In this assessment you will develop an interactive data visualisation, using a Tableau (and possibly another specialist tool like Power BI, Qlik etc), to derive useful insight from a dataset. A successful approach will depend on the nature of your chosen data and the types of questions you set out to answer. For instance, your visualisation might be an information graphic designed for publication on a popular website that tells a factual story around a topic (e.g. changes in third world prosperity over the last decade). Alternatively, your visualisation might be more business-oriented, such as a dashboard designed to help a manager monitor performance or an analytics tool that helps to inform a strategic decision. In any case, your final design and implementation will be a composition of multiple views (charts/graphs) constrained to a single screen that follows theory and best practice in terms of visual representation, presentation and interaction.
You will demonstrate all the module learning outcomes through this assessment. To pass these learning outcomes you need to design and implement an interactive data visualisation using data from a specified domain (e.g. social, business, scientific), which communicates some relevant and non-trivial insight to the user (LO1). You should then critically reflect on the issues faced throughout the process and explaining how you applied theory and technical knowledge to resolve these during design and implementation (LO2). You will start the task by working in a small group where you will discover your data and discuss possible questions around this data. However, the submitted proposal and final solution and report should be an individual effort.
The task is split into two parts: The Proposal and the Final Report and Implementation. The proposal is a formative assessment; it is not graded and its purpose is to help you reflect upon and refine your early ideas. You will begin this part of the task by working as a group to select and prepare a shared dataset. Within your group, you should identify a range of likely personas (types of user) and formulate a pool of questions that these users might want to ask of this data. Individually, you will then choose a persona and up to two relevant questions and develop a proposed solution that you believe will enable effective and efficient answers to those questions. Hence, the aim of the Proposal is to create a prototype (mocked-up) design which shows your visualisation ideas and how you intend to use these views to answer your questions and generally meet all specified requirements.
You will submit the Proposal as two-page report. Whilst this deliverable does not contribute directly to your module grade, you will receive valuable formative feedback and additional marks will be awarded to students who make demonstrable use of this feedback in their final report.
The second and most important part is the Final Report and Implementation. Following your proposal and any feedback received, you will implement and evaluate your design as a dashboard in Tableau (or similar application). You should work individually on all parts of this task. You will submit both your project files and a written report that describes and critically discusses the whole task process. This part contributes 100% to your final grade and you must pass (C- or higher) this part to pass the module overall. Be sure to read and understand the marking criteria, particularly those required for a pass, before proceeding with this part of the task.
At the start of the task, you should form a group of three or four students. Ideally, you will self-select but you can also ask the module leader to allocate you to a group. If you take the first option, then you must inform the module leader of your group membership by Week 20 at the latest. All students who are not registered in a group by the end of this week will be assigned to one. If you are unfamiliar with working with quantitative data it is recommended that you team up with at least one other classmate who is competent in this area.
You should take a structured approach to the task following the following steps:
• Data selection (Group)
• User type/persona specification (Group to pool alternatives, then individual choice)
• Question formulation (Group to pool questions then individual choice)
• Requirements specification (Individual)
• Prototype designs (Individual)
• Implementation (Individual)
• Critical evaluation (Individual)
In your group, you will select an appropriate dataset that is interesting and comprehensible to all members. These data should exist (or be transformable into) a table comprising at least 10 columns (variables) and at least 500 rows (cases) and be in a format that can be downloaded and saved locally (e.g. as an Excel, CSV or SQLite file). If you wish to use a table that you feel is suitable but does not meet these criteria, please discuss with the module leader prior to the Proposal deadline. When you begin working on your individual solution, you are free to extend or adapt the datatable(s) to suit your requirements (e.g. by joining, adding or deriving new columns). However, members of your group should all work on the same ‘base’ dataset.
Try to avoid very large datasets that consume excessive system RAM or ones that require complex, dynamic multi-table joins which may significantly impair the run-time responsiveness of your solution. For instance, if you need to perform lots of joins between multiple source tables, consider merging the required data into a single/smaller number of tables. If your source dataset is very big (i.e. >50 MB), think about how you can reduce its size by sampling rows and/or pruning unnecessary columns.
The data should also come with sufficient metadata and documentation to enable you to formulate valid questions and draw credible conclusions from your analysis. A ‘data dictionary’ (i.e. a table listing variable names, descriptions and domains) of all variables relevant to your analysis should be included in both your proposal and report.
You’ll find it easier to formulate interesting and useful questions if you have a specific user type or ‘persona’ in mind. For instance, if your data relates to crime statistics, you could apply the persona of a ‘house-buyer’ who is moving to a new town or region and wants to find a safe area to live that is also close to their workplace. An alternative persona might be a ‘senior police officer’ who wants to monitor current status and changes of crime levels across their jurisdiction. Taking a specific user perspective should make it relatively easy to think of valid questions along with other requirements that would be relevant to such a person. You can brainstorm personas as a group, but normally a specific persona should only be adopted by one member, unless you are able to think of a large number of relevant and distinct questions. If you experience difficulty thinking up sufficient personas or questions you may need to change or modify your dataset.
You must come up with either one or two distinct questions that your persona might ask. Setting and answering two questions will make it easier to achieve a B grade (see marking scheme below). These should be relevant (to your persona), feasible (using your data) and non-trivial in nature. Most importantly, they should be the kinds of question that are arguably easier to answer using a good visualisation as opposed to standard quantitative analysis method. For instance: “Were average house prices in 2014 higher in London or in Birmingham?” is a poor example because this can be answered simply by computing and comparing two numbers. On the other hand: “How has the ratio of house-prices to median income changed across regions of the UK in the last 10 years?” is a better example because it is an open question that requires the analyst to perceive and interpret complex and possibly subtle patterns in the data and how they change over time.
So, in your proposal, you will declare up to two questions. The second question should be distinct from the first such that it requires a different view of the dataset to answer. Most importantly, each question should be different from those proposed by other group (and class) members and thus require a different solution. If any issues are identified in your proposal feedback, you should revise the question(s) accordingly and provide an explanation of this in relevant section of the final report.
When designing any kind of data visualisation, it is important to consider the user requirements. These relate to the functional behaviours (e.g. views, interactions) and other qualities or criteria (e.g. data/hardware requirements, look and feel) that your final solution needs to provide a good user experience. At least some of the requirements should relate directly to specific questions asked: What data is required to answer this question (perhaps you also need to join additional data to your existing set)? What visual relationship or analytical task does the user need to perform (e.g. correlation, part-whole, nominal comparison; see lecture 3)? Other requirements might relate to relevant aspects of the user’s background (e.g. experience using statistical graphics), context of use (e.g. indoors/outdoors, private/public) and the device through which they will interact with your visualisation. Aim to produce between three and six distinct requirements and present them in your proposal/report as a numbered list (R1, R2, etc). When presenting both your prototype design and final implementation you should make explicit reference to each requirement, explaining how it has been met.
Taking your data, questions and user requirements into account, you will sketch a prototype design (using the Paper Landscapes or similar prototyping method) to illustrate how your intended solution will look and function. You should create your initial design, for the proposal, before you have attempted any kind of implementation i.e. your design at this stage should be your ‘ideal’ solution, guided only by the theory and best practice that you have learnt during the course, not constrained by the limitations (perceived or actual) of your intended visualisation tool. Pay careful consideration to representation (e.g. visual encodings, graph choice), presentation (e.g. view/axis arrangement, colour schemes) and interaction (e.g. view coordination, brushing and filtering) choices throughout the design and implementation process to ensure you meet your stated requirements. The proposed solution should contain at least two different types of chart (e.g. bar, line, choropleth map, Treemap etc.) and at least three separate views (charts) of the data that are composed neatly and coherently into a single screen overview. During both design and implementation, you can assume a maximum resolution of 1080p but smaller is permitted, for instance if your intended display medium is a mobile device.
After the proposal you will refine your design, in response to feedback received and any known constraints imposed by Tableau. You should present both the original prototype and an annotated screenshot of the final implementation in the report, explaining how and why the design evolved during the implementation process. For a higher grade (DSD students especially) you might also devote some space to discussing how HCI/UCD/UX theory, principles or methods were (or could potentially) be applied to improve the design process and overall user experience. Any discussion of this kind must be properly supported with relevant references.
You will then implement your revised design using Tableau. Remember, no evidence of implementation is required at the proposal stage. However, in the final report you should describe, clearly and concisely, how you implemented your solution, discussing any significant issues faced during the process and how you overcame them (or not). You do not need to explain every minute step of the implementation process but there must be enough detail to allow a reasonably competent Tableau user to replicate your work. You must submit all files required to run your final implementation together with the report. For a higher grade, you might attempt a second implementation using a different tool (e.g. Power BI or Qlik). You are not required to describe the implementation at the same level of detail as the Tableau solution but you should submit all required files and, in the report, present a screenshot of the visualisation along with a few sentences (100-200 words) explaining key issues encountered and where/why/how this differs from the proposed design and/or Tableau solution.
At the end of the final report, you should evaluate how successful your implementation(s) was in answering your questions. Extra credit is available (at A grade level) if you are also able to demonstrate how you made an unexpected insight using your visualisation i.e. one that is unrelated to the prior questions. This must be a genuine, non-obvious discovery that was not considered or expected during the planning/design process. Achieving this might require some further exploratory interaction and/or view transformation but cannot involve any additional views that were not specified in the design or required to answer the planned questions. The process of achieving both planned and unplanned insights should be clearly (but concisely) described, using screenshots where helpful. These ‘walkthroughs’ should be sufficiently detailed to allow a competent third-party (i.e. the marker) to repeat the process using the submitted project. Any insights achieved should be stated explicitly and be consistent with the walkthrough description.
Finally, you should provide a reflective discussion on the overall success of the project. This should include some justified critique of the strengths and weaknesses of the tool(s) used, some considered reflection on your overall learning experience during the module and at least one justified personal learning objective you want to achieve in the near future.
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