The Ledger, every show

“Pretty quickly you're gonna realize they're all stepping on each other's toes, and that, that fallout has to go somewhere. Like, not all of these companies can be the next Figma that they all now say that they are, and so it's what happens then, and I think it…”

“They're moving in, like, the semantic layer tool, so basically they're saying, give us raw data, and we will tell you, like, apply semantics to it. The way that they do that now is through SQL. So, like, semantics are kind of, like, air quote semantics. It's b…”

“Dbt is, I think, really popular because it's so easy to get up and running, but it may also eventually be, like, if it had an undoing, to me that would be the undoing, is the thing that was really easy to get up and running doesn't actually solve the real prob…”

“You know, it doesn't undercut everything for Fivetran, but it potentially undercuts the big sources, which I imagine are the things that are, like, the real drivers of revenue for them.”

“The median outcome is the data philosophies of Facebook and LinkedIn and all these kind of early tech companies gets adopted by the enterprise, and so all these sort of modern data tools that tech companies buy today go off and get sold to Coca-Cola and Caterp…”

“If you want to find these folks, there's a really good place to look which is hiring social scientists. So social scientists are trained to think this way. They are analytical thinkers without some of the technical and quantitative background that a lot of STE…”

“If you are a product that has a lot of competition then something like a high NPS is a pretty good sign of product market fit, because people have a lot of choices. You need to know that people, like, actually like your product. It's a thing that they want to …”

“The way we've built DAGs are kind of like, when do I start the, like, when do I kick over the first one, and then we line it up such that we hope the thing arrives at the end. And the way it would make more sense to me is you just tell the thing, I need this t…”

“I guess, ah, seems hard to manage and practice. The way I've seen people describe it is basically it's the thing that you naturally create when you're a very big organization and you can't have a centralized data team that can possibly centralize everything, w…”

“Rather than that living in your BI tool, we think that should live in, in a more generalized layer, and then we just consume from it.”

“I don't think the way we get there is sort of like through open standards and stuff like that. I don't think anybody will actually adhere to that. I think most likely what happens is Snowflake basically says, hey, if you do things in this particular way, we ca…”

“It is strictly like a data access tool. It doesn't tell you what to do with that data after the fact. It doesn't actually tell you how to analyze it. And so this access is not analysis.”

“Me knowing SQL and me knowing how to create a chart doesn't make me an analyst any more than knowing Photoshop makes me a designer.”

“And so when you're, like, looking for product market fit, this is actually probably the best thing you can do, is pay attention to, like, what are your girls in the red dress, ah, that everybody's actually distracted by when you're trying to get them to build …”

“The problem here is, if you just look at daily retention rates for these two things, they will look exactly the same. That the way this math works out is these two things can actually show the exact same retention rates on like a day-by-day basis. And really w…”

“One, if you are a product that needs to be used every day, something like what front is makes sense. Things like DAUs over MAUs, which effectively measures how many days in a month people are using your product. It's really important. A high number there matte…”

“Similarly, if you're, like, a SaaS business that's selling B to B, obviously, in that case, it usually follows much more of, like, a work week kind of pattern. And so if people are using your product more than four days a week, like, how many percentage of you…”

“If your product is a vitamin and not a painkiller, so if your product is something that's kind of a nice to have, you want to actually see that people will be upset if they take it away. So this is actually a metric that I think Superhuman used, the email clie…”

“This is not something where it's like you are pre and post product market fit forever. You are pre and post product market fit for particular types of buyers, for particular products, for particular segments, and all those sorts of things.”

“It is a BI tool basically, but a BI tool built for people who don't like BI.”

“You could potentially incorporate data into that, such that the data sort of surfaced to people in the moment they need it in the product you're trying to use to solve the problem, instead of going to a dashboard and, like, clicking on some stuff. So I think t…”

“And so, ah, a very uncontroversial statement for this group is probably that data driven means it is not dashboards and daily emails to execs, ah, it is decision making and strategic analysis.”

“For the most part these groups average somewhere between like two and five percent. Ah, so about four percent of a company is, is analytics focused.”

“Things that may or may not be true end up becoming part of the culture, and it's very hard to get rid of it once they're there.”

“They're gonna need SQL skills, they're gonna, they might need Python or R skills, they'll need something like Excel but those things are learnable, whereas I think, like, the way of thinking is often harder to teach.”

“There's folks who build models to help machines make decisions, and there's folks who, like, build charts and help people, humans make decisions at a much lower frequency.”

“I think that, that it's basically find problems that you're interested in and try to answer questions about it. That the, if you take a bunch of practice problems, it doesn't really work. It doesn't stick.”

“They really, like, leaned into the way that people were using it, rather than trying to force them to do, do the things they originally wanted them to do.”

“And basically this was us finding, thinking we'd found a product market fit, and we hadn't. That we thought we had found something that worked, we thought it was time to scale, to spend more money on ads, and all that kind of stuff, and it turns out kind of ob…”