week 3

3.3 Data taxonomy

What is a Taxonomy?

Think of a taxonomy like a family tree, but for data. It’s a way of splitting things into groups so we know what type of data we’re dealing with.

 

3.3.1 Definition of qualitative and quantitative, its purpose, and how data is categorised

Quantitative

quantitative data – which basically means numbers. If you can count it or measure it, it’s quantitative.

Two Types of Quantitative Data can be

Discrete Data

• Discrete means things you can count in whole numbers.

• You can’t have half of one, it’s either 1, 2, 3… but not 2.5.

• In IT support/security:

  • How many times a student typed the wrong password.

  • The number of emails flagged as spam.

  • How many viruses an antivirus tool finds.

 “How many login attempts failed this morning?” and you answer “7”, that’s discrete data.

Continuous Data

• Continuous means measurements – and you can have decimals.

• In IT support/security:

  • The server room temperature (22.3°C, 22.4°C, etc.).

  • Bandwidth speed during an esports match (245.6 Mbps).

  • CPU load (%) on a computer.

“What’s the server temperature right now?” and it says “23.5°C” – that’s continuous data.

Both are useful, but in different ways:

• Discrete data is great for counting events – like how many people tried to hack into your system.

• Continuous data is better for monitoring performance – like spotting if your server is overheating or slowing down.

Take Amazon Web Services (AWS) they’re running thousands of servers worldwide they use discrete data to count login attempts and block suspicious ones. At the same time, they use continuous data to monitor server performance. If both types spike at once, they know something is wrong.

Qualitative.

What is Qualitative Data?

Qualitative data is about descriptions, opinions, and categories rather than numbers.

Types of Qualitative Data;

Categorical (or Nominal) Data

Data that can be sorted into groups, but the groups don’t have a natural order.

• In Digital Support & Security:

  • Type of cyberattack: phishing, malware, ransomware, brute force.

  • Operating system: Windows, macOS, Linux.

  • User role: student, staff, admin.

It’s like labels – they tell you what “type” something is, but not which one is bigger or better.

Ordinal Data

Data that can be put in a ranked order, but the gaps between them aren’t necessarily equal.

• In Digital Support & Security:

  • Student feedback on password security training (Poor, Okay, Good, Excellent).

  • Cybersecurity risk ratings: Low, Medium, High, Critical.

  • Priority of support tickets: Urgent, Medium, Low.

So ordinal data has a sense of order, but it’s not really about numbers. “High risk” is more serious than “Low risk,” but we can’t say it’s exactly “two times” more serious.

Quantitative data is great for spotting patterns in numbers – but qualitative data adds the human side:

• What people think

• How people feel

• Why something is happening

NCSC (National Cyber Security Centre, UK):

They collect quantitative data about how many phishing emails are reported but they also collect qualitative data from feedback surveys asking staff how confident they feel spotting phishing emails, by combining the two, they can judge not just how many phishing attempts are happening, but also how well people are prepared to deal with them.

Case Study: College Cybersecurity Awareness

Your college has recently run a campaign to improve cybersecurity awareness among students and staff. The IT support and security team collected both quantitative and qualitative data to see if it worked.

Data Collected:
• Quantitative (numbers):
   - 1,200 phishing emails reported in Term 1.
   - Only 450 phishing emails reported in Term 2.
   - 95% of students logged in successfully without needing password resets.

• Qualitative (opinions/descriptions):
   - “I feel more confident spotting phishing emails now.”
   - “The password rules are still too complicated.”
   - “Training was useful but too short.”
   - Risk ratings given by IT staff: Low, Medium, High.

Task Part 1 – Analysis (20 mins, group work)

Work in small groups and:
1. Identify the quantitative data in the case study.
2. Identify the qualitative data in the case study.
3. Explain how each type of data helps the IT team understand the effectiveness of the campaign.
4. Make a judgement: Do the numbers and opinions show the campaign was successful? Why or why not?

Task Part 2 – Research (Homework or 30 mins independent task)

Each group must research a real-world cybersecurity awareness campaign. Examples:
- NCSC “Cyber Aware” (UK)
- Google Security Checkup
- StaySafeOnline.org (US)
- OR another campaign you find.

For your chosen case:
- Find one example of quantitative data they collected.
- Find one example of qualitative data they used.
- Explain how combining both types of data made their campaign stronger.

Task Part 3 – Group Presentation (15 mins prep + delivery in next lesson)

Prepare a 5-minute presentation to share with the class. Your presentation should include:
1. A short explanation of the difference between quantitative and qualitative data.
2. An analysis of the college case study – was the awareness campaign effective?
3. Findings from your research case study.
4. A recommendation: If you were the IT manager, what would you do next to improve cybersecurity awareness?

Tip: Use visuals like graphs (for quantitative data) and word clouds or quotes (for qualitative data).

Extension / Stretch Task
Design your own mini research survey that collects both quantitative and qualitative data about how safe students feel online. Share 3–5 questions (mix of numerical scales and open-ended questions).

3.3.2 Know the definition for structured data, understand its purpose, and understand that quantitative data is structured.

3.3.3 Know the definition for unstructured data, understand its purpose, and understand that qualitative data is unstructured.

3.3.4 Know the definition for each representation and understand the representations of quantitative data:

• discrete values

• continuous values

• categorical values.

3.3.5 Know and understand the properties of qualitative data:

• stored and retrieved only as a single object

• codified into structured data.

3.3.6 Understand the interrelationships between data categories data structure and transformation and make judgements about the suitability of data categories, data structure and transformation in digital support and security.

Last Updated
2025-09-01 15:01:33