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 →

Partha Srinivasa 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 4 · P 4 · Cm 3 3.85

Q Yes, and we're here at Highland Community Live. We've spoken with Jitesh and Mike. How exactly does Highland help you make sense of the unstructured data?

A Well, I'll tell you that. Two years back, I was with Highland on a keynote with Jitesh when Jitesh joined the company, Two years later, I'm here again. At that time, my biggest focus was we had information, like I said, right? 80 to 90% of our information are on unstructured data, and then structured data is what we use primarily for a lot of things. Now, when you look into that, my goal was to bring all that unstructured data into one common document repository or enterprise content repository. Highland. We migrated out of different companies. I won't name the logos of the different companies. But we moved all of them, so all of our information, our policy documents, our underwriting documents, our claims documents, our medical records, you name it, all of us are sitting into a technology like this, which is enterprise content management. Heavily protected, heavily secured, governed, et cetera, et cetera. But that was becoming more like a document repository for which we were attaching it to the file, claim files or document files or underwriting files. What are we doing now? Is now that I have that information, I'm extracting insights and intelligence out of this unstructured data, trigger a set of workflow. The example I told you, remind your customers to get those documents in a timely fashion. Remind your customers to send an email or send a correspondence automatically to…

AI assessment note: “Highland is coming in as a platform to extract that insights and signals”

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