No Priors Ep. 23 | With Snowflake's CEO Frank Slootman

Key Takeaways

[Music] Our Guest today needs no introduction Frank slootman is the legendary three-time CEO of data domain servicenow and Snowflake and one of the most looked up to leaders in technology for his Relentless execution we're excited to talk to him about what's on the horizon for Snowflake and how he looks at the AI opportunity Frank good to see you thanks for being here absolutely good to see you sir let's start with just a little bit of personal background you have had an amazing journey you grew up in Holland you're the first person in your family to go to college what were you like as a kid and in college and how did you end up in product management and Computing in the U.S yeah that's kind of a you know big wide-ranging uh question I sometimes have to you know go back and figure out what was the method to the madness because you know sometimes your life looks like a random walk you know in other words it's just a series of events that kind of you know go from one to the other but uh you know I was always uh relatively focused discipline kit if if I were to describe myself in almost any uh realm whether it was school or sports or any of those things it's just the nature of the Beast you know I would say and you know definitely uh you know a bit of a chip on my shoulder uh which I generally like in people by the way you need to have a reason to get up in the morning and and and have some of the proof to the world or whoever those are all useful thanks you know obviously I ended up in the U.S because I think the US is obviously much better maybe not obvious but it's obvious to me that it's a much better canvas to uh for for for people like me and obviously we see that all around us right people that come from all over the world here because they have you know far greater opportunity and they would have where they came from and um yeah it certainly is true for me I mean there's no doubt that I would have done in where I came from uh what I've done here so I'm very grateful you know uh having had that opportunity I always tell younger people you know it's very important where you decide to be don't just go where your friends are to the point of choosing the right place be it geography yes and thank you America my parents are also immigrants you talk about being on the right elevator and some of the companies you worked at you know weren't the hottest companies at the time when you joined like tell us about those choices you to elevate it because there's just aspects um of opportunity and circumstances you can't change that is what it is and you're going to be subject to it uh For Better or For Worse and uh therefore you need to choose carefully you know some people think that you know I can will my way to anything that's not true right so uh your choices you make like we just said where are you going to be what industry you're going to be what company you're going to be what people are you going to be with um are all very formative and uh so you you have to make uh you know very uh careful choices because if you combine good choices you know with with great execution you know you get the perfect cocktail for for opportunities for future opportunities and and for having a successful sequence of of experiences so it matters a whole lot a lot and I talked to a lot of people joining entrepreneurial Ventures and they're always trying to figure out where to go um that is often where their friends go and sometimes it's where investor friends will direct them what advice would you have for people choosing that company in terms of the things you can't change you know it's it's a great question I I I get asked a couple times a year to speak to graduating classes at early prominent Business Schools and all that sort of thing and they always ask me is there one message that you have for the graduating class I'm like well you know don't don't go working for some consulting firm you know out of school right try to get a real job in the real economy building real products selling real products he says you really need to feel what it's like you know to sort of be in the drive stream of the economy as opposed to I'm just eating out of somebody else's trough and I start I I kind of sit on the bezel and glide along and I'm feeling good about myself but you haven't really touched a real economy yet and I I really wish that for for people early on in their careers to sort of feel the heat of competition and and and also the occult wins a threat of markets that are you know disappearing because that's the real world and a lot of people choose jobs that are very removed from the real world and I don't think that's helpful for for people's development and their careers how do you think about company versus industry versus role you know often when I talk to people as well I kind of advocate for the choose the right industry and then choose the best company in the industry and the role is secondary do you think that holds true or how would you suggest that people actually find their way yeah I I totally agree with that I think the role is is not that important you'll have many roles okay um and then roles come and go and uh in my first job I took a role I really didn't want but you know being an immaculous in this country Beggars couldn't be choosers and I had to I figured look I'll get in there and I'll make my way from there um you know I was I was in a corporate Planning Group of like six people attached to the CEO of a large computer company I was about as far removed from the real world as I could be and I I didn't want that but that that's all I could you know get into these were the the hey days of affirmative action we didn't have a lot of picks so um and and in hindsight I was right because you know once I got in there you know I he spent two years doing typical MBA stuff you know M A and all this all the presentations for boards and all this kind of stuff but then after that they pretty much gave me uh you know uh whatever I wanted to do was fine with them and from there you know I made my way you've had three just amazing CEO jobs right so I believe you took data domain from um less than three million in Revenue through an IPO and a two billion dollar acquisition by EMC it's servicenow you took it from 75 million of Revenue through an IPO into I think one and at 1.4 or 1.5 billion dollars of Revenue and then snowflake of course has just been an amazing run and it's one of the really seminal companies in the data world how did you go from step one to step two with all these things and in particular you know enjoying data domain I had an academic co-founder I didn't have a product that was commercially scalable yet servicenow you really turbocharged snowflake was growing but you know it was spending a lot of cash so um hey what are the commonalities between those different experiences and more generally what kind of drives you like why what do you have to prove you already had accomplished so much by the time you got to snowflake how do you keep going yeah so let me first sort of correct the record on day of the main that was they had no Revenue no customers nothing there were 15 people there and uh when we first started to you know assort the product it was it had one terabyte of usable space just imagine that okay no it was a while ago you know under around 30 megabytes you know a second so it was useless for for 99.9 percent of of applications um so we're like what are we going to do now and why'd you take the job well I didn't know that um you know I'll tell you what I took the job first of all you know I I got rejected numerous times for for CEO opportunities and the ones that that they were interested in or like second and third string and uh I know people really cautioned me at that time don't hold out you know do not go for a second third string you know daily you need to have really good investors you know we were a startup one out of hundreds at the time you know I'd be walking the halls of of Nea and Greylock and people look to me who are you what company is that oh oh okay we were a no name and we were lectured on you know on other companies that in hindsight ended up being no name so I mean it's it's almost it's almost legendary how Jada domain just manifested itself and by the way I live for that kind of drama no it was great um but we didn't have product Market fit we just didn't and um you know I found a little bit of fit I remember you know meeting with a CIO company that that has been acquired Since By by EMC uh and I was they were testing the products and uh the guy said to me he said you know he said that what product of yours was a real hero here on Friday and I'm like tell me more do tell um but he he explained that you know they had their email database you know backed up on um on our device and uh they had a massive corruption email databases as this happened back then that that's not common anymore and it was four o'clock on the Friday afternoon and they're like oh my God we're going to be recovering from tape here all weekend long we'll be sleeping on cots blah blah blah and uh then they remembered oh we have we have a backup on disc and by seven o'clock that evening they were going home and and obviously you don't need to be a rocket scientist to figure out another use case you can sell a few times more right so we stayed alive and we did do that three million dollars that first year um but I still remember doing the very first contract with like a five thousand dollar uh service deal with Stanford University and they bitched and complained the whole way I'm like well this is going to be a great business yeah you know one of my one of my favorite books which I think is really a Hidden Gem in terms of go to market and sales and startups is tape sucks and I think you get into very great um tactical advice that's lacking from a lot a lot of other books like you get into different Channel strategies and whether you should do them and Partnerships and other things that I just don't think are addressed very well in a lot of business books and you've now written three books and we can come back to the question in terms of you know what continues to drive you and all the rest what drives you to actually share knowledge that way and write a book it looks like with almost every formative experience that you've had you know um I get an awful lot of inbound questions you know can we have coffee can you speak here can you do this can you do that and I'm like I really can't because it just it'll become a full-time job um so I'm like look I'll write uh and by the way the data main book the tape sucks you know I was self-published was Homebrew and it's a very dense book even though it doesn't have that many pages um you know I don't spend a lot of time you know waxing poetic or having a lot of platitudes that's sort of the difference between my writing and then everybody else's there's no filler everything it's super dense everything that I write is I find uh meaningful and and a worthwhile uh sharing but it's really look these books all have had different reasons okay the the last book that I wrote I didn't want to write it okay uh Denise Pearson our CMO really you know pushed me to write it and she also made it easy for me to write it because I had a lot of help along the way I wrote every word of it okay but I did have a ghost writer who just went through and said look you need examples here or nobody will understand is outside of your business you know all that kind of commentary and and explain this better and so he helped me just make the book more consumable rather than this very audience that we normally deal with but the net of the the reason why I wrote amp it up was you know people said hey just like you just said you've had three very successful experiences different times different markets different Technologies different competitors blah blah blah you know what's the secret sauce I mean Americans always think there's a formula that can be extracted and if I just have my hands on that I can just do it too right it's an immediate gratification uh type of thing and the book's really the answer to the question of what do you guys do what do you think explains the success in these companies it's my answer it's it's not that I'm trying to sell that to people at all I don't care whether you agree with me or not I'm just telling you what my best guess my best take is on the answer to that question right um people sometimes go like well I don't agree with this I don't care I mean yeah we I did Kill customer success at every company I've been in I think it's the biggest thing that goes on in Silicon Valley that doesn't mean that I need you to agree with me I'm just telling you what it is right so one of the core messages in amp it up is about the importance of urgency um and you talk a lot about how to create it I guess maybe a more difficult question is why do you think a bunch of CEOs and leaders don't push for more urgency or higher standards well I know you guys have been to a California DMV before you want to see a lack of urgency you know um this is what naturally happens to human beings it's it's just it's innate we slow down to a glacial Pace unless there are people who are going to derive Tempo and pace and intensity and urgency that's what leaders need to do because people naturally slow down they're like well I need to be your annulation you know and then just sort of their mind is wandering off on their next vacation or what they're going to do on the weekend and it's like you know you you need to set you know High Focus high intensity uh High preoccupation you know with with what we're doing I mean the people something ask me what's the message of of your book I'm like read the title okay because that is the message look there is a there is an X Factor there's an enormous amount of room in the margin that is right under your notes okay and you have the opportunity to take it up in the next meeting in the next podcast in the next email in the next slack message you can take it up you know you can push the urgency you can push the standards right you can push the alignment right you have all these opportunities are you taking them it's an easy message but it's really hard to have the mental energy to bring that to every single instance of today right and that that's the that's the message of the book there's a lot of room there there's a ton of room there and people don't realize it because you know I I've seen companies where you know FCC young CEOs they just think I hire a bunch of people and I sit back and wait for greatness they have no idea that they have to relentlessly drive you know every second of the day every interaction and seek the confrontation because you know CEO jobs are in insanely confrontational which is not human nature we don't like it we're naturally confrontational we avoid it I mean I had a Founder CEO once every time somebody had to get fired you know he he had a CFO do it and he stayed home that day because it's just so hard right and it's like I don't have the disposition for it we understand that but there are people in the Enterprise that have to do that stuff okay that fully resonates but another piece that strikes me is people are afraid right that they don't have the right people that um they'll lose in the losing the talent Marketplace if they push hard enough their people will leave right what would you would how would you respond to that well if they leave they should leave okay I mean this is a great thing you know culture shorts and sifts you attract the right ones and you start losing the wrong ones so it's actually quite perfect and people are leaving they're just not your DNA they're not your blood type and uh by the way you need to create your blood type you know around you otherwise you're correct you have nothing but conflict I mean I remember having people after two weeks just said you know what I can't take the patient intensity this place anymore it wasn't me personally it was like everybody was like that you know they were all you know calling people out and driving these expectations they weren't used to and they wanted to go home at four four pm and pick up the kids from school I'm like well you need to go back to HP and sleep in your cubicle this is not the place for you um so you need to about like culture can be incredibly helpful you know to a company uh but culture is not a general thing there's not such a thing as general goodness I mean a culture needs to really enable your mission right and whatever whatever enables your mission effectively is is a good culture is there's no Universal culture that's good you know it depends on you know the type of leadership you have and type of business you have and you know where you are in your journey and all this kind of stuff but you know culture is a very powerful thing because if you don't if you don't fill the void somebody else is going to you know I want to switch over to talking um about Snowflake and and then what's going on in AI um can you just give our listeners a sort of snowflake 101 you know what is the sort of scale and core Innovation and use case of snowflake today um and what we can talk about uh how the how the um company has been evolving from warehousing to Cloud uh the data cloud and application platform and AI after that yeah our our Founders uh probably would argue immediately with you that they were never a warehousing play so they sort of want to forgive me yeah you're forgiven um but there's a reason for it because you know they were dealing with semi-structured data right from the get-go and sort of the workload types were more than just sort of batch uh analytical you know type of stuff which is mostly associated with data warehouse and that's also purely structured data um so there was there was always a broader uh scope and focus but our Founders were two French guys long time you know oracle CTO technologists Architects they were really responsible for making Oracle from the departmental level you probably can't remember that far back but Oracle at one point in time was a departmental platform to the Enterprise platform that it became so things like parallel SQL you know were all things that came you know from them so they they left and you know they wanted to reimagine database management you know for lack of a better award for cloud computing in other words they didn't want to carry technology forward or as little as they could they wanted to reimagine so you know building a data uh base or a data platform whatever you want to call it for cloud computing was very different than just sort of taking a postgres SQL kernel forward and kind of hacking it up important Cloud I'm being very unflattering here but there's plenty of people that have done that um so they did some really uh breakthrough things you know most notably that most people know is the separation of storage and compute I mean back in the day people maybe not remember this but you know I mean you you bought storage and Computing combination you can buy one but not the other whereas in the world of cloud you can come into your compute and and and storage independent of each other and of course it became a consumption model not right away by the way that was sort of an evolution and you know obviously today is by the machine second or compute second but at once upon a time it was you know by the node and there was by the by the machine hour and all that and that was so incredibly fine-grained and granular um that that is completely different but the other thing that they did is they took the control plane out of the cluster itself so the Clusters are now all stateless you know in other words they're clueless which is great because you can run tons of them you know concurrently right so there's not there's not one master the master lives outside of the Clusters so running jobs concurrently is another huge thing because in the world of data warehousing just to use that word again Sarah I mean the reality was you know you have to beg for 2 30 a.m time slot three months from now because you know the cluster was consumed very quickly very easily now it's like there's no limit so this is this is what I often tell investors like I'm not creating them and I'm just enable way it's okay it's so pent up it's insane right and the architecture does that right and then I could also provision workloads either for economy in other words we run the cheapest possible or get running for performance blistering fast and you could make these optimizations and choices so this is this is beautiful stuff right because we we just we just opened up uh the demands in that you know Legacy Marketplace and then of course we started migrating uh you know teradata databases I mean massive teradata plants and by the way I mean it's we're still in the early Innings of that because it's not easy to to move those platforms at all uh but you know tunnel Hadoop of course which is sort of the you know what we used to call Big Data and now old data is Big so that that descriptor doesn't make you know too much sense anymore um you know an old Cloudera and on and on and on tons of Oracle SQL Server I mean so that's that's what we've been doing but you know when I started you know the the the the tagline if you will the positioning or core message was this is the date this is the data warehouse built for the cloud that was no flex message and I'm like okay well I'm gonna stick with that because you know you you taint yourself with a brush pretty soon you can't get it off you which is pretty much what happened to us I mean you just started on Alexa here we go again I have an allergic reaction every time I hear data warehousing because to me it's just a type of workload now it's no longer a market it's no longer an industry where and and you know cloud data management platforms you know are and certainly we are you know we're seeking to become full spectrum workload capable meaning from the most batch analytical to the most streaming online transactional you know massive you know uh scale and and and and extremely low latency from from what you're used to and in all TP type of environments and the reason is we don't want the the whole premise behind the data cloud is that the work comes to the data the data does not go to the work now why does that matter you know because historically the data has always been pumped around to go to the work well you get massive siloing of the data you you don't even have to work at it you're going to get siloing you know whether you try or not because you have a new app you get a new Silo you know because it comes with its own database right and the siloing prevents you from really fully exploiting the potential that that lies within your data because there's no walls that exist between them um so the notion of a data cloud is is kind of a really new data strategy element in the mix and and we Advocate really hard I mean I've I started to see a large Banks I says don't go resilowing your world in the cloud you end up with the same sort of problems you know you have right now in your data science ml AI Etc teams are going to be you know very frustrated uh you know trying to Overlay and blend that data on fine tune and train and do all these fancy things we do now um you know with data so we know we're trying to create an unfettered uh data Universe data orbit that's much bigger than your Enterprise by the way because this is really an ecosystem right it you you have data providers you know in in the world of you know Financial Services you don't get facts said and Bloomberg and and s p and all these tanks um so in in hedge fund they have hundreds and hundreds you know data flows you know coming in so you really need to think of data management as as a much broader orbit uh than than just your your uh your Enterprise and so in the world of of um artificial intelligence or general intelligence uh around data the ability to mobilize data you really need to have a data cost strategy that's also why we are multi-cloud capable because we don't think you can have a data cloud in a single public on a single public Cloud platform by definition you can't right so that's really the strategy and uh um obviously things uh have taken off a lot but there have been multiple iterations in the in the journey you know of snowflake I mean started out off started off just moving uh Legacy uh you know systems for the cloud and taking advantage of the elasticity and the economics and the provisioning all these things uh but now it's much more broadly workload capable and that's a journey that goes on now the other thing that has changed is no longer a database world you know historically a database was just you know a platform that was self-contained and it had standard interfaces like odbc and jdbc that you that the application used to access the data now it's like well wait a second you know we we don't want to operate that way anymore because you're breaching the governor's perimeter so the application needs to execute inside the perimeter of the platform not outside um so we have a programmability platform called snowpark okay and then that's where you know all the applications left we have native application framework all these kinds of things so now you're looking at a very different platform environment very different layer stack than historically what we've had um in the on-premise uh stacked that we've grown up with certainly I grew up with so that's kind of a short a story as I can tell you that's that's really great background and um obviously sniff like has accomplished amazing things and really become Central now to the the Enterprise data world and ecosystem how do you think about what's shifting in AI because I think we went from a world where we had almost like this older version of AI models cnns and rnns and things like that where people doing old school natural language processing or other things and then more recently we've had this big breakthrough wave of generative Ai and it felt like the starting gun for that to some extent was really when Chachi PT came out about six months ago and then gpt4 came out maybe three months ago and then suddenly everybody started building applications against this how has that been showing up or has that been showing up yet in terms of the AI use cases that you see in the Enterprise or your customer requests or has anything really shifted yet in terms of you know the broader Enterprise ecosystem that you deal with just given that often it takes six months for an Enterprise to plan something if it's a very large business and so I feel like the last few months of just the last two quarters have just been a lot of big companies kind of planning against what to do yeah you know first of all large language models are about language okay no surprise um but and it's a huge deal um because you know I was taught the you know the basics of Kobo when I was in school and you know couple stood for common business oriented language well there was nothing common or business oriented about it it was extremely cryptic syntax and all that but compared to assembler and machine code it was amazingly uh you know the syntax was amazingly comprehensible so it's all relative you know in the 80s we had SQL which was back then you know also positioned as something that mere mortals could use to query data so this is all about what what how and what is your relationship with data right and over the years that has you know evolved but it's been immensely frustrating you know for people to get you know access to data in the form that they want and there's a lot of ad hoc and there's a lot of standardized reporting and dashboard and all this kind of stuff but it's been difficult so you know going to natural language is like it's like the last mile here um and that is is an enormous thing I mean the effect on demand will be just enormous because every Mortal if you're semi-literate maybe you're not even literate you can just talk you know you can you can get value from data wow you know so it is an incredibly uh you know big deal but you know the degenerative aspect in terms of content generation that's very cool when you're trying to plan a trip to Yellowstone but when you're in the Enterprise you're dealing with structured proprietary data and you know they're not planning trips to Yellowstone they're gonna you know they're gonna ask really hard questions like an insurance for example they may say um you know we had disproportionate you know bodily injury claims in Florida in the surrounding states didn't have a you know a what explains that b are we gonna have it again next quarter and see what do we do about it do we stop underwriting we change our pricing blah blah blah believe me you're not going to get the answer to that question the large language model so um you got to sort of separate the issues of you know text to Sequel and all that you know which I think are incredibly valuable from going to your structural proprietary data because that's a that's a very different realm so you know I the way I'm trying to think about it right now is yeah we have language models but we're going to see all kinds of other models we're going to see business models okay because the question I just asked you need to understand business models I mean one of the big things that just to stick with insurance for a second one of the biggest things in Insurance in a specific type of insurance like auto insurance auto insurance is Gecko and Progressive and Liberty Mutual and all these people you know telemundry data is is number one through ten for them okay it's limited data is the device you get in your car and it knows when you're speeding and all this kind of stuff um and by the way that that's how they now price risk and they're they're capable of lowering their prices yet increasing their profits because so they're extremely sophisticated and refined use of that data the data is extremely productive you know in terms of you know what what the claims are going to be uh and it's the difference between winners and losers and people who make money and people who don't make money so that's that level of of and by the way that's not even AI That's that's just you know machine learning uh they're really data driven uh and that's already in in Broad use in in other insurance companies that's that is sort of you know where this is all going I need to be able to ask questions that analysts might take weeks and months you know to or bring in McKinsey or Bain or whoever you know to kind of study you know problems right the systems will be able to start giving you Insight uh into those kinds of questions that's really where we live you know proprietary structured Enterprise data that's a totally different realm you know and uh you know and by the way you couple that with language yeah that's pretty powerful so you mentioned Marvel movies you know that's a nice model uh but I imagine in medical we have diagnostic models you know and we have all these different you know levels of intelligence that we can build uh as long as they have the data I mean they're going to be insanely Lightning Fast uh providing Insight you know we acquired this company called Neva uh you know very recently I'm very excited about uh bringing uh the expertise into into the company because you know they're search experts and I'm a search junkie I mean 25 years ago I mean I wish I'd had search you know earlier on my life because it's such a huge thing you know I I just can't help myself I'm always um and and the search is so addicting because it lets you sort of explore everything that's known and ever been written or published or opinionated about and sort of process all that information but the problem with searches it has no context right it just matches on strengths and uh you know if you search on snowflake you might get the company you might get the weather uh you might get the the social phenomenon because it doesn't know it just knows the word and it's it's incredibly and so enrichment and context is really the name of the game in the world of data right we always like to say you one attribute can can make a data attribute go from uh being mundane to being high octane because of the context that they create all of a sudden becomes wildly insightful and impactful and predictive and all these kinds of things so you know in order for for search you know to to get that context and become stateful is those are going to be enormous step forward and you know chat search you know it all becomes one one natural language conversation after a while um so you combine that you know with having this these new levels of intelligence specific to Industries or just subject matters um you know I think is that's really where there's a world of opportunity waiting to unfold still I'm certain that it will you know yes uh you know Neva was a a dear former portfolio company do you imagine that the um snowflake like interface um for users uh changes a great deal over the next you know five ten years in in terms of like supporting more natural language or a broader user set yeah both of those things um you know I think the the there still will be a future for for bi companies business intelligence sort of Cabela's Lookers uh world and you know dashboarding is is done for a number of reasons sometimes it's just you know basically providing data in the consumable format but it's also done because it it's a way to basically tell people this is how I want you to look at the data this is how I want you to understand so there is sort of a guiding element to dashboarding not all analysis is ad hoc based now a lot of it is and uh you know for ad hoc you know nothing is going to be better than than natural language um I at least I'm already using it you know we we push Salesforce data into what we call snow house that's our internal snowflake database as we push everything into and it's just incredibly easy to use already commonly available services and and have you know conversational relationship with that data you know or my two top reps in uh in this country or that market or it is industry now it spits it out in the fraction of a second and and but a beautiful graph attached to it and all of that and so um it's very addicting because it just uh it's just like search right you just keep going and going and going and it becomes like a whole journey um so yeah I definitely democratize access uh anybody semi-literate will be able to get you know way more value than they ever imagined from the data um and it will change you know how products get used I mean bi will not be the same I think I see that as severely affected by by this Evolution you know you made another acquisition of a company called streamlit that I think we're also both familiar with can you talk about um the rationale for that streamlit is is a company that does visualization animation um you know for for python applications but specifically in the world of machine learning the problem with machine learning is um if you're not a programmer it's pretty damn hard to consume you know what what it is and how it works um but Streamwood is almost reflexively reached for by python programmers to basically make a machine learning model consumable by a general business uh user you're actually can manipulate the variables and it just redraws everything visualization animation uh and then that's weird the reason that we acquire Streamwood is hey you know that's certainly we have to have visualization and animation and by the way this also touches the world of bi because a lot of people use streamlit you know for the same reason that they would use bi type of products but this is just much more um you know specific to all kinds of reporting and use cases and uh and and and dashboarding um so what we wanted to do with streamlined is to bring it inside snowflake we call it stream streamlit in Snowflake and the reason is you need to have that hardcore trusted sanctions uh governance perimeter um because otherwise people's people will not allow the business to use these kind of applications governance is a really big deal because the data needs to be sanctioned and trusted and the business should not be able to get in trouble with the data and that's really what we try to do with snowflake we are a hardcore Enterprise grade uh platform and it's really hard I mean you can bring python to your data in two weeks time but the problem is you know people are downloading libraries every couple of weeks to their harsh content and people have no idea what kind of risks they are exposed to in terms of exfiltration and all that we spent two years you know making making python non-porous um and it was an enormous effort to do that but you know you go to large financial institutions I won't let python anywhere near our core data which is not even a conversation and we're like well we're going to do it in a way that you know the people that use Python there are many obviously um but they can do it in the way that they don't violate uh and create exposures to the Enterprise so that's really the the role that we play we talk about governance a lot we talk about data quality a lot and we get into this conversation I don't know how many times a day because you know in a world of AI if you don't have highly organized optimized sanction and trusted data what do you want you know your models to do just kind of train on on a data Lake I call it a landfill you know I mean you have no idea what the hell is in there you know everybody dumps their stuff in there you're going to go train on that it's just absurdity so you're having highly organized optimized sanctioned data is really it's a prerequisite for for old and people publish what they call data products I'm sure you've heard that term uh before a data product is essentially you know I've taken data you know out of it like and I've created into a trusted optimized understood uh object that I can now give to the business and stand behind that's really the role of the chief data officer to to make the data you know trusted organized and optimized and then also that the business can get in trouble you know with either because the data is no good or register breaching all kinds of security and compliance you know aspects of of using data so that's streaml is really important to us uh the great thing about this and open source project so you know people so many people out there are reaching for when they want to publish something and uh you know we're like okay we're going to bring that Insight the Enterprise perimeter and make it high trust you know I go back to sort of the um Journey you described from uh not just a data warehouse but only data warehouse is a first workload to you know broadly you know more online analytics other workloads applications that sit inside snowflake with um you know unified data what are the what are the biggest challenges you guys face in making that Vision come true is it convincing people to like move to you know customers to an entirely new architecture is it building the ecosystem is it just supporting the workloads because it's a it's a very big rewrite of sort of Enterprise architecture overall yeah but it's um you know we are rewriting anyways because of our our migration to Cloud it's like the most disruptive thing ever um and yeah look you know when I was at service now we basically had an on-premise architecture that we hosted in the cloud and by the way I'm I'm not being you know unduly critical here I mean because it was very useful that we were you know a single tenant platform it had all kinds of advantages and we were able to uh to manage it really well through Master standardization and and and things like that I'll give you an example um you know all the Federal Business that we had um that service now was on premise Oracle because you know you could not get in there with the clouds hosted solution just couldn't by the way you still can't I mean the certifications in on federal are so insanely demanding um you know Federalist is is a very small part of our our business because we spent we're in the process for years and years and years uh to meet to meet those standards it's very very hard right um but we are a pure Cloud implementation we can't run on premises I get asked by people you know like I mean I can't even conceive of it you know the way snowflake works right because the commandeers you know resources it's not a it's not a machine-centric uh platform you know um so the it's it's um it is a big change there's there's no doubt and uh as I said earlier you know um you know we we fight the siloaming of data because we're that kind of a company from a data strategy standpoint we really tell people you need a different data strategy for the cloud do not continue with what you've been doing because you've created a massively proliferated bunker outside world and it will not serve you in the world of AI and machine learning and any any level of data science if you want to drive intelligence from data you're going to be in a world of hurt if you keep siloing the data and we tell that to application developers to isvs and say look don't have your own data container okay because instinctively application development I want to have my own my own data layer hanging underneath it I'm like you know what um it's you you're gonna hate it because a it has no value to what you do because you're not a data management expert it's just a utility function you know for you but then you know you're another Silo and the customer is not frustrated because they're going to start pushing their data into Snowflake and now we have pipelines and ETL processes and all this kind of stuff and latency issues Governors issues all this kind of stuff so we we just announced that this relationship with blue yonder for example that says hey we're going to fully re-platform you know on snowflake because in the world of Supply Chain management that's really important because we need to have visibility you know across all the entities that make up a supply chain we only can only do that when you have a single data Universe when you have all these containers it's impossible that's why Supply Chain management has never been platformed because the data problem was unsolvable literally you know um so these and and then the the other thing is the Supply Chain management I mean they run these extremely demanding uh analytical processes right and they run many many times you know uh you know you know per minute per hour and they are very very commanding of resources right so again this is where you know our style of computing is is very very desirable because I can run the process I can run them as fast as I need to I can run as many as as I want to concurrently so all these new architectural things are lending themselves really to use cases that have been there for Generation but you know sprouting management is an email spreadsheet business I mean are they still living in in a world of Microsoft 30 years ago that's insane right because it's one of those use cases that should have been extremely optimized but it isn't right so yeah you're going to be doing re-platforming re-architecting and and reimagining that's what we did snowflake is a reimagination of data management for for cloud computing but it you know as we get through our journey it's looking more and more different than what it what it used to look you mentioned some very large scale Evolutions in terms of just the data World there uh what are some of the other future directions that you're most excited about or the big thrusts that you see coming in terms of data data is going to redefine whole Industries okay and that that's what I find the most interesting and the reason I say that is uh first of all you know nine out of 10 conversations I have with customers are not technology and architecture and all that and migrations it's about industry use cases it's about call centers it's about you know making medicine predictive for example because everybody knows you know Healthcare is is economically you know not viable uh at the scale that we need to deliver it in so data can make us you know predictive and prescriptive right we can if we have enough data you know we can tell who is at risk for what disease when and what they need to do all data driven this is not well we this is not somebody's opinion the data just data doesn't have opinions okay just that's what it is and it gives you the accuracy uh to go with it for the more depth and breadth of data that you have in the more debt certain that stuff becomes but this is this is how uh Healthcare will become uh much more effective obviously because you don't you're no longer reacting to disease and symptoms but ahead of it and every every Healthcare institution you know that we talk to and our customers are this is where they want to go this is where they need to go they don't want to treat disease they want to prevent it and they want to anticipate it so it will change you know Healthcare as an industry but you know I just mentioned you know auto insurance this is a similar type of example in the world of Pharma you know it takes on average 12 years to um you know to to bring a drug to uh to Market well then you've got five years left before your patent runs out what if I could compress that by one two or three years now you've changed the economics of the entire industry right so you know data is is far more important to how in the economics and how the industry functions and and people still realize you know how do your investments in r d reflect this or what are the big areas of thrust that you have right now from an r d small perspective the the hardest part you know for us is you know I have to massively enable we have to massively enable this platform to be incredibly broadly and a capable nuts broadly but also in depth because if it doesn't do what people needed to do or it doesn't do it well they're going to say like well forget it we'll just pump the data over here and now we're back to you know fragmenting and and siloing the data so if if we have the data we have to enable the workloads okay we have to and that's really hard that's really hard man we you mentioned some of the some of the workload types but we do things like Global search okay because in the world of cyber security you know that's incredibly important because a lot of cyber security companies that you know there are partners of ours they are running on the data Cloud they don't they because they couldn't sell to their customers yet another database container customer didn't want it they said look you know bring the data here and then we can combine it with all these other data sources you know vulnerability and then all and then you know our analysts can can search one data Universe instead of 15 of them and try in their head to figure out what does it all mean and do something with it yeah I'm definitely saying a lot of people right now building in terms of um snowflake apps so that they can just uh maintain the data locally within a snowflake instance for a customer but then provide enriched functionality on top of that or access to that data in ways that are really perform it and combine with what the you know uh with what the Company's trying to do more broadly so I think that's been a really great Innovation for the industry um I guess one last question is just around the macro shift so obviously we've we've gone from a zero interest rate environment where everybody was just buying software um like crazy to a world where people are cutting SAS budgets increasingly they're rethinking spend um does the macro environment change your point of point of view on consumption or credit based pricing or you know how you think about um the the pricing and economic model in the in this new regime yeah not really um you know I we have different uh stakeholders that have different opinions on this investors of course love it when you have customers over a barrel and you can keep a gun to their head and they're going to pay you no matter what I don't particularly like that you know when I was at service now you know I I always felt that it was not an equitable relationship that we have with our customers because oftentimes you know they would sign up with us for many millions of dollars and it took them nine months to even get in production they were paying for older users all this time I'm like how how's that Equitable um so one of the things that I really liked about Snowflake and cloud computing and consumption models and the elasticity is that you pay for what you use it's a utility model and and um you know is is that painful sometimes yes I mean I I talked to the CIO of a bank at Las Vegas and he said you know this is my bank's growing three percent snowflakes are only 22 you know and that can't go on forever you know the CFO gets in there and he goes he starts calling on everybody and saying like hey people um you know they basically say this is the size of your Bread Box live with it you're not going to get a new contract but it's and then people need to go back to the to the drawing board they're like okay it's a very fine-grained thing because she can go into snowflake workload and say okay I'm going to downgrade the provision on this I'm going to run this less frequently I'm going to change the retention period on data you can do all these things to to lower your your your consumption of storage and and compute does that hurt us sometimes yes bu