Is there a scale of how much data one can effectively process, something similar to the "Kardashev scale for data"? What would be a name for such a thing? During Memgraph's Community Call (https://youtu.be/ygr8yvIouZk?t=1307), the point is that AgenticRuntimes + GraphRAG moves you up on the "Kardashev scale for data" because you suddenly can get much more insight from any dataset, and everyone can use it (a large corporation does not control it). I found something similar under https://adamdrake.com/from-enterprise-decentralization-to-tokenization-and-beyond.html#productize, but the definition/example looks very narrow.
Ask HN: How to measure how much data one can effectively process or understand?
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Re: Ask HN: How to measure how much data one can effectively process or understand?
#2Here are clickable links: https://youtu.be/ygr8yvIouZk?t=1307, https://adamdrake.com/from-enterprise-decentralization-to-to...
Re: Ask HN: How to measure how much data one can effectively process or understand?
#3[flagged]
Re: Ask HN: How to measure how much data one can effectively process or understand?
#4[flagged]
Yep, amazing points!
Agree with the measures; follow-up question: what's the insight definition? I think exposing some of those measures would help people better understand what the analysis covered, in other words, how much data was actually analyzed. Maybe an additional measure is some kind of breadth (I guess it could be derived from the throughput).
"Informational leverage" reminded me of "retrieval leverage" because yeah, the scale of data didn't change, the ability to extract insights did :D
Re: Ask HN: How to measure how much data one can effectively process or understand?
#5[flagged]
lol comment, ignored.
Re: Ask HN: How to measure how much data one can effectively process or understand?
#6The limiting factor would be the density of information in the source material, followed my the cognitive impedance match of the receiver.
Fir example, a correct grand unified theory isn't useful if you don't know the physics to understand it.
Re: Ask HN: How to measure how much data one can effectively process or understand?
#7[flagged]
Re: Ask HN: How to measure how much data one can effectively process or understand?
#8I would measure data by time to action. If you're not actioning data it's worthless.
Re: Ask HN: How to measure how much data one can effectively process or understand?
#9[flagged]
Yep, amazing points! Agree with the measures; follow-up question: what's the insight definition? I think exposing some of those measures would help people better understand what the analysis covered, in other words, how much data was actually analyzed. Maybe an additional measure is some kind of breadth (I guess it could be derived from the throughput). "Informational leverage" reminded me of "retrieval leverage" bec…
Good question.
By “insight” I mean a measurable reduction in uncertainty that improves decision quality or predictive accuracy.
In practical terms, an insight could be defined as:
•A hypothesis generated and testable from the dataset
•A model parameter adjustment that increases predictive performance
•A structural relationship discovered that reduces entropy in the system representation
So compression efficiency would be something like:
(uncertainty reduced) / (data processed)
Breadth is interesting — I’d treat it as dimensional coverage: how many independent variables or graph regions are meaningfully integrated into the model.
“Retrieval leverage” is a great term. It highlights that the dataset size remains constant, but navigability and relational traversal improve — which increases effective cognitive reach.
Some of these broader ideas around informational sovereignty and anomaly-driven cognition have been explored in independent empirical work, though they’re still niche.
Re: Ask HN: How to measure how much data one can effectively process or understand?
#10Earlier quoted context omitted.
Yep, amazing points! Agree with the measures; follow-up question: what's the insight definition? I think exposing some of those measures would help people better understand what the analysis covered, in other words, how much data was actually analyzed. Maybe an additional measure is some kind of breadth (I guess it could be derived from the throughput). "Informational leverage" reminded me of "retrieval leverage" bec…
Good question. By “insight” I mean a measurable reduction in uncertainty that improves decision quality or predictive accuracy. In practical terms, an insight could be defined as: •A hypothesis generated and testable from the dataset •A model parameter adjustment that increases predictive performance •A structural relationship discovered that reduces entropy in the system representation So compression efficiency woul…
Love it!