这个作业来自澳洲是编写报告并进行可视化分析濒危物种进出口贸易的essay代写
INFS6018 Managing Business Intelligence
场景：
在这个现实生活中的项目中，您需要编写报告并进行可视化分析濒危物种的进出口贸易，考虑到三种不同的设计角色，如下所示。
假设您是一家BI咨询公司，可以独家访问CITES数据。您有三个客户，每个客户都有不同的需求和要求。对于这些客户根据以下信息设计角色。
设计角色是不同类型的用户，这些用户被分组为所谓的“原型”（戈德温，2009年）。原型通常是在深入观察和潜在用户的访谈。由此产生的角色被设计为代表虚构角色（但令人信服的字符，其轮廓总结了整个（现有）相似组的特征用户（Godwin，2009年）。因此，每个角色代表的比典型用户要多得多。
角色通常被命名（例如“桑德拉教授”）以强调以人为本的设计思维。但是，它们不代表单个（命名）用户，而是累积特征已经考虑过需求的一群人中
您的任务：
步骤1：角色设计
您需要设计三个角色来代表三组用户：
对于Jocelyn和Louise，您必须使用2019年及/或之前的CITES数据。
•“联合国Jocelyn” – Jocelyn在联合国工作，并热衷于改善有关野生动物交易爬行动物或哺乳动物（选择1）。
•“非营利性–野生动物保护慈善组织：露易丝” –露易丝为类似的慈善机构工作对世界自然基金会的大自然。 https://www.worldwildlife.org/路易丝（Louise）资源有限，因此只能专注于2个特定国家/地区之间的野生动植物贸易（您可以选择其中2个国家或地区）。
INFS6018管理商业智能学期1 2020
•“零售查理”-查理在澳大利亚的一家大型百货商店工作关系。他们出售许多产品，包括皮草，手袋，鞋子，皮革制品和皮带。 （在2-3个产品之间选择）。
•进一步研究每个角色的角色，工作环境和决策需求，然后使用图1所示的模板描述每个角色。对于每个角色，您被要求陈述三个非平凡的问题，他们将能够通过使用您的BI应用程序。可以通过非常简单的查询来回答问题 , for example, returns only one or two values, will be considered trivial. For example, a question such as “What endangered species are imported into Australia” is considered to be trivial. On the other hand, non-trivial questions involve more than one dimension (see Step 2) and ideally have a potential to offer something interesting, insightful and even unexpected.
Persona
Role
< name of persona> e.g United Nations Jocelyn e.g Wildlife Policy Advocate UN Context of work
< describe the societal context/ work/ role of this persona. Why would this application be of interest?
Key activities
<describe the key activities relevant to this project> (e.g. United Nations Jocelyn is concerned that there is little consistency in how countries legislate for wildlife preservation and that policies may have become outdated
–Wants countries to cooperate
–Wants consistency in wildlife laws across countries (harmonization)
–Wants laws to be relevant to current conditions
–Wants laws to consider future trends BI Questions
Three questions United Nations Jocelyn would use the system to answer or demonstrate.
Question 1:
Question 2:
Question 3:
Figure 1: An Example Design Template
Step 2: Harvest, Clean and Analyse Data
In this step you need to analyse data-related needs of all personas in order to design a single multi-dimensional (MD) model of your data set, using the notation introduced in this unit.
Guided by your MD model, you need to “harvest” data i.e. download relevant .xls data from your main data source and store the resulting data records of the areas in which you are interested in Excel file. The resulting Excel file needs to show metadata of your multi- dimensional model. YOU have to determine which data you select – YOU DO NOT NEED IT
ALL!
A one major challenge you will face is to clean the data you download to ensure consistency and correctness across your data files. The CITES database, for example, holds great value but as it has been built over many years you will find considerable data quality issues which you must resolve. Note: you may or may not choose to use Tableau Prep to do this. No additional points are reserved for using Tableau Prep. Examples of issues you might face include:
Problem
Possible Solution
Use of Latin names for species
Find and replace in xls. with common name.
See CITES Appendix
Animals are not grouped appropriately for Define categories and place animals into your analytic purpose.
meaningful groups for each persona’s analytical purposes. Justify the categorization in the report.
Use different date standards (American Select one standard and ensure its applied (mm-dd-yy) vs European (dd-mm-yy) consistently across XLS. This can be done by formatting cells in xls.
Countries are not consistently named Find and replace in xls with common name E.g., U.K.,United Kingdome, Great (Britain or U.S.A, U.S. or United States or Germany and Deutschland.
Quantities are supplied in different but Find the lowest quantity and transfer all
compatible measures (e.g., k.g., g, m.g.) – quantities into this amount e.g. – k.g. to m.g.
e.g., “comparing apples with apples”
Quantities are supplied in different and Separate the two sets for analytical purposes
incompatible measures or make some sensible assumptions* regarding comparisons and apply that assumption consistently across the dataset.
*
You may need to make some assumptions regarding the data this is fine and usual, but all assumptions should be clearly outlined and justified (i.e., sensible) and included in the report see step 4. For example, “Gross/net trade outputs tend to overestimate trade levels, as –where different quantities have been reported by the importer and the exporter, the larger quantity is presented in the output” – p. 9 – CITES Trade Database – User guide, version 8.
This document further outlines how the database defines different fields.
Note that Steps 1 and 2 are often iterative rather than sequential steps. In other words, design of your MD model and data collection in Step 1, may inspire new possibilities of interesting questions in Step 1, leading to additional data being collected (Step 2)
Step 3: Design of a BI application to answer the stated questions Using the Excel file from Step 3 import your data set into Tableau.
Then proceed to use data visualisation features of Tableau to answer all questions for the three personas, as stated in your design templates (in Step 1). For each persona provide some recommendations based on your analysis to help them reach their goals.
Note that Step 3 may initiate another cycle of iterative design, prompting you to go back to Steps 1 and 2 in order to expand or refine them.
Step 4: Reflection-on-action
This important step requires us to “step-back” and consider our design in broader organisational and societal context. For the purposes of this assignment, you are required to use the PAPA framework to identify and analyse any data related ethical issues that need to
be considered in your project. Even if you think that your use of data and implementation of a
BI application do not pose any ethical risks, you are still required to demonstrate your analysis
of ethical issues using the PAPA framework (relevant documents are posted on Canvas).
Reflect on the challenges you faced when cleaning the data.
Step 5: Draw Conclusions
Summarise findings and think how they can be actioned to help each persona reach their
goals and what you would do next to help them. How would you operationalize the
findings?
Deliverables: Project Files and Group Report
Project Files (Excel and Tableau)
All Excel and Tableau files created in this project need to be uploaded and stored under your
Group folder in Canvas. These files will be used to verify and assess your work.
The files need to be submitted by the deadline for your group report to be considered. Group
reports submitted without corresponding files will not be marked.
Important: Please do not submit your project files through TurnItIn, but upload them under
your group folder on Canvas.
Your group report needs to be submitted through TurnItIn and should include the following
components:
1. Introduction – a brief description of your project and its envisaged value for different
communities of users (e.g. Why would anyone use your analysis?).
2. Design templates for three personas (i.e. 3 tables) clearly showing the corresponding non-
trivial questions (Step 1).
3. A multi-dimensional model of your data set (one model), labelled as Figure 1, with all
attributes listed and clearly defined in a separate table (Step 2) with discussion. As you are a
consultancy servicing three clients (Jocelyn, Louise and Charlie) you should just create one
star diagram will the dimensions for all three clients together.
4.(. A sample of your resulting data set (i.e. the actual Excel records), depicted as Figure 2
Step 2). To create this Figure, you may use print screen of the Excel file & paste it into a Word
document. Note that the shown data set needs to correspond to the MD model.
5. Visual answers for all stated questions (Step 3) – please use print-screen to capture your
resulting Tableau outputs and provide them as Figures (one per questions).
For example:
5.1. Persona “United Nations Jocelyn”
5.1.1. Question 1: <state the question>
Visual Answer:
provide print-screen of your answer>
<
Figure A: A visual answer showing …
Brief summary of results and their meaning and any assumptions
INFS6018 Managing Business Intelligence
Semester 1 2020
5.1.2. Question 2: <state the question>
Visual Answer:
provide print-screen of your answer>
<
Figure B: A visual answer showing …
Brief summary of results and their meaning and any assumptions
5.1.2. Question 3: <state the question>
Visual Answer:
provide print-screen of your answer>
<
Figure C: A visual answer showing …
Brief summary of results and their meaning and any assumptions
5.1.4. Include a summative paragraph outlining some recommendations based on the previous data driven analysis (questions 1-3) to help each persona achieve their goals.
5.2. Persona “Not for Profit – Wildlife Protection Charity: Louise”
5.2.1. Question 1:
…
and so on.
7. Analysis of potential data-related ethical, and quality issues (Step 4)
8. Conclusion: Summarise your lessons learned in this project (Step 5). What would you do differently next time? What would you do next if you had more time and money to make the results more meaningful? Discuss the limitations of your analysis and how you would overcome them if you had more time and money. Note: Limitations are not what you or your group personally found difficult but limitations in your analysis – perhaps because of data quality issues or because of limited access to certain data. Finally, consider what application your analysis might have to other stakeholders and what you would do next.
9. References
Assessment criteria for the Group Report (total 30 marks):
Clear detailed problem analysis, well-developed personas
Quality of questions for each persona
Clear, correct and well-designed multidimensional model
Well prepared and complete data set
Well designed and appropriate visual answers to the stated questions, written explanations and recommendations
Appropriate analysis of the observed and/or potential ethical issues using the PAPA framework Evidence of a deeper level learning and independent thinking, as evident from the conclusions drawn and suggestions to operationalise the findings .
Professional standards of overall presentation and consistent and detailed referencing style.
Please comply with the Academic Honesty and plagiarism guidelines as set out in the unit outline, and note the penalties for late assignments.