11h • 💬 General
Most data pipelines break because they ignore decision latency
Everyone optimizes for:
• data accuracy
• data volume
• data freshness
But very few optimize for decision latency.
Decision latency = the time between a signal appearing and a decision being made.
That’s where value leaks.
A modern data pipeline should be designed backwards:
1️⃣ Decision First What decision must happen?
– pricing change
– alert
– prioritization
– intervention
No decision → no pipeline.
2️⃣ Required Signal Only Which signals directly influence that decision?
Ignore the rest.
Less data = faster truth.
3️⃣ Time Sensitivity Does this decision matter in:
– seconds?
– minutes?
– hours?
Batch pipelines fail real-time decisions by design.
4️⃣ Confidence Thresholds Not every signal needs certainty.
Some decisions need 60% confidence now,
not 95% confidence tomorrow.
5️⃣ Action Output Dashboards don’t close loops.
Actions do.
If your pipeline ends in visualization, you’re still halfway.
Data alchemy isn’t about perfect data.
It’s about timely, decision-ready intelligence.
Speed of understanding beats depth of storage.
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Most data pipelines break because they ignore decision latency
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