00:25:46 · Ron Gabrisko
[00:25:46] S0: Yeah. It's so it's so cool. And so... And we build out these, like, custom demos and then proof of concepts for our customers as well, but like, you know, looking at like a big retailer, you know, they were looking at, okay, how do they, you know, try to maximize revenue from their, you know, short stock. Right? So like, you know, if you're a grocer and you have certain things that are gonna expire, what kind of promotions? And literally, you can see which stores in the network have potential, you know, short life products, and then there's agents that will go look at that data in that store and recommend what promotions they should run for that particular store based on the demographics and all the buy... I mean, that's crazy. Right? Like, you're basically, it's a, you know, a retail knock, right? Gotcha. Network operating center, which is pretty cool. I mean, I gave some of the pharma examples, you know, in banking, they're doing all kinds of fraud detections. So obviously, you know, when you see, you know, an email on your phone, or a text on your phone that, hey, this might potentially be fraudulent, that's, you know, usually Databricks in the background running machine learning models that determine this is way outside your your normal, purchasing, patterns. But again, like, if you think about it, like, a CEO, if they're like, hey, what's happening with my business, you know, again, these retailer, what's happening with with my business? Why is, you know, the Southeast slowing down on sales? Like, normally, they'd go get an analyst. The analyst would put on a bunch of, you know, reports, send them back a report, they would look at it and say, okay, that makes sense, like change this, add this, and they go back and they come back and like the data now is stale, so update the data. Now they literally can just start typing English and say, hey, what's going on in the Southeast? You know, it might be like, hey, these are... The beverages aren't selling as well. You should try doing, you know, different shelf placement. I saw that increase in the Northwest Region. Right? Like, they're literally just interacting with the data and asking questions. It's it's absolutely game changing for CEOs, CFOs, CIOs. So it's it's incredible. So...
00:28:14 · Guillaume Moubeche
[00:28:14] S1: It's exciting exciting times ahead. And For sure. I'm I'm just wondering, do you sometimes have, like, you're focusing on enterprise and very large enterprise deal. So the data for some companies is equivalent to gold, I would say, and they don't wanna share it with with everyone around them. So is, you know, like, do do you sometimes struggle to close deals because of how the data is handled and this kind of thing? Because we've seen it a lot. Some companies typically don't want to use like Anthropic, don't want to use like OpenAI, etcetera, because they don't want to share kind of like their data with some some like AI companies. So how exactly do you navigate this from your perspective and and the enterprise clients you work with?
00:29:11 · Ron Gabrisko, Guillaume Moubeche
[00:29:11] S0: Yeah. I mean, we never... You never give your data to us. Like, that's the whole, you know, great part about Databricks is you store it in open storage, you own your data. Like, there's a lot of companies out there that are like, you know, you give your data to them, and then, you know, you have to make sure it's safe, all that kind of stuff. You never give your data to Databricks. It stays in open storage, in open data formats, Delta, Iceberg. Again, that's the key, right, to... And and, you know, that provides an even playing field too for the entire industry. Like, let the best engines and the less... The best products win that work on top of that data. And so that's a big thing about Databricks is don't don't give your data to us, don't give it to anyone because that's gonna be, like you said, your goal. That's your that's your secret sauce, right, is how you make new products and new predictions and make decisions on that data. So don't give it to us, don't give it to anybody. That's a that's a big key of the Lakehouse and how we built how we built out the data world. [00:30:19] S1: Nice. Can you walk us through, like, the process to close such a big deal? Like, what are the different stages? Who's involved? How do you handle, like, the the pressure on these kind Yeah. Of
00:30:34 · Ron Gabrisko
[00:30:34] S0: I mean, listen, like, you know, all of our customers started from, you know, one use case and, you know, decided to use us for, you know, for at least some kind of business use case or migration. And as you start to expand your presence inside of those accounts, like, our main thing is we're looking for more use cases. Right? We're looking for where we can add value either on the business side or, you know, retiring legacy technologies, so we, you know, we retire a lot of on prem technology Hadoop, old data warehouses, because you can consolidate all that, and when you consolidate all that, one, it saves you a bunch of cost, but now if you have all your data organized in the... In, under Databricks in our unique catalog, now I can do all these cool AI data science use cases. And so if you wanna take an enterprise or, you know, even, you know, some of the digital natives or any of the startups and transform your company and be able to use data to make decisions, you gotta get it all organized. Right? And so, you know, we'll start with going to each BU and starting to show the value of Databricks. We usually use those internal champions as references, and as companies start to use data, it's amazing product. It's the best product I've ever sold, and so they, you know, will continue to use it, and we'll kinda predict, okay, here are the other 30 projects you wanna use Databricks for. Right? Like, if we're inside of a large bank or a large insurance company, they might be using it for claims fraud and using it for how do I automate claims processing? How do I automate claims pre authorizations, like, etcetera, etcetera? And we're understanding kind of that... Those usage patterns. And, you know, for us, then we're gonna build out, okay, how do we make a strategic plan with that customer to make sure that they can, you know, grow with us and we can continue to add value? And so usually, we'll do a business case. It'll show them that they can save or make billions of dollars, and, you know, for the larger deals, we're usually connected to CEOs, CFOs, CIOs to make those decisions, and, you know, we have enough champions that they're like, yeah, Databricks is the best product and best partner in the industry to work with to to do these transformations. And so, you know, most of those are years of work. They don't happen overnight, but I assume.
00:33:04 · Ron Gabrisko, Guillaume Moubeche
Exciting times. [00:33:06] S1: You have, like... I mean, yeah, I'm just wondering, you know, like, how do you come up with with the pricing eventually? Because, of course, you have like usage based, but there is a difference from selling, as you mentioned, you know, when you got started, it was around like 20 k deal up to like 100 of millions. So how exactly when it comes to the the business case, you mentioned the, I guess, like value based pricing where you essentially try to identify how much value you're gonna bring to the company, whether it's the amount of money you're gonna help them save, how do you come up with the number, like, because in enterprise sales, obviously, like, the pricing is not on the website. Like, there is not... If I go on Databricks, there is not like, hey, here's yours.
00:33:53 · Ron Gabrisko, Guillaume Moubeche
[00:33:53] S0: Or is it? We we put all our... I mean, it's all We're usage not doing value based pricing. Mean, I think Palantir does that, but it's... All our pricing's on the website. It's super transparent. It's all usage based. So the the biggest part is we can see those usage patterns. Like, know for these 20 use cases, based on this this amount of data, we can kinda predict what kind of usage. You know, we already saw, like, we we might have three years of data of how your usage is going up, and then we layer on these new use cases so we know that that might accelerate the usage a little bit. And the reason we're doing value is like, you're not gonna spend, you know, even a million dollars or $10,000,000 unless you know you're getting, you know, 10 x that in value, and so, you know, we have a team that goes in and says, okay, well, here's how that's gonna impact your revenue, here's how you save costs to be able to justify that and make sure, you know, and we're doing quarterly reviews with that company to say, okay, we're on track, we wanna go back and measure that, but, you know, we're not doing value based pricing. I know some of the enterprise software like Palantir does, but, you know, we don't we don't do that. It's all usage based. It's all super transparent. Just go to the website and... [00:35:07] S1: Okay. So you're keeping like the the very like open source mentality and you know what you pay for and... Okay.