Data, information and knowledge
Data are recorded facts such as 35 °C, "Class 11B", a photo or a sound clip. Information is data that has been organised so that it means something: "Today was 5 °C hotter than average". Knowledge is understanding built from information: "Heat waves are getting more common here, so schools need shade."
The same data can be useful or useless depending on whether it is correct, complete, up to date and relevant to a question.
Data as raw material
A raw material is something that is processed into a finished product. Data is processed by sorting, cleaning, counting, comparing and modelling to make information, charts and predictions.
- Sales records → which products to stock.
- Hospital records → which diseases are rising.
- Photos and labels → a program that recognises objects.
Rule of thumb: better raw material gives better results. Mistakes in the data lead to mistakes in the answer ("garbage in, garbage out").
Data as a means of production
A means of production is anything used to make goods and services, such as machines, tools and land. Data now plays this role too.
- Farming: soil sensors say when and how much to water, so crops grow with less water.
- Factories: machine data predicts breakdowns before they happen.
- Shops and delivery: data on orders and routes saves time and fuel.
- Online services: data about what people read helps improve content and recommendations.
Data does not wear out when used and can serve many users at once, but it must be collected, stored and kept up to date.
Data as infrastructure, and how to protect its value
Infrastructure is the base that many other activities depend on, like roads, power lines and water pipes. Shared data systems such as maps, weather data, population records, payment systems and health records serve schools, hospitals, shops and farms at the same time.
How value grows
- More correct data points.
- Fresh data, updated regularly.
- Joining data from different sources to see new patterns.
How value is lost or harmed
- Wrong, missing or biased data.
- Data that is too old.
- Leaks, theft or use without permission; unfair treatment of groups.
Responsible use: collect only what is needed, keep it safe, get consent when personal data is used, follow the law, and be honest about limits.
Key formulas and definitions
- [object Object]
- [object Object]
- [object Object]
- [object Object]
- [object Object]
- [object Object]
Worked examples
1. A survey has 30 answers and 80% are correct. How many correct data points are there?
30 × 80 ÷ 100 = 24 correct points.
2. Two shops each have 25 correct sales records. Joining them gives 50 records. If 10% of the joined records have errors, how many are correct?
10% of 50 = 5 errors, so 45 correct records.
3. A farm with sensors uses 600 L of water per plot and one without uses 800 L. What percent of water does the sensor farm save?
Saved = 200 L. 200 ÷ 800 × 100 = 25%.
4. Explain with one example why a map app is a data infrastructure.
Many apps (delivery, taxi, travel) use the same map and traffic data, just as many houses use the same road or power line.
Common mistakes
- Using data and information as the same thing. Information is data organised so it has meaning.
- Thinking more data is always better. Wrong or old data can lower value.
- Believing data gets "used up". Data can be copied and shared, but it can become out of date.
- Ignoring privacy. Valuable data about people must be protected and used fairly.