Intent: Develop a business intelligence solution that adds value and competitive advantage.
Objective(s): This assessment task addresses the following subject learning objectives (SLOs):
- Assess ethical considerations in decision-making processes and practices for a business intelligence system in a real organisation.
- Design business intelligence applications to add value and competitive advantage in an organisation.
- Apply business intelligence and analytics tools to solve real-world problems and interpret results.
Groupwork: Group, group & individually assessed
Weight: 45% (30% group, 15% individually). The group and individually assessed components are distributed in both, report and presentation.
Task: Students are to develop a business intelligence conceptual solution for a real-case scenario. Business Intelligence life cycle will be used as a road-map to understand how data and information transform into intelligence, how different applications/technologies can be used to analyse and report data.
This assessment task is divided into two components:
(1) Report – 25% (20%-group assessed and 5%-individually assessed)
(2) Presentation – 20% (10%-group assessed and 10%-individually assessed)
Length:
- Report:3000 words (+/- 10%). References, Table, diagram/figure/chart, Cover page, Table of contents/figures/tables, Appendix are NOT counted towards the word limit.
- Presentation:Approximately 15 minutes per group (including the Q&A) where each group member must present for at least 2 minutes.
Due Date & Time:
- Report & Presentation slides on Canvas: Sunday, 9 May 2021 by 23:59 (Week 10)
- In-Class Presentations: Week 11/12
Feedback: Marks and feedback will be provided via Canvas.
Late Penalty: Work submitted late without an approved extension is subject to a late penalty of 10% of the total available marks deducted per calendar day that the assessment is overdue. Work submitted after five calendar days is not accepted and a mark of zero is awarded. For example, if this assessment task is submitted (up to) 24 hours after the deadline without an extension, you will have 4.5 mark deducted from your awarded mark.
Submission Instructions:
- Please submit your report as a Word file (.doc or .docx) or PDF via Turnitin and your presentation slides on Canvas.
- Use the following format in naming the report/presentation: (The submitting group member’s) Student surname + Student number + Assg3. For example, a student called Smith Bob with the student ID 12345678 will have the following name for their assignment file: Bob12345678Assg3.docx.
- Soft copy submissions via other sources such as email, Teams, or Canvas message will NOT be accepted or marked.
Report Structure:
- Please use 12-point Arial or Times New Roman font style and single line spacing. The size of headings should be 14-point Arial or Times New Roman font style.
- You can use the numbered headings and sub-headings. The task descriptions will help you to understand the important tasks for the assignment. You can use it as headings in your report. This will also help you to track that you do not miss anything from the marking criteria.
- All text in the report should be in appropriate academic language.
- All tables/figures/diagrams/charts are numbered and have a caption.
- For bibliography, you must use the appropriate UTS APA style (7th)for citing and referencing research. Please see more information on referencing here: UTS APA Reference GuideLinks to an external site..
- Please refer to the marking rubric to ensure you address all the assessment criteria.
Task Descriptions & Marking Guide: The descriptions for the tasks and the allocated marks are written in front of them in square brackets [].
If you plan to use Tableau for this assignment, the process to complete some of the tasks could be different to what is mentioned for Power BI below.
Include the screen shots for each performing tasks in your report as the focus is on learning the process i.e. the road-map to understand how data and information transform into intelligence, how Power BI/Tableau can be used to analyse and report data and NOT only the end product i.e. the dashboard.
Task 1: Data Acquisition [3 marks, individually assessed]
Choose a publicly available data set from Kaggle (www.kaggle.com (Links to an external site.)), include the Kaggle link and explain:
- Why you have selected a particular data set?
- What problem would be solved from it?
Task 2: Metadata Analysis [2 marks, group assessed]
Based on your problem definition from Task 1, you will create a data dictionary or data catalogue to provide data descriptions/definitions of the data. A data dictionary contains metadata from your chosen data set. It is usually represented in a table format.
HINT: If the field/attribute list is too long (> 30) in your chosen data set, you can include the top 10-15 attributes related to your problem definition and provide a complete list in the Appendix.
Task 3: Data Loading [2 marks, group assessed]
Load your flat file i.e. a csv (from Task 1) on Power BI. Upload a copy of the flat file (csv) with your report on Canvas.
HINT: A flat file is a type of file that has only one data table and every row of data is in the same structure. The file does not contain hierarchies.
Task 4: Data Transformation [3 marks, group assessed]
In this task, you will shape your chosen data set and simplify it’s structure. It is important to shape your data to ensure that it meets your needs and is suitable for use in reports. You can perform actions such as renaming columns, changing text to numbers, removing rows/columns, setting the first row as headers, changing column data types, replacing values/null values, remove duplicates, and much more.
In your report, include all the actions you have used to shape your initial data with a brief description for each.
Task 5: Data Profiling [3 marks, group assessed]
This task is related to the refinement of the chosen data set such as determining data anomalies and data statistics such as row counts, value distributions, minimum and maximum values, averages, and so on. This concept is important because it allows you to shape and organise the data before you start creating the visualisations.
In your report, include all data anomalies and statistics and the actions/transformations you have applied with a brief description for each.
HINT: Data anomalies are outliers within your data. Determining what those anomalies are can help you identify what the normal distribution of your data looks like and whether specific data points exist that you need to investigate further.
Power Query Editor in the Power BI:
- Determine data anomalies by using the Column Distribution feature
- Understand data statistics by using the Column Quality and Column Profile features
Task 6: Data Presentation [6 marks, group assessed]
- Create visualisations of the insights. HINT:Include at least FOUR different visualisations and explain why you have chosen them. [2 marks]
- Create a static dashboard. [4 marks]
Task 7: Interpretation [4 marks, group assessed]
Explain how visualisations created can help the target audience (e.g. general public/government/organisation/industry).
Task 8: Reflection [2 marks, individually assessed]
Use the following questions to create a comprehensive self-assessment and reflection response including your experience after doing this group project. Include examples to illustrate your reflections. This should reflect and draw upon the concepts and terminology you have learned in doing the tasks included in this project. Please DO NOT describe the process you followed to do a particular task.
- What did/do I enjoy about this project?
- What parts of it do I particularly like? Dislike? Why?
Additional Penalties:
- References: Missing references or Incorrect reference style used – up to 1 mark
- Report: Missing structure, format, and/or spelling, grammatical errors – up to 2 marks
You will be graded according to the rubric below.
Frequently Asked Questions (FAQs):
Task 1 is individually assessed. Does it mean every group member needs to select a dataset?
As a group only ONE dataset should be selected. It is individually assessed for the following sub-questions.
- Why you have selected a particular data set?
- What problem would be solved from it?
What should we include in a data dictionary?
A data dictionary must contain field/column names, data types, data format, field sizes, descriptions, examples.
For Task 3, do we need to include something in the report?
Yes. You can include that the file loaded was a flat one with one table only and additionally may include total number of rows and columns. Include a screen shot within Power BI/other tool to show that the file is successfully loaded.
BI Life Cycle: Report
| BI Life Cycle: Report | ||||||
| Criteria | Ratings | Pts | ||||
| This criterion is linked to a Learning OutcomeTask 1
Data Acquisition [Individually assessed] |
|
3 pts | ||||
| This criterion is linked to a Learning OutcomeTask 2
Metadata Analysis [Group assessed] |
|
2 pts | ||||
| This criterion is linked to a Learning OutcomeTask 3
Data Loading [Group assessed] |
|
2 pts | ||||
| This criterion is linked to a Learning OutcomeTask 4
Data Transformation [Group assessed] |
|
3 pts | ||||
| This criterion is linked to a Learning OutcomeTask 5
Data Profiling [Group assessed] |
|
3 pts | ||||
| This criterion is linked to a Learning OutcomeTask 6
Data Presentation [Group assessed] |
|
6 pts | ||||
| This criterion is linked to a Learning OutcomeTask 7
Interpretation [Group assessed] |
|
4 pts | ||||
| This criterion is linked to a Learning OutcomeTask 8
Reflection [Individually assessed] |
|
2 pts | ||||
| Total Points: 25 | ||||||
