Creating a data visualisation and keeping its data secure: HSC Enterprise Computing Data Visualisation
“Research, source, organise and store data appropriate for a data visualisation; design and develop a data visualisation for a specific scenario to represent trends, patterns and relationships, and illustrate predictive analysis incorporating big data; investigate and implement methods to maintain data security, including cybersecurity and data backup”
Create a data visualisation by defining the question and audience, sourcing and checking data, cleaning, organising and storing it, then designing and developing visuals that show trends, patterns, relationships and clearly marked predictions. Protect the data with authentication, role-based access, encryption and minimisation, plus tested backups following the 3-2-1 rule.
Jump to a section
What this dot point is asking
This is the practical outcome of the focus area. You need to describe (and in class, carry out) the process of creating a visualisation for a scenario: gathering and preparing data, designing and developing visuals that show trends, patterns, relationships and predictions, and keeping the data secure.
The answer
1. Research, source, organise and store data
- Define the question and audience first, so you know what data is needed.
- Research and source: primary data (surveys, sensors, internal systems) and secondary data (government open data, ABS, industry reports). Check relevance, accuracy, validity, reliability, ownership and licence.
- Organise: clean the data (duplicates, missing values, inconsistent formats and units), structure it in tables with consistent fields, join datasets on common keys (date, location) and document it with metadata and a data dictionary.
- Store: choose storage that suits the volume and sharing needs (spreadsheet, database, cloud data warehouse), with access controls and backups.
2. Design and develop the visualisation
- Sketch first: plan layout, chart types and interactions on paper or in a wireframe tool.
- Show trends with line charts, patterns with heat maps or seasonal charts, relationships with scatter plots, and comparisons with bar charts.
- Illustrate predictive analysis: extend trends or models forward (dashed projected lines, uncertainty bands), clearly separated from actual data, with assumptions stated.
- Incorporate big data: aggregate and filter large datasets, use drill-down so users can move from overview to detail.
- Develop and test: build in the chosen tool, then test with users against UX criteria and check accuracy against the source data.
3. Maintain data security
Cybersecurity
- Authentication with strong passwords and multi-factor authentication.
- Authorisation with role-based access and least privilege.
- Encryption in transit (HTTPS) and at rest.
- Patching, antivirus and firewalls; logging and monitoring of access.
- Data minimisation and de-identification in shared visuals.
Data backup
- Regular automated backups (full, incremental or differential).
- The 3-2-1 rule: three copies, two media, one off-site.
- Version history to recover from mistakes or ransomware.
- Regular restore testing.
A school's sports department wants a dashboard of student participation in sport over five years.
- Source: export participation records from the school's database; collect enrolment numbers per year.
- Organise: remove duplicate records, standardise sport names, calculate participation rate per year group.
- Store: a school-managed cloud database with staff-only access.
- Design: line chart of participation rate over five years, stacked bar of sports by year group, scatter of participation against distance from school, and a projection for next year.
- Security: students' names are removed from the dashboard dataset; staff log in with MFA; the database is backed up nightly with monthly restore tests.
- Skipping data preparation
- Cleaning and organising usually takes most of the time and decides accuracy.
- Presenting predictions as facts
- Distinguish projected values visually and state assumptions.
- Treating security as only passwords
- Include access control, encryption, minimisation and backup.
Practice questions
Original practice questions graded from foundation to exam level, each with a full worked solution. Try them before revealing the solution.
foundation3 marksOutline three steps to prepare a downloaded dataset of local air quality readings before visualising it.Show worked solution →
- Check the source and licence (for example a state environment agency's open data) and record the metadata.
- Clean the data: remove duplicate readings, handle missing values (flag or interpolate) and fix inconsistent date formats and units.
- Organise and store it: structure it as a table with one row per reading (date, time, station, pollutant, value) and save it in a secure, backed-up location.
Marking guide: 1 mark per step.
core4 marksA small online business stores its sales dashboard data in a cloud spreadsheet shared by link. Recommend four methods to maintain the data's security.Show worked solution →
- Replace link sharing with named accounts and role-based access, so only staff can view and only the owner can edit.
- Turn on multi-factor authentication for all accounts to stop stolen passwords being enough.
- Enable version history and scheduled backups to a separate storage service (following the 3-2-1 rule) so data can be restored after deletion or ransomware.
- Remove unnecessary personal data (customer names and addresses) from the dashboard dataset, keeping only what the charts need.
Marking guide: 1 mark per justified method.
exam7 marksDescribe how you would design and develop a data visualisation for a local council that shows trends in water use over ten years, relationships with rainfall and population, and a prediction for the next three years. Include how you would secure the data.Show worked solution →
- Research and source
- Obtain monthly water use from the water utility, rainfall from the Bureau of Meteorology and population estimates from the ABS. Record sources, licences and dates.
- Organise and store
- Clean and align the datasets by month and suburb, store them in a database or structured tables, and document fields in a data dictionary.
- Design
- Sketch a dashboard for councillors: a headline figure (total use this year versus last), a line chart of water use over ten years, a scatter plot of monthly use against rainfall, a chart of use per person to separate population growth from behaviour, and a map by suburb.
- Predictive analysis
- Fit a model (for example regression using population growth, average rainfall and seasonality) and show the next three years as a dashed projected line with an uncertainty band and stated assumptions.
- Develop and test
- Build in a business analytics tool or spreadsheet, add filters for suburb and year, and test with councillors against criteria such as "can identify the highest-use suburb within 20 seconds".
- Security
- Use role-based access (public version shows aggregated data only), multi-factor authentication for editors, encryption, logging of changes, and automated backups to a separate location with regular restore tests.
Marking guide: 1 mark each for sourcing, organising, design, prediction, development and testing, and security (6 marks), 1 mark for a coherent process tied to the scenario.