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For a typical hackathon demo, create a small fixture from scratch that matches the screens and user flows you need to show. Use hand-authored JSON or CSV for a few predictable states, or a generator such as Faker when you need more variety. Don’t copy customer, coworker, event-participant, or social-profile records and change a few fields: sampled rows still represent real people. A convincing demo fixture is not the same as statistically representative data or a privacy-reviewed release.
Start with what the prototype needs to show
List the screens in the demo journey, then note the fields and relationships each screen actually uses. A profile page may need a display name, fictional email, status, and linked orders; a dashboard may need dates and amounts that produce the intended view. Avoid inventing extra personal details just to make a record look realistic.
The UK Government’s Data and AI Ethics Framework recommends limiting data to its purpose and considering synthetic or anonymised data, including for testing. For a short demo, the useful goal is often to make the interface and its flows behave plausibly—not to imitate a population.
Choose the simplest fixture that fits
| Approach | Best suited to | Trade-off or limit |
|---|---|---|
| Hand-authored JSON or CSV | A short demo with a few known UI states and no need for statistical realism | Gives direct control, but you maintain relationships and edge cases yourself. |
| Faker in Python | Programmatically creating varied records, localized values, and repeatable test data | Field generators make plausible-looking values; they do not establish statistical fidelity or privacy. |
| Microsoft Synthetic Data Showcase | Teams exploring privacy-oriented techniques or aggregate views | Suitability depends on the use case and risk model; its documentation describes utility risks and attribute-inference concerns. |
| Statistical synthesis from real data | Work requiring selected population relationships or group structure | Needs more governance and assessment of both data utility and disclosure risk. |
The Office for National Statistics (ONS) notes that simple synthetic data matching properties such as row count, columns, or file size can help estimate code or process behavior and support development while access to real data is arranged. More complex methods may preserve selected statistical properties, but “Synthetic data will not preserve all features of the real data they represent.” Choose the level of complexity to match the question the data must answer; a visually convincing fixture alone does not need to mimic population statistics.
Build records around screens and flows
Hand-author a small, deliberate set
For a compact demo, write a few records directly in the format your app consumes. This makes it easy to guarantee a particular name, status, amount, or relationship appears when the presenter needs it. Use fictional contact-like values and avoid combinations that could accidentally point to a real person.
Generate variety when it saves time
Faker’s documentation describes generators for common fields, locales, and custom workflows. Use these for values such as names, dates, and addresses when variety is useful, then set important status values and relationships deliberately. A random generator should not decide whether the key success, error, or empty state appears during a live demo.
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Include explicit edge cases
- A normal successful path that supports the main story.
- An empty state and a record with an optional field missing.
- Long text, boundary values, and invalid input for layout or validation checks.
- Linked records that resolve correctly, such as a user with related orders.
Use only fields your prototype needs. Keep the values fictional, and inspect combinations of dates, locations, roles, and events rather than assuming that removing a name eliminates every identifying clue.
Make the demo repeatable
Faker supports seeding a generator: using the same methods and the same Faker version reproduces the same output. Its documentation also warns that results can change across patch versions, so pin the exact version if the generated output itself matters. Keep the generation script, schema, and fixture version with the project so teammates can recreate the records.
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- Define the fixture schema and the few UI states the demo must cover.
- Write the records by hand or generate them with Faker, making critical edge cases explicit.
- If using Faker, seed it and pin the version; preserve the script alongside the app.
- Run the UI and integration paths against those fixtures, checking constraints, relationships, and visible edge states.
Validate what the fixture can—and cannot—prove
Check whether each value looks plausible in its screen context, whether the app accepts the intended constraints, and whether linked records resolve. A fixture can reveal layout, validation, and integration problems, but it is not evidence that the prototype will perform well on production data. ONS and the UK Government Digital Service (GDS) both caution that synthetic data has limitations: GDS writes, “Synthetic data is just as vulnerable to weakness, bias, omission and so on, as real-world data.” Poorly designed data can contain unrealistic patterns, skew, or missing relationships, so test it against the specific behavior you intend to demonstrate.
Do not treat random values from a generator as representative measurements. If your task requires population relationships, model evaluation, or statistical conclusions, use a separate quality assessment and privacy review appropriate to that purpose. A demo fixture built to exercise screens does not establish those properties.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep real source records out of ordinary demo fixtures
Changing a name while retaining a rare combination of dates, locations, roles, or events can leave identifying clues. GDS warns that anonymised material may be reconstructable in some circumstances. A generator is not a privacy guarantee either: ONS says synthetic data should be unlikely to accurately reproduce real records.
If the work genuinely requires generation from real people’s records, keep it in an approved environment, document why each field is needed, assess disclosure risk before release, and have the responsible data owner approve distribution. ONS assigns public-sharing decisions to the information asset owner and data controller and calls for detailed disclosure-risk assessment for publicly shared synthetic data.
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When specialized synthesis is justified
Microsoft’s Synthetic Data Showcase documentation describes differential privacy for situations where cumulative privacy loss across repeated releases needs to be quantified, and k-anonymity synthesizers for one-off releases that need precise combination counts at a selected privacy resolution. The project also cautions that k-anonymity approaches may be unsuitable when attribute inference through homogeneous groups is a concern. These are recommendations for that project’s techniques, not universal prescriptions; the right choice depends on the data, threat model, and intended audience.
For ordinary hackathon fixtures created without personal source records, hand-authored files or Faker are usually the proportionate starting point. Statistical synthesis is a different undertaking, with privacy and utility questions that a good-looking demo alone cannot settle.
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