Abhi Sivasailam
@abhisivasailam.bsky.social
Fast-talker. Burrito connoisseur.
That ...wasn't my take 😅 Actually, the markets are smaller, GTM harder, and the most and NFC more entrenched, and this all says much more about the market weakness of the acquired than the strength/optimism/momentum of the acquirers
January 16, 2025 at 8:25 PM
That ...wasn't my take 😅 Actually, the markets are smaller, GTM harder, and the most and NFC more entrenched, and this all says much more about the market weakness of the acquired than the strength/optimism/momentum of the acquirers
Lol it was actually a last-second re-add that I had removed. Anyway, I'm generally interested in AI enabled first-/middle-mile data work. Pretty uninterested in all the last-mile stuff like text to SQL. Y'all doing this stuff?
December 12, 2024 at 2:52 AM
Lol it was actually a last-second re-add that I had removed. Anyway, I'm generally interested in AI enabled first-/middle-mile data work. Pretty uninterested in all the last-mile stuff like text to SQL. Y'all doing this stuff?
No, I pay for Claude, I just use it a *lot*.
November 30, 2024 at 9:30 PM
No, I pay for Claude, I just use it a *lot*.
Would argue it's a lot easier to downshift from BigCo context than to extrapolate out from SmallCo. Basically every org has more to learn from AMZN than [SmallCo]
November 30, 2024 at 3:39 PM
Would argue it's a lot easier to downshift from BigCo context than to extrapolate out from SmallCo. Basically every org has more to learn from AMZN than [SmallCo]
At least, this gets you very far. And especially when you can tie those queries to context from the BI tool and get that "semantic" context from charts, dashboards, labels, etc..
November 24, 2024 at 4:42 AM
At least, this gets you very far. And especially when you can tie those queries to context from the BI tool and get that "semantic" context from charts, dashboards, labels, etc..
Also, the notion that a "semantic layer" is necessary for text to SQL and AI affordances makes little sense to me (outside of, yes, making metrics easier to query)
November 24, 2024 at 4:01 AM
Also, the notion that a "semantic layer" is necessary for text to SQL and AI affordances makes little sense to me (outside of, yes, making metrics easier to query)
I just think the "semantic" part is overblown. Call a "metrics layer" what it is.
November 24, 2024 at 4:00 AM
I just think the "semantic" part is overblown. Call a "metrics layer" what it is.
November 24, 2024 at 3:58 AM
I just think the "semantic" part is overblown. Call a "metrics layer" what it is.
Idk what got into Chris tonight but I'm here for it. Go off king.
November 24, 2024 at 3:53 AM
Idk what got into Chris tonight but I'm here for it. Go off king.
Also will add that Idiosyncratic "levers" usually "hang on" the Common. Or in the parlance I use when talking about metric trees, the Idiosyncratic are usually "Influences" and the Common are usually "Components".
November 23, 2024 at 3:10 AM
Also will add that Idiosyncratic "levers" usually "hang on" the Common. Or in the parlance I use when talking about metric trees, the Idiosyncratic are usually "Influences" and the Common are usually "Components".
Some companies win through operational excellence; some win through innovation.
IME (read: anecdata), most winners do the former category, but most of the biggest winners do the latter.
IME (read: anecdata), most winners do the former category, but most of the biggest winners do the latter.
November 23, 2024 at 3:07 AM
Some companies win through operational excellence; some win through innovation.
IME (read: anecdata), most winners do the former category, but most of the biggest winners do the latter.
IME (read: anecdata), most winners do the former category, but most of the biggest winners do the latter.
I'd ditch the FE
November 18, 2024 at 4:04 AM
I'd ditch the FE
(The right answers to (2) are obviously "Neither: Fritz's" or "frozen custard is overrated: Oberweiss" 😌)
November 18, 2024 at 3:56 AM
(The right answers to (2) are obviously "Neither: Fritz's" or "frozen custard is overrated: Oberweiss" 😌)
Let's just get the rest of this out of the way:
1. Imos or CWP?
2. Ted Drews or Andy's?
1. Imos or CWP?
2. Ted Drews or Andy's?
November 18, 2024 at 3:55 AM
Let's just get the rest of this out of the way:
1. Imos or CWP?
2. Ted Drews or Andy's?
1. Imos or CWP?
2. Ted Drews or Andy's?
lol I thought the same thing when I saw this a few weeks ago and keep forgetting to ask. I'm West County Rockwood :)
November 18, 2024 at 3:54 AM
lol I thought the same thing when I saw this a few weeks ago and keep forgetting to ask. I'm West County Rockwood :)
I keep forgetting to ask where you're from in MO (and if stl area, "where did you go to HS" 😀)
November 18, 2024 at 3:48 AM
I keep forgetting to ask where you're from in MO (and if stl area, "where did you go to HS" 😀)
1. Making all those things you mention above work well (causal model, operating mechanisms, etc).
2. True, proactive exploration/"insight"-harvesting.
3. Shifting up the value chain and looking more like a profit center: customer-facing data work, data commercialization/monetization, etc...
2. True, proactive exploration/"insight"-harvesting.
3. Shifting up the value chain and looking more like a profit center: customer-facing data work, data commercialization/monetization, etc...
November 17, 2024 at 7:13 PM
1. Making all those things you mention above work well (causal model, operating mechanisms, etc).
2. True, proactive exploration/"insight"-harvesting.
3. Shifting up the value chain and looking more like a profit center: customer-facing data work, data commercialization/monetization, etc...
2. True, proactive exploration/"insight"-harvesting.
3. Shifting up the value chain and looking more like a profit center: customer-facing data work, data commercialization/monetization, etc...
More basic than any of those things is just having data models and metrics that are stable, reliable, complete, and expressive enough for a data team (and a would-be Staff DA) to not have to be in constant reactive mode. Rare, actually.
November 17, 2024 at 6:34 PM
More basic than any of those things is just having data models and metrics that are stable, reliable, complete, and expressive enough for a data team (and a would-be Staff DA) to not have to be in constant reactive mode. Rare, actually.
There _are_ staff DAs, there just aren't that many of them. In no small part, this is bc maximal Staff DA leverage requires an organizational data maturity that very few cos have realized.
November 17, 2024 at 6:22 PM
There _are_ staff DAs, there just aren't that many of them. In no small part, this is bc maximal Staff DA leverage requires an organizational data maturity that very few cos have realized.