Covers the full form (Data, Information, Knowledge, Wisdom)

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.

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1) Data

Raw facts without context.

• Examples: 25, 32, 47; Delhi, Rain, 2025; sensor output 45°C.
• LIS: Unprocessed circulation logs, accession numbers, and individual keywords.

Unprocessed • Objective • Unorganized

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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

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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

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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

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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

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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.

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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).

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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.

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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).

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Summary: DIKW shows how libraries transform raw records into meaningful services and wise policies. It’s a ladder from facts to action.