Even with Cloudflare’s move, free access to information on the internet—a vast sea of information — will still be available.
The differentiator, as it should be, is the expert filtering mechanisms that create context based on strand commonality, accessing deep and relevant archives.
As Shira Yoskovitch once stated regarding Peloton’s misreading of the data that could have enabled them to avoid (or at least limit the impact) of the pandemic on their supply chain, having access to data is not the same as understanding it.
In the context of Shira’s observation, we need to understand the distinction between having clean data and actually understanding it.
The difference between having clean data and understanding clean data is subtle but critical—especially in procurement, supply chain, and AI-driven decision environments, such as those governed by the Hansen Fit Score.
Having Clean Data
Means the data is technically correct:
- No duplicates, blanks, or errors
- Fields are normalized (e.g., units standardized: “kg” vs “kilogram”)
- Consistent formats (e.g., dates, currencies, taxonomies)
- Aligned to data governance and compliance rules
- Passes validation checks (syntax-level and relational integrity)
Example: A supplier master list contains no typos, adheres to consistent naming conventions, and includes complete contact records.
BUT—clean data alone does not mean insight.
Understanding Clean Data
Means the data’s context, implications, and limitations are understood:
- Why was this data captured this way?
- What business rules or historical decisions shaped it?
- What patterns or outliers signal strategic insight?
- What is missing even in a “clean” dataset?
- Are different stakeholders interpreting it differently?
Example: You understand why certain suppliers always show higher unit prices—because they’re in a low-volume, high-expedite region. Clean but misunderstood data might wrongly trigger their removal.
Why the Difference Matters
In Hansen’s Framework
- Strand Commonality warns that patterns across domains are only valid if their context is correctly interpreted.
- Agent-based models suffer if agents act on misunderstood clean data.
- Metaprise Alignment fails when enterprise views data as correct but misaligned with intent or behavior (e.g., KPIs vs operational truth).
Bottom Line
“Clean” data can still be misleading if misunderstood.
A procurement team with 99% valid supplier data can still make the wrong decisions if it doesn’t grasp supplier risk dynamics, market triggers, or embedded bias in their taxonomy to name only a few strand or agent influences.
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Here is the visual overlay contrasting “Clean Data” versus “Understood Data” across six critical dimensions:
- Clean data scores high in completeness, consistency, and accuracy, but low in strategic relevance and interpretability.
- Understood data reverses that trend—strong in context, meaning, and strategic use, even if technically “clean” standards are lower.
Procurement Insights
Do you know the difference between having clean data and understanding clean data?
Posted on July 21, 2025
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Even with Cloudflare’s move, free access to information on the internet—a vast sea of information — will still be available.
The differentiator, as it should be, is the expert filtering mechanisms that create context based on strand commonality, accessing deep and relevant archives.
As Shira Yoskovitch once stated regarding Peloton’s misreading of the data that could have enabled them to avoid (or at least limit the impact) of the pandemic on their supply chain, having access to data is not the same as understanding it.
In the context of Shira’s observation, we need to understand the distinction between having clean data and actually understanding it.
The difference between having clean data and understanding clean data is subtle but critical—especially in procurement, supply chain, and AI-driven decision environments, such as those governed by the Hansen Fit Score.
Having Clean Data
Means the data is technically correct:
Example: A supplier master list contains no typos, adheres to consistent naming conventions, and includes complete contact records.
BUT—clean data alone does not mean insight.
Understanding Clean Data
Means the data’s context, implications, and limitations are understood:
Example: You understand why certain suppliers always show higher unit prices—because they’re in a low-volume, high-expedite region. Clean but misunderstood data might wrongly trigger their removal.
Why the Difference Matters
In Hansen’s Framework
Bottom Line
30
Here is the visual overlay contrasting “Clean Data” versus “Understood Data” across six critical dimensions:
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