 From the CUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Everyone, welcome to this CUBE Conversation. I'm John Furrier here in the Palo Alto Studios. During the pandemic, we're not in person. Usually we are, but we are doing remote interviews. And as a lead-up to ThoughtSpot Beyond 2020, an event coming, a virtual event coming up, we've got two awesome visionaries here to have a conversation around data and the role of data. Cindy Housen, who's the Chief Data Strategy Officer at ThoughtSpot and Kent Graziano, Chief Technical Evangelist at Snowflake, which has been great success. Welcome to the program. Thanks for coming on. Thanks for having us, John. So, Kent. Happy beer. Dave Vellante, who's just a fanboy of Snowflake. I mean, he's just gushing over the success of the company, obviously Frank Slutman, we've known for years. Congratulations on your success. Great stuff. Yeah, thank you very much. Well, the topic I want to get into immediately is obviously data. You know, we're seeing in the heels of Amazon re-invent conference the role of data in the cloud and also on-premise. You're seeing both things going on and companies are adopting this. Now it's a do or die situation for companies to either get on board with a full-on data strategy. Can you guys talk about how that move to the cloud is imperative and so important? Yeah, I mean, as you said, John, it's a do or die moment. And we've seen, even pre-pandemic, many organizations were in the process of modernizing their cloud data and analytics, moving to the cloud, but COVID has really just accelerated that. The ones that innovated sooner here are performing better and the ones that are still dragging their heels, the laggards, I am not convinced they will survive. Ken, your thoughts on the data, you guys are born in the cloud data company. You can't get any more born in the cloud than you guys. Obviously, I started out in the on-prem world. I've been with Snowflake for five years now, but exactly what Cindy was saying there and I've been telling folks as I've talked to them over the last five years that things are changing. The world is changing, things are changing. And this was even pre-pandemic. Things were changing faster than anyone could have imagined and the only way to really keep pace with the growth of data and the diversity of data, in my mind, was to go to the cloud. And this concept of having a data cloud where we can easily share and govern data is the game changer, right? And making customers and organizations so much more successful by being able to do things with data that they just couldn't do in the on-prem world, elasticity and the power in the cloud is just giving people unprecedented access to do just amazing things. Whether you're a startup or a big company or on-premise trying to transform with digital transformation, you're either inventing or reinventing or creating a category or redefining a category. And data is going to be the critical piece of it and the cloud can actually scale that. So I want to get your thoughts on this notion of reinvention. How does data become? Because you could be a category creator and redefine a category, but the people have to understand, the customers have to first understand that their problem that they have is something that can be solved with data. This is a critical moment of connection, the product market fit kind of thing where they go, okay, I get it now. Cindy, when do they have that moment, the aha moment of, I see the problem, I got to do this. Yeah, well, there's two things, the aha moment. And John, I have to preface this. If I may, many people listening to you may not have met me or Kent until now. Kent and I go way back, both previously independent analysts, but we remain with this North Star of helping our customers unlock the value of data. So I don't want people to think, oh, we're pushing cloud because that's what we work for these companies now. It really is a belief you have to use this to innovate faster. So when did that aha come? It depends. For some people it's only just now staring at them and that's why there's been a lot of churn in leadership. But let's go back even a few years ago, you can take Walmart as an example as they were maybe losing to Amazon, they went to digital, they went to cloud and are now competing beautifully. So it happens at different paces. Capital One, of course, was earlier here. There's a lot of financial services, organizations that really are moving too slowly to the cloud. And you see how well Capital One is doing versus some of the others that have moved too slow. Well, Kent, you guys go way back. You know, you've seen the old school, old guard as Andy Jassy at Amazon calls it. But there is a real shift happening now, finally. It's not just the old school data warehouse model anymore, there's new requirements and there's new benefits for being in the cloud that you don't get on-prem or with a data warehouse. You know, you've gotten a different kind of access to a more scale, maybe another company with an API. So the idea of connecting in the cloud, cloud native is completely different. Can you share your view on how that helps people understand the cloud better? Yeah, and I've certainly seen that. Like I grew up in the on-prem data warehouse world which is where Cindy and I met. And what I'm seeing now is the lines are being blurred between some of what we would have thought of as the traditional silos of data in the on-prem world. The data lake and the data warehouse or foremost in my mind is with the data cloud, that line is not really there anymore. It's now about the workload in the use case than it is about, I'll say the structure of the data or the location of the data. We're able to eliminate the data silos by getting them all up into a platform like Snowflake. And the form of the data is less important than it was. We can start with a very raw form and be doing data profiling and having data scientists look at it and maybe even feeding a machine learning engine in the process. And then as you discover the important bits in that data, maybe curate it some more because we do need some data governance, we need some data quality. And that goes more into what you would think of traditionally as a data warehouse type format or a data mark format for running and supporting dashboards. But we're now able to unify all this data and really get to this concept of having a single source of truth and be agile at the same time. That's one of the things that attracted me to Snowflake out of my independent consulting world at the time to jump on board with Snowflake is this, I was just so amazed at what we could do in the cloud with that power and the elasticity that we just was unheard of and unthinkable in the on-prem world that we just can make so much more progress and so fewer constraints, faster time to value, all kinds of things like that that just were amazing to me. Okay, Ken, it's been too long since we've jointly met with customers. You use dashboard, that's a dirty word. We're trying to get rid of those, we'll say CloudFlight. Well, that's a good point. I mean, let's talk about the dashboard is what people are comfortable with. That's what they're used to is kind of the first gen. But now going beyond the traditional analytics, this is where you start to see machine learning and AI become the value. And that's the one thing that's constant now is, okay, data is accessible. You get cloud scale, massive amounts of data. How fast can you put it to work? Sounds trivial, but it's not. What do you guys react to that comment? Yeah, and it's not trivial on the impact, but I would say it's become more trivial to make it happen because you have that unlimited compute or elastic compute, snowflake separates the compute and storage. So you can do analytics that were just not possible in an on-premises world, on-premises discourages experimentation because of the high fixed cost to even get going. And with ThoughtSpot, the AI-driven insights lets you find the anomalies, the correlations without a data scientist on all your data. So granular, every, you know, terabytes, just millions of records within your snowflake data warehouse. And I think it's also combining the different workloads that in the past used to be separate, right? Kent, they would take the data out and do it on the desktop or in the data lake even, the data scientists anyway. Yeah, exactly. I mean, well, in the past they were, the repositories themselves were even separate, right? You often had very different technologies. I've worked with customers that would have data replicated across two massive data warehouses, one for loading, one for reporting, and then they'd be extracting that very same data into a Duke cluster to put it in the same place with the semi-structured data so the data scientists could go at it. So they really had three copies of that same data and the amount of engineering and synchronization required to make that work so that everybody was sort of working off of the same data. And we've been able to now eliminate all of that with a snowflake to put it all in one place just once. And let everyone work on it and really democratize the access to that data in one place. So whether it is machine learning and AI being one of the really big use cases that's certainly growing now and getting into it faster, driving that time to value in those insights with products like ThoughtSpot to be able to get in there and make it so much easier for professionals to look at that data and analyze that data and find those insights that they really need. You know, that's a great point. You mentioned, you know, the old way of setting up a Duke cluster and all the time. You know, we all know what happened there. I mean, there was too much engineering going into setting up clusters, then getting the value out of the clusters and then income spark and then it comes to Amazon. Hello, you know, goodbye, Hadoop, right? So Cloudera certainly has shifted. They merged with Hortonworks. You know, they're going back into the clouds. Smart, smart move. But the data world has changed. Obviously, you guys are leaders in this new data in the cloud phenomenon with new business models, new value propositions. But I got to ask you about kind of the old personnel files that are out there. You talk about people. You know, there's people's jobs who works the DBA or I ran the data whereas I set up those clusters. So, you know, I hear what you're saying, Ken, but like the data administrators do their jobs go away. So take me through the impact because this is a big challenge to how to redeploy and how to retrain or leverage the existing personnel. Yeah, and I've been using the agile term refactor. We have to refactor the database administrator's job to be more of an architect or a platform builder. And we're talking more now about having, you know, data coaches, data storytellers, Cindy's talking about that all the time. It's different skill sets, but folks that have been in the space for a while are very adaptable. And if they're data experts at some level, then it's, you know, just looking at it a little differently. And in reality, when I talk to DBAs, when you look at it and say, well, where do you really get the most joy out of your work? It's delivering the value. Nobody's overly excited about backup and recovery, right? That's not where they're getting their job satisfaction from. It's getting the business access to the data. And so now with the advances in technology, we're able to give them that opportunity to really become, you know, data providers and to work in partnership with the business to get the business access to the data they need from new sources, different data types, and but in a more timely manner, rather than having to spend 70% of their day working on really manual mundane administration just to keep the platform up and running. And we've had customers tell us that they've seen as, you know, 50, 60, 70, 80% reduction or more in the amount of administration necessary, which means that their staff is actually more productive. And that's going to be a good shift, Cindy. Take us through the shift because, you know, one megatrend that's happening, you see chips coming out there with more horsepower, with built-in machine learning. You're seeing this kind of new layer of democratization for insights and storytelling and analytics. And then you got this embedded model. You guys do search embedded into all your activities. You got three layers, almost a stack of data software, you know, built in, easy to use and simple and then completely forgotten by the user because it's built into some app somewhere. Right, so you're starting to see this change. How does that affect, like, who works on stuff? Yeah, so it does shift. You have to think the analyst. We talk about the analyst of the future in a way similar to what Kent was saying with the DBAs, trying to become data engineers. The analysts of the future really want to be the strategic business champions. And even a research report from TDWI talked about how most feel beaten down. They can't keep up with it. But 36% would say, if you freed up our time, we would become more strategic business advisors. So that's kind of the core analyst now. The embedded that you're talking about is really where data becomes a product and it's the product managers that are embedding data in these applications. But this people change management is super hard. In fact, Harvard Business Review said the lack of accounting for people change management is one of the top reasons why technology is not adopted for these frontline decision makers. We can make it easy, consumer grade, but if we're not looking at how we change these people's roles, it's still a tough hill to climb. Well, I got to ask you both kind of the real question that's kind of the middle of the table here is, you both have seen ways of innovation before. What's going on now? And it's pretty obvious, it's playing out in the real world right now. It's in full display as we see it with COVID and digital transformation. How do people do it? What's the playbook? How do you advise folks are saying, because you see both sides of the table, you've been there. You now see the other sides, Snowflake and ThoughtSpot. What's the mindset? What's the playbook? What do people do? How do they get going? Yeah, so start small with the business outcome, with your biggest pain or your biggest opportunity, learn, figure out how you're going to change the people and then run fast, run faster than you ever have before. The rate of creative destruction has never been faster. Yeah, in the agile world, they talk about failing fast. So exactly to Cindy's point, things are changing so rapidly, you don't have time to sit around and mull it over for very long. And so really adopting an agile mindset is very important to being successful today. And certainly with the pandemic, we've seen many organizations come to the top. And those were folks that were able to rapidly adapt. And in part, that is their mindset, the willingness to adapt, not to sit around and overly complicate the issue, overly discuss the issue to many committees, all of that, but really getting into that mindset of, what can we do today? What technology do we have at hand to take advantage of today to make a significant difference? And that's where at Snowflake, we've certainly seen an increase in adoption from many of our customers where they're actually using Snowflake more, they're creating new use cases and they're able to use that flexibility and the agility of the platform to make significant business changes in a short period of time. But back to Cindy's point, you've got to have the right culture in place, and the right mindset in place to even see that as a possibility. You know, the three things that make business go great, you make things easy to use and simple and provide value fast is a really good formula. You guys do that. Ken, congratulations on your success at Snowflake. I know Frank Slutman's going to be speaking at the Thought Spot Beyond 2020. You guys had great business success. Your customers are voting with their wallet. Thought Spot, you guys are having an innovative formula doing very well at the AI and built in search and all the greatness. The new models are here. And so congratulations. Thanks for watching theCUBE. I'm John Furrier. To learn more about Snowflake and Thought Spot working together, check out Beyond 2020. It's a virtual event on December 9th and 10th. And you can register at thoughtspot.com slash beyond 2020. That's thoughtspot.com beyond 2020. I'm John Furrier from theCUBE. Thanks for watching this CUBE conversation.