3.1 Data, Information & Knowledge

Data consists of raw, unprocessed facts and figures. On its own, data has little meaning.

Example: Temperature readings or login timestamps.

Information is data that has been processed, organised, or structured so that it has meaning and context.

Example: An average daily temperature calculated from readings.

Knowledge is the understanding gained from information combined with experience, skills, and judgement.

Example: Using temperature trends to plan cooling system upgrades.

Data generated by humans includes surveys and forms, where users input information manually.

AI systems generate data through automated analysis and learning. There is a risk of feedback loops where biased data reinforces inaccurate outcomes.

Sensors collect environmental data such as temperature, acceleration, vibration, sound, light, and pressure.

IoT devices generate continuous data from smart objects such as thermostats, lights, security cameras, and trackers.

Transactional data includes customer records, membership details, timestamps, and shopping basket contents.

Ethical data practices ensure data is collected, stored, and used responsibly, fairly, and legally.

Data value can be assessed using:

• Quantity – volume of data available
• Timeframe – how current the data is
• Source – how reliable the data source is
• Veracity – accuracy and trustworthiness of data

Organisations use data and information to:

• Analyse patterns and trends
• Monitor system performance such as load and outages
• Track user activity and resource access
• Deliver targeted marketing
• Assess threats and opportunities

Data must be suitable for its intended purpose. Organisations must evaluate how data is generated, transformed into information, and applied as knowledge, particularly in digital support and security contexts.