3.3 Data Taxonomy
3.3.1 Data categories
Quantitative Data
Quantitative data is numerical and can be measured or counted.
Its purpose is to support calculations, comparisons, and statistical analysis.
Examples: number of users, response times, storage capacity.
Examples: number of users, response times, storage capacity.
Qualitative Data
Qualitative data is descriptive and non‑numerical.
Its purpose is to provide context, meaning, and insight into user behaviour
or experiences.
Examples: user feedback, comments, incident descriptions.
Examples: user feedback, comments, incident descriptions.
3.3.2 Structured data
Structured Data
Structured data is organised into predefined formats such as rows,
columns, and fields. It is easy to search, analyse, and process.
Quantitative data is typically structured.
Examples: databases, spreadsheets, tables.
Quantitative data is typically structured.
Examples: databases, spreadsheets, tables.
3.3.3 Unstructured data
Unstructured Data
Unstructured data does not follow a predefined format.
It is often more complex to analyse and process.
Qualitative data is typically unstructured.
Examples: emails, images, videos, free‑text reports.
Qualitative data is typically unstructured.
Examples: emails, images, videos, free‑text reports.
3.3.4 Representations of quantitative data
Discrete Values
Discrete values are whole numbers that cannot be subdivided.
Examples: number of logins, number of network devices.
Examples: number of logins, number of network devices.
Continuous Values
Continuous values can take any value within a range,
including fractions and decimals.
Examples: temperature readings, bandwidth usage.
Examples: temperature readings, bandwidth usage.
Categorical Values
Categorical values group data into named categories.
Examples: incident priority levels (low, medium, high).
Examples: incident priority levels (low, medium, high).
3.3.5 Properties of qualitative data
Qualitative Data Properties
Qualitative data:
- Is stored and retrieved as a single object
- Can be codified into structured data for analysis
3.3.6 Interrelationships and suitability
Making Judgements About Data Use
Data category, structure, and transformation method are closely related.
Organisations must select appropriate data types and structures
to ensure accuracy, efficiency, and suitability in digital support
and security contexts.