Why we visualise data, software features and finding patterns: HSC Enterprise Computing Data Visualisation
“Explain the purposes of data visualisation, including simplifying understanding, telling a story and highlighting significant results; describe how features of software contribute to a better understanding of datasets through data visualisation, including spreadsheets, creative design applications and combining applications to track trends and forecast; identify patterns in data by interpreting and comparing datasets for an enterprise, social or ethical issue to highlight trends and for predictive data analytics”
Data visualisation simplifies understanding, tells a story and highlights significant results. Spreadsheets give accurate charts, trendlines and forecasts, design applications make visuals engaging, and combining tools lets enterprises track trends and forecast. Comparing datasets reveals trends, seasonality, correlations and outliers that support predictive analytics.
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What this dot point is asking
This is the "why" of data visualisation. You need to explain the three purposes NESA names, describe how software features help people understand data (including combining applications), and use visualisations to compare datasets, identify trends and support prediction.
The answer
The three purposes
- Simplifying understanding: people read shapes, colours and positions faster than rows of numbers.
- Telling a story: a sequence of visuals with titles and annotations explains what happened and why it matters.
- Highlighting significant results: colour, size and labels draw attention to outliers, targets and key changes.
How software features help
- Spreadsheets: charts, trendlines, forecast functions, conditional formatting, pivot charts, sparklines and dashboards with slicers. Best for accurate calculation and quick analysis.
- Creative design applications: layout, typography, icons, colour palettes and branding for infographics and reports aimed at general audiences.
- Combining applications: analyse in a spreadsheet or database, visualise in a business intelligence tool, then refine in a design tool; or link live data to a dashboard so charts update automatically. Combining tools lets enterprises track trends over time and forecast (trendlines, moving averages, projected values with uncertainty bands).
Choosing a visual
| Purpose | Chart |
|---|---|
| Trend over time | Line chart |
| Compare categories | Bar or column chart |
| Part of a whole (few categories) | Pie or stacked bar |
| Relationship between two variables | Scatter plot |
| Distribution | Histogram or box plot |
| Location | Map or heat map |
| Connections | Network diagram |
Identifying patterns by comparing datasets
Comparing datasets reveals relationships that one dataset alone cannot. For an enterprise issue (sales against advertising), a social issue (public transport use against fuel prices) or an ethical issue (loan approvals by postcode), look for:
- Trends: steady rises or falls.
- Seasonality: regular repeating patterns.
- Correlations: variables moving together.
- Outliers: unusual points that need investigating.
- Gaps between groups: differences that may signal unfairness.
These patterns feed predictive data analytics: extending a trend, modelling seasonality or using regression to forecast.
An electricity retailer compares daily household usage with daily maximum temperature for two summers.
- Scatter plot: usage against temperature shows a curve; usage rises sharply above 32 degrees (air conditioning).
- Line chart over time: shows usage peaks on the same days as heatwaves.
- Pattern: a strong relationship above a temperature threshold.
- Prediction: using next week's temperature forecast, the retailer predicts peak demand and buys extra electricity in advance.
- Story: a dashboard headline, "Heatwaves drive 40% higher demand", with the scatter plot and forecast.
- Listing chart types without purpose
- Always link the chart to what the audience needs to see.
- Treating a trend as a guarantee
- Forecasts carry uncertainty; say so.
- Comparing datasets with different time periods or units
- Align them first.
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 each purpose of data visualisation using a council's report on household recycling rates.Show worked solution →
- Simplifying understanding: a bar chart of recycling rates by suburb is easier to read than a table of 40 numbers.
- Telling a story: a line chart with annotations shows rates rising after new bins were introduced in 2024.
- Highlighting significant results: the two suburbs below target are coloured red so councillors notice them.
Marking guide: 1 mark per purpose applied to the scenario.
core4 marksDescribe how a spreadsheet and a creative design application could be combined to track and communicate a trend in a charity's donations.Show worked solution →
The spreadsheet stores monthly donations, calculates totals and percentage change, and produces a line chart with a trendline and a forecast for the next six months (for example with the FORECAST function or a trendline extended forward).
The chart is exported into a creative design application, where the charity adds its branding, a clear headline ("Donations up 18% since the appeal launched"), icons and short explanations to create an infographic for supporters on social media.
Combining them keeps the analysis accurate (calculations stay in the spreadsheet) while making the message engaging for a general audience.
Marking guide: 2 marks for spreadsheet features, 1 mark for design application features, 1 mark for the benefit of combining.
exam6 marksA health department compares two datasets: weekly flu cases and weekly vaccination numbers over three years. Explain how visualisation could identify patterns in this social issue and support predictive analytics, and discuss one caution.Show worked solution →
- Visualising the datasets
- A dual line chart (or two aligned line charts with the same time axis) shows both series over three years. A scatter plot of vaccination numbers against flu cases a few weeks later shows whether higher vaccination is associated with fewer cases.
- Patterns
- The line charts reveal seasonality (cases peak each winter), whether vaccination campaigns start before peaks, and whether peaks are smaller in years with higher vaccination. A heat map by region and week can show where outbreaks start.
- Predictive analytics
- Seasonal patterns plus current vaccination rates can feed a forecast of next winter's cases, shown as a projected line with an uncertainty band, helping plan hospital staffing and vaccine orders.
- Caution
- Correlation is not causation: fewer cases may also reflect a milder strain or less testing, and dual-axis charts can exaggerate relationships if the scales are chosen poorly. The department should state limitations and use consistent scales.
Marking guide: 2 marks for suitable visualisations, 2 marks for patterns identified, 1 mark for predictive use, 1 mark for a caution.