Data Foundations · Module 2
Data, information, knowledge, judgement
I want a simple model in your head that stays useful even when the tools change, and DIKW works because it forces you to separate raw observations from meaning before.
Previously
What data is and why it matters
Data starts as recorded observations, for example numbers on a meter, text in a form, or pixels in a photo.
This module
Data, information, knowledge, judgement
I want a simple model in your head that stays useful even when the tools change, and DIKW works because it forces you to separate raw observations from meaning before.
Next
Units, notation, and the difference between percent and probability
Data work goes wrong when people are casual about units.
Progress
Mark this module complete when you can explain it without rereading every paragraph.
Why this matters
Suppose a dashboard shows “12.4”, which could be 12.4 kWh, 12.4 MWh, 12.4 percent, 12.4 incidents, or 12.4 minutes, so the number itself is not the problem and the missing context is.
What you will be able to do
- 1 Explain data, information, knowledge, judgement in your own words and apply it to a realistic scenario.
- 2 DIKW is useful when it keeps facts separate from interpretation and decision.
- 3 Check the assumption "Interpretation is stated" and explain what changes if it is false.
- 4 Check the assumption "Judgement is owned" and explain what changes if it is false.
Before you begin
- No previous technical background required
- Read the section explanation before using tools
Common ways people get this wrong
- Facts and opinions mixed. When facts and opinions mix, dashboards become arguments.
- Automation without judgement. Automating a bad interpretation scales harm quickly.
I want a simple model in your head that stays useful even when the tools change, and DIKW works because it forces you to separate raw observations from meaning before you make decisions.
Worked example. A number without context is a rumour
Worked example. A number without context is a rumour
Suppose a dashboard shows “12.4”, which could be 12.4 kWh, 12.4 MWh, 12.4 percent, 12.4 incidents, or 12.4 minutes, so the number itself is not the problem and the missing context is.
My opinion is that if you cannot answer “what does this represent” and “what would make it wrong”, you do not have information yet. You have vibes with a font size.
Common mistakes (DIKW edition)
Common mistake
Treating charts as truth
Reality: A chart is someone's interpretation of data. Always ask how the data was produced, what was included, and what was excluded before trusting the picture.
Common mistake
Mixing measurement with meaning
Reality: The sensor measured something and you are interpreting it, so the number 21.5 from a thermometer is a measurement while "the room is comfortable" is your judgement.
Common mistake
Skipping uncertainty
Reality: Many datasets are estimates, not direct observations. If you do not know the confidence interval or the sampling method, you do not really know what the number means.
Verification. Prove you can separate meaning from numbers
DIKW verification drill
If you can do these three steps, you are reasoning instead of guessing.
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Write one metric definition with its unit
Include the decision the metric is meant to support.
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Name one realistic failure mode
Examples include missing data, unit mismatch, selection bias, or duplication.
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Design one detection check
State exactly how you would catch the failure before it reaches a decision.
Mental model
DIKW as a map
DIKW is useful when it keeps facts separate from interpretation and decision.
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1
Data
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2
Information
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3
Knowledge
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4
Judgement
Assumptions to keep in mind
- Interpretation is stated. If you hide interpretation, people treat it as fact and make bad decisions.
- Judgement is owned. A judgement without an owner becomes a default nobody can defend.
Failure modes to notice
- Facts and opinions mixed. When facts and opinions mix, dashboards become arguments.
- Automation without judgement. Automating a bad interpretation scales harm quickly.
Check yourself
Quick check. DIKW
0 of 4 opened
What is the useful point of DIKW
It forces you to separate raw observations from meaning, patterns, and decisions, so you stop mixing facts with interpretation.
Scenario. A dashboard shows “12.4”. Name two bits of context you need
The unit and definition, plus scope such as time window, source, and whether the value is measured or estimated.
What turns data into information
Context and agreed meaning such as units, definitions, and how it was collected.
What turns information into knowledge
Patterns you can explain well enough to support a decision or action.
Artefact and reflection
Artefact
A short module note with one key definition and one practical example
Reflection
Where in your work would explain data, information, knowledge, judgement in your own words and apply it to a realistic scenario. change a decision, and what evidence would make you trust that change?
Optional practice
Complete one guided exercise and explain your decision in plain language