>>Yeah. >>All right. Welcome back to our third session, which is all about administering analytics at Global Scale. We're gonna be discussing how you can implement security data compliance and governance across the globe at for large numbers of users to ensure thoughts. What is open for everyone across your organization? So coming right up is Cheryl Zang, who is a senior director of product management of Thought spot, and Kendrick. He threw the sports sports director of Systems Engineering. So, Cheryl and Kendrick, the floor is yours. >>Thank you, Tina, for the introduction. So let's talk about analytics scale on. Let's understand what that is. It's really three components. It's the access to not only data but its technology, and we start looking at the intersection of that is the value that you get as an organization. When you start thinking about analytics scale, a lot of times we think of analysts at scale and we look at the cloud as the A seven m for it, and that's a That's an accurate statement because people are moving towards the cloud for a variety of reasons. And if you think about what's been driving, it has been the applications like Salesforce, Forcados, Mongo, DB, among others. And it's actually part of where we're seeing our market go where 64% of the company's air planning to move their analytics to the cloud. And if you think of stock spotted specifically, we see that vast majority of our customers are already in the cloud with one of the Big Four Cloud Data warehouses, or they're evaluated. And what we found, though, is that even though companies are moving their analytics to the cloud, we have not solved. The problem of accessing the data is a matter of fact. Our customers. They're telling us that 10 to 25% of that data warehouse that they're leveraging, they've moved and I'm utilizing. And if you look at in General, Forrester says that 60 to 73% of data that you have is not being leveraged, and if we think about why you go through, you have this process of taking enterprise data, moving it into these cubes and aggregates and building these reports dashboards. And there's this bottleneck typically of that be I to and at the end of the day, the people that are getting that data on the right hand side or on Lee. Anywhere from 20 to 30% of the population when companies want to be data driven is 20 to 30% of the population. Really what you're looking for now it's something north of that. And if you think of Cloud data, warehouse is being the the process and you bring Cloud Data Warehouse and it's still within the same framework. You know? Why invest? Why invest and truly not fix the problem? And if you take that out and your leverage okay, you don't necessarily have the You could go directly against the warehouse, but you're still not solving the reports and dashboards. Why investing truly not scale? It's the three pillars. It's technology, it's data, and it's a accessibility. So if we look at analytics at scale, it truly is being able to get to that north of the 20 to 30% have that be I team become enablers, often organization. Have them be ableto work with the data in the Cloud Data warehouse and allow the cells marking finding supplies and then hr get direct access to that. Ask their own questions to be able to leverage that to be able to do that. You really have to look at your modern data architecture and figure out where you are in this maturity, and then they'll be able to build that out. So you look at this from the left to right and sources. It's ingestion transformation. It's the storage that the technology brains e. It's the data from a historical predictive perspective. And then it's the accessibility. So it's technology. It's data accessibility. And how do you build that? Well, if you look at for a thought to spot perspective, it truly is taking and driving and leveraging the cloud data warehouse architectures, interrogated, essay behind it. And then the accessibility is the search answers pen boards and embedded analytics. If you take that and extend it where you want to augment it, it's adding our partners from E T L R E L t. Perspective like al tricks talent Matile Ian Streaming data from data brings or if you wanna leverage your cloud, data warehouses of Data Lake and then leverage the Martin capability of your child data warehouse. The augmentation leveraging out through its data bricks and data robot. And that's where your data side of that pillar gets stronger, the technologies are enabling it. And then the accessibility from the output. This thought spot. Now, if you look at the hot spots, why and how do we make this technology accessible? What's the user experience we are? We allow an organization to go from 20 to 30% population, having access to data to what it means to be truly data driven by our users. That user experience is enabled by our ability to lead a person through the search process. There are search index and rankings. This is built for search for corporate data on top of the Cloud Data Warehouse. On top of the data that you need to be able to allow a person who doesn't understand analytics to get access to the data and the questions they need to answer, Arcuri Engine makes it simple for customers to take. Ask those questions and what you might think are not complex business questions. But they turn into complex queries in the back end that someone who typically needs to know that's that power user needs to know are very engine. Isolate that from an end user and allows them to ask that question and drive that query. And it's built on an architecture that allows us to change and adapt to the types of things. It's micro services architecture, that we've not only gone from a non grim system to our cloud offering, in a matter of of really true these 23 years. And it's amazing the reason why we can do that, do that and in a sense, future proof your investment. It's because of the way we've developed this. It's wild. First, it's Michael Services. It's able to drive. So what this architecture ER that we've talked about. We've seen different conversations of beyond its thought spot everywhere, which allows us to take that spot. One. Our ability to for search for search data for auto analyzed the Monitor with that govern security in the background and being able to leverage that not only internally but externally and then being able to take thought spot modeling language for that analysts and that person who just really good at creating and let them create these models that it could be deployed anywhere very, very quickly and then taking advantage off the Cloud Data warehouse or the technology that you have and really give you accessibility the technology that you need as well as the data that you need. That's what you need to be able to administer, uh, to take analytics at scale. So what I'm gonna do now is I'm gonna turn it over to Cheryl and she's gonna talk about administration in thought spot. Cheryl, >>thank you very much Can take. Today. I'm going to show you how you can administrator and manage South Spot for your organization >>covering >>streaming topics, the user management >>data management and >>also user adoption and performance monitoring. Let's jump into the demo. >>I think the Southport Application The Admin Council provides all the core functions needed for system level administration. Let's start with user management and authentication. With the user tab. You can add or delete a user, or you can modify the setting for an existing user. For example, user name, password email. Or you can add the user toe a different group with the group's tab. You can add or delete group, or you can manage the group setting. For example, Privileges associated with all the group members, for example, can administrate a soft spot can share data with all users or can manage data this can manage data privilege is very important. It grants a user the privileges to add data source added table and worksheet, manage data for different organizations or use cases without being an at me. There is also a field called Default Pin Board. You can select a set of PIN board that will be shown toe all of the users in that group on their homepage in terms off authentication. Currently, we support three different methods local active directory and samel By default. Local authentication is enabled and you can also choose to have several integration with an external identity provider. Currently, we support actor Ping Identity, Seaside Minor or a T. F. S. The third method is integration with active directory. You can configure integration with L DAP through active directory, allowing you to authenticate users against an elder up server. Once the users and groups are added to the system, we can share pin board wisdom or they can search to ask and answer their own questions. To create a searchable data, we first need to connect to our data warehouses with embraced. You can directly query the data as it exists in the data warehouse without having to move or transfer the data. In this page, you can add a connection to any off the six supported data warehouses. Today we will be focusing on the administrative aspect off the data management. So I will close the tap here and we will be using the connections that are already being set up. Under the Data Objects tab, we can see all of the tables from the connections. Sometimes there are a lot of tables, and it may be overwhelming for the administrator to manage the data as a best practice. We recommend using stickers toe organize your data sets here, we're going to select the Salesforce sticker. This will refined a list off tables coming from Salesforce only. This helps with data, lineage and the traceability because worksheets are curated data that's based on those tables. Let's take a look at this worksheet. Here we can see the joints between tables that created a schema. Once the data analyst created the table and worksheet, the data is searchable by end users. Let's go to search first, let's select the data source here. We can see all of the data that we have been granted access to see Let's choose the Salesforce sticker and we will see all of the tables and work ship that's available to us as a data source. Let's choose this worksheet as a data source. Now we're ready to search the search Insight can be saved either into a PIN board or an answer. Okay, it's important to know that the sticker actually persist with PIN board and answers. So when the user logging, they will be able to see all of the content that's available to them. Let's go to the Admin Council and check out the User Adoption Pin board. The User Adoption Pin board contains essential information about your soft spot users and their adoption off the platform. Here, you can see daily active user, weekly, active user and monthly active user. Count that in the last 30 days you can also see the total count off the pin board and answers that saved in the system. Here, you can see that unique count off users. Now. You can also find out the top 10 users in the last 30 days. The top 10 PIN board consumers and top 10 ad hoc searchers here, you can see that trending off weekly, active users, daily, active users and hourly active users over time. You can also get information about popular pin boards and user actions in the last one month. Now let's zoom in into this chart. With this chart, you can see weekly active users and how they're using soft spot. In this example, you can see 60% of the time people are doing at Hawk search. If you would like to see what people are searching, you can do a simple drill down on quarry tax. Here we can find out the most popular credit tax that's being used is number off the opportunities. At last, I would like to show you assistant performance Tracking PIN board that's available to the ad means this PIN board contains essential information about your soft spot. Instance performance You this pimple. To understand the query, Leighton see user traffic, how users are interacting with soft spot, most frequently loaded tables and so on. The last component toe scowling hundreds of users, is a great on boarding experience. A new feature we call Search Assist helps automate on boarding while ensuring new users have the foundation. They need to be successful on Day one, when new users logging for the first time, they're presented with personalized sample searches that are specific to their data set. In this example, someone in a sales organization would see questions like What were sales by product? Type in 2020. From there are guided step by step process helps introduce new users with search ensuring a successful on boarding experience. The search assist. The coach is a customized in product Walk through that uses your own data and your own business vocabulary to take your business users from unfamiliar to near fluent in minutes. Instead of showing the entire end user experience today, I will focus on the set up and administration side off the search assist. Search Assist is easy to set up at worksheet level with flexible options for multiple guided lessons. Using preview template, we help you create multiple learning path based on department or based on your business. Users needs to set up a learning path. You're simply feeling the template with relevant search examples while previewing what the end user will see and then increase the complexity with each additional question toe. Help your users progress >>in summary. It is easy to administrator user management, data management, management and the user adoption at scale Using soft spot Admin Council Back to you, Kendrick. >>Thank you, Cheryl. That was great. Appreciate the demo there. It's awesome. It's real life data, real life software. You know what? Enclosing here? I want to talk a little bit about what we've seen out in the marketplace and some of them when we're talking through prospects and customers, what they talk a little bit about. Well, I'm not quite area either. My data is not ready or I've got I don't have a file data warehouse. That's this process. In this thinking on, we have examples and three different examples. We have a company that actually had never I hadn't even thought about analytics at scale. We come in, we talked to them in less than a week. They're able to move their data thought spot and ask questions of the billion rose in less than a week now. We've also had customers that are early adoption. They're sticking their toes in the water around the technology, so they have a lot of data warehouse and they put some data at it, and with 11 minute within 11 minutes, we were able to search on a billion rows of their data. Now they're adding more data to combine to, to be able to work with. And then we have customers that are more mature in their process. Uh, they put large volumes of data within nine minutes. We're asking questions of their data, their business users air understanding. What's going on? A second question we get sometimes is my data is not clean. We'll talk Spot is very, very good at finding that type of data. If you take, you start moving and becomes an inner door process, and we can help with that again. Within a week, we could take data, get it into your system, start asking business questions of that and be ready to go. You know, I'm gonna turn it back to you and thank you for your time. >>Kendrick and Carol thank you for joining us today and bringing all of that amazing inside for our audience at home. Let's do a couple of stretches and then join us in a few minutes for our last session of the track. Insides for all about how Canadian Tire is delivering Korean making business outcomes would certainly not in a I. So you're there

>> Narrator: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hi, everyone. Welcome to this CUBE conversation. I'm John Furrier here in the Palo Alto Studios. Yeah, during the pandemic, we're not in person. Usually we are, but we are doing remote interviews and as a lead-up to ThoughtSpot Beyond 2020 a virtual event coming up, we got two awesome visionaries here to have a conversation around data and the role of data. Cindi Howson, who's the Chief Data Strategy Officer at ThoughtSpot and Kent Graziano, Chief Technical Evangelist at Snowflake which has been great success. Welcome to the program. Thanks for coming on. >> Thanks for having us, John. >> So Kent, >> Yeah, happy to be here. >> Dave Volante who's just a fan boy of Snowflake. I mean, he's just gushing over the success of the company. I see Frank Slootman who you've known for years. Congratulations on your success. Great stuff. >> Yeah, thank you very much. >> Well, the topic I want to get into immediately is obviously data. You know, we're seeing in the heels of Amazon reinvent conference, the role of data cloud in the cloud and also on premise, you're seeing both things going on and companies are adopting this. Now it's a do or die situation for companies to either get on board with a full on data strategy. Can you guys talk about how that move to the cloud is imperative and so important? >> Yeah, I mean, as you said, John, it's the do or die moment and we've seen even pre-pandemic, many organizations were in the process of modernizing their cloud data and analytics moving to the cloud, but COVID has really just accelerated that. The ones that innovated sooner here are performing better and the ones that are still dragging their heels, the laggards, I am not convinced they will survive. >> Kent, do you have thoughts? You guys are born in the cloud data company. I mean, you can't get any more born in the cloud than you guys. >> No, obviously I started out in the on-prem world. I've been with Snowflake for five years now, but exactly what Cindi was saying there. And I've been telling folks, as I've talked to them over the last five years, that it's things are changing. The world is changing, things are changing and this was even pre-pandemic. Things were changing faster than anyone could have imagined and the only way to really keep pace with the growth of data and the diversity of data in my mind was to go to the cloud and this concept of having a data cloud where we can easily share and govern data is the game changer, right? And making customers and organizations so much more successful by being able to do things with data that they just couldn't do in the on-prem world. The elasticity and the power in the cloud is just giving people unprecedented access to do just amazing things. >> Yeah, whether you are a startup or a big company or on-premise trying to transform with digital transformation, you're either inventing or reinventing or creating a category or redefining a category and data is going to be the critical piece of it. And the cloud can actually scale that. So I want to get your thoughts on this notion of re-invention. How does data become because you could be a category creator and redefine a category, but the people have to understand, the customers have to first understand that their problem that they have is something that can be solved with data. This is a critical moment of connection, the product market fit kind of thing, where they go, okay, I get it now. Cindi, when do they have that moment? The aha moment of, I see the problem I got to do this. >> Yeah, well, there's two things. The aha moment and, John, I have to preface this. If I may, you know, many people listening to you may not have met me or Kent until now, Kent and I go way back, both previously independent analysts but we remain with this North star of helping our customers unlock the value of data. So I don't want people to think, oh, we're pushing cloud because we work for these companies. Now, it really is a belief. You have to use this to innovate faster. So when did that aha come? It depends, for some people it's only just now staring at them and that's why there's been a lot of churn in leadership, but let's go back even a few years ago, you can take Walmart as an example as they were maybe losing to Amazon, they went to digital, they went to cloud and are now competing beautifully. So it happens at different paces. Capital One, of course, was earlier here, there's a lot of financial services, organizations that really are moving too slowly to the cloud. And you see how well Capital One is doing versus some of the others that have moved too slow. >> Well, Kent, you guys go way back. You know, you've seen the old school, old guard as Andy Jassy at Amazon calls it, but there is a real shift happening now finally. It's not just the old school data warehouse model anymore, there's new requirements and there's new benefits for being in the cloud that you don't get on-prem or with a data warehouse. You know, you've got a different kind of access to more scale, maybe another company with an API. So the idea of connecting in the cloud, cloud native is completely different. Can you share your view on how that helps people understand the cloud better? >> Oh, yeah. Yeah, and I've certainly seen that. Like I grew up in the on-prem data warehouse world which is where Cindi and I met. And what I'm seeing now is the lines are being blurred between some of what we would have thought of as the traditional silos of data in the on-prem world. The data lake and data warehouse are foremost in my mind is with the data cloud, that line's not really there anymore. It's now about the workload and the use case than it is about, I'll say the structure of the data or the location of the data. We're able to eliminate the data silos by getting them all up into a platform like Snowflake and the form of the data is less important than it was. We can start with a very raw form and be doing data profiling and having data scientists look at it and maybe even feeding a machine learning engine in the process. And then as you discover the important bits in that data, maybe curated, some are cause we do need some data governance, we need some data quality. And that goes more into what you would think of traditionally as a data warehouse type format or a data mart format for running and supporting dashboards. But we're now able to unify all this data and really get to this concept of having a single source of truth and be agile at the same time. That's one of the things that attracted me to Snowflake out of my independent consulting world at the time to jump on board with Snowflake, I was just so amazed at what we could do in the cloud with that power and the elasticity that was unheard of and unthinkable in the on-prem world that we just can make so much more progress. And so, you know, fewer constraints, faster time to value, all kinds of things like that that just were amazing to me. >> Okay. Kent, it's been too long since we've jointly met with customers. You used dashboard, that's a dirty word. We're trying to get rid of those. We'll say cloud flying. >> Well, that's a good point. I mean, let's talk about the dashboard is what people are comfortable with. That's what they're used to, is kind of the first gen but now going beyond the traditional analytics this is where you start to see machine learning and AI become the value and that's the one thing that's constant now is okay, data's accessible. You get cloud scale, massive amounts of data. How fast can you put it to work? Sounds trivial, but it's not. What do you guys react to that comment? >> Yeah, and it's not trivial on the impact, but I would say it's become more trivial to make it happen because you have that unlimited compute or elastic compute, Snowflake separates the compute and storage. So you can do analytics that were just not possible in an on-premises world, on-premises discourages experimentation because of the high fixed costs to even get going. And with ThoughtSpot, the AI driven insights lets you find the anomalies, the correlations without a data scientist on all your data. So granular, every, you know, terabytes, just millions of records within your Snowflake data warehouse. And I think it's also combining the different workloads that in the past used to be separate, right? Kent, they would take the data out and do it on the desktop or in the data lake even, the data scientists anyway. >> Yeah, exactly. I mean, well in the past the repositories themselves were even separate, right? You often have very different technologies and I've worked with customers that would have data replicated across two massive data warehouses, one for loading, one for reporting. And then they'd be extracting that very same data into Hadoop cluster to put it in the same place with the semi-structured data, so the data scientists could go at it. So they really had three copies of that same data and the amount of engineering and synchronization required to make that work so that everybody was sort of working off of the same data. And we've been able to now eliminate all of that with Snowflake to put it all in one place, just once and let everyone work on it and really democratize the access to that data in one place. So whether it is, you know, machine learning and AI being one of the really big use cases that's certainly growing now and getting to it faster, you know, driving that time to value in those insights with products like ThoughtSpot to be able to get in there and make it so much easier for professionals to look at that data and analyze that data and find those insights that they really need. >> Yeah. You know, that's a great point. You mentioned, you know, the old way of setting up a dupe cluster and all the time, you know, we all know what happened there. I mean, there was too much engineering going into setting up clusters than getting the value out of the clusters and then in comes Spark and then in comes to Amazon. Hello, you know, Goodbye Hadoop. Right, so Cloudera certainly has shifted, they merged with Hortonworks. You know, they're going back into the clouds, smart, smart move. But the data world has changed. Obviously you guys are leaders in this new data in the cloud phenomenon with new business models, new value propositions. But I got to ask you about kind of the old personnel files that are out there. You talk about people, you know, there's people's jobs, where's the DBA? I ran the data where I set up those clusters. So, you know, I hear what you're saying, Kent, but like the data administrators, do their jobs go away? So take me through the impact because this is a big challenge to how to redeploy and how to retrain or leverage the existing personnel. >> Yeah, and I've been using the agile term refactor, we have to refactor the database administrator's job to be more of an architect or a platform builder. And we're talking more now about having, you know, data coaches, data storytellers. Cindi's talking about that all the time is it's different skillsets, but folks that have been in the space for awhile are very adaptable. And if they're data experts at some level, then, you know, it's just looking at it a little differently. And in reality, when I talk to DBAs, when you look at it and say, well, where do you really get the most joy out of your work? It's delivering the value. Nobody's overly excited about backup and recovery, right? That's not where they're getting their job satisfaction from, it's getting the business access to the data. And so now with the advances in technology we're able to give them that opportunity to really become, you know, data providers and to work in partnership with the business to get the business access to the data they need from new sources, different data types, but, you know, in a more timely manner rather than having to spend 70% of their day working on really manual mundane administration just to keep the platform up and running. And we've had customers tell us that, that they've seen is, you know, 50, 60, 70, 80% reduction or more in the amount of administration necessary, which means that their staff is actually more productive... >> And that's going to be a good shift. Cindi take us through the ship because, you know, one mega trend that's happening and you see chips coming out there with more horsepower, with built-in machine learning, you're seeing this kind of new layer of democratization for insights and storytelling and analytics and then you've got this embedded model and you guys do search embedded into all your activities. You've got three layers, almost a stack of data of software, you know, built in, you know, easy to use and simple and then completely forgotten by the user because it's built into some apps somewhere, right? So you're starting to see this change. How does that affect like who works on stuff? >> Yeah, so it does shift. You have to think the analyst, we talk about the analyst of the future in a way similar to what Kent was saying with the DBAs trying to become data engineers, the analysts of the future really want to be this strategic business champions and even a research report from TDWI talked about how most feel beaten down, they can't keep up with it, but 36% would say if you freed up our time, we would become more strategic business advisors. So that's kind of the core analysts now, the embedded that you're talking about is really where data becomes a product and it's the product managers that are embedding data in these applications. But this people change management is super hard and in fact, Harvard Business Review said the lack of accounting for people change management is one of the top reasons why technology is not adopted for these frontline decision makers. We can make it easy, consumer grade, but if we're not looking at how we change these people's roles, it's still a tough hill to climb. >> Well, I got to ask you both kind of the real question that's kind of in the middle of the table here is you both have seen waves of innovation before, what's going on now? And it's pretty obvious, it's playing out in the real world right now, it's in full display as we see it with COVID and digital transformation how do people do it? What's the playbook? How do you advise folks who are saying, cause you see both sides of the table, you've been there. You now see the other sides, Snowflake and ThoughtSpot. What's the mindset, what's the playbook? What do people do? How do they get going? >> Yeah. So start small with the business outcome, with your biggest pain or your biggest opportunity, learn, figure out how you're going to change the people and then run fast, run faster than you ever have before. The rate of creative destruction has never been faster. >> Yeah. In the agile world they talk about failing fast, so exactly to Cindi's point. Things are changing so rapidly, you don't have time to sit around and mull it over for very long. And so really adopting an agile mindset is very important to being successful today. And certainly with the pandemic, we've seen, you know, many organizations come to the top and those were folks that were able to rapidly adapt. And in part that as their mindset, the willingness to adapt not to sit around and overly complicate the issue, overly discuss the issue, too many committees, all of that, but really getting into that mindset of what can we do today? What technology do we have at hand to take advantage of today to make a significant difference? And that's where, you know, Snowflake we've certainly seen an increase in adoption from many of our customers where they're actually, you know, using Snowflake more, they're creating new use cases and they're able to use that flexibility and the agility of the platform to make significant business changes in a short period of time. But back to Cindi's point, you've got to have the right culture in place, right? And the right mindset in place to even see that as a possibility. >> You know, there are three things that make business go great. You make things easy to use and simple and provide value fast is a really good formula, you guys do that. Kent, congratulations on your success at Snowflake. I know Frank Slootman is going to be speaking at the ThoughtSpot Beyond 2020. You guys had great depths of business success, your customers are voting with their wallet. ThoughtSpot, you guys are having innovative formula, doing very well as well to AI and built in search and all the greatness, the new models are here. And so congratulations. Thanks for watching theCUBE. I'm John Furrrier. To learn more aboutS Snowflake and ThoughtSpot working together, check out Beyond 2020. It's a virtual event on December 9th and 10th and you can register at thoughtspot.com/beyond2020, that's thoughtspot.com/beyond2020. I'm John Furrier from theCUBE, thanks for watching this CUBE conversation. (upbeat music)

>> Narrator: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Everyone welcome to this special CUBE conversation. I'm John Furrier, host of theCUBE here in our Palo Alto studios. During this time of the pandemic, we're doing a lot of remote interviews, supporting a lot of events. theCUBE virtual is our new brand because there's no events to go to, but we certainly want to talk to the best people and get the most important stories. And today I have a great segment with a world-class entrepreneur, Ajeet Singh co-founder and executive chairman of ThoughtSpot. And they've got an event coming up, which is going to be coming up in December 9th and 10th. But this interview is really about what it takes to be a world-class leader and what it takes to see the future and be a visionary, but then execute an opportunity because this is the time that we're in right now is there's a lot of change, data, technology, a sea change is happening and it's upon us and leadership around technology and how to capture opportunities is really what we need right now. And so Ajeet I want to thank you for coming on to theCUBE conversation. >> Thanks for having me, John. Pleasure to be here. >> For the folks watching, the startup that you've been doing for many, many years now, ThoughtSpot you're the co-founder executive chairman, but you also were involved in Nutanix as the co-founder of that company as well. You know, a little about unicorns and creating value and doing things early, but you're a visionary and you're a technologist and a leader. I want to go in and explore that because now more than ever, the role of data, the role of the truth is super important. And as the co-founder, your company is well positioned to do that. I mean, your tagline today on the website says insight is the speed of thought, but going back to the beginning, probably wasn't the tagline. It was probably maybe like we got to leverage data, take us through the vision initially when you founded the company in 2012. What was the thinking? What was on your mind? Take us through the journey. >> Yeah. So as an entrepreneur, I think visionary is a very big term. I don't know if I qualify for that or not, but what I'm really passionate about is identifying very large markets, with very, very big problems. And then going to the white board and from scratch, building a solution that is perfectly designed for the big problem that the market might be facing from scratch. And just an absolute honest way of approaching the problem and finding the best possible solution. So when we were starting ThoughtSpot, the market that we identified was analytics, analytics software. And the big problem that we saw was that while on one hand, companies were building very big data lakes, data warehouses, there was a lot of money being spent in capturing and storing data how that data was consumed by the end-users, the non-technical people, the sales, marketing, HR people, the doctors, the nurses, that process was not changing. That process was still stuck in old times where you have to ask an analyst to go and build a dashboard for you. And at the same time, we saw that in the consumer space, when anyone had a question they wanted to learn about something, they would just go to Google and ask that question. So we said, why can't analytics be as easy as Google? If I have a question, why do I have to wait for three weeks for some data experts to bring some insights to me for most simple questions, if I'm doing some very deep analysis, trying to come up with fraud algorithms, it's understood, you know, you need data expert. But if I'm just trying to understand how my business is doing, how my customers are doing, I shouldn't have to wait. And so that's how we identified the market and the problem. And then we build a solution that is designed for that non-technical user with a very design thinking UX first approach to make it super easy for anyone to ask that question. So that was the Genesis of the company. >> You know, I just love the thinking because you're solving a problem with a clean sheet piece of paper, you're looking at what can be done. And it's just, you can bring up Google because you know, you think about Google's motto was find what you're looking for. And they had a little gimmicky buttons, like I'm feeling lucky, which just took you to a random webpage at that time while everyone else was tryna build these walled gardens and this structural apparatus, Google wanted you in and out with your results fast. And that mindset just never came over to the enterprise and with all that legacy structure and all the baggage associated with it. So I totally loved the vision, but I got to ask you, how did you get to beachhead? How did you get that first success milestone? When did you see results in your thinking? >> Yeah, so I mean, I believe that once you've identified a big market and a big problem, it comes down to the people. So I sort of went on a recruit recruiting mission and I recruited perhaps the best technology and business team that you can find in any enterprise segment, not only just analytics, some of the early engineers, my co-founder, he was at Google before that, Amit Prakash, before that he was at Microsoft working on Bing. So it took a lot of very deliberate effort to find the right kind of people who have a builder's mentality and are also deep experts in areas like search large-scale distributed systems. Very passionate about user experience. And then you start building the product, you know, it took us almost, I would say one and a half three years to get the initial working version of the product. And we were lucky enough to engage with some of the largest companies in the world, such as Walmart who are very interested in our solution because they were facing these kinds of problems. And we almost co-developed this technology with our early customers, focusing on ease of use, scale, security, governance, all of that, because it's one thing to have a concept where you want to make access to data as easy as Google, you have a certain interface people can type and get an answer. But when you are talking about enterprise data and enterprise needs, they are nowhere similar to what you have in consumer space. Consumer space is free for all, all the information is there you can crawl it and then you can access it. In enterprise, for you to take this idea of search, but make it production grid, make it real and not just a concept card. You need to invest a lot in building deep technology and then enabling security and scalability and all of that. So it took us almost , I would say a two and a half to three years to get to the initial version of the product and the problem we are solving and the area of technology search that we are working on. We brought it to the market. It's almost an infinite game. You know, you can keep making things easier and easier. And we've seen how Google has continued to evolve their search over time And it is still evolving. We just feel so lucky to be in this market, taking the direction that we have taken. >> Yeah. It's easy to talk a big game in this area because like you said, it's a hard technical problem because it'll structural data, whether it's schema databases or whatever, legacy baggage, but to make it easy, hard. And I like what you guys go with this, find the right information and put it in the right place, the right time. It's a really hard problem. And the beautiful thing is you guys are building a category while there's spend in the market that needs the problem today. So category creation with an existing market that needs it. So I got to ask you, if you could do me a favor and define for the audience, what is search-driven analytics? What does that mean from your standpoint? >> Yeah, what it means is for the end user, it looks like search but under the hood is driving large scale analytics. I like to say that our product looks like a search engine on the surface, but under the hood, it's a massive number crunching machine. So Search and AI driven analytics. There's two goals there. One, if the user has, any user and we're talking about non-technical users here, we're not talking about necessarily data experts, but if a user has a question, they should be able to get an answer instantly. They shouldn't have to wait. That is what we achieve with Search and with Spot IQ, our AI engine, we help surface insights where people may not even know that those are the questions they should be asking because data has become so complex. People often don't even know what question they should be asking. And we give them a pool that's very easy to use, but it helps surface insights to them. So there is both a pool model that we enabled through Search and a push model that we enable through Spot IQ. >> So I have to ask you that you guys are pioneering this segment you're in first. And sometimes when you're first, you have arrows in your back as you know, it's not all the beginners survive, they get competition copies, but you guys have had a lead. You had success. What's different today as you have competition coming in trying to say, "Oh, we got Search too." So what's different today with ThoughtSpot? How are you guys differentiated? >> Yeah. I mean, that's always a sign of success. If what you are trying to do, if others are saying we have it too, you have done something that is valuable. And that happens in all industry. I think the best example is Tesla. They were the first to look at this very well-known problem. I mean, we haven't had a very sort of unique take on the existence of the problem itself. Everybody knows that there is a problem with access to data, but the technology that we have built is so deep that it's very, very hard to really copy it and make it work in real world with Tesla in automotive industry in cars, there is obviously so many other companies that have launched battery powered cars, electric cars, but there is Tesla and there is all the other electric cars which are a bit of an afterthought, because if you want to build an analytics product, where Search is at the core, Search cannot be added on the top, Search has to be the core, and then you build around it. And that requires you to build a fundamental architecture from the ground up. And you can't take an existing BI product that is built for dash boarding and add a search bar. I have always said that adding a search bar in a UI is perhaps, you know, 10 to 20 lines of JavaScript code. Anyone can add it and there is so much open source stuff out there that you can just take it and plug it. And many people have tried to do that, but taking off the shelf, Search technology that is built for unstructured data and sticking it on to a product that is required to do analytics on enterprise data, that doesn't work. We built a search technology that understands enterprise data at a very deep level, so that when our customers take our product and bring it into their environment, they don't have to fundamentally change how they manage their data. Our goal is to add value to their existing enterprise data Cloud Data Warehouses and deliver this amazing Search experience where our Search engine is enable to understand what's in their data Lake, what's in their Cloud Data Warehouse. What are the schema, the tables, the joints, the cardinality, the data archive, the security requirements, all of things have to be understood by the technology for you to deliver the experience. So now that said, we pride ourselves in not resting on our laurels. You know, we have this sort of motto in the company. We say we are only 2% done. So we are on our own sort of a continuous journey of innovation. And we have been working on taking our Search technology to the next level. And that is something really powerful that we are going to unveil at our upcoming conference, Beyond, in December. And that is one to create even more distance between us and the competition. And it's all driven by what we have seen with our customers, how they're using our product or learnings what they like, what they don't like, where we see gaps and where we see opportunity to make it even easier to deliver value to our customers and our users. >> I think that's a really profound insight you just shared, because if you look at what you just said around thinking about Search as an embedded architectural foundational, you know, embedded in the architecture, that's different than bolting on a feature where you said Java code or some open source library. You know, we see in the security market, people bolted on security had huge problems. Now, all you hear is, "Oh, you got a big security in from the beginning." You actually have baked Search into everything from the beginning. And it's not just a utility, it's a mindset. And it's also a technology metadata data about data software, and all kinds of tech is involved. Am I getting that right? I mean, cause I think this is what I heard you say. It's like, you got to have the data. >> This is totally right. I mean, if I can use an analogy, there is Google search and obviously Yahoo also tried to bring their own search Yahoo search Yahoo actually, Yahoo versus Google is a perfect example or a perfect analogy to compare with ThoughtSpot versus other BI product Yahoo was built for predefined content consumption. You know, you had a homepage, somebody defined it. You could make some customizations. And there is predefined content you can consume it. Now, they also did add search, but that didn't really go so far. While Google said, we will vary from scratch ability to crawl all the data, ability to index all the data and then build a serving infrastructure that deliver this amazing performance and interactivity and relevance for the user. Relevance is where Google already shined. And you can't do those things until you think about the architecture from the ground up. >> Ajeet I'm looking forward to having more deep dive conversations on that one topic. But for the folks who might not be old enough, like me to remember Google back at that time, Yahoo was the best search engine and it was directory basically with a keyword search. It was trivial, technically speaking, but they got big. And then the portal wars came out, we got to have a portal. Google was very much not looked down as an innovator, but they had great technical chops and they just stayed the course. They had a mission to provide the best search engine to help users find what they're looking for. And they never wavered. And it was not fashionable about that time to your point. And then Yahoo was number one, then Google just became Google and the rest is history. So I really think that's super notable because companies face the same problem. What looks like fashionable tech today might not be the right one. I think that's... >> Yeah, and I totally agree. And I think a lot of times in our space, there's a lot of sort of hype around AI and machine learning. We as a company have tried to stay close to our customers and users and build things that will work for them. And a lot of stuff that we are doing, it has never been done before. So it's not to say that along the way, we don't have our own failures. We do have failures and we learn from them. >> Yeah. Yeah. Just don't make the same mistake twice. >> Yeah, I think if you have a process of learning quickly, improving quickly, those are the companies that will have a competitive advantage. In today's world, nobody gets it right the first time. If you're trying to do something fundamentally different, if you're copying somebody else, then you're too late already. >> I totally agree. >> If you do something new, it's about how fast you penetrate And that's... >> That's a great mindset. That's a great mindset. And I think that's worth capturing calling out, but I got to ask you because what's first of all, distinguished history and I love your mindset and just solving problems, big problems. All great. I want to ask you something about the industry and where you guys were in 2012 alright when you started the company, you were literally in what I call the before Cloud phase. Cause it was before Cloud companies and then during Cloud companies and then after Cloud, you know, Amazon clearly took advantage of that for a lot of startups. So right around 2012 through 2016, I'd call that the Amazon is growing up years. How did the Cloud impact your thinking around the product and how you guys were executing because you were right on that wave. You were probably in the sweet spot of your development. >> Yeah. >> Pre business planning. You were in the pre-business planning mode, incomes, Amazon. I'm sure you're probably using Amazon cause your starters and all start up sort of use Amazon at first, but I just think about, do we all have found premise with a data center? How did that impact you guys? And how does that change today? >> Certainly. Yeah it's been fascinating to see how the world is evolving how enterprises have also really evolved in depth, thinking on how they leverage the cloud infrastructure now. In the Cloud, there is the compute and storage infrastructure. And then you have a Cloud Data Warehouse, the analytics stack in the Cloud. That's becoming more popular now with a company like Google, having BigQuery and then Snowflake really amazing concepts and things like that. So when we started, we looked at where our customers are , where is their data. And what kind of infrastructure is available to us at the time there wasn't enough compute to drive the search engine that we wanted to build. There were also not any significant Cloud Data Warehousing at the time, but our engineering team our co-founders, they came from companies like Google, where building a Cloud based architecture and elastic architecture, service oriented architecture is in their DNA. So we architected the product to run on infrastructure that is very elastic that can be run practically anywhere. But our initial customers and applies the Global 2000. They had their data on-prem. So we had started more with on-prem as a go-to-market strategy. and then about four and a half years ago, once cloud infrastructure I'm talking about the compute infrastructure started to become more mature, we certified our software, to run on all three clouds So today we have more than 75 to 80% of our customers already running our software in the Cloud. And as now, because we connect to our primary data sources, our Cloud Data Warehouses, Cloud Data Lakes. Now with Snowflake and BigQuery and Synapse and Redshift, we have enough of our customers who have deployed Cloud Data Warehouses. So we are also able to directly integrate with them. And that's why we launched our own hosted SaaS Offering about a month ago. So I would say our journey in this area has been sort of similar to companies like Splunk or Elastic, which started with a software model initially deployed more on-prem, but then evolved with the customers to the Cloud. So we have a lot of focus and momentum and lot of our customers, as they're moving their data to the Cloud, they're asking us as well to be in the Cloud and provide a hosted offering. And that is what we have built for the last one year. And we launched it a month ago. >> It's nice to be on the right side of history. I got to say, when you're on the way to be there. And that also makes integrations easy too. I love the Cloud play. Let's get to the final segment here. I want to get your thoughts on your customers, your advice. There's a huge untapped opportunity for companies when it comes to data, a lot of them are realizing that the pandemic is highlighting a lot of areas where they have to go faster and then to go to Cloud, they're going to build modern apps more data's coming in than ever before. Where are these untapped opportunities for customers to take advantage of the data? And what's your opinion on where they should look and what they should do? >> Yeah, I really think that the pandemics has shown for the first, the value of data to society at large, there is probably more than a billion people in the world that have seen a chart for the first time in their life. Everybody is being... and COVID has done some magic. But everybody was looking at charts of infection and so on and so forth. So there is a lot more broad awareness of what data can do in improving our society at large for the businesses of course, in the last six, seven months, you heard it enough from lot of leaders that digital transformation is accelerating. Everybody is realizing that the way to interact in the world is becoming more and more digital expecting your customers to come to your branch to do banking is not really an option. And people are also seeing how all the SaaS companies and SaaS businesses, digital businesses, they have really taken off. So if a company like Zoom can suddenly have a a hundred, $150 billion valuation, because you are able to do everything remote, all the enterprises are looking to really touch their customers and partners in a lot more digital way than they could do before. And definitely COVID has also really created this almost, you know, pool buckets of organization. There is lot of companies that have tremendously benefited from it. And there a lot of companies that have been poorly affected, really in a difficult place. And I think both of them for the first category, they are looking at how do I maintain this revenue even after COVID, because one of this thing, you know, hopefully early next year we have a vaccine and things can start to look better again sometime next year. But we have learned so much. We have attracted so many new customers, how do we retain and grow them further? And that means I need to invest more and more in my technology. Now, companies that are not doing well, they really want to figure out how to become more operationally efficient. And they are really under pressure to get more value from there and both categories, improving your revenue, retaining customers. You need to understand the customer behavior. You need to understand which products they are buying at a fine grain level, not with the law of averages, not by looking at a dashboard and saying our average customer likes this kind of product. That one doesn't really work. You have to offer people personalized services and that personalization is just not possible at scale, without really using data on the front lines. You can't have just manager sitting in their office, looking at dashboards and charts and saying these are the kinds of campaigns I need to run because my average customer seems to like these kinds of offers. I need to really empower my sales people, my individual frontline workers, who are interfacing with the customer to be able to make customized offers of services and products to them. And that is possible on the data. So we see a really, a lot more focus in getting value from data, delivering value quickly and digital transformation broadly but definitely leveraging data in businesses. There is tremendous acceleration that is happening and, you know, next five years, it's all going to be about being able to monetize data on the front lines when you are interfacing with your customers and partners >> Ajeet, that's great insight. And I really appreciate what you're saying. And you know, I wrote a blog post in 2007. I said, data will be the new development kit. Back then we used to call development kits, software user development. >> John, you are the real visionary. It took me until 2012 to be able to do this. >> Well, it wasn't clear, but you saw other data was going to have to be programmed be part of the programming. And I think, what you're getting at here is so profound because we're living 2020 people can see the value of data at the right time. It changes the conversations, it changes what's going on in the real time communications of our world with real-time access to information, whether that's machine to machine or machine to human, having data in the right place, changes the context. >> Yap. >> And that is a true, not a tech thing, that's just life, right? I think this year, I think we're going to look back and say, this was the year that everyone realized that real time communications, real-time society needs real time data. And I think it's going to be more important than ever. So it's a really big problem and important one. And thank you for sharing that. >> Yeah. And actually you bring up a very good point programming, developing big data. Data as a development kit. We are also going to announce a new product at Beyond, which will be about bringing ThoughtSpot everywhere, where a lot of business users are in their business applications. And by using ThoughtSpot product, using our full experience, they can obviously do enterprise wide analytics and look at all the data. But if they're looking for insights and nuggets, and they want to ask questions in their business workflows. We are also launching a product capability that will allow software developers to inject data in their business applications and enable and empower their own business users to be able to ask any questions that they might have without having to go to yet another BI product. >> It's data as code. I mean, you almost think about like software metaphors, where's the compiler? Where's the source code? Where's the data code? You start to get into this new mindset of thinking about data as code, because you got to have data about the data. Is it clean data, dirty data? Is it real time? Is it useful? There's a lot of intelligence needed to manage this. This is like a pretty big deal. And it's fairly new in the sense in the science side. Yeah, machine learning has been around for a while and you know, there's tracks for that. But thinking of this way as an operating system mindset, it's not just being a data geek. You know what I'm saying? So I think you're on the right track Ajeet. I really appreciate your thoughts here. Thank you. >> Thank you John. >> Okay. This is a cube conversation. Unpacking the data. The data is the future. We're living in a real-time world and in real-time data can change the outcomes of all kinds of contexts. And with truth, you need data and Ajeet Singh co-founder executive chairman of ThoughtSpot shares his thoughts here in theCUBE. I'm John furrier. Thanks for watching. (soft upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Hey welcome back, everyone. Live here in Las Vegas, this is theCUBE's coverage of Informatica World 2018. I'm John Furrier co-host of theCUBE with Peter Burris, my co-host for the next two days. Chris Wojdak who's the Production Architect at Symcor, a Canadian leading financial processing services provider. Welcome to theCUBE. >> Thank you, great to be here, guys. >> So first explain, about in one minute, what the company does and your role. >> Yeah, so Symcor was formed by the three largest banks in Canada, over 20 years ago. We have a proven ability to work effectively as a utility service structure type of model. Symcor is a leading business processing and client communications provider in Canada, supporting banks, telecommunications, insurance, and retail companies in Canada. >> John: And your role there is to do what? Deployment of data, deployment? Be specific. >> Yeah, specifics, one of the things that I work on is strategic initiatives. Everything from data-driven architectures to the strategies, where we want to take the company and how do we, how does the technology line up to the business needs. Such that I'm a Senior Architect in the office as a CTO. >> So what's your data look like? I mean, obviously, you're an Informatica customer. >> Are you happy with Informatica? And are they helping you out? And what's the, tell us about, tell us what's going on. >> Anybody who knows me will know that I'm a pretty blunt guy, so when I say this, I do mean it is, Informatica has done tremendous things for us. Their products actually just work. It's very easy to get value out of our data using Informatica. Our time to market has decreased from months to weeks with them. So we're extremely happy with the maturity of their products and services that we get from them. >> So as you think about the role that, that the architecture's played, and you being a, a good example of that. The architect used to be the individual that would look at the physical assets, and how you thought about the physical assets should be put together in response to a known process, >> Chris: Correct. >> and a known application. And now, as you mention, a data-first orientation requires thinking about the arrangement of assets that have to be architected around very differently. >> Absolutely. >> How has the role of architecture changed? Certainly where you are, but in response to this notion of data first. >> Yeah, so one of the biggest challenges that we have is how do we ethically use that data for fraud prevention and detection purposes 'cause that's one of the key areas that we're trying to grow as one of our key initiatives, which is digital and data services. And where we struggle with that is how do we effectively use our data? So we work with our internal teams, like our privacy and data governance teams to come up with a data governance policy, a comprehensive one at that. How do we ethically use this data now for our services? That's the biggest thing that's changed as opposed to just taking our process and gluing it together. How can you use that without breaking laws and things like that? That's the biggest change I see. >> And what's the relationship between architecture, data architecture, or architecture generally and the role that security's playing? We have a feeling that because data can be shared, because it can be copied, 'cause it can be moved, privatizing that data is essential to any business strategy and security historically has played a major role in thinking about how we privatize data. How does security fit into that governance, ethical kind of model? >> Yeah, and we are a security first type of company over anything else a lot of times. They definitely have a seat at the table. We've had to deploy certain things, I'm not sure if you heard of format preserving encryption architectures and techniques to help enable not only to satisfy the governance, but to drive value legally to our businesses, and our clients. >> How do you look at data as a platform, and how is your data laid out? You made a comment earlier which I liked, which was, Informatica products just works. We've been covering them for a few years. One of the things that got my attention was horizontally scaling the data across systems, not just a point product, >> Chris: Exactly. >> more of a platform. How, from your standpoint, do you look at platforms for you? As you re-platform with data, you are digitizing a lot of services, you're actually enabling new services. What is it about the data platform, and how are you guys thinking about it? >> Well, when we're thinking about it, how do we manage data in a centralized spot, and deliver microservices on top of that data in one spot? How do we, because we can't afford to have data in a million data warehouses, or sporadically throughout the organization, it's not an effective use of data. So the way we've tried to structure it is as soon as we get the data in, we keep it in one spot, which in our case would be the Tera Hadoop cluster. Fully encrypted using format preserving encryption as our mechanism to securing the data. And then from there, running microservices on top of our Hadoop stack power byte Informatica, to drive value out of that data. And where the biggest bang for our buck a lot of times is is that, mainly we have old mainframe data file structured data that's hard to parse and deal with. Well, we can store it in Hadoop, save the space, 'cause it's highly compressed, like X9 or EBCDIC, use Informatica to just get at it in a matter of minutes, to drive value in weeks versus months in a traditional model >> Talk about the microservices architecture because that's kind of a methodology, kind of a mindset. Is it like the classic cloud, Kubernetes containers, or you think of it more of endpoint APIs, talk about how you define microservices. >> Yeah, so microservices, where we've leveraged microservices is essentially in our in our new development models where we're utilizing node.js, and react, single page application development, where we have this in the front end just talking to microservice, specifically, delivering on a specific need only. And then we're leveraging things like, for instance, Kubernetes in the backend, where we deploy those microservices, but we're dealing with it from a single page application perspective, really the more modern web development approach is. >> So you're bringin' data into the application, via microservices, so you can have the centralized location, microservices handles the interaction, and it inputs that into the application? >> Right, and then which also, we have to rework the security infrastructure, and approach to it, because we couldn't use the old school, let's see, Jade session, cookie, now we're using token-based authentication, and all these challenges there, right? >> Hey, I love it, we're at a data show, and we're talkin' Kubernetes, and orchestration containers, and microservices, and it's awesome. (laughing) (Chris laughing) >> But that's what those, that's what those technologies are deployed for, right? >> I know, I'm just saying, it's great! >> But I want to push you on this. >> Chris: Yeah, sure. >> So, today, Symcor provides, as you said, a, this enormous facility for looking up past banking transactions or past banking statements for a variety of different banks in Canada. But, I presume you're looking at providing new services in the future. I can imagine that a centralized resource for a human being looking up an old banking statement, well you got, four, five seconds to get the job done, it's probably pretty good. But when you start talking about, maybe moving to fraud detection, or some other types of services, does that start to change the way you think about your data architecture? 'Cause now you're doing something that's much more close to real-time, how's that going to effect the way you think about things? >> Oh, it was a, we've been on a journey, right? On a digital data transformation journey, literally at Symcor because of that. We started off with some in-house built solutions that we have actually patents on, on how to properly warehouse data. We have one of the largest Canada data warehouses for check images, like a 2.6 petabytes in Canada, and we have to somehow, how do we drive value out of this as a data warehouse type of mentality solution, how do we drive value? So how do we move now into more of the Hadoop, the Cassandra's world, to get that real-time batch processing and get insights, and how do we do that ethically as well, right? And secure, how do we secure? Those are the three biggest things that we have to look at in our journey to get there, hasn't been easy, 'cause different paradigms, different understandings-- >> So let me make sure I got that, new technologies to reduce the response times, ethical use of the data, >> The data. >> and secure control in reference to the data? >> Correct, to protect it, yes. >> So how is that changing then, how you think, do you see it staying centralized, do you see it becoming, moving some of the data, some of the responses out closer to some of your banks, who are actually doing the fraud detection? >> Well, we see it, 'cause we're trying to get into this space, and do it on their behalf because, we have that overarching kind of look at this, so how do we just do it ethically, right? So, when some of our owner banks, for example, send this data, well we can provide services overarching to provide insights across the board, something they can not let's say, do on their own, without our help, type of thing. >> Real quick, define data ethics, 'cause you mentioned ethics many times. Do you mean securely, anonymized, what does that mean for you? >> Well, to me it means like that old, you know, 20 years ago for example, I would take my wallet, maybe put it in my vault, in my vault at home, physically protected, it's safe. Well how do I protect that data now, not only from potentially breaches, but how do I protect to make sure my privacy isn't at risk, that someone's not using it for, for improper use, things like that, that's how define ethical use, right? >> What're you doin' now that you couldn't do before, we're seeing this awesome cloud, you mentioned, Kubernetes gets me pumped up, because that's kind of a horizontal orchestration, you talk about multi-cloud, these are things that are, coming into sight with those kinds of technologies. There's an old way, there's a new way, right? (laughs) So we're seeing this transformation, what's different now for you, that you couldn't do before? >> Yeah, before it was hard to drive insights, because we didn't have the scalability horizontally, or vertically, so things like Hadoop, Informatica and Hadoop the way we can scale our web applications with microservices that's what's made the big difference, is the techniques that are being developed to get down to real-time processing, get the answer quicker and faster, and drive value to our clients faster. What's really important is, when they moved to digital channels, you know, fraud becomes a problem it's growing, in incidents and complexity. We see an opportunity now, where we can provide this fraud detection and prevention services as they change and go to digital channels, were there for the ride, type of thing. >> Chris, it's a great interview, I'd love to follow up with you and learn more about your environment. Final question, I heard you got the Informatica innovation award honoring, congratulations! >> Thank you. >> Advice to other folks doing cutting edge stuff that might be interested in in that kind of status? >> Yeah, words of advice there would be, try to push the limits. Never give up, try to push the limits on the design patterns and design approaches. You'd be amazed at what you can achieve if you really push those limits. >> Great story, love what you guys are doing out of Canada, Toronto area, Chris thanks for comin' on theCUBE, appreciate your stories. theCUBE live coverage here in Las Vegas for Informatica World 2018, I'm John Furrier, Peter Burris, we'll be back after this short break. (bubbly music)

from San Francisco it's the queue covering Red Hat summit 2018 brought to you by Red Hat hey welcome back everyone's two cubes live coverage here in San Francisco California at Moscone West I'm John for a co-host of the cube with my analyst this week co-host John Troy a co-founder of tech reckoning our next guest is Dave Abrams executive general manager of data at Insurance Australia group welcome to the cube thanks for having me we were just you know talking on an off-camera before we came on about the challenges of data as cloud scale you guys have been around for many many years yeah you're dealing with a lot of legacy yeah you guys out right on the front step what's going on with you take a minute to explain what you guys do in your role in your environment absolutely now it's you know so we're we're large insurance trying we we've got offices in New Zealand and across Southeast Asia so we're kind of expanding out in our in our reach but um we've been around for a hundred odd years and and we've really grown a lot through merger and acquisition over time and so what that's meant ah this is a bit of a byproduct of those kind of merge and acquisition process is that data has been siloed and fragmented in different brands and different products and so it's been hard to get for example just a holistic view of a customer what does the customer have all the products they hold you know are they a personal customer as well as a business caste and all that sort of stuff doesn't kind of line up so we've had that big challenge in we've been working over the last couple of years to even just kind of consolidate all that unify that data into one platform so that we can see across the group from from a holistic perspective and and build that single view of customer and that's now helped us sort of understand you know what our customers are doing in and what's important to them and how we can better support them and yeah and offer better services and what are you doing here at Red Hat this week what's what's the objective what are you doing what do you have you know I'm speaking you talking the folk what's the what's the solution with Red Hat well so yeah we're primarily here as a result of the Innovation Awards so we you know we were nominated and we're successful in our in our award for that category in our region which was wonderful we we're really honored with that so we're here because of that we sharing our customer story with the rest of the Red Hat team and the rest of the open-source community around really what it's meant for us to use open source within a big corporate that's kind of traditionally been based on a lot of vendor technology right a live Ben driven predominantly by the big tech vendors you know that have come in and sort of helped us build big solutions and platforms which which were great and wonderful in the fact that you know they they were there and they lasted like ten years plus and that was all good but now because things are changing so fast we need to be more adaptable and and unfortunately those platforms become so entrenched into the organization and and and sort of lock you in that it's a to adjust into it to be adaptable you can't you can't take it out very easily it doesn't even stack up sometimes from a business case so why would we take that technology out we'll just have to dig deeper and we'll just have to spend more right so we're trying to we're trying to re reverse-engineer some of that and the role open source for you guys have been part of new systems recruiting talent everything director what's been a benefit the impact of absolutely it's huge inand you're right I think one of the biggest benefits for us that that really plays out is there is in the talent side right for our people to say not only are we transitioning our organization as a whole and the way we the way we operate but we're really transitioning out people we're transition from kind of the work force that we that we had and they've got us to where we are today but we're now setting ourselves up for the workforce of the future and it is a different skill set it is a different way of approaching problems so you know bringing bring this new technology to the table and allowing people to experiment to learn and to update their skills and capabilities exactly what we what we need for our company so we're pushing that hard yeah that's great it's like a real cultural shift give me maybe transfer transfer over a little bit to the actual tech problem you had right so you multiple countries multiple data warehouses multiple systems yours so what were you looking at and then what was the solution that you kind of figured out and then when yeah when so when I first started the roll a couple of years back we had something like 23 different separate individual data warehouses there were all sort of interconnected and dependent on each other and had copies of each other in each other and it was just it was a little bit of a mess so so the first challenge was to really sort of rationalize and clean up a lot of that so so that's that's what we spent a fair bit of time upfront doing which was basically really acquiring the organization's data from a massive amount of call source systems so in the vicinity of I think we take data from roughly about 150 to 200 call systems and we want to take that data essentially in as close to real time as we possibly can and pump that into her into a and to a new clean unified data Lake right just to make that data all line up so that was the big challenge in the first instance and then the second instance was really a scale problem right so getting the right technology that would help us scale into you know because we've predominately been using our own data centers and keeping a lot of stuff you know in that sort of on-prem mode but we really wanted to be able you know self scale to not only to be able to you know take advantage of cloud infrastructure just to give us that extra computing that extra storage and processing but really also to be able to leverage the the commoditization that's happening in cloud right because you know all all cloud companies around the world commoditizing technology like machine learning and you know artificial intelligence so that it's it's it's available to lots of organizations and the way we see it is really that that we're not going to be able to compete or out engineer those those companies so we need to make it you know accessible and available for our people to be able to use and leverage that innovation on our work as well as is you know do some some smart stuff ourselves are using infrastructures of service OpenStack or what's your solution I mean what are you guys doing solution is yet to use I've been stack is is our first sort of real step into infrastructure-as-a-service so that's really helped us set up like I was showing this morning set up the capability for us to turn our scale in a really cost-efficient way and we've ported a lot of our traditional dedicated you know applications on infrastructure that you know was like appliance based and things like that on to OpenStack now so that we can it gives us a lot more portability and we can move that around and put that in the place where we think gets us the best value so so that's really helped I'm kind of curious you work with Red Hat consulting and was I was I was curious about that process did you was that the result of a kind of a bake-off or we were already Red Hat customers and said oh hey by the way can you give us some advice yeah it really came about I mean we've been working with Red Hat for many years you know and it started back just sort of in the support area of Linux and and rel and using that kind of capability and rit has been there for us for quite a long time now and I think we've sort of done some some Explorer exploratory type exercise with them around you know I've been shifting and The Container well but but what really started the stick was just getting their expertise in from our OpenStack perspective and when you that was a key platform that we really wanted to dive into an enable and so having them there is our partner and helping us provide that extra consulting knowledge and expertise was was what we really needed helped us deliver on that project and we delivered in a mazing ly tight timeframe so it was a fast delivery faster live what about the business impact why people look at OpenStack and some of these new technologies and certainly with the legacy stuff going on you have got all these things everywhere what was the actual business benefits can you highlight like did you get like faster time-to-market was it like a claims issue and what were the key things that you look back and saying well we kicked ass and we did these three things I mean really what it boils down to as faster time-to-market right and just the ability to move quicker so to give you an example the way we used to work is it would take you say probably weeks maybe even longer to to provision and get infrastructure stood up and ready to go for different projects so I meant that there was all this lead time that projects nearly go through before they could start to write code and even start to add value to to customer so we wanted to sort of take that away and and and and that was a that was a big hindrance to to be able to experiment and to be on a play we think so again we want to take that out of the picture in and really free people up to sort of say well the infrastructure is done and it spins up in a matter of seconds now on OpenStack and you can get on with the job of trying something out experimenting and actually delivering and writing code that will that will produce an outcome to launch new applications what was a specific outcome that came from standing up putting that over stack together I see you experimenting result not adding yeah not only in the app spice but more so the biggest the biggest sort of benefit with God is really in the data space where we've now been able to essentially stand up our entire data stack using open source technology and we've never been able to do that before and this is you know this is this is the environment it's allowed us to do that by just allowing for us to do that test and trial and say you know he's kafir you're gonna be the right tool for us is it you know is he gonna we're gonna use Post Chris whatever that is it's allowed us to sort of really do that in a rapid way and then figure that thing out and start to move forward so you know ask our kiss you guys have done a lot of work out there good work so I gotta ask you the question with kubernetes containers now part of the discussion as a real viable way to handle legacy but also new software development projects how do you look at that what it's what's the your your reaction to that as that practitioner yeah you guys excited yeah yeah things in motion what's your what's your color um absolutely it's in fact it's been something that we've kind of had on the radar for quite a while because we've we've we've been working with containers so dock in particular and and and one of the things that you know you come across this just management of containers and just ongoing maintenance of of those kind of things where they start to get a little bit unwieldy a little bit out of control so you know we've been trying to we try to start which started off trying to build our own you know in solution to that is there's a lot of corporates are doing quickly found out less that's it that's a huge engineering challenge so things like kubernetes that have now come along and the investment that's been put in that platform will really open up that avenue for and even seeing just the the new innovation that's been put into our OpenShift here that sort of takes a lot of that management and service you know administration out of the out of the equation few is wonderful for a company like us because at the end of the day we're an insurance company right we're not a we're not a technology engineering company while although we have some capability it's never going to be our our strengths right we're really here to service our customers and and to help them in the times when they need our help you guys are a data company data is critical for any trivet yeah how how is you how we've become more data-driven as a result of all this yeah so so now that we've got our data all in one place and we're able to get their single views of customers we're able to put that data now into the hands of people that can really add value to us so for example into our analytics teams and get them to look for optimization in price or in service claims processing all those kind of good things that that are helping our customers reduce the the time frames that they would normally go through in that part of that experience and I think one of the other things is not only that but also enrich our digital capability right and rich that digital channel so make it more convenient for customers you know where it used to be that customers would come along and it's literally like coming to the organization for the first time every time you know I say fill in that form again from blank you're like we don't know anything about you but now we're able to enrich your form exactly it's very painful I see your name and you know you wanted to show your house tell us all about that house you know what does it made of you know what what type of roof material what's the wall we know all that we've probably seen that house ten times already so why wouldn't we just be able to pre-populate that kind of information and make it more convenient forecasting personalization becomes critical absolutely absolutely I like the way you underscored and told the story just like with cloud you just can't take your broken old IT apps and just throw them up at the cloud you had to you had to do a data exercise and you had to do a consolidation and the cleaning strong and sure that involved open source but you didn't get the tech stack first first you have to picture picture data app and and that was a key part here yeah so that's difficult and that's you know that's one of the things that I think we really we really invested in it was because a lot of the time what we've seen is organizations have sort of attacked the low-hanging fruit like the the the kind of the external the digital data that they might be able to get but not that offline data that's been you know one and and generated by the branch and the call centers and all those kind of areas and we dug in deep and invested in that space and got that right first which really helped us a lot to accelerate and now we're I think we're in a better position we can definitely take advantage of that yeah thanks for sharing your insights here in the cube I gotta ask you a final question as the folks watching that they're looking at you say wow this guy he got down and dirty fixed some things he's gone forward innovative what advice would you give someone watching is pregnant practitioner what have you learned what's the learnings that you've that have been magnified out of this process for you and your view going forward yeah yeah there's a there's a lot of learnings we can share but I think some of the key ones is you know I think there's sometimes a bit of a bit of a sort of attempt to try and solve everything yourself right and and we definitely did that where I try and build it all yourself and do everything right but it's it's a challenge and and use partners and look for look for you know things that are kind of gonna help you accelerate and give you some of the foundational work you don't have to build yourself right you don't have to build everything yourself and I think that acknowledgement is really key so that was one of the big things for us the other thing is you know just just investing early and getting things right upfront life pulling your data and consolidating it into into a single platform even though that takes a lot of time and and it's and it's quite challenging to sort of go back and redo things that's actually a huge investment in a big winter to really help you accelerate at the end that investment upfront does does pay off so congratulations on your Innovation Award thank you Davis is general manager at I I AG insurance Australia group here inside the cube sharing the best practices it's it's a world you got to do the homework upfront open source is the way it's and it's an operating model for innovation the cube bringing you all the action here on day two of coverage stay with us for more live right after this short break

>> Narrator: Live from San Francisco, it's The Cube! Covering AWS Summit 2017, brought to you by Amazon Web Services. (upbeat music) >> Hi, welcome back to The Cube. We are live in San Francisco at the AWS Summit at Moscone Center. Really excited to be here. A tremendous amount of buzz going on. I'm Lisa Martin with my cohost George Gilbert and we're very excited to have Lowell Anderson, product marketing guru at AWS. Welcome back, Cube alumni! >> Lowell: It's great to be here, Lisa, thank you. >> Great to have you here as well. The keynote this morning was so energetic with Werner and Nextdoor is going to be on the program in a little bit. Over a thousand product launches last year. Not only are there superpowers now that AWS, I like that. You don't have a T-shirt, but maybe next time. But I think the word that I heard most today so far is customer. And I think that it's such a, and as AWS really talks about, it's a really differentiated way of thinking, of doing business. I'd love to understand what the products that were announced today. Walk us through some of the key highlights there. Customer logos were everywhere. So talk to us about how customers are influencing the development of the new services and products coming from AWS. >> Yeah, well, you know, for us, customers are always core to what drives our innovation. It's how we start, we start with what our customers want, and we work backwards from that to try to deliver a lot of the new features and services that we talked about today. And Werner covered a huge breadth of things, but they really fall into maybe four or five categories. He started talking about, directly for developers, talking about what we're doing with a product called CodeStar, which is designed to really help developers build and deploy software applications in the Cloud. He also then went and talked about our new marketplace, SaaS Contracts' capability, which makes it super easy for customers to sign up and purchase SaaS applications using multi-year contracts on AWS, but it also makes it easier for ISVs to make their offerings available for our customers. So again, really trying to make that easy for customers. We talked a lot about what we're doing in artificial intelligence, with the general availability of Amazon Lex today, and then a really entertaining video with Polly, where we saw that avatar speaking and the new whispering capability, so adding a lot more value to our suite of artificial intelligence services. Some exciting stuff in analytics, where we talked about Redshift Spectrum, which is the new search capability on Amazon Redshift that allows customers to search not just the data in their Redshift database, but also search all the unstructured data they have in S3. And then some really exciting announcements here on the database space with DynamoDB DAX, which is an accelerator for DynamoDB. And we also talked about the availability of a new version of Aurora for Postgres. So a lot of new capabilities, both in databases, big data, analytics, machine learning and artificial intelligence, and our offerings for SaaS Contracts as well. >> And that was all before lunch. (laughs) >> Lowell: Yeah, a lot of stuff. >> Lowell, following up on, in order of, let's say the comments on AI and the announcements made there. Microsoft, Google, Amazon all have gone beyond frameworks and tools to fully trained services that a normal developer can get their hands around. But in the areas of conversational user interface, natural language understanding, image recognition. Why do you think that those three vendors, the three vendors have been able to make such progress in those areas, to make that capability accessible, and there's so many other areas where we're still down in the weeds? >> I think there's, we sort of see it in, sort of focusing in maybe three different areas that are really targeted at what our customers are asking for. We have some very sophisticated customers who really want to build their own deep learning and machine learning applications, and they want services like MXNet, which is a highly scalable deep learning framework, that they can do and build these deep learning models. So there's a very sophisticated, targeted customer who wants that. But we also have customers that want to build and train and create prediction algorithms, and they use Amazon Machine Learning, which is a managed service which allows them to look at their past transactional data and build prediction models from it. And then the third piece is kind of what you mentioned, which is services that are really designed for the average developer, so they can really easily add capabilities like chatbots and speech and visual recognition to their applications with a simple API interface. And I think what you touched on is, why did we focus here, Well I think, as Andy also talked about today, that it's really early days in this space. And we're going to see a really, really strong amount of innovation here. And Amazon, which has been doing this for many, many years, and thousands of developers focused on this in our retail side, we're really working hard to bring that technology out, so that our customers can use it. And Lex, which is based on Alexa, which we're all familiar with from using the Echo. Bringing that out and making that type of capability available for average developers to use is a piece of that. So I think you're just going to continue to see that and over the course of the next year you're going to see continued new services coming from us on machine learning and artificial intelligence, across all those three spectrums. >> So let me jump to another subject which is really a hot button for our customers, both on the vendor side and the enterprise side, which is the hybrid cloud, I don't know whether we should call it migration or journey or endpoint. But let's take a couple scenarios. Let's say you're a Hadoop customer, and you've got Cloudera on-prem, you're a big bank, you've put an instance of it on Amazon and on Azure so that you can move your data around and you're relatively free. >> Lowell: Sure. >> Now the big use case has been data warehouse offload. So all of a sudden you have two really great data warehouses that are best in class on Amazon. With Redshift, with now the significant expansion of it, and Snowflake, and then you have Teradata, which now can take their on-prem capabilities and put them on the Cloud. How does the customer weigh the cost/benefit of lowest common denominator versus-- >> Yeah, yeah, sure. I think for us and for our customers it's not a one-size-fits-all. Every customer approaches this differently, and so what our focus has been on is to give them the range of choice. So if you want to use Cloudera, you can deploy it on EC2 and you can manage that yourself, and that's going to work great for you. But if you want a more managed service where maybe you don't want to have to manage the scalability of that Cloudera deployment, maybe you want to use EMR and deploy your Hadoop applications on EMR which manages that scalability for you. And so you make those tradeoffs and each of our customers makes those tradeoffs for different reasons and in different ways and at different times. And so our focus has always been to really try to give them that flexibility, to give them services where they can make the choice themselves about which direction they want to go for their individual applications, and maybe mix it up and try different ways of running these types of applications. And so we have a full range of those types of, from the ability to deploy those directly onto EC2 and manage it themselves, all the way to fully managed services that we maintain all the scalability and management and monitoring ourselves. >> One of the interesting things that Andy Jassy said in his fireside chat just in the last hour or so about HyperCloud was that most enterprises are going to operate in HyperCloud for the next several years, and there are those customers that are going to have to, or want to have their own data centers for whatever type of application. But something also that he brought up in that context, and I know you know a lot about this, George, is VMware. So when I was looking at the announcement that was made in the last six months or so about VMware, vSphere-based cloud services, VMware has just sold off their vCloud Air, kind of competing product, wondering with the VMware Cloud on Amazon, how does that... what are really the primary drivers there? Is that sort of a way to take those VMware customers eventually towards hybrid cloud, or is that an opportunity to maybe compete with some of the other guys who might have more traction in the legacy application migration space? >> I think for us, it's again, it comes back to our customers saying, some of our workloads that maybe for a long period of time have been deployed on VMware and we've been using VMware ESX for many, many years on-premise, and we have these applications that have been deployed for many years there, and they're highly integrated, they use specific features of VMware, and maybe we also like using VMware's management tools, we like using vCloud to manage all of these different instances of our VMware virtualization platform, but we just want to run it in the Cloud, because we want that scalability. When you deploy that stuff on-premise, you're still kind of locked in. Every time you want to expand, you've got to go out and you've got to buy more hardware. You really don't have the agility to expand that business, both as it grows, or as it declines. So you're paying for that hardware to power it and run it no matter what. And so they're telling us we'd like to get some of this up into the Cloud, but we don't want necessarily to have to, we've built these apps, we're comfortable with how they're running them, but we want to run them up in the Cloud and we want to do it with low risk. And that's what this VMware relationship is about, is letting those enterprises that have spent years building and maintaining and using VMware and their various management tools, to do that up in the Cloud. That's really what it's about. >> So let's switch gears to another topic that Andy talked about, since all his topics were topical. Edge computing and IIoT. That's another big shift that's coming along and changing the architecture so we have more computing at the edge again, and huge amounts of data. Obviously there's many scenarios, but how do you think customers will basically think through this, or how should they think through how much analytics and capability is at the edge, that issue of should it look like what is in the Cloud? Or should it be really tight and light and embedded? >> I think we're seeing just an increasing range. And also a really interesting mix, where you have some very intelligent devices, your laptop and so on, that is connected to the Cloud and it has a pretty significant amount of processing power, and so there can be applications that run on that machine that are very sophisticated. But if we're going to start to expand that universe of edge devices out to simple sensors for pipelines, and simple ways to monitor the thermostat in your home, and simple ways to measure and monitor and track all sorts of, you know, automobiles and so on, that there's going to be a range of different on-premise or edge types of compute, that we need to support in the Cloud. And so I think what Andy's saying is that we want to build the Cloud to be the system that can act as the, has the analytics power to ingest data from these maybe tens of millions of different devices, which will have a range of different compute power, and support those applications on a case by case basis. >> We've got to wrap things up here, and I know this conversation could continue for many hours. I think what we've heard here today is a tremendous amount of innovation, and I made the joke, all announced before lunch, but really it was. We're seeing the flexibility, we're seeing the customers really drive the innovation. Also the fact that AWS starting in the startup space with the developers, that's still a very key target market for you, even as things go up to the enterprise. So continued best luck with everything going forward. We're excited to be at re:Invent in just, what, five or six months from now, and with many, many more thousands of people and hearing the great things that continue to come from the leader in public cloud. >> Lowell: All right. Thank you, Lisa. >> Thanks for joining us, Lowell, we appreciate it. Next time I want the superpower T-shirt. (laughs) >> (laughs) Okay, I'll take you up on that. >> All right. I'm Lisa Martin for my cohost George Gilbert. Thanks so much for watching, stick around. We are live at the AWS Summit in San Francisco, and we will be right back. (upbeat music)