DIKW Pyramid → Signal Extraction → Horizontal Funnel

This document supports an 11″×14″ poster showing how raw data becomes wisdom — and especially where signal is separated from noise.

🔷 Classic DIKW Pyramid (ASCII Visual)

               /‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\
              / Wisdom = judgment      \
             /  applied to patterns     \
            /_____________▲______________\
           /                              \
          /   Knowledge = insight +        \
         /     (cause/effect patterns)      \
        /_________________▲__________________\
       /                                      \
      /   Information = structured and         \
     /      contextualized data                 \
    /_____________________▲______________________\
   /                                              \
  /      Data (raw facts & measurements)           \
  ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾▲‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾

This classic form has been widely used to model how raw observations — facts, logs, readings — are successively enriched with meaning and purpose all the way to wisdom. :contentReference[oaicite:1]{index=1}

⭐ Where Signal Is Extracted

Between Data → Information the following transformations happen:

• Validation & cleaning of raw values
• Filtering out noise and non-relevant bits
• Structuring and contextualizing streams of measurements
• Adding description (who, what, where, when)
• Detecting obvious patterns that make the data meaningful

This zone is where signal rises out of noise — the rest of the pyramid depends on it. :contentReference[oaicite:2]{index=2}

🧠 Intermediate Transition Activities

At each boundary between levels there are key activities that shape meaning:

📌 Data → Information

• Cleaning: remove duplicates, errors
• Structuring: columns, units, timestamps
• Contextualizing: what does this mean?
• Filtering noise: focus on patterns, not static

📌 Information → Knowledge

• Analysis: calculate trends, correlations
• Synthesis: combine contexts to find relationships
• Modeling: forming mental or computational models
• Interpretation: what does this tell us?

📌 Knowledge → Wisdom

• Judgment: deciding what actions make sense
• Evaluation: weighing trade-offs and consequences
• Experience: applying pattern recognition in context
• Strategy: picking a direction with foresight

These transitions show that DIKW is less “siloed stages” and more a workflow of transformation — which makes the funnel view useful. :contentReference[oaicite:3]{index=3}

📉 Horizontal Funnel (ASCII Visual)

|---------------------- Data (lots of noise) ----------------------|
|------------------ Information ------------------|
|--------------- Knowledge -------|
|-------- Wisdom --------|

As data moves right, noise is shed, meaning is concentrated, and actionable focus emerges — because the higher levels require less volume but more clarity than the levels below them.

📝 Quick Reference Definitions

• Data: raw recordings without context — they don’t tell a story yet.
• Information: organized and contextualized data giving answers to who/what/when/where.
• Knowledge: interpretation of information, revealing how things relate.
• Wisdom: judgment applied to knowledge — what to do and why.
  (Definitions adapted from common DIKW frameworks.) :contentReference[oaicite:4]{index=4}

📍 Notes on DIKW

The pyramid is conceptually useful but not strictly linear: many processes in analytics and decision science iterate back and forth between levels. The poster can help teams think about workflow and transformations, not just boxes in a graphic. :contentReference[oaicite:5]{index=5}