User experience (UX) in data visualisation: HSC Enterprise Computing Data Visualisation
“Use graphic design tools to assist in the graphic development of a data visualisation; explain how user experience (UX) influences the development of effective data visualisations, including relevance to the audience, audience interpretation, customisation and live analysis; develop and implement criteria for evaluating the effectiveness of user experiences; investigate the impact of emerging hardware and software technologies on user interface (UI) and UX design and development”
Graphic design tools and principles make visualisations readable and accessible. UX shapes effective visuals through relevance to the audience, audience interpretation, customisation and live analysis. Evaluate UX with measurable criteria such as effectiveness, efficiency, satisfaction, learnability and accessibility through user testing. Emerging technologies such as voice, AI, mobile and AR are changing how people interact with data.
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What this dot point is asking
A visualisation succeeds only if the people using it can understand and act on it. You need to use graphic design tools, explain how UX shapes effective visualisations using four named factors, write and apply UX evaluation criteria, and investigate how new hardware and software are changing UI and UX.
The answer
Graphic design tools and principles
Graphic design tools (colour pickers and palettes, layout grids, typography, icons, vector editing) support principles that make visuals readable:
- Hierarchy: the most important number or chart is largest and first.
- Contrast: highlight what matters; mute the rest.
- Alignment, proximity and white space: group related items and reduce clutter.
- Consistency: the same colours mean the same categories everywhere.
- Accessibility: sufficient contrast, colour-blind-safe palettes, text labels and alternative text.
How UX influences effective visualisations
- Relevance to the audience: show the measures, detail and language the user needs for their decision.
- Audience interpretation: match the chart type and complexity to the audience's data literacy and culture; label clearly; avoid ambiguous colours or icons.
- Customisation: let users filter, change date ranges, choose measures and save views, so one dashboard serves many users.
- Live analysis: connect to live data so users see current information and can explore it interactively, with a clear "last updated" time.
Developing and implementing UX evaluation criteria
Good criteria are specific and measurable:
- Effectiveness: percentage of test users who complete a task correctly.
- Efficiency: time or number of clicks to complete a task.
- Satisfaction: user ratings or survey responses.
- Learnability: how quickly a new user becomes competent.
- Accessibility: compliance with accessibility guidelines (contrast, keyboard access, screen reader labels).
Implement them through usability testing (observe real users doing set tasks), surveys, analytics (where users click or drop off) and A/B testing of two designs, then refine and retest.
Emerging hardware and software and UI/UX
- Touch, gesture and mobile: visuals must work on small screens with tap-to-drill interactions.
- Voice and natural language: users ask "What were sales in March?" and the tool generates a chart.
- AI-assisted analytics: software suggests charts, highlights anomalies and writes plain-language summaries.
- Augmented and virtual reality: 3D data exploration, overlaying data on real environments (maintenance data on machinery).
- Wearables and large displays: glanceable data on watches; wall-sized control-room dashboards.
These make data more accessible and immediate, but raise new design challenges: less screen space, explaining AI-generated insights, accessibility of new modes, and privacy of voice and location data.
A café owner and her accountant use the same sales dashboard.
- Relevance: the owner sees today's sales, top items and staff costs; the accountant's saved view shows monthly profit and tax categories.
- Interpretation: simple bar and line charts with plain labels for the owner.
- Customisation: each user saves their own filters.
- Live analysis: the owner's view updates every 15 minutes from the point-of-sale system.
- Evaluation criterion: the owner can identify the best-selling item today within 10 seconds on her phone; testing shows she can, after the top items chart is moved to the top of the mobile layout.
- Confusing UI with UX
- UI is what users interact with; UX is how the whole experience works for them.
- Writing vague criteria ("easy to use")
- Make criteria measurable.
- Designing for yourself
- Test with the real audience.
Practice questions
Original practice questions graded from foundation to exam level, each with a full worked solution. Try them before revealing the solution.
foundation3 marksExplain how graphic design principles improve a data visualisation, with two examples.Show worked solution →
Graphic design principles direct attention and reduce effort to understand data.
- Contrast and colour: highlighting the key bar in a strong colour and greying the rest makes the main result obvious.
- Alignment and white space: aligned charts with clear spacing and consistent fonts make a dashboard easier to scan.
Marking guide: 1 mark for the general purpose, 1 mark per example.
core4 marksDevelop four criteria to evaluate the user experience of a school attendance dashboard used by year advisers, and explain how you would test one of them.Show worked solution →
Criteria.
- Advisers can find a student's attendance rate in under 30 seconds (efficiency).
- Advisers correctly identify students below 90% attendance (effectiveness).
- The dashboard meets accessibility guidelines, such as sufficient colour contrast and text labels (accessibility).
- Advisers rate it 4 or more out of 5 for usefulness (satisfaction).
Testing criterion 1. Give five advisers realistic tasks ("Find Sam Lee's attendance this term"), time them and note where they hesitate. If the average exceeds 30 seconds, add a search box and improve navigation, then retest.
Marking guide: 2 marks for four measurable criteria, 2 marks for a valid test procedure.
exam6 marksA state emergency service wants a public bushfire dashboard. Explain how UX considerations (relevance to the audience, audience interpretation, customisation and live analysis) should shape its design, and assess the impact of one emerging technology.Show worked solution →
- Relevance
- The public need to know whether they are at risk now and what to do, so the main view is a map of current fires and warning levels near the user, not detailed fire behaviour statistics.
- Audience interpretation
- Use the nationally recognised warning levels and colours with text labels and plain-language actions, large readable text, and icons that work for people with low literacy or English as an additional language. Avoid colour-only coding.
- Customisation
- Users can save locations (home, school, family) and choose alerts for those areas, making the dashboard personally relevant.
- Live analysis
- Data must update in near real time, show when it was last updated, and load quickly on mobile networks, because out-of-date information is dangerous.
- Emerging technology
- Location-aware mobile devices and push notifications let the dashboard alert users automatically when they enter a warning area. This greatly improves relevance and speed, but depends on phone coverage (which may fail during fires) and on users granting location access, so it must be backed up by radio and other channels.
Marking guide: 1 mark per UX consideration applied (4 marks), 2 marks for assessing an emerging technology's impact with a limitation.