DIKW is a conceptual hierarchy that shows how raw facts become meaningful insights and finally lead to judicious action. Each level adds context, interpretation, and judgment.
1) Data
Raw facts without context.
- Examples:
25, 32, 47;Delhi,Rain,2025; sensor output45°C. - LIS: Unprocessed circulation logs, accession numbers, and individual keywords.
Unprocessed • Objective • Unorganized
2) Information
Processed data with context and meaning.
- Examples: “Temperature in Delhi is 45°C today”, monthly rainfall chart.
- LIS: A catalog record; a report of daily footfall by hour.
Context • Organized • Useful
3) Knowledge
Internalized information understood through experience.
- Examples: Knowing 45°C is dangerous; understanding monsoon patterns.
- LIS: A librarian’s expertise to recommend the best resource for a user’s query.
Understanding • Experience • Application
4) Wisdom
Judicious application of knowledge with foresight and ethics.
- Examples: Advising heatwave safety; planning sustainable water usage.
- LIS: Policy-making for equitable access; long-term collection strategy.
Judgment • Foresight • Ethics
Quick Comparison Table
| Level | Key Question | What It Adds | LIS Example |
|---|---|---|---|
| Data | — | Raw values | Click logs, issue/return timestamps |
| Information | What? | Context & organization | Monthly circulation report by subject |
| Knowledge | How? | Interpretation & patterns | Understanding peak hours & user needs |
| Wisdom | Why? | Judgment & action | Adjust staffing, acquire targeted collections |
The DIKW Pyramid (Why → How → What → Facts)
Wisdom (Why?)
─────────────────
Knowledge (How?)
─────────────────
Information (What?)
─────────────────
Data (Facts)
Exam Tip: Remember the flow: Data → Information → Knowledge → Wisdom. Each level is not just more data—it's more context, meaning, and judgment.
DIKW in Libraries & Knowledge Management
- Collection Development: Use issue data (Data) → subject trends (Info) → reader profiles (Knowledge) → balanced, inclusive policies (Wisdom).
- Reference Services: Query logs (Data) → FAQ topics (Info) → staff training focus (Knowledge) → proactive user-education programs (Wisdom).
- Digital Libraries: Metadata records (Data) → curated subject guides (Info) → discovery strategies (Knowledge) → open access & equity decisions (Wisdom).
FAQ
Is information always “better” than data?
No. Information is data in context. If the context is wrong or misleading, information quality suffers. High-quality data and appropriate context both matter.
Can knowledge exist without information?
Practical know-how can be tacit (in people’s heads), but it arises from repeated exposure to information and experiences over time.
Where do ethics enter the DIKW model?
Ethics become critical at the wisdom level—when decisions affect people and communities (e.g., privacy, inclusivity, accessibility).
Summary: DIKW shows how libraries transform raw records into meaningful services and wise policies. It’s a ladder from facts to action.
