6031MKT Big Data Coursework Assignment Brief 2026 | CU

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Published: 30 Mar, 2026

Category	Assignment	Subject	Computer Science
University	Coventry University	Module Title	6031MKT Big Data

Word Count	4000 Words
Assessment Type	Applied Core
Assessment Title	CW Individual Audit / Plan (Standard_FA)
Academic Year	2026

6031MKT Assignment Brief

6031MKT Assignment Task

Task summary:

This is an individual assessment: Audit / Plan.

This assessment will require you to meet the Learning Outcomes (LOs) set out in this document by producing a Written 4,000 words (+ or - 10% or equivalent).

Create an Audit / Plan Project with justification in relation to the Big Data mix. Students should explain Big Data concepts, strategies and academic theories, as well as justify their decisions in an appropriate manner.

Students should discuss how Big Data planning, decisions and use of tools can help achieve marketing goals. They should also evaluate the effect and implications that the Big Data proposed solution(s) can have. If applicable, they should explore current regulations, data privacy and /or CSR concerns as part of a safeguarding strategy and the need for ethical and sustainable practices.

Students should also use academic writing and APA referencing.

Task details:

Use research and analysis to produce a critical comparison as a table between "traditional" data and "Big Data" strategies. You MUST use mainstream academic theories and business literature to discuss the main differences in:

a) Uses and implementation.
b) Features and important business players (E.g., software makers).
c) Advantages and limitations.

Present it in your assessment as an IMAGE (inserted into the document, 1 PAGE maximum size). The "image" can be a diagram, infographic or table, however extra points will be awarded for creativity and professional execution. Use appropriate in-text citation in your image BUT all references are to be added in the "References" section of your assessment. You MUST also include evidence of your progression in developing the image in your Appendix section as SCREENSHOTS. You must include at least three (3) screenshots.

You are required to mine a social media data set for an influencer of your choice (whose content is in English). You can choose any free tool seen in class or of your choice to mine the data, you MUST provide evidence that it was you who mined it (Example: screenshots, video or similar.

Use Excel, PowerBI or a data manipulation software of your choice, to clean, analyse and gain insights from the data by:

1) Manipulating, cleaning, splitting and concatenating the data. Transforming data types accordingly.
2) Creating Pivot Tables, calculating fields and filtering results. Creating charts from the Pivot Tables to help you identify 3 key main insights from your analysis (these can be areas of success or failures).
3) Critically describe your observations and present 1 recommendation (solution) using the academic frameworks studied in class. Use links to academic theories in the form of in-text citations.

Submission Instructions:

Submission via: TurnItIn
Limit: Project
Number of students per CW submission: Individual
Grade release date: 10 working days

The assessment must be submitted before 18:00 (UK time) on 08/12/2025 18:00:00. No paper copies are required. You can access the submission link through Aula.

Please ensure that you have submitted your work using the correct file format; unreadable files will receive a mark of zero. The Faculty accepts any of CU's approved file formats. PDF files will not be accepted.

Your coursework will be given a zero mark if you do not submit a copy through Turnitin. Please take care to ensure that you have fully submitted your work.

All work submitted after the submission deadline without a valid and approved reason (see below) will be given a mark of zero.

Students MUST keep a copy and/or electronic file of their assignment.

The University wants you to do your best. However, we know that sometimes events happen which mean that you can’t submit your coursework by the deadline – these events should be beyond your control and not easy to predict. If this happens, you can apply for an extension to your deadline for up to five working days, or if you need longer, you can apply for a deferral, which takes you to the next assessment period (for example, to the resit period following the main Assessment Boards). You must apply before the deadline.

You will find information about the process and what is or is not considered to be an event beyond your control at

Checks will be made on your work using anti-plagiarism software and approved plagiarism checking websites.

Development of Skills and Attributes

This module is designed to help the students achieve all the graduate attributes, as specified below. Discussions during the lectures and activities during the seminars help in achieving the following graduate attributes.

By undertaking this assessment, you will develop skills to Think Creatively (TC): Level 6

Can keep an open mind and critically evaluate primary and secondary research as well as relevant marketing theory to explore complex areas of investigation

6031MKT Marking and Feedback

How Will My Assignment Be Marked?

Your assignment will be marked by the module team.

How will I Receive My Grades and Feedback?

Provisional grades will be released once internally moderated.

Feedback will be provided by the module team alongside grades release, which can be accessed via the module’s Aula page.

Your provisional marks and feedback should be available within 10 working days.

What Will I be Marked Against?

Details of the marking criteria for this task can be found at the bottom of this assignment brief.

Assessed Module Learning Outcomes

The Learning Outcomes for this module align with the marking criteria, which can be found at the end of this brief. Ensure you understand the marking criteria to ensure successful achievement of the assessment task. The following module learning outcomes are assessed in this task:

Critically evaluate and apply data mining techniques to collect data from analytical platforms and databases.
Justify and apply insight methods to gather qualitative and quantitative data.
Analyse and critique data to gain insight into relevant marketing issues.
Synthesise and develop strategic marketing decisions based on the critical evaluation of data.

Assignment Support and Academic Integrity

If you have any questions about this assignment, please see the Student Guidance on Coursework for more information.

Spelling, Punctuation, and Grammar:

You are expected to use effective, accurate, and appropriate language within this assessment task.

Academic Integrity:

The work you submit must be your own, or in the case of groupwork, that of your group. All sources of information need to be acknowledged and attributed; therefore, you must provide references for all sources of information and acknowledge any tools used in the production of your work. We use detection software and make routine checks for evidence of academic misconduct.

It is your responsibility to keep a record of how your thinking has developed as you progress through to submission. Appropriate evidence could include: version-controlled documents, developmental sketchbooks, or journals. This evidence can be called upon if we suspect academic misconduct. We strongly suggest you regularly save your work to your university OneDrive account.

If using Artificial Intelligence (AI) tools in the development of your assignment, you must reference which tools you have used and for what purposes you have used them. This information must be acknowledged in your final submission.

Definitions of academic misconduct, including plagiarism, self-plagiarism, and collusion, can be found on the Student Portal. All cases of suspected academic misconduct are referred for investigation, the outcomes of which can have profound consequences for your studies. For more information on academic integrity, please visit the Academic and Research Integrity section of the Student Portal.

Support for Students with Disabilities or Additional Needs:

If you have a disability, long-term health condition, specific learning difference, mental health diagnosis or symptoms and have discussed your support needs with health and wellbeing, you may be able to access support that will help with your studies.

If you feel you may benefit from additional support, but have not disclosed a disability to the University, or have disclosed but have yet to discuss your support needs, it is important to let us know so we can provide the right support for your circumstances. Visit the Student Portal to find out more.

Unable to Submit on Time?

The University wants you to do your best. However, we know that sometimes events happen which mean that you cannot submit your assessment by the deadline or sit a scheduled exam. If you think this might be the case, guidance on understanding what counts as an extenuating circumstance and how to apply is available on the Student Portal.

Administration of Assessment

Assignment Category: Written
Attempt Type: Standard_FA
Component Code: CW

Assessment Marking Criteria

	Holistic Feedback
80 to 100%	The submission ascertains a vast understanding of the requirements of the assessment. The application of Big Data is exceptionally relevant and justified with no corrections needed. Exceptional demonstration of research effort and reading to develop theoretical understanding and knowledge in research methods (qualitative and quantitative data). Exceptional interpretation and description of relevant content associated with this area of study within the brief's requirements. Exceptional critical analysis is used in the application of data mining techniques. Exceptional evidence of understanding data for strategic decision making. Exceptional ability to manage insight and analysis from Big Data tools. Vast use of key data to interpret real-life data and develop actionable insights. Exceptional command to critically assess and apply relevant big data tools, measures and techniques. The discussions of the roles and functions of Big Data for marketing are entirely relevant to the industry and supported by examples. Outstanding ability to assess data privacy and security issues, undertake analysis and build arguments. Exceptional evidence in the use of metrics and analytics for making recommendations regarding Big Data activities. Outstanding knowledge of the benefits and usefulness of data for strategic decision-making. Distinction effort in presentation, clarity of expressions, management of citations and references. The assessment structure is exceptionally developed, with a vast use of evidence with relevant explanations. Exceptional presentation, referencing with no corrections needed.	.
70 to 79%	The submission ascertains an extensive understanding of the requirements of the assessment. The application of Big Data is excellently relevant and justified, with very few corrections needed. Excellent demonstration of research effort and reading to develop theoretical understanding and knowledge in research methods (qualitative and quantitative data). Excellent interpretation and description of relevant content associated with this area of study within the brief's requirements. Excellent critical analysis is used in the application of data mining techniques. Excellent evidence of understanding data for strategic decision-making. Excellent ability to manage insight and analysis from Big Data tools. Extensive use of key data to interpret real-life data and develop actionable insights. Excellent command to critically assess and apply relevant Big Data tools, measures and techniques. The discussions of the roles and functions of Big Data for marketing are highly relevant to the industry and supported by examples. High ability to assess data privacy and security issues, undertake analysis and build arguments. Excellent evidence in the use of metrics and analytics for making recommendations regarding Big Data activities. High knowledge of the benefits and usefulness of data for strategic decision-making. Distinction effort in presentation, clarity of expressions, management of citations and references. The assessment structure is excellently developed, with extensive use of evidence with relevant explanations. Excellent presentation, referencing with very few corrections needed.	.
60 to 69%	The submission ascertains plenty understanding of the requirements of the assessment. The application of Big Data is mainly relevant and justified with few corrections needed. Very good demonstration of research effort and reading to develop theoretical understanding and knowledge in research methods (qualitative and quantitative data). Very good interpretation and description of relevant content associated with this area of study within the brief's requirements. A very good critical analysis is used in the application of data mining techniques. Very good evidence of understanding data for strategic decision making. Very good ability to manage insight and analysis from Big Data tools. Plenty of use of key data to interpret real-life data and develop actionable insights. Very good command to critically assess and apply relevant Big Data tools, measures and techniques. The discussions of the roles and functions of Big Data for marketing are closely relevant to the industry and supported by examples. Considerable ability to assess data privacy and security issues, undertake analysis and build arguments. Very good evidence in the use of metrics and analytics for making recommendations regarding Big Data activities. Considerable knowledge of the benefits and usefulness of data for strategic decision-making. Merit effort in presentation, clarity of expressions, management of citations and references. The assessment structure is mainly developed, with plenty of use of evidence with relevant explanations. Very good presentation, referencing with a few corrections needed.	.
50 to 59%	The submission ascertains considerable understanding of the requirements of the assessment. The application of Big Data is satisfactorily relevant and justified, with some corrections needed. Good demonstration of research effort and reading to develop theoretical understanding and knowledge in research methods (qualitative and quantitative data). Good interpretation and description of relevant content associated with this area of study within the brief's requirements. Good critical analysis is used in the application of data mining techniques. Good evidence of understanding data for strategic decision making. Good ability to manage insight and analysis from Big Data tools. Considerable use of key datum to interpret real-life data and develop actionable insights. Good command to critically assess and apply relevant big data tools, measures and techniques. The discussions of the roles and functions of Big Data for marketing are reasonably relevant to industry and supported by examples. Above Average ability to assess data privacy and security issues, undertake analysis and build arguments. Good evidence in the use of metrics and analytics for making recommendations regarding Big Data activities. Above-average knowledge of the benefits and usefulness of data for strategic decision-making. Pass effort in presentation, clarity of expressions, management of citations and references. The assessment structure is satisfactorily developed, with considerable use of evidence with relevant explanations. Good presentation, referencing with some corrections needed.	.
40 to 49%	The submission ascertains some understanding of the requirements of the assessment. The application of Big Data is sufficiently relevant and justified, with several corrections needed. Acceptable/adequate demonstration of research effort and reading to develop theoretical understanding and knowledge in research methods (qualitative and quantitative data). Acceptable/adequate interpretation and description of relevant content associated with this area of study within the brief's requirements. Acceptable/adequate critical analysis is used in the application of data mining techniques. Acceptable/adequate evidence of understanding data for strategic decision making. Acceptable/adequate ability to manage insight and analysis from Big Data tools. Some use of key data to interpret real-life data and develop actionable insights. Acceptable/adequate command to critically assess and apply relevant big data tools, measures and techniques. The discussions of the roles and functions of Big Data for marketing are partially relevant to the industry and supported by examples. Average ability to assess data privacy and security issues, undertake analysis and build arguments. Acceptable/adequate evidence in the use of metrics and analytics for making recommendations regarding Big Data activities. Average knowledge of the benefits and usefulness of data for strategic decision making. Pass effort in presentation, clarity of expressions, management of citations and references. The assessment structure is sufficiently developed, with some use of evidence with relevant explanations. Acceptable/adequate presentation, referencing with several corrections needed.	.
Fail Outcomes not met. 30 to 39%	The submission ascertains a weak understanding of the requirements of the assessment. The application of Big Data is hardly relevant and justified, with multiple corrections needed. Limited demonstration of research effort and reading to develop theoretical understanding and knowledge in research methods (qualitative and quantitative data). Limited interpretation and description of relevant content associated with this area of study within the brief's requirements. Limited critical analysis is used in the application of data mining techniques. Limited evidence of understanding data for strategic decision making. Limited ability to manage insight and analysis from Big Data tools. Weak use of key data to interpret real-life data and develop actionable insights. Limited command to critically assess and apply relevant big data tools, measures and techniques. The discussions of the roles and functions of Big Data for marketing are minimally relevant to industry and supported by examples. Low ability to assess data privacy and security issues, undertake analysis and build arguments. Limited evidence in the use of metrics and analytics for making recommendations regarding Big Data activities. Low knowledge of the benefits and usefulness of data for strategic decision-making. Outcomes Not Met: effort in presentation, clarity of expression, and management of citations and references. The assessment structure is hardly developed, with weak use of evidence and relevant explanations. Limited presentation, referencing with multiple corrections needed.	.
Fail Outcomes not met. 0 to 29%	The submission ascertains a very limited understanding of the requirements of the assessment. The application of Big Data is insufficiently relevant and justified, with many corrections needed. Minimal demonstration of research effort and reading to develop theoretical understanding and knowledge in research methods (qualitative and quantitative data). Minimal interpretation and description of relevant content associated with this area of study within the brief's requirements. Minimal critical analysis is used in the application of data mining techniques. Minimal evidence of understanding data for strategic decision making. Minimal ability to manage insight and analysis from Big Data tools. Very Limited use of key data to interpret real-life data and develop actionable insights. Minimal command to critically assess and apply relevant big data tools, measures and techniques. The discussions of the roles and functions of Big Data for marketing are not relevant to the industry and are not supported by examples. Low ability to assess data privacy and security issues, undertake analysis and build arguments. Minimal evidence in the use of metrics and analytics for making recommendations regarding Big Data activities. Low knowledge of the benefits and usefulness of data for strategic decision-making. Outcomes Not Met: effort in presentation, clarity of expression, and management of citations and references. The assessment structure is insufficiently developed, with very limited use of evidence with relevant explanations. Minimal presentation, referencing with many corrections needed.	.

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