Ask HN: How to measure how much data one can effectively process or understand?

[mbuda]

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.

[mbuda]

Here are clickable links: https://youtu.be/ygr8yvIouZk?t=1307, https://adamdrake.com/from-enterprise-decentralization-to-to...

[allinonetools_]

Interesting question. In practice, I’ve found the limit isn’t how much data exists but how much you can turn into action without friction. The clearer and faster the feedback loop, the more data you can effectively “use,” regardless of volume.

[mikewarot]

The 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.

[rgavuliak]

I would measure data by time to action. If you're not actioning data it's worthless.

[mbuda]

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

[Natfan]

lol comment, ignored.