 From around the globe, it's theCUBE, presenting ActiveDQ, Intelligent Automation for Data Quality, brought to you by IOTAHO. Welcome to the sixth episode of the IOTAHO Data Automation series on theCUBE. We're going to start off with a segment on how to accelerate the adoption of Snowflake with Glenn Grossman, who's the Enterprise Account Executive from Snowflake in Yusef Kahn, head of data services from IOTAHO. Gentlemen, welcome. Good afternoon, good morning, good evening, Dave. Good to see you, Dave. Indeed, good to see you. Okay, Glenn, let's start with you. I mean theCUBE hosted the Snowflake Data Cloud Summit in November, and we heard from customers going from, love the tagline, zero to Snowflake, you know, 90 minutes, very quickly. And of course, you want to make it simple and attractive for enterprises to move data and analytics into the Snowflake platform. But help us understand once the data is there, how is Snowflake helping to achieve savings compared to the data lake? Absolutely, Dave, it's a great question. You know, it starts off first with the notion and kind of we coined it in the industry or t-shirt size pricing. You know, you don't necessarily always need the performance of a high-end sports car when you're just trying to go get some groceries and drive down the street 20 miles an hour. The t-shirt pricing really aligns to, depending on what your operational workload is to support the business and the value that you need from that business, not every day do you need data every second of the moment, might be once a day, once a week. And through that t-shirt size pricing, we can align for the performance according to what the environmental needs of the business, what those drivers are, the key performance indicators to drive that insight to make better decisions. It allows us to control that cost. So to my point, not always, do you need the performance of a Ferrari? Maybe you need the performance and gas mileage of the Honda Civic, if you would, just to get and deliver the value of the business, but knowing that you have that entire performance landscape at a moment's notice. And that's really what allows us to hold and get away from how much is it going to cost me in a data lake type of environment. Got it, thank you for that. Yusef, where does Io-Taho fit into this equation? I mean, what's unique about the approach that you're taking toward this notion of mobilizing data on Snowflake? Well, Dave, in the first instance, we profile the data itself at the data level, so not just at the level of metadata. And we do that wherever that data lives. So it could be structured data, could be semi-structured data, could be unstructured data, and that data could be on-premise, it could be in the cloud, or it could be on some kind of SaaS platform. And so we profile this data at the source system that is feeding Snowflake within Snowflake itself, within the end applications and the reports that the Snowflake environment is serving. So what we've done here is take our machine learning discovery technology and make Snowflake itself the repository for knowledge and insights on data. And this is pretty unique. Automation in the form of RPA is being applied to the data both before, after, and within Snowflake. And so the ultimate outcome is that business users can have a much greater degree of confidence that the data they're using can be trusted. The other thing we do, which is unique is employee data RPA to proactively detect and recommend fixes to data quality. So that removes the manual time and effort and cost it takes to fix those data quality issues if they're left unchecked and untouched. So that's key, two things there. Trust, nobody's going to use the data if it's not trusted, but also context. If you think about it, we've contextualized our operational systems, but not our analytics systems. So this is a big step forward. Glenn, I wonder if you can tell us how customers are managing data quality when they migrate to Snowflake because there's a lot of baggage in traditional data warehouses and data lakes and data hubs. Maybe you can talk about why this is a challenge for customers and like, for instance, can you proactively address some of those challenges that customers face? Yeah, we certainly can. They have data quality, legacy data sources are always inherent with DQ issues. Whether it's been master data management and data stewardship programs over the last really almost two decades right now, you do have systemic data issues. You have silo data. You have information operational data stores, data marts, it became a hodgepodge. When organizations are starting their journey to migrate to the cloud, one of the things that we're first doing is that inspection of data. First and foremost, even looking to retire legacy data sources that aren't even used across the enterprise, but because they were part of the systemic long running operational on-premise technology, it stayed there. When we start to look at data pipelines as we onboard a customer, we want to do that error. We wanna do QA and quality assurance so that we can and our ultimate goal eliminate the garbage in, garbage out scenarios that we've been plagued with really over the last 40, 50 years of just data in general. So we have to take an inspection where traditionally it was ETL. Now in the world of Snowflake, it's really ELT. We're attracting, we're loading, we're inspecting, then we're transforming out to the business so that these routines could be done once and again, give great business value back to making decisions around the data instead of spending all this long time always re-architecting the data pipeline to serve the business. Got it, thank you, Glenn. Now, you said, of course, Snowflake's renowned for, I mean, customers tell me all the time, it's so easy. It's so easy to spin up a data warehouse. It helps with my security. Again, it simplifies everything. But so, you know, getting started is one thing, but then adoption is also a key. So I'm interested in the role that Io Tahoe plays in accelerating adoption for new customers. Absolutely, Dave, I mean, as Glenn said, you know, every migration to Snowflake is going to have a business case and that is going to be partly about reducing spend in legacy IT servers, storage, licenses, support. All those good things that CIOs want to be able to turn off entirely ultimately. And what Io Tahoe does is help discover all the legacy undocumented silos that have been built up, as Glenn says, on the data estate across a period of time, build intelligence around those silos and help reduce those legacy costs sooner by accelerating that whole process. Because obviously the quicker that IT and CDOs can turn off legacy data sources, the more funding and resources going to be available to them to manage the new Snowflake-based data estate on the cloud. And so turning off the old, building the new, go hand in hand to make sure those numbers stack up, the program is delivered and the benefits are delivered. And so what we're doing here with Io Tahoe is improving the customers ROI by accelerating their ability to adopt Snowflake. Great, and we're talking a lot about data quality here, but in a lot of ways that's table stick. Like I said, if you don't trust the data, nobody's going to use it. And Glenn, I mean, I look at Snowflake and I see obviously the ease of use, the simplicity, you guys are nailing that. The data sharing capabilities, I think are really exciting because everybody talks about sharing data, but then we talk about data as an asset. Everybody wants to hide and hold it. And so sharing is something that I see as a paradigm shift and you guys are enabling that. So what are the things beyond data quality that are notable that customers are excited about that maybe you're excited about? Dave, I think you just cleared it out. It's this massive data sharing play part of the data cloud platform. Just as of last year, we had a little over about 100 people, 100 vendors in our data marketplace. That number today is well over 450 and it is all about democratizing and sharing data in a world that is no longer held back by FTPs and CSVs and then the organization having to take that data and ingest it into their systems. You're a Snowflake customer, want to subscribe to an SMP data source as an example. Go subscribe it to it, it's in your account, there was no data engineering, there was no physical lift of data and that becomes the most important thing when we talk about getting broader insights, data quality. Well, the data's already been inspected from your vendor, it's just available in your account. It's obviously a very simplistic thing to describe behind the scenes as what our founders have created to make it very, very easy for us to democratize not only internal with private sharing of data, but this notion of marketplace and sharing across your customers. Marketplace is certainly on the top of all of my customers' minds and probably some other areas that might have heard out of our recent cloud summit is the introduction of Snowpark and being able to do where all this data is going towards is MI and AL, along with our partners at IOTAHO and RPA Automation is what do we do with all this data? How do we put the algorithms and targets? Now we'll be able to run in the future our Python scripts and Java libraries directly inside Snowflake, which allows you to even accelerate even faster, you know, which people found traditionally when we started off eight years ago just as a data warehousing platform. Yeah, I think we're in the cusp of just a new way of thinking about data. I mean, obviously simplicity is a starting point, but data by its very nature is decentralized. You're talking about democratizing data. I like this idea of the global mesh. I mean, it's a very powerful concept. And again, it's early days, but a key part of this is automation and trust. Yusef, you work with Snowflake and you're bringing active DQ to the market. What are customers telling you so far? Well, Dave, I mean, the feedback so far has been great, which is brilliant. So, I mean, firstly, there's a point about speed and acceleration, so that's the speed to insight really. So where you have inherent data quality issues, whether that's with data that was on-premise and being brought into Snowflake or on Snowflake itself, we're able to show the customer results and help them understand their data quality better within day one, which is a fantastic acceleration. Related to that, the cost and effort to get that insight is it's a massive productivity gain versus where you're seeing customers who've been struggling sometimes to remediate legacy data and legacy decisions that they've made over the past couple of decades. So that cost and effort is much love and it would otherwise have been. Thirdly, there's confidence and trust. So you can see CDOs and CIOs got demonstrable results that they've been able to improve data quality across a whole bunch of use cases for business users in marketing and customer services, for commercial teams, for financial teams. So there's that very quick kind of growth, in confidence and credibility as the projects get moving. And then finally, I mean, really all the use cases for the Snowflake depend on data quality really, whether it's data science and the kind of snow park applications that Glenn has talked about. All those use cases work better when we're able to accelerate the ROI for our joint customers by very quickly pushing out these data quality insights. And I think one of the things that the Snowflake have recognized is that in order for CIOs to really adopt enterprise wide, it's also, as well as the great technology that Snowflake offers, it's about cleaning up that legacy data estate, freeing up the budget for CIOs to spend it on the new modern data estate that lets them mobilize their data with Snowflake. So you're seeing this kind of progression with simplifying the analytics from a tech perspective. You bring in federated governance, which brings more trust, then you bring in the automation of the data quality piece, which is fundamental. And now you can really start to, as you guys were saying, democratize and scale and share data. Very powerful guys. Thanks so much for coming on the program. Really appreciate your time. Dave, thank you. I appreciate it as well.