| 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 |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Management - Business School |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
This unit is important because it gives students sound knowledge and clear understanding of the implications of big data analytics in marketing within the frameworks of ethical, legal, social, and digital responsibility. It focuses on both the risks and benefits of the adoption of big data analytics for marketers. This unit will 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. Methods of analysing data sets will be considered as well as ways of sourcing them to facilitate their usage for marketing purposes. Various applications in a range of marketing context will be critically assessed.
How does this unit fit into your programme of study?
This unit fulfils the purpose of familiarising students with the ethical, organisational, societal, and environmental implications of the adoption of big data analytics for marketing purposes. Students will learn about the complexity of balancing between the increasing demand for (marketing) data to competitively innovate and the legal and social obligation to ensure safe and ethical treatment of data.
An overview of the content
The unit will explore the distinction 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 using data to optimise the quality of the marketing decisions.
How will students, personally, be different as a result of the unit?
Upon completion of the unit, students will have formed a deep understanding of purpose, application and value of market data to an organisation and particularly to those working in marketing. They will have acquired knowledge of how to exploit big data analytics for marketing purposes within an ethical, legal, and digital and social responsibility.
Learning Outcomes
At the end of this unit, students will be able to:
ILO 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.
ILO 2 – Given a set of market insights objectives, compose a strategy for identifying and harvesting appropriate data.
ILO 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.
ILO 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 will be conducted through ten lectorial sessions of 3 hours (total 10*3 = 30 hours) across TB2. These comprise a combination of one-hour lecture talks and two-hour tutorial-style discussions that focus on the aspects taught in the lecture. The latter will involve deep academic conversations, case studies, debates, tasks, as well as interesting talks from people in the industry. Optional advice and feedback hour sessions for additional support are available. Students will be required to do some preparation reading and activities before the sessions. All learning material will be available on the unit’s Blackboard page. Additional readings will also be provided on Blackboard to support students who wish to acquire a deeper learning about big data analytics and marketing intelligence. The discussion board on Blackboard will also be used to complete tasks and strengthen peer interaction. It is also a place for students to ask any questions about the unit. Students are advised to set aside time to review the weekly material and plan when they will work through them. Planning will allow them to learn in an efficient and organised manner and can prevent them from stress and anxiety in the long term.
Tasks which help you learn and prepare you for summative tasks (formative):
Forms of formative assessment will include readings and task preparation before each session (ILOs 1, 2, 3 & 4).
Tasks which count towards your unit mark (summative):
There will be two main components of the summative assessment. The first one consists of a data analysis group report that will help students to collect, analyse, interpret data and present the results in a way that is comprehensible to a layperson (ILOs 1&2); the second one is based on an individual report aimed at proposing a sustainable solution for the given marketing objective (ILOs 3&4)
When assessment does not go to plan
Students who do not pass the unit overall and who are eligible for a reassessment, will be re-assessed using like-for-like assessments:
If the student does not pass the data analysis group report (50%) they will be reassessed with an individual data analysis report with the same data but addressing a different marketing objective (500 words) (ILOs 1 and 2).
If the student does not pass the individual report (50%) they will be reassessed with another individual report with the same data but different marketing objective (2,500 words) (ILOs 3 and 4).
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EFIMM0059).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
