The first split is the one you will be asked for most often, and it is simple: is the data a measurement, or a description?
Quantitative
- Numbers you can count or measure
- Steps walked, price paid, minutes watched
- Easy to add up, compare and chart
- Tells you HOW MUCH, and nothing about why
Qualitative
- Words, images, sounds — descriptions
- A review, an interview, a photo
- Harder to compare, needs reading or coding first
- Tells you WHY, and rarely how much
The best answers use both: A shop's till data says sales fell 12%. Its reviews say the new packaging is hard to open. Neither number nor comment explains the drop on its own.
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Metadata is data about other data. A photo's metadata is not the picture: it is the time, the camera, and often the exact place the picture was taken. People share the photo without thinking about the rest of it.
Metadata you carry around without noticing
- Photos — time, date, camera, and often the exact location.
- Messages — who, to whom, when, how often, from where. Not what you said.
- Files — who made it, who edited it, and when.
- Web pages you visit — the address, the time, and how long you stayed.
Why metadata is examined: Metadata is often treated as less private than content, and it can reveal more. Who you called at 3 a.m., and how often, says a great deal without anyone reading a word.
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The guide also names the kinds of data by subject, because different kinds carry different risks and different rules. You are not asked to memorise all of them — you are asked to say why the kind matters.
| Kind of data | Example | Why the kind matters |
|---|---|---|
| Medical | A diagnosis, a prescription | Most tightly protected; a leak cannot be undone |
| Financial | Transactions, credit score | Directly usable for fraud |
| Geographical | Where a phone has been | Reveals home, work, worship, health visits |
| Meteorological | Rainfall, temperature | Usually open, and valuable to everyone |
| Cultural | What you read, watch, listen to | Reveals belief and identity indirectly |
| Transport | Journeys taken, tickets bought | Pattern of life, and who you travel with |
Real-world examples you can name
The EU General Data Protection Regulation (GDPR) — in force since May 2018
A regulation giving people rights over data held about them — to see it, correct it, have it erased and take it elsewhere — and requiring a lawful basis before an organization may process it at all. Fines can reach 4% of a company's worldwide annual turnover.
Who it affected: Everyone in the EU, and any organization anywhere that handles EU residents' data.
Aadhaar, India's biometric ID system — launched 2009; Supreme Court ruling September 2018
A national identity number linked to fingerprints and iris scans, used to authenticate access to services. The Supreme Court upheld the scheme for welfare and tax purposes but struck down its use by private companies, and reporting has linked authentication failures to people being denied rations.
Who it affected: Over a billion enrolled residents, and in particular those whose fingerprints scan poorly — manual labourers and older people.
How this is tested — you have to name the type of data a system uses and say why the type changes what is at stake. It comes up two ways:
Paper 1 — structured question
- Part a: identify types of data a described system collects
- Part b: explain why one type is more sensitive than another
Paper 2 — source-based question
- Q1: read what kind of data a source presents
- Q2: explain how a source is using quantitative evidence
The trap: listing instead of choosing: “It collects data” is worth nothing. Name the type, and then the risk follows: geographical data reveals where you sleep, medical data cannot be taken back.
Identify two types of data a fitness tracker collects.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.
Explain one reason why the metadata from a photo can be more revealing than the photo itself.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.