Data are recorded symbols, measurements, or observations; information is data organized and interpreted within a context; knowledge involves understanding and accepting claims on the basis of evidence, justification, or experience. The movement from one category to another requires interpretation by a knower or a technological system.
| Concept | Meaning | Example |
|---|---|---|
| Data | Unprocessed records without sufficient context | A sensor records “18.2” |
| Information | Data given organization, meaning, and context | The room temperature is 18.2°C |
| Knowledge | Understanding supported by interpretation and justification | Knowing that the room is cooler than the recommended temperature because the sensor is reliable and the recommended range is known |
This distinction is often presented as a hierarchy: data are processed into information, and information contributes to knowledge. However, from a Theory of Knowledge perspective, the process is not automatic. Choices about what to collect, how to classify it, and which context to apply influence the resulting information.
Technology can collect and process enormous quantities of data, identifying patterns that a human knower might miss. Nevertheless, a pattern does not necessarily become knowledge merely because an algorithm detects it. Its significance must be interpreted, and the reliability of the data, method, assumptions, and source must be evaluated.
The categories can also overlap. What counts as information for one knower may function as raw data for another. For example, a table of temperature readings is information to a homeowner but may become data for a climate scientist conducting a larger statistical analysis.
A common misconception is that data are completely objective while knowledge is subjective. Data collection already involves decisions about measurement, selection, categories, and instruments, so data are not necessarily neutral.
Exam technique: In a TOK essay or exhibition, do not merely define the three terms. Use a specific real-world example to analyze how interpretation, technology, evidence, and justification transform data into knowledge, while questioning whether that transformation is reliable.
