The assignment gives students the possibility to demonstrate their ability to evaluate and analyse a business problem. They apply data visualisation and data analytics to provide business intelligence to a fictional company.
The learning outcomes being assessed
This assessment assesses the learning outcomes 1 - 3.
Overview of assignment
As a business analyst, you are commissioned to provide intelligence to the senior management to evaluate the current pattern of individual and team-level performance. You are asked to analyse the current performance pattern using business intelligence to provide recommendations for the company to improve its performance. Moreover, you should propose performance dashboards calibrated based on historic data to enable the firm to monitor its future performance.
You submit two documents the first should comprise the following four elements:
An executive summary (about one page)
A non-technical report of 2.000 words (+/- 10%)
A technical appendix summarising your methodology A performance dashboard (about one page)
The second document is the R code that enables me to replicate your analysis.
Please also keep in mind:
Electronic copy must be tidy, and not show any history of track changes. If you use includes graphics, please ensure these are of a high quality.
The first page (slide) should include the title, all names, and students’ numbers.
Use the Harvard reference style and provide a reference list at the end of your slides.
You must acknowledge your source every time you refer to others’ work, using the Harvard Referencing system NO PLAGIARISM
12 Noon, Friday 20th of May 2020.
Please note that this is the final time you can submit – not the time to submit! Your feedback and mark for this assignment will be provided via return of the assignment.
1. Effective summary of key results in the executive summary and performance dashboards; stating the key findings/ key recommendations and presenting an easy
to grasp visualisation that enables an informed decision making ..............................................
2. Subject knowledge, application of skills and communication skills in the report (non-technical and technical): evidence of factual and conceptual understanding of the subject and evidence of analysis and evaluation, ability to collate, categorise and apply ideas and information and to develop and sustain a coherent argument; quality of the recommendations; structure and organisation of the text content,
layout and formatting, use of visuals, command of syntax, language and grammar,
in-text and end-of-text referencing (if applicable) .....................................................................
3. Replicability of the analysis – a programme code that allows an expert to replicate,
understand and amend the analysis ........................................................................................
Description of the call centre data
The union agreed to release aggregated data that link the employee (call centre agent) to his or her individual performance. The performance data including the ID for each agent is the call centre data. It is available for the last couple of weeks. In addition, you have some personal information about each agent in the HRM data.
Variable definition in the call centre data
time of the day when the call has been recorded (in hours)
duration of the call with the call centre agent (in seconds)
duration, the costumer spent waiting to be connected to an agent (in seconds)
each costumer has been asked to rate their satisfaction with the service after the call via a 10 point Likert scale on the app, 1 is very unsatisfied and 10 is very satisfied
each caller chooses one of four options before connected to an agent, the options are technical support, delivery problem, return problem and complaint
if the call centre agent could not resolve the problem, the agent could forward the call to an expert (for technical problems only)
anonymous number for the call centre agent
Variables in the HRM data
indicating if male of female employee
age of the respondent at the beginning of the period (in years)
number of months working in the call centre
three broad categories of education degree including apprenticeship, some college and university
four categories describing the employee’s likely ethnic origin including British, Black, Asian and other