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 →

Shant Hovsepian no published score: only 1 usable exchange 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
1on raw tape
0redirected or not addressed
Answered raw tape D 4 · C 3 · P 3 · Cm 2 3.15

Q So this is why you didn't want to send the slides in advance?

A This is why I made sure not to send the slides in advance. Um, I also had to, uh, artificially give you a tan. It was like a serious problem with this photo compared to Ryan, but they do, right, you know, ladies and gentlemen, they do look alike. Um, all right. That just set, like, sort of the theme for the rest of this talk. Um, so I'm the CTO, and, uh, this shade of gray doesn't look great here, but I apologize. CTO and co-founder of Arcadia Data. Sort of our founding vision was create business value from big data. That was generic enough that we could kind of do whatever we wanted and still claim that we're sticking to our vision. Um, so we came out of stealth mode in June, and we just announced the GA release of our product. Uh, been rapidly growing, and we're definitely focused on the Fortune 200. So, we see a lot of customers who are struggling with data, big and small. And, um, what I'm gonna sort of try to talk about today, a lot of these, you know, NYC data-driven talks are supposed to be less promotional and more informative. So, I'm gonna do my best to sort of talk about what we've been seeing in the field, what our customers complain about, and in particular, I'm gonna try to answer, um, a question around BI on big data. Specifically, you know, you don't use previous generation architectures to store your big data, so why should you use previous generation BI tools …

AI assessment note: “This is why I made sure not to send the slides in advance.”

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