The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Felix Van de Maele no published score: no usable exchanges on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

clear all ✕
1exchanges match
0on raw tape
0redirected or not addressed
Answered produced feed D 5 · C 5 · P 4 · Cm 4 4.60

Q that I lose the audience because they don't understand, uh, kind of the data side of what I described. Cause you, you are very, I mean, you're an engineer, right? So semantic web, things like this, people may not understand. Is there a way you can dumb this down so that, and I'm not calling my audience dumb, but even for me, right? Can you dumb this down for me?

A Absolutely. I love to use a bit of an old analogy. Let's say you have a library and library you have books and you have these index cards and this index cards tell you where to find a book, who the author is, who's last rented it. It's very similar to organizations with data, right? So you have lots of databases that store the data. These are the books of a library. What we do, we are the index cards. We help people, users find where the data is, what it means, How they can use it, uh, what the quality is, these types, these types of things. And, and ultimately what we want to get is to get to is that we call it the Amazonification of data, where ultimately users just shop for data like they are on Amazon. They, they, they put it in their data basket, so to speak, and then it goes to approval work workflows to actually get access to it. And so they can run the analytics.

AI assessment note: “Let's say you have a library... What we do, we are the index cards.”

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