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Assessment

Summary of Assessment Details

Assessment 1: Individual Essay (30% of the final mark)
Assessment type:	Individual Essay
Essay topic:	*Big Data: Hype or a gold mine?* Please see Assessment 1 Outline section for further details and instructions.
Length and format:	1,250 words (±10%) in MS Word or PDF
Marking scale:	0-100%, where 40% is a pass
Submission date:	March 24^th, 2018 by 5:00pm. Work submitted after this date will not be marked, unless it is accompanied by an approved extenuating circumstances (EC) form.
Submit via:	Canvas/Turnitin
Marking criteria:	· Presentation 20% · Descriptive Content 25% · Analytical Content/ Analysis and Discussion 25% · Conclusions and Recommendations 20% · Sources and Referencing 10% Please see Assessment 1 marking criteria section for further details.
Feedback date:	April 27^th, 2018 by 5:00pm
Feedback via:	Canvas/Turnitin

Assessment 2: Group report (70% of the final mark)
Assessment type:	Group report. Groups should consist of maximum 3 members from the same seminar/workshop group. Report your teams to your tutor by 2^nd of February 2018, or your will randomly assigned to a group.
Report topic:	*Understanding Virgin media customer churn* Please see Assessment 2 Outline section for further details and instructions.
Datasets:	VirginMediaChurn_Jan2018.csv VirginMedia_Feb2018.csv (both available on Canvas)
Length and format:	3,500 words (±10%) in MS Word, PDF or MS PowerPoint
Marking scale:	0-100%, where 40% is a pass
Submission date:	April 30^th, 2018 by 5:00pm. Work submitted after this date will not be marked, unless it is accompanied by an approved extenuating circumstances (EC) form.
Submit via:	Canvas/Turnitin
Assessment criteria:	· Analytical Approach and Execution 30% · Findings 20% · Recommendations 20% · Presentation 20% · Sources and Referencing 10% Please see Assessment 2 marking criteria section for further details.
Feedback date:	May 22^th, 2018 by 5:00pm
Feedback via:	Canvas/Turnitin

Feedback and Results:

All assessments are subject to the usual process of internal and external moderation. Internally a sample of work will be blind marked by another lecturer and compared to those of the module leader. Samples are then sent to the external moderator, who assures the quality and consistency of marking. Feedback and provisional results will be returned to you within 15 working days of the submission date. Please note working days exclude Saturdays and Sundays, bank holidays and any other day on which the University is closed i.e. Christmas period.

Extenuating Circumstances:

Where illness or another verifiable cause will prevent a student from completing an assessment, he/she should contact the Module Leader as soon as possible (i.e. IN ADVANCE of the assessment due date). The Module Leader will then provide advice on the most appropriate course of action (i.e. whether the illness/difficulty constitutes Extenuating Circumstances). Further details of this process and the associated rules and regulations can be found in the UG Student Handbook. Please note the Extenuating Circumstances (EC) procedure only applies when something serious and unexpected happens. For example: If you are taken ill just before or during an exam; If you are involved in an accident or serious incident that prevents you attending the University just before an exam or assignment deadline; If you experience a bereavement or family illness that prevents you attending the University just before an exam or assignment deadline. All applications for Extenuating Circumstances will need to present evidence supporting the application. Depending on the circumstances, examples of evidence could include: a medical certificate, a death certificate etc. Again, please refer to the UG Student Handbook for the rules and regulations surrounding this process. Late coursework (except for those accompanied by an EC form signed by the module leader, year tutor or programme leader) will not be accepted.

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Assessment 1: Outline, Assessment Criteria and Feedback

Individual essay: “Big Data: Hype or a gold mine?” (Submission: March 24^th, 2018 by 5:00pm)

In recent years, there have been many discussions on the importance of data-driven insight in increasingly digital economy. This requires companies to be able to collect, process and learn from big (and small) data. There is a range of evidence showing that companies utilising big data analytics and machine learning are performing significantly better than companies that ignore or have no access to these technologies and competences. On the other hand, opposing voices claim that big data require significant investments that will most likely never be paid off and that more data does not imply a better insight. Furthermore, it is argued that insatiable corporate thrust to track everything and record it as big data is not only costly and but in many cases illegal and/or unethical. Whatever the case, big data is one of the most prominent contemporary business themes. Your task is to produce an individually written essay that critically evaluates pros and cons of adopting big data analytics in a corporation. The essay’s title is: “Big Data: Hype or a gold mine?” Your analysis should use contemporary evidence alongside academic literature and must also reflect on technological, managerial and ethical issues pertaining to big data. Your report must also provide analysis of the impact that big data may have on various stakeholders. Your conclusions and views should be informed by the analysis that you conduct.

Students are strongly recommended to commence this coursework early during the semester. Further details on the marking scheme and the criteria relevant to each area are detailed below. All students who submit will be provided feedback by April 27th, 2018 by 5:00pm via Canvas/TurnitIn.

Assessment 1 marking criteria

Criterion	Max	0-39%	40-49%	50-59%	60-69%	70-100%
Presentation and Structure	20	Unsatisfactory presentation of the essay. The structure does not facilitate the logical progression of the essay and potentially does not include all the basic requirements of an essay. Limited evidence of additional reading or academic content used to support. Spelling and grammatical errors throughout.	Presentation poor. The essay is structured in a generally logical manner but does not fully embrace the requirements of an essay format. Basic but unsatisfactory level of additional reading undertaken i.e. some evidence of research beyond lecture materials. Some spelling and grammatical errors.	Presentation adequate. The essay is structured in a way that is logical and appropriate but without explicit guidance for the reader. Clear evidence of a good level of research beyond lecture material. Only minor spelling and grammatical errors.	The essay is written and presented very well, supported with a wide range of relevant academic and contemporary sources of information illustrating a wide level of additional reading. The essay is structured logically and is coherent with the aim of the assignment. Little/no spelling or grammatical errors.	The essay is written and presented in a professional manner (i.e. outstanding). All fundamentals of good presentation are effectively addressed. The essay is structured logically written succinctly and sign-posting used throughout. Clear evidence of thorough research of the topic area and a wide range of academic and contemporary sources used to inform. No spelling or grammatical
Descriptive content	25	Poor use of literature and other sources of information. Little to no description of key concepts, theories or frameworks. Little/no examples provided.	Limited sources used to inform descriptive content, contemporary or academic. Descriptive content needs further development. Limited integration between theories and contemporary evidence (examples).	Key sources of both contemporary and academic information used and partially linked. Some description and integration of all concepts. Broader range of theoretical and contemporary sources needed to improve.	Strong description of case and all elements e.g. case for and against, theories, ideologies and examples. Wide use of literature that has been used coherently to inform the proposed arguments.	Excellent description of all elements of the case. Outstanding use of literature both academic and contemporary practice to build proposed arguments. Focused fully on the task and fully and well referenced.
Analysis and Discussion	25	Poor use of literature. Points not backed up. Proposed arguments have not been effectively analysed against the required concepts. No integration/discussion key concepts and how they are associated e.g. theory, practice etc. Little to no application to proposed arguments.	Limited analysis. Analysis needs much further development. Need to increase the use of theoretical and/or practical sources of information. Limited discussion demonstrating how all elements link together.	Arguments for and against big data analysed against key concepts e.g. various technological, ethical, managerial and ideological positions. Analysis is appropriate and well conducted but greater use of critical evidence would improve.	Arguments for and against big data comprehensively analysed. A wide range of concepts used, clearly understood and some examples of critique included. Could be further improved by extending the critical nature of the evaluation.	Excellent analysis that illustrates an exceptional understanding of the link between theory and practice. A wide range of concepts and practical information used to produce a concise and critical evaluation.
Conclusion	20	No conclusion or appropriate recommendations provided. There is a need to provide suggestions based on the previous analysis on whether big data analytics show be adopter or not and why.	Weak conclusion. Limited recommendations. Few appropriate to discussion. Use of analysis and theory to support suggestions is limited/unclear.	Appropriate conclusion and recommendations drawn based on analysis conducted. Greater use of theory would benefit the work. Some use of examples and literature to develop recommendations.	Relatively strong and appropriate conclusion and recommendations drawn. These are based on theory and evidence and are logically based on the previous analysis conducted. Supported fully by examples or literature.	Conclusion and recommendations are strong and fully integrating theory and practice. Recommendations are not limited to scope of proposed argument but have potential to be generalised i.e. are practical and demonstrate solid understanding of the issues pertaining to big data analytics. Logical based on prior analysis, fully supported and clearly thought through evident from supporting materials and points.
Sources and Referencing	10	Harvard referencing system not used or used incorrectly. No in-text references. No references provided at the end or very limited number of references provided. Absence of a systematic approach to referencing.	Harvard referencing system used. Few references used. Absence of a systematic approach to referencing. Occasional absence of in-text references. An imbalance of academic and industrial sources. Reference list not provided in Alphabetic order.	Harvard referencing system used correctly with occasional inconsistencies. A fair range of references used. Different types of references balanced. References list provided in Alphabetic order.	Correct and consistent use of Harvard referencing both in text and at the end of report. Wide range of good quality, up to date references used. Good balance of academic and industrial sources. References list provided in Alphabetic order.	Harvard referencing used both in text and at the end of report correctly. A very wide range of up to date, good quality references used. An excellent and well-balanced mix of both industrial reports and academic sources used. References list provided in Alphabetic order.

Assessment 2: Outline, Assessment Criteria and Feedback

Group report: Understanding Virgin Media customer churn (Submission April 30^th, 2018 by 5:00pm)

Sir Richard Branson has just welcomed you into his spasious office at Virigin Media headquarters in London. Quite concerned with many of his customers switching to competitors he said: „Recently we have found out that many customers have left us for our competitors. My business intelligence department is abroad on a team-building event and until they come back I am concerned that more customers will leave. This situation requires immediate, insight-driven actions that will stop and hopefully reverse customer churn. Therefore, I have a couple of analytical tasks that I need solved in the best of your capacity. Once you create insight, I expect that you come up with a set of strategic and tactical recommendations on how to act to prevent or possibly reverse customer churn.

Task 1: Identify who of our current customers is next to leave Virgin Media. You are provided with two files (database extracts). The first one VirginMediaChurn_Jan2018.csv includes information about:

· Customers who left Virgin Media in January 2018 – the column is called Churn

· Services and extras that each customer has signed up for – Landline, MultipleLines, Broadband, Powerline Kit, Online Backup, Gadget Rescue, Cable TV and Video On Demand

· Customer account information – how long they have been a customer (tenure), contract type, payment method, paperless billing, monthly charges, and total charges

· Demographic info about customers – gender, employment and marital status and if they have any dependents

The second file is VirginMedia_Feb2018.csv includes everything listed above except Churn. Building on the knolwedge obtained from the first file, try to predict who of our current customers is likely to leave. To build prediciton model, choose the best prediction technique, explain which variables are most important to predicting churn and establish who will churn. Provide a detaled explanation of your analytical approach – i.e. workflow model and its elements. Explain step by sep what was done to get the best possible predicitons. Provide detailed overview of the outputs and elaborate your findings. Provide links to your workflows and files (be explicit in which file you have saved predicted churners). Do not forget that my DBI team is very picky so make sure you do a rock solid modelling and analysis.

Task 2: Once you have established who is likely to churn, check if different types of churners exist. If so, establish how many segments there are, profile identified segments using available data and give appropriate names to the segments. Same like in the previous task, elaborate your approch and your findings. Provide link to your workflow model and files (be explicit in which file you have saved your classification results).

Task 3: I also want to know WHY our customer leave. Unfortunately, we cannot see this in our databases and we have no time to commission market research. Instead, I would like you to see what have people tweeted recently about VirginMedia. This might signpost to the potential churn causes. I would like to see key themes and sentiment of the tweet corpus mentioning our business name. Anything above this minimum requirement would be a big plus. Same like in the previous tasks, elaborate your approch and your findings. Provide links to your workflow models and files.

Once you have solved all the tasks, you need to provide recommendations on what actions VirginMedia should take to prevent and potentionally reverse churn. While you have creative freedom, your recommendations have to be linked with the insight you have created and other external research. Please note that there will be two audiences reading this report. First audience is made of myself and my board of directors. Given that we are extremely busy we do not like technical and leghty reports that require hours of reading. Instead we prefer intuitive infographs, with very little text that can be easily understood. We are willing to read up to 3 infographic slides. Please put these in the executive summary so that we do not have to struggle finding them in your report. The second audience is my DBI team. They will be interested to throroughly read and inspect your analytical approach and findings. Therefore, be elaborate and precise and provide links for my team to inspect your model workflows, working files and outputs. Justify your decisions in building models and demonstrate that you know how different widgets/tools function. And once again, bear in mind you do all of these analyses to help us stop the churn. Good luck!“

Sir Richard Branson, Virgin Media

Further instructions and expectation

All analyses must be performed in Orange Data Mining software. If necessary install add-ons that will assisst you in completeing your tasks. In explaining your analytical approach you are allowed to insert workflow screenshots. It is vital that your data mining models are well annotated and made available for your tutors to download. Therefore, save all your workflows and data files in cloud (Dropbox, Google drive etc.) and provide links to each task’s solution so that your tutors can inspect it when marking. When making infographics you can use any software you want. Please note, you are not allowed to write to or call Virgin Media or Sir Richard Branson in any way or form. Please note, producing digital business intelligence report is a very demanding task. Start working on it as soon as possible and make sure that you do not delegate tasks back to the client.

Suggested report structure

Digital Business Intelligence reports can take various forms and structures. We would recommend but not insist that your report follows some of the structures given below. You can make your report in MS Word or MS PowerPoint with later giving you more flexibility in building infographics. However, the final report structure and report organisation is ultimately your decision. The suggested plan structures:

Alternative 1:

Alternative 2:

Cover page
Table of contents
Executive summary infographics
1. Task 1
   1.1. Approach
   1.2. Findings
2. Task 2
   2.1. Approach
   2.2. Findings
3. Task 3
3.1. Approach
   3.2. Findings
4. Recommendations
References
Appendices

Cover page
Table of contents
Executive summary infographics
1. Task 1
   1.1. Approach
   1.2. Findings
1.3. Recommendations
2. Task 2
   2.1. Approach
   2.2. Findings
2.3. Recommendations
3. Task 3
3.1. Approach
   3.2. Findings
3.3. Recommendations
4. Conclusion
References
Appendices

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