>>Yeah, >>yeah. Hello and welcome to Day two at Beyond. I am so excited that you've chosen to join the building a vibrant data ecosystem track. I might be just a little bit biased, but I think it's going to be the best track of the day. My name is Mallory Lassen and I run partner Marketing here, a thought spot, and that might give you a little bit of a clue as to why I'm so excited about the four sessions we're about to hear from. We'll start off hearing from two thought spotters on how the power of embrace can allow you to directly query on the cloud data warehouse of your choice Next up. And I shouldn't choose favorites, but I'm very excited to watch Cindy housing moderate a panel off true industry experts. We'll hear from Deloitte Snowflake and Eagle Alfa as they describe how you can enrich your organization's data and better understand and benchmark by using third party data. They may even close off with a prediction or two about the future that could prove to be pretty thought provoking. So I'd stick around for that. Next we'll hear from the cloud juggernaut themselves AWS. We'll even get to see a live demo using TV show data, which I'm pretty sure is near and dear to our hearts. At this point in time and then last, I'm very excited to welcome our customer from T Mobile. They're going to describe how they partnered with whip pro and developed a full solution, really modernizing their analytics and giving self service to so many employees. We'll see what that's done for them. But first, let's go over to James Bell Z and Ana Son on the zero to search session. James, take us away. >>Thanks, Mallory. I'm James Bell C and I look after the solutions engineering and customer success teams have thought spot here in Asia Pacific and Japan today I'm joined by my colleague Anderson to give you a look at just how simple and quick it is to connect thought spot to your cloud data warehouse and extract value from the data within in the demonstration, and I will show you just how we can connect to data, make it simple for the business to search and then search the data itself or within this short session. And I want to point out that everything you're going to see in the demo is Run Live against the Cloud Data Warehouse. In this case, we're using snowflake, and there's no cashing of data or summary tables in terms of what you're going to see. But >>before we >>jump into the demo itself, I just like to provide a very brief overview of the value proposition for thought spot. If you're already familiar with thought spot, this will come as no surprise. But for those new to the platform, it's all about empowering the business to answer their own questions about data in the most simple way possible Through search, the personalized user experience provides a familiar search based way for anyone to get answers to their questions about data, not just the analysts. The search, indexing and ranking makes it easy to find the data you're looking for using business terms that you understand. While the smart ranking constantly adjust the index to ensure the most relevant information is provided to you. The query engine removes the complexity of SQL and complex joint paths while ensuring that users will always get thio the correct answers their questions. This is all backed up by an architecture that's designed to be consumed entirely through a browser with flexibility on deployment methods. You can run thought spot through our thoughts about cloud offering in your own cloud or on premise. The choice is yours, so I'm sure you're thinking that all sounds great. But how difficult is it to get this working? Well, I'm happy to tell you it's super easy. There's just forced steps to unlock the value of your data stored in snowflake, Red Shift, Google, Big Query or any of the other cloud data warehouses that we support. It's a simple is connecting to the Cloud Data Warehouse, choosing what data you want to make available in thought spot, making it user friendly. That column that's called cussed underscore name in the database is great for data management, but when users they're searching for it, they'll probably want to use customer or customer name or account or even client. Also, the business shouldn't need to know that they need to get data from multiple tables or the joint parts needed to get the correct results in thought spot. The worksheet allows you to make all of this simple for the users so they can simply concentrate on getting answers to their questions on Once the worksheet is ready, you can start asking those questions by now. I'm sure you're itching to see this in action. So without further ado, I'm gonna hand over to Anna to show you exactly how this works over to you. Anna, >>In this demo, I'm going to go to cover three areas. First, we'll start with how simple it is to get answers to your questions in class spot. Then we'll have a look at how to create a new connection to Cloud Data Warehouse. And lastly, how to create a use of friendly data layer. Let's get started to get started. I'm going to show you the ease off search with thoughts Spot. As you can see thought spot is or were based. I'm simply lobbying. Divide a browser. This means you don't need to install an application. Additionally, possible does not require you to move any data. So all your data stays in your cloud data warehouse and doesn't need to be moved around. Those sports called differentiator is used experience, and that is primarily search. As soon as we come into the search bar here, that's what suggestion is guiding uses through to the answers? Let's let's say that I would wanna have a look at spending across the different product categories, and we want Thio. Look at that for the last 12 months, and we also want to focus on a trending on monthly. And just like that, we get our answer straightaway without alive from Snowflake. Now let's say we want to focus on 11 product category here. We want to have a look at the performance for finished goods. As I started partially typing my search them here, Thoughts was already suggesting the data value that's available for me to use as a filter. The indexing behind the scene actually index everything about the data which allowed me to get to my data easily and quickly as an end user. Now I've got my next to my data answer here. I can also go to the next level of detail in here. In third spot to navigate on the next level of detail is simply one click away. There's no concept off drill path, pre defined drill path in here. That means we've ordered data that's available to me from Snowflake. I'm able to navigate to the level of detail. Allow me to answer those questions. As you can see as a business user, I don't need to do any coding. There's no dragon drop to get to the answer that I need right here. And she can see other calculations are done on the fly. There is no summary tables, no cubes building are simply able to ask the questions. Follow my train or thoughts, and this provides a better use experience for users as anybody can search in here, the more we interact with the spot, the more it learns about my search patterns and make those suggestions based on the ranking in here and that a returns on the fly from Snowflake. Now you've seen example of a search. Let's go ahead and have a look at How do we create a connection? Brand new one toe a cloud at a warehouse. Here we are here, let me add a new connection to the data were healthy by just clicking at new connection. Today we're going to connect Thio retail apparel data step. So let's start with the name. As you can see, we can easily connect to all the popular data warehouse easily. By just one single click here today, we're going to click to Snowflake. I'm gonna ask some detail he'd let me connect to my account here. Then we quickly enter those details here, and this would determine what data is available to me. I can go ahead and specify database to connect to as well, but I want to connect to all the tables and view. So let's go ahead and create a connection. Now the two systems are talking to each other. I can see all the data that's available available for me to connect to. Let's go ahead and connect to the starter apparel data source here and expanding that I can see all the data tables as available to me. I could go ahead and click on any table here, so there's affect herbal containing all the cells information. I also have the store and product information here I can make. I can choose any Data column that I want to include in my search. Available in soft spot, what can go ahead and select entire table, including all the data columns. I will. I would like to point out that this is important because if any given table that you have contains hundreds of columns it it may not be necessary for you to bring across all of those data columns, so thoughts would allow you to select what's relevant for your analysis. Now that's selected all the tables. Let's go ahead and create a connection. Now force what confirms the data columns that we have selected and start to read the medic metadata from Snowflake and automatically building that search index behind the scene. Now, if your daughter does contain information such as personal, identifiable information, then you can choose to turn those investing off. So none of that would be, um, on a hot spots platform. Now that my tables are ready here, I can actually go ahead and search straight away. Let's go ahead and have a look at the table here. I'm going to click on the fact table heat on the left hand side. It shows all the data column that we've brought across from Snowflake as well as the metadata that also brought over here as well. A preview off the data shows me off the data that's available on my snowflake platform. Let's take a look at the joints tap here. The joint step shows may relationship that has already been defined the foreign and primary care redefining snowflake, and we simply inherited he in fourth spot. However, you don't have toe define all of this relationship in snowflake to add a joint. He is also simple and easy. If I click on at a joint here, I simply select the table that I wanted to create a connection for. So select the fact table on the left, then select the product table onto the right here and then simply selected Data column would wish to join those two tables on Let's select Product ID and clicking next, and that's always required to create a joint between those two tables. But since we already have those strong relationship brought over from Snow Flag, I won't go ahead and do that Now. Now you have seen how the tables have brought over Let's go and have a look at how easy is to search coming to search here. Let's start with selecting the data table would brought over expanding the tables. You can see all the data column that we have previously seen from snowflake that. Let's say I wanna have a look at sales in last year. Let's start to type. And even before I start to type anything in the search bar passport already showing me all those suggestions, guiding me to the answers that's relevant to my need. Let's start with having a look at sales for 2019. And I want to see this across monthly for my trend and out off all of these product line he. I also want to focus on a product line called Jackets as I started partially typing the product line jacket for sport, already proactively recommending me all the matches that it has. So all the data values available for me to search as a filter here, let's go ahead and select jacket. And just like that, I get my answer straight away from Snowflake. Now that's relatively simple. Let's try something a little bit more complex. Let's say I wanna have a look at sales comparing across different regions, um, in us. So I want compare West compared to Southwest, and then I want to combat it against Midwest as well as against based on still and also want to see these trending monthly as well. Let's have look at monthly. If you can see that I can use terms such as monthly Key would like that to look at different times. Buckets. Now all of these is out of the box. As she can see, I didn't have to do any indexing. I didn't have to do any formulas in here. As long as there is a date column in the data set, crossbows able to dynamically calculate those time bucket so she can see. Just by doing that search, I was able to create dynamic groupings segment of different sales across the United States on the sales data here. Now that we've done doing search, you can see that across different tables here might not be the most user friendly layer we don't want uses having to individually select tables. And then, um, you know, selecting different columns with cryptic names in here. We want to make this easy for users, and that's when a work ship comes in. But those were were sheet encapsulate all of the data you want to make available for search as well as formulas, as well as business terminologies that the users are familiar with for a specific business area. Let's start with adding the daughter columns we need for this work shape. Want to slack all of the tables that we just brought across from Snowflake? Expanding each of those tables from the facts type of want sales from the fax table. We want sales as well as the date. Then on the store's table. We want store name as well as the stay eating, then expanding to the product we want name and finally product type. Now that we've got our work shit ready, let's go ahead and save it Now, in order to provide best experience for users to search, would want to optimize the work sheet here. So coming to the worksheet here, you can see the data column that we have selected. Let's start with changing this name to be more user friendly, so let's call it fails record. They will want to call it just simply date, store name, call it store, and then we also want state to be in lower case product name. Simply call it product and finally, product type can also further optimize this worksheet by adding, uh, other areas such as synonyms, so allow users to use terms of familiar with to do that search. So in sales, let's call this revenue and we all cannot also further configure the geo configuration. So want to identify state in here as state for us. And finally, we want Thio. Also add more friendly on a display on a currency. So let's change the currency type. I want to show it in U. S. Dollars. That's all we need. So let's try to change and let's get started on our search now coming back to the search here, Let's go ahead. Now select out worksheet that we have just created. If I don't select any specific tables or worksheets, force what Simply a search across everything that's available to you. Expanding the worksheet. We can see all of the data columns in heat that's we've made available and clicking on search bar for spot already. Reckon, making those recommendations in here to start off? Let's have a look at I wanna have a look at the revenue across different states for here today, so let's use the synonym that we have defined across the different states and we want to see this for here today. Um yesterday as well. I know that I also want to focus on the product line jacket that we have seen before, so let's go ahead and select jacket. Yeah, and just like that, I was able to get the answer straight away in third spot. Let's also share some data label here so we can see exactly the Mount as well to state that police performance across us in here. Now I've got information about the sales of jackets on the state. I want to ask next level question. I want to draw down to the store that has been selling these jackets right Click e. I want to drill down. As you can see out of the box. I didn't have to pre define any drill paths on a target. Reports simply allow me to navigate to the next level of detail to answer my own questions. One Click away. Now I see the same those for the jackets by store from year to date, and this is directly from snowflake data life Not gonna start relatively simple question. Let's go ahead and ask a question that's a little bit more complex. Imagine one. Have a look at Silas this year, and I want to see that by month, month over month or so. I want to see a month. Yeah, and I also want to see that our focus on a sale on the last week off the month. So that's where we see most. Sales comes in the last week off the month, so I want to focus on that as well. Let's focus on last week off each month. And on top of that, I also want to only focus on the top performing stores from last year. So I want to focus on the top five stores from last year, so only store in top five in sales store and for last year. And with that, we also want to focus just on the populist product types as well. So product type. Now, this could be very reasonable question that a business user would like to ask. But behind the scenes, this could be quite complex. But First part takes cares, or the complexity off the data allow the user to focus on the answer they want to get to. If we quickly have a look at the query here, this shows how forceful translate the search that were put in there into queries into that, we can pass on the snowflake. As you can see, the search uses all three tables as well shooting, utilizing the joints and the metadata layer that we have created. Switching over to the sequel here, this sequel actually generate on the fly pass on the snowflake in order for the snowflake to bring back to result and presented in the first spot. I also want to mention that in the latest release Off Hot Spot, we also bringing Embraced um, in the latest version, Off tosspot 6.3 story Q is also coming to embrace. That means one click or two analysis. Those who are in power users to monitor key metrics on kind of anomalies, identify leading indicators and isolate trends, as you can see in a matter of minutes. Using thought spot, we were able to connect to most popular on premise or on cloud data warehouses. We were able to get blazing fast answers to our searches, allow us to transform raw data to incite in the speed off thoughts. Ah, pass it back to you, James. >>Thanks, Anna. Wow, that was awesome. It's incredible to see how much committee achieved in such a short amount of time. I want to close this session by referring to a customer example of who, For those of you in the US, I'm sure you're familiar with who, Lou. But for our international audience, who Lou our immediate streaming service similar to a Netflix or Disney Plus, As you can imagine, the amount of data created by a service like this is massive, with over 32 million subscribers and who were asking questions of over 16 terabytes of data in snow folk. Using regular B I tools on top of this size of data would usually mean using summary or aggregate level data, but with thoughts. What? Who are able to get granular insights into the data, allowing them to understand what they're subscribes of, watching how their campaigns of performing and how their programming is being received, and take advantage of that data to reduce churn and increase revenue. So thank you for your time today. Through the session, you've seen just how simple it is to get thought spot up and running on your cloud data warehouse toe. Unlock the value of your data and minutes. If you're interested in trying this on your own data, you can sign up for a free 14 day trial of thoughts. What cloud? Right now? Thanks again, toe Anna for such awards and demo. And if you have any questions, please feel free to let us know. >>Awesome. Thank you, James and Anna. That was incredible. To see it in action and how it all came together on James. We do actually have a couple of questions in our last few minutes here, Anna. >>The first one will be >>for you. Please. This will be a two part question. One. What Cloud Data Warehouses does embrace support today. And to can we use embrace to connect to multiple data warehouses. Thank you, Mallory. Today embrace supports. Snowflake Google, Big query. Um, Red shift as you assign that Teradata advantage and essay Bahana with more sources to come in the future. And, yes, you can connect on live query from notable data warehouses. Most of our enterprise customers have gotta spread across several data warehouses like just transactional data and red Shift and South will start. It's not like, excellent on James will have the final question go to you, You please. Are there any size restrictions for how much data thought spot can handle? And does one need to optimize their database for performance, for example? Aggregations. >>Yeah, that's a great question. So, you know, as we've just heard from our customer, who there's, there's really no limits in terms of the amount of data that you can bring into thoughts Ponant connect to. We have many customers that have, in excess of 10 terabytes of data that they're connecting to in those cloud data warehouses. And, yeah, there's there's no need to pre aggregate or anything. Thought Spot works best with that transactional level data being able to get right down into the details behind it and surface those answers to the business uses. >>Excellent. Well, thank you both so much. And for everyone at home watching thank you for joining us for that session. You have a few minutes toe. Get up, get some water, get a bite of food. What? You won't want to miss this next panel in it. We have our chief data strategy off Officer Cindy, Housing speaking toe experts in the field from Deloitte Snowflake and Eagle Alfa. All on best practices for leveraging external data sources. See you there

>>Hi, I'm Frank's Luqman CEO of Snowflake. And welcome to the Snowflake Data Cloud Summit. I'd like to take the next few minutes to introduce you to >>the data cloud on why it matters to the modern enterprise. As an industry, we have struggled to mobilize our data, meaning that has been hard to put data into service of our enterprises. We're not living in a data economy and for most data central how we run our lives, our businesses and our institutions, every single interaction we have now, whether it's in social media, e commerce or any other service, engagement generates critical data. You multiply this out with the number of actors and transactions. The volume is overwhelming, growing in leaps and bounds every day. There was a time when data operations focused mostly on running reports and populating dashboards to inform people in the enterprise of what had happened on what was going on. And we still do a ton of that. But the emphasis is shifting to data driving operations from just data informing people. There is such a thing as the time value off data meaning that the faster data becomes available, the more impactful and valuable it ISS. As data ages, it loses much of its actionable value. Digital transformation is an overused term in our industry, but the snowflake it means the end to end automation of business processes, from selling to transacting to supporting to servicing customers. Digital processes are entirely disinter mediated in terms of people. Involvement in are driven into end by data. Of course, many businesses have both physical and digital processes, and they are >>intertwined. Think of retail, logistics, delivery services and so on. So a data centric operating discipline is no longer optional data operations Air now the beating heart >>of the modern enterprise that requires a massively scalable data platform talented data engineering and data science teams to fully exploit the technology that now is becoming available. Enter snowflake. Chances are that, you know, snowflake as a >>world class execution platform for a diverse set of workloads. Among them data warehousing, data engineering, data, lakes, data, science, data applications and data sharing. Snowflake was architected from scratch for cloud scale computing. No legacy technology was carried forward in the process. Snowflake reimagined many aspects of data management data operations. The result was a cloud data platform with massive scale, blistering performance, superior economics and world class data governance. Snowflake innovated on a number of vectors that wants to deliver this breakthrough. First scale and performance. Snowflake is completely designed for cloud scale computing, both in terms of data volume, computational performance and concurrent workload. Execution snowflake features numerous distinct innovations in this category, but none stands up more than the multi cluster shared stories. Architectural Removing the control plane from the individual cluster led to a dramatically different approach that has yielded tremendous benefits. But our customers love about Snowflake is to spin up new workloads without limitation and provisioned these workloads with his little or as much compute as they see fit. No longer do they fear hidden capacity limits or encroaching on other workloads. Customers can have also scale storage and compute independent of each other, something that was not possible before second utility and elasticity. Not only can snowflake customer spin up much capacity for as long as they deem necessary. Three. Utility model in church, they only get charged for what they consumed by the machine. Second, highly granular measurement of utilization. Ah, lot of the economic impact of snowflake comes from the fact that customers no longer manage capacity. What they do now is focused on consumption. In snowflake is managing the capacity. Performance and economics now go hand in hand because faster is now also cheaper. Snowflake contracts with the public cloud vendors for capacity at considerable scale, which then translates to a good economic value at the retail level is, well, third ease of use and simplicity. Snowflake is a platform that scales from the smallest workloads to the largest data estates in the world. It is unusual in this offer industry to have a platform that controversy the entire spectrum of scale, a database technology snowflake is dramatically simple fire. To compare to previous generations, our founders were bent on making snowflake, a self managing platform that didn't require expert knowledge to run. The role of the Deba has evolved into snowflake world, more focused on data model insights and business value, not tuning and keeping the infrastructure up and running. This has expanded the marketplace to nearly any scale. No job too small or too large. Fourth, multi cloud and Cross Cloud or snowflake was first available on AWS. It now also runs very successfully on mark yourself. Azure and Google Cloud Snowflake is a cloud agnostic platform, meaning that it doesn't know what it's running on. Snowflake completely abstracts the underlying cloud platform. The user doesn't need to see or touch it directly and also does not receive a separate bill from the cloud vendor for capacity consumed by snowflake. Being multi cloud capable customers have a choice and also the flexibility to change over time snowflakes. Relationships with Amazon and Microsoft also allow customers to transact through their marketplaces and burned down their cloud commit with their snowflakes. Spend Snowflake is also capable of replicating across cloud regions and cloud platforms. It's not unusual to see >>the same snowflake data on more than one public cloud at the time. Also, for disaster recovery purposes, it is desirable to have access to snowflake on a completely different public cloud >>platform. Fifth, data Security and privacy, security and privacy are commonly grouped under the moniker of data governance. As a highly managed cloud data platform, snowflake designed and deploys a comprehensive and coherent security model. While privacy requirements are newer and still emerging in many areas, snowflake as a platform is evolving to help customers steer clear from costly violations. Our data sharing model has already enabled many customers to exchange data without surrendering custody of data. Key privacy concerns There's no doubt that the strong governance and compliance framework is critical to extracting you analytical value of data directly following the session. Police Stay tuned to hear from Anita Lynch at Disney Streaming services about how >>to date a cloud enables data governance at Disney. The world beat a >>path to our door snowflake unleashed to move from UN promised data centers to the public cloud platforms, notably AWS, Azure and Google Cloud. Snowflake now has thousands of enterprise customers averaging over 500 million queries >>today across all customer accounts, and it's one of the fastest growing enterprise software companies in a generation. Our recent listing on the New York Stock Exchange was built is the largest software AIPO in history. But the data cloth conversation is bigger. There is another frontier workload. Execution is a huge part of it, but it's not the entire story. There is another elephant in the room, and that is that The world's data is incredibly fragmented in siloed, across clouds of old sorts and data centers all over the place. Basically, data lives in a million places, and it's incredibly hard to analyze data across the silos. Most intelligence analytics and learning models deploy on single data sets because it has been next to impossible to analyze data across sources. Until now, Snowflake Data Cloud is a data platform shared by all snowflake users. If you are on snowflake, you are already plugged into it. It's like being part of a Global Data Federation data orbit, if you will, where all other data can now be part of your scope. Historically, technology limitations led us to build systems and services that siloed the data behind systems, software and network perimeters. To analyze data across silos, we resorted to building special purpose data warehouses force fed by multiple data sources empowered by expensive proprietary hardware. The scale limitations lead to even more silos. The onslaught of the public cloud opened the gateway to unleashing the world's data for access for sharing a monetization. But it didn't happen. Pretty soon they were new silos, different public clouds, regions within the and a huge collection of SAS applications hoarding their data all in their own formats on the East NC ations whole industries exist just to move data from A to B customer behavior precipitated the silo ing of data with what we call a war clothes at a time mentality. Customers focused on the applications in isolation of one another and then deploy data platforms for their workload characteristics and not much else, thereby throwing up new rules between data. Pretty soon, we don't just have our old Silas, but new wants to content with as well. Meanwhile, the promise of data science remains elusive. With all this silo ing and bunkering of data workload performance is necessary but not sufficient to enable the promise of data science. We must think about unfettered data access with ease, zero agency and zero friction. There's no doubt that the needs of data science and data engineering should be leading, not an afterthought. And those needs air centered on accessing and analyzing data across sources. It is now more the norm than the exception that data patterns transcend data sources. Data silos have no meaning to data science. They are just remnants of legacy computing. Architectures doesn't make sense to evaluate strictly on the basis of existing workloads. The world changes, and it changes quickly. So how does the data cloud enabled unfettered data access? It's not just a function of being in the public cloud. Public Cloud is an enabler, no doubt about it. But it introduces new silos recommendation by cloud, platform by cloud region by Data Lake and by data format, it once again triggered technical grandstands and a lot of programming to bring a single analytical perspective to a diversity of data. Data was not analytics ready, not optimized for performance or efficiency and clearly lacking on data governance. Snowflake, address these limitations, thereby combining great execution with great data >>access. But, snowflake, we can have the best of both. So how does it all work when you join Snowflake and have your snowflake account? You don't just >>avail yourself of unlimited stories. And compute resource is along with a world class execution platform. You also plug into the snowflake data cloud, meaning that old snowflake accounts across clouds, regions and geography are part of a single snowflake data universe. That is the data clouds. It is based on our global data sharing architectures. Any snowflake data can be exposed and access by any other snowflake user. It's seamless and frictionless data is generally not copied. Her moves but access in place, subject to the same snowflake governance model. Accessing the data cloth can be a tactical one on one sharing relationship. For example, imagine how retailer would share data with a consumer back. It's good company, but then it easily proliferate from 1 to 1. Too many too many. The data cloud has become a beehive of data supply and demand. It has attracted hundreds of professional data listings to the Snowflake Data Marketplace, which fuels the data cloud with a rich supply of options. For example, our partner Star Schema, listed a very detailed covert 19 incident and fatality data set on the Snowflake Data Marketplace. It became an instant hit with snowflake customers. Scar schema is not raw data. It is also platform optimize, meaning that it was analytics ready for all snowflake accounts. Snowflake users were accessing, joining and overlaying this new data within a short time of it becoming available. That is the power of platform in financial services. It's common to see snowflake users access data from snowflake marketplace listings like fax set and Standard and Poor's on, then messed it up against for example. Salesforce data There are now over 100 suppliers of data listings on the snowflake marketplace That is, in addition to thousands of enterprise and institutional snowflake users with their own data sets. Best part of the snowflake data cloud is this. You don't need to do or buy anything different. If your own snowflake you're already plugged into the data clouds. A whole world data access options awaits you on data silos. Become a thing of the past, enjoy today's presentations. By the end of it, you should have a better sense in a bigger context for your choices of data platforms. Thank you for joining us.

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by VM Wear and its ecosystem partners. >> Welcome back, everyone. Live cube coverage here in Mosconi, north of the Emerald 2019. I'm Javert David launch their 10th year covering the emerald. We here with this team Cube alumni Von Stuart, vice president technology at pier Storage. Great to see you guys another year, another privilege to sit >> down and have a little chat. >> Another. Another year that Vienna where doesn't die of something storage doesn't go away every year. Containers is going to kill the end where this is revealing. The EM wears resiliency as virtualization platform is just second to none has been, well, document. We've been talking about it because the operational efficiencies of what they've done has been great. You guys air kicking butt in storage on again, a sector that doesn't go away. You gotta put the data somewhere. Eso stores continues toe do Well, Congratulations. What's the big What's the big secret? Thanks. >> Well, we just shared our cue to >> financial results last week. 28% year on your growth. We are the by far the fastest growing storage company, and I think there's a lot of disruption for the legacy vendors. Right now. They're getting hit on all angles. Next. Gen. If vendors like us followed by the cloud as well this platforms like H. C, I think it's been it's been a tough sledding for similar legacy vendors. >> Talk about your relationship with the end, where and why that's been so important for pure because again, again, resiliency operations. At the end of the day, that's what the rubber hits the road, making developers happy, but operating it's a key. Yeah, if you look at >> so that's a really good question. If you look at our business, Veum, where is the number one platform deployed on top of pure storage platforms? And that's probably the case for most of the storage vendors because of their dominant position in the infrastructure. That means, as VM were evolves their product platforms right. Well, that's the pivotal acquisition Veum or Claude Foundation via McLaren AWS. And as that'll expand, you have to as a partner continued to jointly innovate, sometimes hand in hand. Sometimes, you know, on parallel paths to drive value into that that market for those customers or you're not gonna make it. And our investments of engineering wise are significant. We've had a large number of new capability that we've ruled out through the years that are specific to VM, where that are either integrations or enhancements to our platform. You know, we believe through external data points, we are the number one V balls vendor, which is, you know, which was something that being were launched about 78 years back. That kind of dip, but has risen back up. Um, and >> we were key, >> I think, um, design partner right now with the cloud platforms, the Via MacLeod Foundation as well as, ah, humor coordinative us. >> So, as you know, this is our 10th year VM world. You go back to 2010. There was what I used to call the storage cartel. And you weren't part of it, right? Had early access to the AP eyes you had. So obviously e m c was in there. Um, you were really the on lee sort of newbie to reach escape velocity. Your storage. Now there's basically two independent storage companies over a billion dollars. You guys a net up. Um, so >> when I was at both, >> you saw you saw >> the opportunity and okay, leaned in hard. Yeah, there >> was a time when he's >> paid off. But so why do you think, um, you were able to be one of the rare ones to achieve escape velocity when many people said that will never happen. You'll never see another $1,000,000,000 storage company. And then I'm interested in how you're achieving number one in Viv balls. In a world where it seems like, you know, the ecosystem is getting a little tighter between Dow Wand VM where? But how do you guys thrive in that dynamic? >> I think there's a challenge for all vendors in terms of market and try to get your message through right. If you if you one better does something well, the rest of the market tries too obvious. Get that. We've been fortunate enough that through our channel ecosystem, our system's integrator partners right to actually be able to demonstrate the technology that gain there enthusiasm to drive it into the market and then actually demonstrated to the customers. And so how does that show up? Uh, I think it's fair to say our platforms are more intelligent, they're more automated and they they operated a greater scale. Then then the competitors and you can look at this through one lens and say, Well, it's Veum or a P I says in that Make all the storage the same And it's like it does from a via more operational standpoint, but it doesn't mean how you deliver on that value Prop or what us. A platform deliver above and beyond is at parody, and that's really where we demonstrate a significant difference. Let me give you one example. We have a lot of customers. Ah, a lot customer growth in the last 12 months around Custer's who are deploying eight c i, along with all flash raise. Right? And David Floyd had reached out recently and said, Well, wouldn't one, you know, compete with the other? It's like, Yes, there's overlap. But what we're finding from customers is they're looking to say if my applications need to be more cost effective, easy to manage its scale, we actually want to put it on all flash rain, You say, How could that be? I'll give you one simply example. Do you know what it takes anywhere from 10 x 200 x, less time to upgrade your V and where infrastructure on a shared array. Then if it's on on hyper converged because you don't have to go through the evacuation and rehydration of all your data twice right? And so things like that, they're just really simple that you wouldn't pick up in like a marketing scheme. If you are a customer at scale, you go well. I can't afford 100 man hours. I can afford woman. And so it's It's simple things like that. It's rapid provisioning. It's not having Silas that are optimized for performance or availability or cost. It's about saying, you know your time to implement is one time life cycle on hardware. But it's probably something happens every quarter for the next three years, right? >> So this is your point about >> innovation in the innovative vendors. Your the modernization of storage is planning for these use cases where the old way didn't work. >> Yeah, yeah, you mentioned that you were 10 years now, and one of things that I've said over the last six or seven years being up yours, one of things I think is really interesting about pure is that our founder, John Call Grove, came out of the volume manager and file system space at Veritas, right? He was the founder for those products. He understood the intersection between managing a storage array and your application, and that goes through our ethos of our products, where I think a lot of storage platforms, a start up platforms come from George guys who worked on the Harbour side. And so they take a faster, you know, Piper faster from the media, and they make another box that behaves like the other box from an operational perspective. >> So he said, a C I a compliment or competitors. I'm still not sure which. Maybe it's both and then say, Same question for V. San. Yeah, how do you So, >> um, on air that we've put a lot of investment in and started one with via more around the middle of last year was putting V sand with pure storage flash race together, and what you see that materialized now is when you look at via MacLeod Foundation or via MacLeod in eight of us. The management domains must be visa, and that's so that you can have an instant out of the box controlled, um, management plane that Veum where you know, executes on and then you have workload domains and those could be on ah, hyper converge platform. Or they could be on third party storage. And when you put those on pure, then you again, all the advantages that we bring to bear as an infrastructure with all the same simplicity scale in lifecycle management that you get from from just, you know, the VM where std see manager. And so it works very well together. Now, look, I'm sure what I share with you here. They'll be some folks who are on the V sand team that they themselves are to be like, you know, B s. But that's the nature of our business. One >> of these I want to get your thoughts on this side. Vons. You've always >> been kind of on the cutting edge on all the conversations we've had. I gotta ask you about the container revolution, which not new doctor came out many many years ago. Jerry Chen when he funded those guys and we covered that extensively upset there was a small changed kubernetes is all the rage orchestrating the containers is a pivotal role in all the action happening here. It's big part of how things were with the app side. So the question is, how does continues impact the storage world? How do you see that being integrated in? There's talk of putting Cooper names on bare metal, so you start to see HC. I come back. Devices are important, she started. See hardware become important again with that? >> Well, I love you. Drop of pivotal there, right? First off, kudos to Vienna, where for the acquisition pill, little guys are exceptional. What they don't have is a lot of customers, but the customers they do have our large customers, right? So we've got a fair amount of pivotal on pure customers, and they are all at scale. So I think it's a great acquisition for VM, where by by far the most enterprise class form of containers today, >> and they've always kind of been the fold. Now they're officially in the fold. Yes, formalize it. >> And so now that the road map that was shared in terms of what via Moore looks to do to integrate containers into the Essex I platform itself right, it's managing V, EMS and containers next year. That's perfect in terms of not having customers have to pick or choose between which platform and where you're going to play something, allow them to say you can deploy on whichever format you want. It runs in the same ecosystem and management, and then that trickles down to the gun in your storage layer. So we do a lot of object storage within the container ecosystems. Today, a lot of high performance objects because you know the file sizes of instances or applications is much larger than you know, a document filed that you or I might create online. So there's a big need around performance in that space, along with again management at scale. It's >> interesting we sent about about Pivotal and I, By the way, I like the acquisition, too, because I think it was cheap. Any time you can pick up $4 billion asset for 800 million in cash, you know gets my attention. But Pivotal was struggling in the marketplace. The stock price never even came close to its I po. You know, it's spending patterns were down. Do you feel as though the integration will VM Where will supercharge Pivotal? >> I absolutely agree that I've had this view that the container ecosystem was really, um uh, segmented you had comes that built their products off a container. So save your twitter or your Facebook, right? The platform that your customers and interact interact with is all ran by containers. Then you have an enterprise. You have containers, which was more kind of classic applications. Right? And that would take time for the applications to be deployed. And so what did you see now for Mike stuff, right? See if you can run as a container. Right? Run is a container. As the enterprise app start to roll over, the enterprise will start to evolve from virtual machines, two containers. And so I think it's the timing's right. That's not to dismiss any of where people I think is built the brand right now, which is helping companies build next gen platforms. You know, after big sure that I don't name drop customers references to pull back there. Yeah, I think the time is right. >> I'm interested in how you guys can further capitalized on containers. And we've been playing around with this notion of of data assurance containers, Fring complexity. And so, you know, complexities oftentimes your friend, because you're all about simplifying complexity. But so how do you capitalize on this container trend in the next 3 to 5 years? So you've got storage >> needs for containers that either tend to be ephemeral or persistent. And I think when containers were virtually created, it was always this notion that would be ephemeral. And it's like, Yeah, but where's the data reside? Ultimately, there's been significant growth around data persistence, and we've driven that in terms of leveraging the flecks of all drivers that have been put into the community, driving that into our pure service orchestrator RPS O'Toole, which supports pivotal in kubernetes derivatives. Today again, we've got proven large scale installs on this. So it's it's, um, it's providing the same class of storage. Service is simplicity and elegance in your integrations that we have for Vienna, where we've been doing that across pivotal already. Pivotals. Interesting, right? They don't validate hardware, the only validate software. So they validate our P S O and having that same value prop for that that infrastructure, because they are scale, you never find a small scale containers ecosystem, and I keep referencing that point when you get to scale considerations around. What does it take to allow that environment to to remain online and holly performance are significant considerations and weak cell >> There. We'll talk about your event coming up. You guys have pierced accelerate September 17th and 18th Coming up Osti the VM where ecosystem that you're part of here. Big part of that. You guys have a lot of customers. I know you can reveal any news, but what's expected at this show? What can people who are interested in either attending or my peach in some of the notable things that might be happening >> lot orange? We know that >> one. Number two I know the cubes gonna be there >> for two days will be there for two days. >> So hopefully you guys will get a load of conversations with both our our team, product management, engineering, maybe some of leadership, but also customers. I think customers are always the best statement you can make about how your how you're doing and market. I think you will see from us a number of announcements that I am prohibited to share today, but some really big things that we're gonna introduce the market. So it should be excited for that. And some just a great showing of our partner. Our alliance ecosystem will be there. Obviously, VM will be there in force as well as red hat with the open >> again, there's gonna be a cloudy >> future for you. It's girls would be very analytical. It's going to be there elastics going to be there. So, you know, >> you guys like to do first of these shows. I mean, kind of I don't view it first with an all flesh array, but probably one of the first if not first the evergreen thing ticked off a lot of people like, Why didn't we think of that? You were first with sort of bundling envy. Any in the whole thing. The announcement you guys made with video. That was before anybody else. You know, your whole cloud play you like, you like to be first, So we expect another first next month. Hopefully we >> will deliver, and, uh, you're not gonna get me to leak anything. >> Thanks for the insight, Vice President. Reality Lions, that pier storage. David, let me stay with us for more coverage. Robin Madlock. CMO is coming on and, of course, tomorrow. Michael Dell, Pat Girl singer and more and more great guest senior vice presidents from VM wear from all different groups. We'll be asking the tough questions here in the Cube. Thanks for watching.

>> Live from Barcelona, Spain. It's the cue covering Sisqo. Live Europe, Brought to you by Cisco and its ecosystem partners. >> Come back. Everyone live here in Barcelona is the key. Exclusive coverage of Sisqo Live twenty nineteen. John for David Want my co host for the week, and Stupid Man was also here, doing interviews. Our next two guests is Mike Bundy, senior director of Global Cisco Lines with pure storage and Z, who's in charge of Christ Francisco. Welcome to the Cube. Thanks for joining >> us. Thank you for having us here. >> Also one, but we're in the definite zone. It's packed with people learning really use cases. Get rolling up the sleeves. Talk about the Cisco pure relationship. How do you guys fit into all this? What's the alliance? >> You understand? >> Sure. So we have a partnership with Cisco, primarily around a solution called flashback in the Converse infrastructure space. And most recently, we've evolved a new use case, an application together for our official intelligence that Z's business unit have just released a new platform that works with Cisco and in video to accomplish. You know, customer application needs mainly in machine learning, but but all aspects of our official intel it >> Hey, Eyes, obviously hot trend in machine learning. But today it's Cisco. The big story was, it's not about the data center as much anymore is. It's the data at the center of the value proposition, which spans the on premises I ot edge and multiple clouds. So data now is every where you gonna store it? So it's going to start in. The cloud is on premises. Data at the center means a lot of things you can programme with its gotta be addressable and has be smart and aware and take advantage of networking. So, with all that is a background backdrop, what is the A I approach? How should people think about a I in context to storing data using data, not just moving package from point A to point B? But you're storing it? You're pulling it out. You're in agreeing into apple cases. A lot of moving parts there. What's that? >> Yeah, you got a really good point here. When people think about machine learning traditional age, they just think about training. But we look at this more than Chinese. The whole did a pipeline that starts with collecting the data stored the data, analyze the data between the data and didn't deploy it and then for the data back. So it's really a vory. It's a cycle there, right? It's it's where you need to consider >> how you actually collect the data from the edge, how you store them in the speed that you can and give the data to the training side. So I believe way work was pure. We try to create this as a whole data pipeline and thinking about entire data movement and the star, which need that would look here. >> So we're in the definite zone, and I'm looking at the machine learning with Python ML library >> center >> Flow of Apache sparked a >> lot of this data >> science type stuff, but increasingly a ISA workload that's going mainstream. But what The trends that you guys are seeing in terms of, you know, traditional, I tease involvement is >> it's still sort of >> a I often an island. What are you seeing there? So I'll take a take a gas stab at it. So, really, every major company industry that we work with have you know, Aye, aye. Initiatives. It's the core of the future for their business. So, no, what we're trying to do is partner with I t to get ahead of the large infrastructure demands that will come from those smaller, innovative projects that Aeryn pilot mode so that they are a partner to the business and the data scientist, rather than, you know, a laggard in the business. The way that you know, sometimes there the reputation that that I guess we want to be the infrastructure solid, you know, like a cloud like experience for the data scientists. So they can worry more about the applications, the data, what it means the business and less about the infrastructure. Okay. And so you guys are trying to simplify that >> infrastructure, whether it's converged infrastructure. No other sort of unifying approaches is Are you seeing the shift of a sort of that heavy lifting of people out now? Shifting resource is, too. You work loads like a I Maybe you could discuss trends, are there? >> Yeah, absolutely. So I think I started was more like a data signs experiment. Right? You see, want to date, assigns a couple of data science experiment. Now it's really getting into ministry. More and more people report into that and us. Apologize. Mike, Mike, The way we start that questions my deep apology. I need a GP or something. >> Like, I need to >> store the data better. >> Your fortnight? Yes. >> So as Micah's had early on, right? It's it's not just the data scientist is actually all a challenge as well. And I think was Cisco, where twenty do was pure. Here is, you know, that Cisco thing. We're saying we're breach right. We want to bridge the gap between the data scientists and the it and make it not just as experiments, but a scale at production level and be wedded to actually, Crew will impact with the technology infrastructure that we can table >> might talk about yours position You guys have announced here in the cloud. Yes, he's seeing that software. Focus software is the key here. Or you can get to a software model. Aye, aye. And she learned Only we're talking about is software data is now available to be addressed and managing that software. Lifecycle. How is this Corolla software for you guys? With converge infrastructure at the San Francisco announce your downstage day, we'll converge infrastructure to the edge. >> Yeah, so if you look at the plant, one that we built, that's it's referenced by being called the data hub. The data hub has a very tight synergy, with all the applications referring to spark tenser PLO, etcetera, etcetera cafe. So we look it as the next generation analytics, and the platform has a super layer on top of all those applications because that that's going to really make the integration possible for the data scientists. They could go quicker and faster. What we're trying to do underneath that is used the data hub that no matter what the size, whether it's small data, large data transaction based or more bulk data warehouse type applications, you know the data hub in the flash blade solution or need handle all of that very, very different and probably more optimizing and easier than traditional legacy infrastructures, even tradition, even even even flash, you know, from some of our competitors. Because, you know, we've built this a purpose built application for that, you know, not trying to go backwards in terms of technology, >> I want to put both you guys on the spot for a question. We hear infrastructure is code for going on many, many years since the few started at nine years ago. Infrastructures code. Now it's here. The network's programmable infrastructures, programmable storages, programmable What a customer! Or someone asked you. How is infrastructure Network's in storage, Programmable. And what do I do? I'm used to provisional storage. I've got servers. I'm going cloud. What do I do? How do I become? A. I enabled that I could program the infrastructure. How do you guys answer that question? >> So a lot of that comes to the infrastructure management layer, right? How do you actually using policy and using the white infrastructure managing to make the right configuration want? And I think one thing from program eligibility is also flexibility. Instead of having just a fixed conflagration. What we're doing with pure here is really having that flexibility right where you can put pure Star Ridge different kind of star, which was different, kind off. Compute that you have. No matter. It's we're talking about two are used for you. That kind of computing power is different and connects with a different Star wars, depending on what the customer use cases. So that flexibility driven by the driven to the proper program ability that is managed by the infrastructure. Imagine a layer, and we're extending that So pure and Cisco's infrastructure management actually tying together it's really single pane of glass was in decide that we can actually manage both pure and Cisco. That's the program ability that we're talking >> about. Get pure storage and to end manageability. >> Where's the Cisco compute its A single pane of glass. >> So what do I buy? I want to get started. What? What do you got for me? What you have, it's pretty simple. Three basic components, you know, Cisco Compute and a platform for machine learning that's powered by and video GP. Use Cisco Flash Blade, which is the data hub and storage component and then network connectivity from the number one network provider in the world. Francisco. Very simple. It's askew. It's a solution. It's very, very skewed. It's very simple. It's data driven, so you know it's not tied to a specific skew. It's more flexible than that. So you have a better optimization of the network. You know you don't buy a one thousand Siri's ex. Okay, Only used fifty percent of it. It's very customized. Okay, so I can customize it for my whatever data science team or my workloads and provisioning for multipurpose. Same way of service provider would ifyou're a large organization >> trend trend around Breaking Silas has been being discussed heavily. Talk about multiple clouds on premise and cloud and edge all coming together. How should companies think about their data architecture on? Because Silas Air good for certain things to make multi cloud work and all this and to end and intent based networking and all the power of a eyes around the corner. You gotta have the date out there, right? It's gotta be horizontally scaleable of you. How do you break down those silos? Twitter advises air use cases or anarchic for architecture. >> You know what I think? It's a classic example of how it has evolved to not think just silos and be multi cloud. So you know, we've advocate is is you have a date, a platform that transpires the entire community, whether its development, test engineering production applications and that, you know, runs holistically across the entire organization that would include on from it would include integration with the cloud. Because most you know cos now require, That s so you could have different levels of high availability or lower cost if your data needs to be archived. So it's really, you know, building and thinking about The data is on platform across the across the company and not just you know, silos for >> replication never goes away. Never. It's gonna be around for a long, long time. >> Deaf tests never goes away. Yeah, >> you thought some >> s o i. D On top of that, We believe where you infrastructure should go is where the data goes, right? You want to follow that where the data is, And that's exactly why I want a partner was pure here because we see a lot of the data sitting today in the very important infrastructure which is built by pure storage and want to make sure that we're not just building a sidle box sitting there where you have for the data in there all the time, but actually connected our chips. Silver was pure storage in the most manageable way. And it's the same kind of manager layer you're not thinking about All have to manage all the Sala box or the shadow it that some day that time would have under their desks. Right. That's the least thing you want it. >> And the other thing that came up in the Kino today, which we've been seeing on the Cuban, all the experts reaffirm, is moving data cost money got late in sea. Costs also just cost to move traffic around, so moving compute to the edge of moving. Compute to the data has been a big hot trend. How is the computer equation changed? I got storage. I'm moving. I'm not just moving packets around. I'm storing it and moving it around. How does that changed the computers? It put more emphasis on the computer. >> Wait, It's definitely putting a lot more emphasis on computer. I think it's where you want to compute to happen, right? You can pull all the data and I want it happen in the centre place. That's fine if that's the way you want to manage it. If you have, if you have already simplify the data, you want to put it in that way. If you want to do it at the edge near where the data sources, you can also do the cleaning there. So we want to make sure that no matter how you want to manage it. We have the portfolio that can actually help you to manage. And >> his alternative alternate processors mentioned video first. Yeah, you would deal with them in other ways to you've got to take advantage of technologies like uber, Nettie says. Example. So you can move the containers where they need to be and have policy managers for the computer requirements. And also, you know, storage so you don't have contention or data and integrity issues. So embracing those technologies and a multi cloud world, it's very, very >> like. I want to ask you a question around customer trends. What are you seeing as a pattern from a customer standpoint as they prepare for a I and start re factory? Some of their end or resource is. Is there a certain use case that they set up with pure in terms of how they set up their storage? Is it different by customers? Are a common trend that you see >> there are some commonalities, you know, like take financial services want trading as an example. We have a number of customers that leverage our platform for that. Is this very you know, time sensitive, high availability data? So really, I think the customers the trend over all of that would be a step back. Take a look at your data and focus on how can I correlate, Organize that and really get it ready so that whatever platform used from a story standpoint, you're you're thinking about all aspects of data and get it in a format in a forum where you can manage and catalog, because that's kind of the sentence. >> I mean, it really highlights all the key things that would say it in storage for a long time. I availability integrity of the data. And now you got at patient developers programming with data. This's a hole with a P IIs. Now you're slinging FBI's around like it's Tom mentioned me its weight should be. This is like Nirvana finally got here. How far along are we in the progress? How far we earlier we moving the needle? Where the >> customers himself a partnership partnership. Deanna >> and General, You guys were going to say, You got you got storage, You got networking and compute all kind of working together. That's reflex school elastic like the cloud >> I my feeling, mike, contract me or you can disagree with me. I think right now, if we look at all the wood analysts saying what we're saying, I think most of the companies more than fifty percent of companies either have deployed a Emma or are considering implant off deploying that right. But having said that, we do see that we're seeing at a relatively early stage because the challenges off making a deployment at scale where data scientist and I'd really working together, right? You need that level of security in that level, off skill ofthe infrastructure and software involving Devon I. So my feeling is where stew At a relatively early stage, >> I think we are in the early adopter face. You know, we've had customers for last two years. They've really been driving this way, worked with about seven of the automated car, you know, driving Cos. But, you know, if you look at the data from Morgan Stanley and other analysts, is about a thirteen billion dollars infrastructure that's required for a eye over the next three years from twenty, nineteen, twenty, twenty one. So you know, that is probably six x seven x what it is today, so we haven't quite hit that. >> So people are doing their homework right now. You are the leader. >> Its leaders in the industry, not mastering everybody else is going to close that gap. So that's where you guys come into helping that scale way built this. This platform with Cisco on is really flashback for a I is around scale for, you know, tens and twenties of petabytes of data that will be required for >> these targeted solution for a I with all the integration pieces Francisco built in. Yes. Great. We'll keep track of a look sighting. We think it's cliche to say future proof, but this, in this case, literally is preparing for the future. The bridge? >> Yes. Future. Yes. You >> know, as the news is good, it's acute coverage. He live in Barcelona with more live coverage after this short break. Thanks for watching. I'm John Barrier, but David won't they stay with us. >> Thank you.

>> Hello, and welcome to the special. Keep conversation here in Palo Alto, California John Furrier, Co-Host of the Cube. We're here with Jonathan Rosenberg, CTO chief technology officer and head of AI for Five9. Jonathan. Great. Great to see you. Thanks for coming in. >> Thanks. My pleasure to be here. >> So you've had a stellar career? Certainly. Technical career going way back to Lucent Technologies. Now here at Five9, Cisco along the way. You've been a really technical guru. You've seen the movie before. This's happening. Every wave of innovation, multiple ways you've been on. Now you're on the next wave, which is cloud AI, CTO Five9. Rapidly growing company. Yes, it is. What attracted you to five? >> Yeah, Great question. There's actually a lot of things that brought me to Five9. I think probably the most important thing is that I've got this belief, and I'm very motivated for myself. A least to do technology and innovate and create new things. And this belief that were on the cusp of the next generation of technology in the collaboration industry. And that next generation is going to be powered by artificial intelligence, and one of the ways I sort of talked about this is that if you look at the entire history of collaboration, up til now meetings, telephony, messaging was to figure out, a way to get the bits of data from one person to another person fast enough to have a conversation. That's it. You know, once we got the audio connected, we just moved the audio packets in the video packets and messaging from one place to another. And we didn't actually analyze any of that because we couldn't. We didn't have the technology to do that. But now, with the arrival of artificial intelligence and particular speech recognition, natural language processing, we can apply those technologies to that content and take all this dark data that's been basically thrown away the instant it was received, to process it and do things. And that is going to completely transform every field of collaboration, from meetings to messaging, to telephony. And I believe that so strongly, that is, That's great. That's going to be my next job. I wanna work on that. And it's going to start in the Contact Center because a contact center is the ideal place to do that. It's the tip of the spear for AI in collaboration, >> and it's in a really great area. Disruptive innovation are absolutely so Take us through the impact was one of things I have observed in this industry is you have You know, I don't want to say mainframe clients served to go back to date myself, but there was that wave of client server computer >> mainframes. Cool again. We just called clout. Now, hey, is >> exactly. So you have these structural industry waves take us through the waves of how we got here and what's different now? And why can't the old guard or the older incumbents surviving if you're not out in front that next wave your driftwood. So what? What's What's his ways mean? Why is this important? What has to change to be successful? >> Exactly. So there's been this this whole like you said these waves. So the first wave of telecommunications was like hardware: circuit switching, big iron switches, sitting in telco data centers, you know, And then that era transitioned to software and that was with the arrival voiceover IP and technologies like SIP, and that made it more less expensive. And anyone could do it, and it transformed the industry. The next wave, the third wave were still like halfway through and in some areas, actually, just beginning contact, center was early here, the third wave is cloud, right is now we're moving that software to a totally new delivery vehicle that allows us to deliver innovation and speed. And that wave has now enabled us to start the next wave, which is on ly in its infancy, which is AI right, and the application of machine learning techniques to automate all kinds of aspects of how people communicate in collaborate. >> I think cloud is a great example of Seen a. I, which had been a concept around when I was in computer science. Back in the eighties, there was a guy you know theory, and it's the science of it is not so much change, but computing's available. The data to be analysed for the first time is available. Yeah, you mentioned analyzing the bits writings. There's now a key part. What does it actually mean? Teo. Someone who's has a contact center has a large enterprise. Says, you know what? I got to modernize. How does A I fit them? What is actually going on, >> right? Great question. So a I actually consult lots different problem at the end of the day again, Hey, eyes like this, Let's. It's the biggest buzz word right on. It's in my title. So, like I'm a little guilty, right? >> We'll get a pay raise for, But >> what? It comes down to this, really this Korean machine learning, which is really like a fancy new algorithmic technique for taking a bunch of data and sort of making a decision based on it. So And it turns out, as we've learned that if you have enough data and you can have enough computing and we optimize the algorithms, you could do some amazing things, right? And it's been applied to areas like speech recognition and image recognition and all these kind of things. Self driving cars that are all about decision process is, Do I go left? I go right? Is this Bob? Is this Alice? Did the users say and or did they say or write those air all decision process? Is that these tools economy? What does it mean? The Contact Center? It means everything in the context. And if you look at the conduct center. It's all about decision. Process is, you know, where should this call get routed? What's the right agent to handle the call right now? When the agent gets the call, what kind of things should they be saying? What I do with the call after the call is done, How should the agent use their time? All those things are decision processes and their key to the contact center. So so, aye, aye. And Emily going to transform every aspect of it and, most importantly, analyzing what the person is saying connecting with the customer, allowing the age to >> be more. You know, I think this is really one of the most cutting edge areas of the business. And the technology and throw in CEO was talking about an emotional cognitive recognition around. Yeah, connecting with customers and data certainly is going to be a part of that. But as machine learning continues to get it, Sea legs. Yeah, you seeing kind of two schools of thought? I call it the Berklee School. Hard core mathematics. Throw math at it. And then you've got this other side of a machine learning which is much more learning. Yeah, it's less math. More about adaptive and self learning. One's deterministic one's non deterministic is starting to see these use cases where Yeah, there's a deterministic outcome, right throw machine learning at a great exactly helped humans come curate, create knowledge, create value that you've got a new emerging use case of non deterministic, like machine learning environments where I could be driving my test Look down the road or my company's run the Contact Center. I gotto understand what's gonna happen before it happens. Right? Talkabout this. What's your thoughts on this is This isn't really new, pioneering area. What's your view on >> this? Yeah, so I think it actually straight sort of a key point. I wantto narrow enough from what she said, which is that a lot of these problems still, it's about the combination of man and machine, right? It's that there's things that you know are going to be hard for the machine to predict. So the human in their usage of the product, teaches the machine, and the machine, as it observes, helped the human achieved mastery. And that human part, by the way, is even more important in the conduct centre than anywhere else. At the end of the day, your customer and you call up, you're reaching for human connection. You're calling this. You want to talk, you've got a problem. You need someone to not just give you the answers, but empathize with youto understand you. Right? And if you go back to anything about the best experience you've ever had when you called up for support or get a question answered. He was like it was someone who understood you who's friendly, polite, empathetic, funny. And they knew exactly what they were doing, right? And they solve it for you. So the way I think about that, is that actually the future of the context. Dinner is a combination of human and machine, and the human delivers the heart, and the machine delivers the master. >> And I just noticed your I'm looking at Twitter, right? And you just tweeted this forty minutes to go the future of Contact Center. Nice. A combination of human and machine human delivers heart. The machines lose mastery. I think this is so important because unpacking that words like trust come out True relationship. So you asked about my experiences is when I've gotten what I needed, You know, all ledger, the outcome I wanted. Plus I felt good about right. I trusted it. I trusted the truth. It was. And he's seeing that in media today with fake news. You're seeing it with Digital has kind of almost created, anonymous, non trustworthy its data. There's been no real human. Yeah, packaging. So I think you're I'm hearing you You're on the side of humans and machines, not just machines being the silver bullet. >> Absolutely, absolutely. And again, it goes back to sort of the history of the contact centre has been this desire to, like, just make it cheaper, right? But as the world is changing, and as customer experience is more important than ever before and is now, technology is enabling us to allow agents and human beings to be more effective through this. The symbiotic relationship that we're going to form with each other, like we can actually deliver amazing customer experiences. And that's what really matters. And that idea of trust I want to come back to that word that's like super Central to this entire thing. You know, you have that as a user, you have to trust the brand you have to trust the information you're getting from the agent. You have to trust the product that you're calling them talking about, and that's central to everything that we need to do. In fact, it's a It's a fundamental aspect of our entire business. In fact, if you again think about it for a moment here, we're going to customers who are looking to buy a context, and we're saying, Trust us, we're going to put it in the cloud, We're going to run it, We're going to operate it for you and we're going to deliver a great, highly reliable experience that takes trust to sew one of things that back to your early early question. Why did come two, five, nine? One of the things it has done is build this amazing trust with its customers to its huge, amazing reliability. Up time, a great human process of how we go in work with our customers. It's about building trust in every single >> way. So I want to put in the spot because I know you've seen many ways of innovation. You've seen a lot of different times, but now it's more accelerated. Got cloud computing at a much more accelerated innovation cycle. So as users expect interact with certain kind of environment. Roman talked about this in his interview. CEO Control. So you just want to be served on the channels that they want to be served in. So having a system that they have to go to to get support, They wanted where they are. And so how is the future of the customer interaction? Whether it's support our engagement is going to take place in context to nonlinear discovery, progression, meaning or digging a service themselves in the organic digital space. I honestly want to go to a site per se. How do you see the future evolving around this notion of organic discovery? Talking to their friends, finding things out? Does that impact how Five9 sees the future? >> Yeah, absolutely. And I think it gets back to sort of an old idea of Omni channel. I mean, this is something that the context people been talking about for, like forever, like the last ten years, right? And and its original meeting was just this idea. Oh, you know, you can talk to us via chat, or you can send us an e mail or you can send us a text or you could call us right and we'll work with you on any of those, like you said. Actually, what's more interesting is as customers and users moved between those things, and it actually switches from reactive to proactive right where we actually treat those channels as well. Depending on what the situation is, we're going to gather information from all these different data sources, and then we're going toe, find the right way to reach out to you and allow you to reach out to us in the most official. >> So you see a real change in user expectation experience with relative rule contact? >> Yeah, I mean, I mean, the one thing that technology is delivered is a change in user expectations on how things work. And if you look at the way we as human beings communicate with each other, it's dramatically different today than it was really just just a few years ago. >> So, Johnny, let's look under the hood now in terms of the customer environment, because certainly I've seen Legacy after Legacy sisters being deployed. It's almost like cyber security kind of matches the same kind of trend that in your world, which is throw money at something and build it out. So there's a lot of sprawl of solutions out there and trying to solve these problems. How does the customer deal with that? And they're going forward there on this new wave. They want to be modernized, but they got legacy. They had legacy process, legacy, culture. What's the key technical architecture, How you see them deploying this? What's the steps of the patient and her opinion? >> It will surprise you not one drop when I say it's go to the cloud, all right, and there are real reasons for it and by the way, this is going to be going to be talking about this at Enterprise Connect. So, So tune in Enterprise Connect. I'm going to be talking about this. Um, there's a ton of reasons, five huge ones, actually, about why people need to get to the cloud. And one of them is actually one of the ones we've been talking about here, which is a lot of this. Modernization is rooted in artificial intelligence. It turns out you just cannot do artificial intelligence on promise you cannot. So the traditional gear, which used to be installed and operated by legacy vendors like a VIA, you know, they go in, and Genesis, they go in the install a thing and it works just for one customer at a time. The oly way artificial intelligence works is when it gathers data across multiple customers. So multi tendency and artificial intelligence go hand in hand. And so if you want to take any benefit from the stuff that we've been talking about this conversation, the first step is you gotta take your context int the cloud just to begin building and adding your data on the set and then leverage the technologies and they come out >> So data is the central equation And in all this because good data feed's good machine learning good machine learning feeds Great a. I So data is the heart of this, yes. So data making data in the cloud addressable seems to be a key. Thought Your reaction and what are you guys doing with? >> Absolutely, absolutely. And this is, by the way, another reason why I joined five nine, that I've been speculating here. I said, All right, if Date if ya if the future is about a I miss, I said, That's what I want to do in collaboration. You need data to do that. You actually have to work for a company that has a lot of data. So market leadership matters. And if you go look at the contact center and you go look at all the industry and analyst reports like it made it pretty obvious, like who to go to there is like the leader in cloud Conduct. Sonar with with tons of agents and tons of data is Five9 and ah, and so that's That's why you're so building the data aggregating data. That's one of the first things I'm working on here is how do we increase and utilize the data that we've been gathering for years. >> And and a lot of that we've had this conscious with many customs before about Silas Silas. Kill innovation When it comes to data address ability, your thoughts on that and what customs Khun due to start thinking about breaking down those silent >> exactly so In fact, Silas have been a big part of the history of especially on premise systems. Once in fact, Afghan one silo for inbound contacts and are different for outbound. Different departments, by the way, also had their own different comic centers. And then you had other tools that on the other data, if you don't like a separate tool over there for serum and a different tool over there for WFOR debut Fam and something else for Q M. And all these things were like barely integrated together in the cloud that becomes much more natural. Spring these technologies together and the data can begin to flow from the systems in and out of each other. And that means that we have a much greater access to data and correlated data across these different things that allows us to automate all over the place. So it's this positive reinforcement sile cycle that you only get one year when you've gone to the club. >> The question I want to ask you, it's more customers on pretend I'm a customer for second. I won't ask you, Jonathan, what's the core innovation for me to think about and bring to my organization? If I want to go down the modern monitors you. How do you answer that question? What is the core innovation? Stretch it. I should have Marcy moving through the cloud is one beyond that is itjust cloud. Then what else? What, Juanito? Be preaching internally and organizing my culture >> around. Yeah, great questions. So, I mean, I think the cloud is sort of the enabler of many of these different pieces of innovation. Right? So velocity and speed is one of them. And then setting up and adjusting these things used to be super super hard. Ah, you wanted to add agents seats? Oh, my gosh, enough to go binding hardware and racket stack boxes and whatever. So even simple things like reactive nous, right? That's something that's important to talk about is that many of our customers and our businesses are highly seasonal. Right? We've seen like someone showed me a graph. This was like, Oh, my gosh, it was It was a company that was doing ah, telethon. And they said, Here's how many agents they have over this year. It was like two agents, and then it shut up. It's like five hundred agents of phones. Two days exactly. Drop back down. And I'm like, if you think about a business like that, you could never even do that. And so the so cloud is nice, but the way you talk about it, and as an I t buyer of these technologies, you talk your business owners about reacted nous speed, velocity, right? That's what matters to a business and then customer experience. >> You're one of the things that just to kind of end of second, I want to get your thoughts on. I'm gonna bring kind of industry trend. That's I think, might be a way to kind of talk about some of these core problems on data. Most mainstream people look at Facebook and saying, Well, what a debacle. They used my data. These men against me. I'm not in control of my data. You're seeing that weaponization people saying elections were rigged. So weaponizing data for bad is this content, and this context ends right? An infrastructure that's right, >> that's right. >> But there's also the other side, which is, you actually make it for good. So you started thinking about this people starting to realize Wow, I should be thinking about my data and the infrastructure that I have to create a better outcome. That's right, Your thoughts on that as people start to think about II in terms of the business context, right? How did they get to that moment where they can saying, I don't want anyone weaponizing did against me. I want to use it for good. How did the head of the company comes back to >> trust, by the way, right? Is that you know, on and to some degree that's an uphill battle due to some of these debacles that you just talked about. But Contact Center is a different beast of the whole thing. And interestingly, it's an area where there's already been an assumption by users that when they interact with the contact center, that data is sort of used to improve the experience. I mean, every contacts and the first thing I say, by the way, this call may be recorded for training. Um, honoring purses, Captain, that they are right. It's it's already opt in. There's an assumption that that's exactly how that is being used. So it's This is another reason. By the way, what's a contact center is? It was the tip of the spear because it was a place where there was already permission, where the data is exactly the kind of stuff that had already been subject to analysis and Attock customer expectation that that's actually what was happening. The expectation was there they building action, that data what was missing. So now we're filling in the ability to action on that All that data with artificial intelligence >> and final question. What's your vision going forward? A CTO and aye, aye. What's the vision of Five9? What do what do you see? The twenty miles stair for Five9 within consciousness. We just talked about >> it. So? So it's It's about revolution. I'll be honest. Right on. I tell people like, I'm not like an incremental, steady Eddie CTo like I do things because I want to make big changes. And I believe that the context and R is on the cusp of a massive change. And my boss, Rohan said this and this has been actually central to how I'm thinking about this. The Contact Center in the next five years will be totally different than the twenty five years before that. It's a technologist. I say. Wow, five years like that's not very long in terms of softer development. That's what we were going pretty much rewrite our entire stack over the next five years. And show. What should that start to look like? So for me, it's about how do we completely reimagine every single aspect of the context center to revolutionize the experience by merging together, human and machine and totally new >> and the innovation strategies cloud in a cloud and and and data great job and great to have you on pleasure. Great, great conversation. Quick plug for you guys. Going to be a enterprise, connect to Cuba. Lbi. They're covering the event as well. What you going to talk about that? What? Some of the interactions? What will be the hallway conversations? What's your objective? What's your focus >> exactly? So so I'm going to be having my own session. We're going to be talking about the five reasons that you may not think about to goto context on the cloud. I've hinted already. A James of them. I think we're too well. That's you can you know, A. I is clearly central and I'm going to start to talk about the other four. >> Great, great conversation. A lot of change. Massive change happening. Great innovation Stretch. Great mission here at Five9. Great, great mission around. Changing and reimagine. More change the next five years in the past twenty five years. Again cloud computing eyes doing it will be winners. Will be losers will be following it here on the Cube. Jonathan Rosenberg, CTO ahead of AI at Five9. I'm John Furrier with the Cube. Thanks for watching.

>> Live from Orlando, Florida It's the que covering service now. Knowledge seventeen Brought to you by service now. >> Welcome back to Sunny Orlando. Everybody, This is the Cube, the leader Live tech coverage. My name is Dave Volonte, and I'm here with my co host, Jeffrey Walter Wall coverage of service now. Knowledge seventeen. Jason, Johannes. Here he is. A long time cube along Lamis, a managing director at Accenture. Jason, great to see you again. >> Thanks so much. Appreciate it. >> So when Jeff and I did our for our first service now knowledge in twenty thirteen, we walked around the floor. We saw a company called Cloud Share pose. Uh, we said, you know, for this company to become a billion dollar company, they really have tto evolve the ecosystem, and that's exactly what's happened. But But before we get into that, take us through how you got to Accenture. >> Yeah. So let's see, I had an eleven year career Att. IBM decided tto leave that for no good reason other than to go try something new and way were responsible for a small company called Navigant. Nah, Vegas was one of the first service now partners in the ecosystem. We thought maybe if we had a few good years there, we might pick up some VC funding or something like that. Things moved a lot faster than we had expected. And one one twenty, thirteen We're required by Cloud Sherpas. I became president of service now, Business Unit was a new line of business in Cloud Sherpas, which was really aspiring and was a cloud services brokerage across sales force, Google and service. Now and then, of course, the good news here at the twenty fifteen, we move on to extension er and then I get the opportunity to lead the global platform team for service >> now at Accenture. So before we get into that, when you were a navigates, did you ever do a raise or did you not have two? >> Didn't have to be police tracked it all the way through. So >> what sort of people in our audience are always interested in fascinated the entrepreneur get started? That was with sort of customer funding and sort of getting getting projects, >> you know, it started like a lot of partners did at that point in time. I mean, really, the ecosystem was served by partners nobody ever heard of. Right, And, uh and so they all started kind of one deployment at a time and you see some companies that might have been doing implementations for other it some tools or something of that nature started to gravitate to this thing called service hyphen now dot com at the time, right? And, uh, couple logo changes elimination of Iife in later. Here we are over a billion dollars in the service now ecosystem and on their way to four billion by twenty twenty. >> And you guys were there early. So what advantages that did that give you? >> So I think what it taught us early on is kind of how to build, uh, and create service now, consultants, which was, you know, something that the very little of the ecosystem had at that point in time. Um, it wasn't is quite a straightforward. It's just saying, Let's take somebody who did Platform X or or, you know, application Why? And go, you know, go work on service now The first people that were rolling through while they had big company logos, they they did tend to be early adopters and those types of folks that would be kind of earlier in line. So, you know, there's kind of a whole different requirement. Hold this a different necessity. At the time, I would say two thousand, two thousand. It was really kind of the anti other platforms or other tools kind of crowd. And then we move into where we are today, which is, you know, market leading Sim tool moving rapidly into other spaces. HRC sm etcetera. So >> do you find they're still on expertise? Shortage in the marketplace? And >> there is >> How are you feeling? Not >> so. I consider US Foundation Lee a learning organization. We were back then, and we are now with over a hundred certified trainers on service. Now we had fifty of them here at the event, training on behalf of service, now largest of any partner, and we've turned that internally. So while we've very publicly recently made several acquisitions, one in Europe one in Germany are UK, Germany and, of course, Canada. We also organically, in the last fourteen months, crew Accenture's sort of Haitians more than one hundred thirty percent. So we have that training capability, and we can use that to incubate our next consultants that our next certified resource is on the platform. Did you guys know platforms are so broad? You really have to, you know, be broad and deep to be successful, like kind of scale we're at right now. And so it's important that we're kind of climbing down as deep as we can the platform as quickly as possible since Agent and did a century by Cloud services an accelerator or really, Was that there their first kind of big play with service? Now there's quite a big business case around it, because at the time he was a sales force company of with company and a service down company. So I think the answer is a little different for each of the platforms. But I'LL give you the service now platform. So what we did is we took a practice in Cloud Sherpas that was about the same size of centuries practice, and we brought them together, right. We unified the organization, which is kind of a different model for X ensure having a global platform lead on a global platform team where there's a direct line management relationship versus managing across the axes, but what that gives us an ability to kind of globally incubate skills globally moved to, You know where the center of gravity needs to be now versus where it needed to be then and so it came together quite nicely. On top of that, you see us making these few acquisitions. We'd just be three in the last six months. And it's, you know, kind of round out our global presence and capability. So we saw as we brought the organisations together, there were few. Geography is where we needed toe accelerate, Right? I mentioned we were accelerating our certifications one hundred thirty thirty percent more than doubled their staff in that time. We now have more than fifteen hundred certified Resource is in two thousand service now, resource is an extension. And, uh and that was largely through organic efforts Post cloud Sherpas acquisition. Now we layer in these additional acquisitions on top really gives it that full global capability. >> And obviously extent you had a sales force business yet folding that didn't have ah, Google businesses. Well, >> yeah, So platforms and of course, you know, absent in e mail, etcetera. So you know, they're on their way and kind of kind of re adjusting or kind of Swiss Ling for that practices. Well, but obviously my my interest in my >> phone is the service now, Okay. And then you said two thousand a trained now, professionals, >> just over two thousand service. Now, resource is in our platform team over fifteen hundred service now. Certifications. >> Uh, okay. And that's obviously global. Yeah, And then the other thing, the other big team we're hearing is that service now starting to penetrate, you know, different industries. And that's where you guys come in. I mean, you have deep, deep industry knowledge and expertise when if you could talk about how the adoption of service now is moving beyond sort of horizontal, I t into specific industries. >> So that's our big pivot. And that's the future of service. Now is a platform, not an I t. Sm tool, in my opinion. And I think the one of the foundational tenets behind the acquisitions, you see, with, like, dxy and of course, uh, of course, you know, cloud Sherpas to Accenture. Um, one of the things service that has to do to reach their market capitalization has become more than just a ninety seven, too will become a platform. Um, when you start have this platform conversations, you start having conversations that air well outside of it, they'd become business conversations. I'm sure you made the keynote this morning and heard about going horizontal across that full very often. Silas size departments in business. That's the way work gets done. And that's where the opportunity is. We find that most commonly when we're talking to prospects and customers, they want to talk about others in their sector, in their domain. What have you done with customers like me somewhere else and you end up having a conversation. So we did this here. We did that there. We did this over here, right across that whole platform. We're going deep into service now. Catalyst Model, which they just released here at acknowledged seventeen. And the reason for that is because that's where we're moving. We're creating an entire conversation across the platform, so we're certainly gonna have an industry lends to the same conversation. But we're going to bring more to that. We're gonna bring the integration stacked that we're gonna be in the custom ap Stop to that. We're gonna be the configured abstract to that. Of course you're gonna bring those outside of T APS to that. >> And the catalyst is what the gold standard of partners. >> Yeah, it really is. I mean, the service now just release the program to the partners just a few days ago. There are three partners that have catalyst today. There'LL be more of a course in time. Ours is focused on the financial sector, which we have really found to be a high growth area for us in the platform. And we also had a significant amount of domain and intellectual property in that space. That was easy for us to aggregate and really hit the market running with that one. But we'LL have more intime retail and a few others coming very quickly. And so that's where you're building a solution on top of service. Now you got exactly right cell as a solution across the platform. So just it's important not to think of it as just a new individual app or just a individual integration. But it's important to think of something much bigger >> than that. And then, you know, we're obviously it feels like we're on the steep part of the S curve. You predicted this a couple years ago that the future of service now is beyond me. But you were there doing the heavy lifting with getting people to buy into a single c M d b. Adopt the service catalog, you know, do a host things that were necessary to really take leverage. And in the early days, there was some friction in order to get people to do that. It was political, didn't really see, you know, the long term benefits, that they would maybe do it in a little pocket of opportunity. Has that changed as it changed dramatically? And how has that affected your ability to get leverage with customers, specifically the customers themselves getting leverage in other areas? >> You know, customers they're all trying to digitize, right? Everyone's trying to digitize, and it's a digitize, er die moment. It really has been digitized by moments for the last several years. Um, there's only so many places going to be able to do that. And what's so important about service now is the ability to actually bring that across work flows across organisations to relate to people in a user interface and a design that they're familiar with. You know, service now does a fantastic job. That's why we've been here in this sector. So order this software so long. But, you know, it's it's, uh, it's it's imperative anymore. It's not something that are seeing our clients have an option, too, except a reject. It's a demand. >> Yes, I want to I want to stay on this, uh, point for just a minute. I've said several times today and Jeff, you and I have talked about this that in the early days, the names that you saw in the ecosystem, you know, no offense, but like cloud Sherpas, you know, it was not a widely known brand. And now you've got the big I mean, except yours. You know, not number one, number one or number two. And what what you do on. So that lends an air of credibility. Two customers, they feel the comfort level. You've got global capabilities, got the ability to go deeper. So where do you see >> stay? Tune? It's also validation. I mean, when you're a start up company, that is a tremendous validation that a company like a century, they don't make small bets, you know, they're not going to They're not going to come and try to build a practice around your solution unless they feel like they could make some serious >> coin. So it feels Jason like we're on the cusp of Ah, you know, decade, Plus, you know, opportunity Here. You feel that way? >> I think there are other platforms that kind of paved the way of what you should expect to see out of the service now. But in my opinion service now does it better? Um, you know, I'm envisioning a place where, as service now is moving towards, you know, there's four billion mark that we're moving. We're having comments to our stack to write in that process and and the type of industrialization and rugged ization that you'd expect to see in a digital kind of movement in a digital world, you know, the least single a platform of records, a single place of record. It becomes so important for so many reasons, people adopted service down because the best of what it did, and it's extremely capable platform. But just start layering things like a I and chat bots and some of these things as well, especially a I. It needs a single source of record to make its best decisions. And if you don't have that someplace, you're not going to get the value out of a I. So not only the service now happy automate now very tactically kind of down your Peredo chart, but it's set you up for the future because it gives you that contacts that place where you can warehouse the information and let your automated solutions get in there and kind of ripped and release the best of of the solutions that they have a party available. >> I wonder if we get a riff on the sort of structure of the software business for a minute. I mean, you know, it's much different today. Like you said, everybody's going, going digital. You've got this whole big data trend going on, and a eyes now seems to be really. But if you look at some previous examples, I mean, Salesforce's an obvious example. You got used to have a sales force practice. I still do. I was in your company in your smaller company, and and I guess Oracle is the other one I look at. They had the system of record with the database ago. Probably go back to IBM Devi, too, but it was sort of that database was the main spring, uh, and then you know, Salesforce's sort of came from from C R M. But sales force It seems like there it's not the greatest workflow engine in the world. It seems like there's a lot of called the sex where service now seems to have the potential to really permeate throughout the organization. I wonder if you could give us your perspectives from you know, your your experience and in these businesses, how do you compare service now? Other software companies? >> Well, you know, a lot of software companies. Um, there's a lot of room, right? So it's It's very regular that we see successfactors workday or sales force and service now in office and azure. All kind of kind of sitting in the same place is a W s et cetera. Um, you know, those are just going to be natural. There's gonna be those that grow and scale and those that do not. But one of the things that I think it's most powerful about a service now, is it my opinion? It's got the best workflow capability to span across those different stacks, and that gives you your Swiss army knife, right? That gives you your ability too almost integrate with anything you want to in a meaningful way by directionally uniter, actually etcetera to bring that data in an enriched away into a single repository and then the layer these other things like Aye, aye and chat bots. On top of that, you get that console experience. A lot of the executives I'm talking to you right now are wrestling things with things like universal cues or a single approval Q. Or things of that nature search now does that really easy. That's an easy thing to do. What isn't easy right is making sure you aggregate all those things up in a meaningful way to a single source and then putting in somebody's hand that they can actually do something with contacts. But it's in St John. Donnie in the Kino talked about what? What's cool about centric? Uh, entry is you cross all those different silos where, if you're coming in, is the CIA right amount for your coming in as a marketing automation after you're coming in as a pick, your favorite silo SAS app. You don't have the benefit of being involved in so many kind of cross silo processes where service now came in, uh, check. They said it is our homies, uh, Frankie, So to say so you're already kind of touching, which gives you a better footprint from which to now go up into those. There are many organisations in a business that understand their underlying technology. But tonight, T Wright brothers, they kind of understand the blueprint. But, you know, I've seen a lot of articles about the rise of the chief digital officer. Anything like that. Reality is the CEO is a digital officer. Now, if they're not, they're not gonna be that CEO very long. And they need to be able to work within the context of digitizing everything. >> Well, this gives him a platform to actually deliver that value across the enterprise. So Alright, Jason, Hey, it's great to see you again. Thanks so much for coming on. Sharing your perspectives and congratulations on all the great success and continue. >> Appreciate it. Thank you very much. And >> I keep it right there, buddy. Jeff and I'll be back with our next guest right after this. We're live from service now. Knowledge seventeen. This is cute