exercise (50%) – 2,500 words max. report

Students will be presented with a marketing scenario and given ‘dummy’ data. They will be required to individually format the data, analyse it, interpret it and present the results in a way that is comprehensible to a layperson (ILO 2, 3,4).

Students will receive a summative mark for this piece of assessment. Additionally, they will receive formative comments on areas of strength and weakness as well as constructive comments as to how to improve their work, two weeks prior to submission of their work. Formative feedback will be provided in the forms of generic in class feedback and individual verbal feedback.

The problem-based approach to the delivery of this unit will require students to present their work regularly at small group sessions. Students will receive verbal feedback from their tutor as well as peer to peer feedback on their work from fellow students.

2 Hour Open Book Case Study Exam (50%)

Students will prepare a data management case study prior to the exam. In the 2 hour exam students will be asked to answer questions on aspects of the case and their interpretation of the issues involved (ILO 1,2,3). Students will receive a summative mark for their work on this piece of assessment.

Reading and References

Core text for this Unit

Marr, B. (2015). Big Data: Using SMART big data, analytics and metrics to make better decisions and improve performance. John Wiley & Sons.

Recommended reading

Marr, B. (2016). Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. John Wiley & Sons.

Kitchin, R. (2014), The data revolution: big data, open data, data infrastructures & their consequences, SAGE Publications Ltd, London.

Foreman, J.W. (2013), Data Smart: Using Data Science to Transform Information Into Insight, John Wiley and Sons, London

Liebowitz, J. (2013), Big data and business analytics, CRC Press, Boca Raton, FL.

Academic and Practitioner Journals

Journal of Management Information Systems

Journal of Management Science

Decision Support Systems

Journal of Consumer Research

European Journal of Information Systems

Computers in Human Behavior

Information and Management

International Journal of Information Management

Applied Intelligence 

Harvard Business Review

The Economist



Other news media

The BBC website (

Any/all broadsheet newspapers