>>The Cube presents UI Path Forward five, brought to you by UI Path. >>Hi everybody. Welcome to Las Vegas. We're here in the Venetian, formerly the Sans Convention Center covering UI Path Forward five. This is the fourth time the Cube has covered forward, not counting the years during Covid, but UiPath was one of the first companies last year to bring back physical events. We did it at the Bellagio last year, Lisa Martin and myself. Today, my co-host is David Nicholson, coming off of last week's awesome CrowdStrike show back here in Vegas. David talking about UI path. UI path is a company that had a very strange path, as I wrote one time to IPO this company that was founded in 2005 and was basically a development shop. And then they realized they got lightning in a bottle with this RPA thing. Yeah. And Daniel Deez, the founder of the company, just really drove it hard and they really didn't do any big kind of VC raise for several years. >>And then all of a sudden, boom, the rocket ship took off, kind of really got out over their skis a little bit, but then got to IPO and, and has had a very successful sort of penetration into the market. The IPO obviously has not gone as well. We can talk about that, but, but they've hit a billion dollars in arr. There aren't a lot of companies that, you know, have hit a billion dollars in ARR that quickly. These guys had massive valuations that were cut back, obviously with the, with the downturn, but also some execution misuses. But the one thing about UiPath, Dave, is they've been very successful at penetrating customers. And that's the thing you always get at forward customer stories. And the other thing I'll, I'll, I'll add is that it started out with the narrative was, oh, automation software, robots, they're gonna take away jobs. The opposite has happened, the zero unemployment. Now basically we're heading into a recession, we're actually probably in a recession. And so how do you combat a recession? You put automation to work and gain if, if, if, if inflation is five to 7% and you can get 20% from automation. Well, it's a good roi. But you sat in the keynotes, it was really your first exposure to the company. What were your thoughts? >>Yeah, I think the whole subject is interesting. I think if you've been involved in tech for a while, the first thing you think of is, well, hold on a second. Isn't this just high tech scripting? Aren't you essentially just automating stuff? How, how cool can that possibly be? >>Well, it kinda was in the >>Beginning. Yeah, yeah. But, but, but when you dig into it, to your, to your point about the concern about displacing human beings, the first things that can automate it are the mundane and the repetitive tasks, which then frees individuals up frontline individuals who are doing those tasks to do more strategic things for the business. So when you, when we, you know, one of the things that was talked about in the keynote was this idea of an army of citizen developers within an organization. Not, you know, not just folks who are innovating and automating at the core of enterprise applications, but also folks out on the front line automating the tasks that are interfering with their productivity. So it seems like it's a win-win for, for everybody throughout the enterprise. >>Yeah. So let's take a, let's take folks through the, the keynote to, basically we learned there are 3,500 people here, roughly, you know, we're in the Venetian and we do a lot of shows at, at the Venetian, formerly the San Convention Center. The one thing about UiPath, they, they are a cool company. Yeah, they are orange colors, kinda like pure storage, but they got the robots moving around. The setup is very nice, it's very welcoming and very cool, but 300 3500 attendees, including partners and UiPath employees, 250 sessions. They've got a CIO, automation council and a pickleball court inside this hall, which pickleball is, you know, all the rage. So Bobby, Patrick and Mary Telo kicked it off. Bobby's the cmo, Mary's the head of branding, and Bobby raised four themes. It it, this is a tool that it's, this is RPA is going from a tool to a way of operating and innovating. >>The second thing is, the big news here is the UI path business platform, something like that. They're calling, but they're talking about about platform and they're really super gluing that to digital transformation. The third is really outcomes shifting from tactical. I have a robot, a software robot on my desk doing, you know, mimicking what I do with the script to something that's transformative. We're seeing this operationalized very deeply. We'll go into some examples. And then the fourth theme is automation is being featured as a strategic line item in annual reports. Bobby Patrick, as he left the stage, I think he was commenting on my piece where I said that RPA automation is more discretionary than some other things. He said, this is not discretionary, it's strategic. You know, unfortunately when you're heading into a recession, you can, you can put off some of the more strategic items. However, the flip side of that, Dave, is as they were saying before, if you're gonna, if if you're, if you're looking at five to 7% inflation may be a way to attack that is with automation. Yeah. >>There's no question, there's no question that automation is a way to attack that. There's no question that automation is critical moving forward. There's no question that we have moved. We're in the, you know, we're, we're still in the age of cloud, but automation is gonna be absolutely critical. The question is, what will UI path's role be in that market? And, and, and when you hear, when you hear UI path talk about platform versus tool sets and things like that, that's a critical differentiator because if they are just a tool, then why wouldn't someone exploit a tool that is within an application environment instead of exploiting a platform? So what I'm gonna be looking for in terms of the, the folks we talked to over the next few days is this question of, you know, make the case that this is actually a platform that extends across all kinds of application environments. If they can't seize that high ground moving forward, it's it's gonna be, it's gonna be tough for them. >>Well, they're betting the company on >>That, that's Rob Ensslin coming in. That's why he's part of the, the equation. But >>That platform play is they are betting the company. And, and the reason is, so the, the, the history here is in the early days of this sort of RPA craze, Automation Anywhere and UI path went out, they both raised a ton of money. UI Path rocketed out to the lead. They had a much e easier to install, you know, Automation Anywhere, Blue Prism, some of the other legacy business process folks, you know, kind of had on-prem, Big Stacks, UiPath came in a really simple self-serve platform and took off and really got a foothold in the market. And then started building or or making some of these acquisitions like Process Gold, like cloud elements, which is API automation. More recently Reiner, We, which is natural language processing. We heard them up on the stage today and they've been putting that together to do not just rpa but process mining, task mining, you know, document automation, et cetera. >>And so Rob Ins insulin was brought in from Google, formerly Google and SAP, to really provide that sort of financial and go to market expertise as well as Shim Gupta who's, who's the cfo. So they, they, and they were kinda late with that. They sort of did all this post ipo. I wish they had done it, you know, somewhat beforehand, but they're sort of bringing in that adult supervision supervision that's necessary. Rob Sland, I thought was very cogent. He was assertive on stage, he was really clear, he was energetic. He talked about the phases, e r p, Internet cloud and the now automation is a new S-curve. He quoted a Forester analyst talking about that. He also had a great quote. He said, you know, the old adage better, faster, cheaper, pick two. He said, You don't have to do that anymore with automation. He cited reports from analysts, 50% efficiency improvement, 40% productivity improvement, 40% improvement in customer satisfaction. >>And then what I always, again, love about UiPath is they're no shortage of customers. They do as good a job as anybody, and I think I would say the best of, of, of getting customers to talk about their experiences. You'll see that on the cube all this week, talked about Changi airport from Singapore. They're adding 50 able to service 50 million new customers, new travelers with no new headcount company called Vital or retail. And how you say that a hundred thousand employees having access to it. Uber, 150% ROI in one year. New York state getting 1.2 million relief checks out in two weeks and identifying potentially 12 billion in fraud. They also talk about 25% of the, of the UI path finance team is digital. And they've, they've only incremented headcount, you know, very slightly one and a half times their revenue's grown. What a 10 x? And really he talked about how to, for how to turn automation into a force multiplier for growth. And to your point, I think that's their challenge. What were your thoughts on Rob ens insulin's keynote? >>First of all, in addition to his background, Rob brings a brand with him. Rob Ensslin is a brand, and that brand is enterprise overarching platform. Someone you go to for that platform play, not for a tool set. And again, I'll, I'll say it again. It's critically important that they, that they demonstrate this to the marketplace, that they are a platform worth embracing as opposed to simply a tool set. Because the large enterprise software providers are going to provide their own tool sets within their platforms. And if you can't convince someone that it's worth doing two things instead of one thing, you're, you're, you're never gonna make it. So I've had experiences with Rob when he was at Google. He's, he's, he's the right person for the job and I, and I I I buy into his strategy and narrative about where we are and the critical nature of automation question remains, will you I path to be able to benefit from that trend. >>So a couple things on that. So your point about sap, you know, is right on EY was up on stage. They, EY is a huge SAP customer and they chose UI path to automate their SAP installation, right? And they're going all in with UI path as a partner. Of course. I I often like to say that the global system integrators, they like to eat at the trough, right? When you see GSIs like EY and others coming into the ecosystem, that means there's business being done. We saw Orange up on stage, which was really interesting. >>Javier from Spain. Yeah. Yep. >>Talking about he had this really cool dashboard and then Ted Coomer was talking about the business automation platform and all the different chapters and the evolution. They've gotta get to a platform play because the thing I failed to mention is Microsoft a couple years ago made a tuck in acquisition and got it to this market really providing individual automations and making it, you know, it's Microsoft, they're gonna make it really easy to add it really >>Cheaply. SAP would tell you that they have the same thing and, >>And then, and then just grow from that. So UiPath has to pivot to a platform play. They started this back in 2019, but as you know, it takes a long time to integrate stuff. Okay. So they're, they're, they're working through that. But this is, you know, Rob ends and put up on the, the slide go big, I, I tweeted, took a page outta Michael Dell. Go big or go home. Final thoughts before we break? >>I think go big or go home is pretty much sums it up. I mean this is, this is an existential mission that UiPath is on right now, starting to stay forward. They need to seize that high ground of platform versus tool set. Otherwise they will never get beyond where they are now. I I I, I do wanna mention too, to folks in the audience, there's a huge difference between a billion dollar valuation and a billion dollars in revenue every year. So, so, you know, these, these guys have reached a milestone, there's no question about that. But to get to that next level platform, platform, platform, and I know we'll be, we'll be probing our guests on that question over the next couple years. >>Yeah. And the key is obviously gonna be keep servicing the customers, you know, all the financial machinations and you know, they reduced yesterday their guidance from the high end being 25% ARR growth down to roughly 20% when you, when you factor out currency conversions. UiPath has a lot of business overseas. They're taking that overseas revenue and converting it back to dollars though dollars are appreciated. So they're less of them. I know this is kind of the inside baseball, but, but we're gonna get into that over the next two days. Dave Ante and Dave, you're watching the Cubes coverage of UI path forward, five from Las Vegas. We'll be right back, right after this short break.

>>Welcome to the customer. Obsession begins with data session. Uh, thank you for, for attending. Um, at Cloudera, we believe that a custom session begins with, uh, with, with data. Um, and, uh, you know, financial services is Cloudera is largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five, uh, top stock exchanges, eight out of the 10 top wealth management firms and all four of the top credit card networks. Uh, so as you can see most financial services institutions utilize Cloudera for data analytics and machine learning. Uh, we also have over 20 central banks and it doesn't or so financial regulators. So it's an incredible footprint, which glimpse Cloudera, lots of insight into the many innovations that our customers are coming in up >>With >>Customers have grown more independent and demanding. Uh, they want the ability to perform many functions on their own and, uh, be able to do it. Uh, he do them on their mobile devices, uh, in a recent Accenture study, more than 50% of customers, uh, are focused on, uh, improving their customer experience through more personalized, uh, offers in advice. The study found that 75% of people are actually willing to share their data for better personalized offers and more efficient and intuitive of services >>Together. And >>A better understanding of your customers use all the data available to develop a complete view of your customer and, uh, and better serve them. Uh, this also breaks down, uh, costly silos, uh, shares data in, in accordance with privacy laws and assists with regulatory adherence. So different and organizations are going to be at different points in their data analytics and AI journey. Uh, there are several degrees of streaming and batch data, both structured and unstructured. Uh, you need a platform that can handle both, uh, with common, with a common governance layer, um, near real time and real real-time sources help make data more relevant. So if you look at this graphic, looking at it from left to right, uh, normal streaming and batch data comes from core banking and, uh, and lending operations data in pretty much a structured format as financial institutions start to evolve. >>Uh, they start to ingest near real-time streaming that comes not only from customers, but also from, from newsfeeds for example, and they start to capture more behavioral data that they can use to evolve their models, uh, and customer experience. Uh, ultimately they start to ingest more real-time streaming data, not only, um, standard, uh, sources like market and transaction data, but also alternative sources such as social media and connected sources, such as wearable devices, uh, giving them more, more data, better data, uh, to extract intelligence and drive personalized actions based on data in real time at the right time, um, and use machine learning and AI, uh, to drive anomaly detection and protect and predict, uh, present potential outcomes. >>So this >>Is another way to look at it. Um, this slide shows the progression of the big data journey as it relates to a customer experience example, um, the dark blue represents, um, visibility or understanding your customer. So we have a data warehouse and are starting to develop some analytics, uh, to know your customer and start to provide a better customer 360 experience. Uh, the medium blue area, uh, is, uh, customer centric or where we learn, uh, the customer's behavior. Uh, at this point we're improving our analytics, uh, gathering more customer centric information to perform, uh, some more exploratory, uh, data sciences. And we can start to do things like cross sell or upsell based on the customer's behavior, which should improve, uh, customer retention. The light blue area is, uh, is proactive customer inter interactions or where we now have the ability, uh, to predict customers needs and wants and improve our interaction with the customer, uh, using applied machine learning and, and AI, uh, clap the Cloudera data platform. >>Um, you know, business use cases require enabling, uh, the end-to-end journey, which we referred to as the data life cycle, uh, what the data life cycle, what is the data life cycle that our customers want to take their data through to enable the end-to-end data journey. If you ask our customers, they want different types of analytics, uh, for their diverse user bases to, to help them implement their, their, their use cases while managed by a centralized security and governance later layer. Uh, in other words, um, the data life cycle to them provides multifunction analytics, uh, at each stage within the data journey, uh, that, uh, integrated and centralized, uh, security, uh, and governance, for example, uh, enterprise data consists of real-time and transactional type type data. Examples include, uh, clickstream data, web logs, um, machine generated, data chatbots, um, call center interactions, uh, transactions, uh, within legacy applications, market data, et cetera. >>We need to manage, uh, that data life cycle, uh, to provide real enterprise data insights, uh, for use cases around enhance them personalized customer experience, um, customer journey analytics, next best action, uh, sentiment and churn analytics market, uh, campaign optimization, uh, mortgage, uh, processing optimization and so on. Um, we bring a diverse set of data then, um, and then enrich it with other data about our customers and products, uh, provide reports and dashboards such as customer 360 and use predictions from machine learning models to provide, uh, business decisions and, and offers of, uh, different products and services to customers and maintain customer satisfaction, um, by using, um, sentiment and turn analytics. These examples show that, um, the whole data life cycle is involved, um, and, uh, is in continuous fashion in order to meet these types of use cases, uh, using a single cohesive platform that can be, uh, that can be served by CDP, uh, the data, the Cloudera data platform. >>Okay. Let's, uh, let's talk about, uh, some of the experiences, uh, from our customers. Uh, first we'll talk about Bunco, something there. Um, Banco Santander is a major global bank headquartered in Spain, uh, with, uh, major operations and subsidiaries all over Europe and north and, and south America. Uh, one of its subsidiary, something there UK wanted to revolutionize the customer experience with the use of real-time data and, uh, in app analytics, uh, for mobile users, however, like many financial institutions send them there had a, he had a, had a large number of legacy data warehouses spread across many business use, and it's within consistent data and different ways of calculating the same metrics, uh, leading to different results. As a result, the company couldn't get the comprehensive customer insights it needed. And, uh, and business staff often worked on multiple versions of the truth. Sometimes there worked with Cloudera to improve a single data platform that could support all its workloads, including self-service analytics, uh, operational analytics and data science processes in processing 10 million transactions, daily or 30,000 transactions per second at peak times. >>And, uh, bringing together really, uh, nearly two to two petabytes of data. The platform provides unprecedented, uh, customer insight and business value across the organization, uh, over 80 cents. And Dera has realized impressive, uh, benefits spanning, uh, new revenues, cost savings and risk reductions, including creating analytics for, for corporate customers with near real-time shopping behavior, um, and, and helping identify 7,000 new corporate, uh, customer prospects, uh, reducing capital expenditures by, uh, 3.2 million annually and decreasing operating expenses by, uh, 650,000, um, enabling marketing to realize, uh, 2.4 million in annual savings on, on cash back on commercial transactions, um, and protecting 3.7 million customers from financial crime impacts through 95, new proactive control alerts, improving risk and capital calculations to reduce the amount of money. It must set aside, uh, as part of a, as part of risk mandates. Uh, for example, in one instance, the risk team was able to release a $5.2 million that it had withheld for non-performing credit card loans by properly identifying healthy accounts miscategorized as high risk next, uh, let's uh, talk about, uh, Rabo bank. >>Um, Rabobank is one of the largest banks in the Netherlands, uh, with approximately 8.3 million customers. Uh, it was founded by farmers in the late 19th century and specializes in agricultural financing and sustainability oriented banking, uh, in order to help its customers become more self-sufficient and, uh, improve their financial situations such as debt settlement, uh, rebel bank needed to access, uh, to a varied mix of high quality, accurate, and timely customer data, the talent, uh, to provide this insight, however, was the ability to execute sophisticated and timely data analytics at scale Rabobank was also faced with the challenge of, uh, shortening time to market. Uh, it needed easier access to customer data sets to ensure that they were using and receiving the right financial support at the right time with, with, uh, data quality and speed of processing. Um, highlighted as two vital areas of improvement. Robert bank was looking to incorporate, um, or create new data in an environment that would not only allow the organization to create a centralized repository of high quality data, but also allow them to stream and, uh, conduct data analytics on the fly, uh, to create actionable insights and deliver a strong customer service experience. >>Rabobank >>Leverage Cloudera due to its ability to cope with heavy pressures on data processing and its capability of ingesting large quantities of real-time streaming data. They were able to quickly create a new data lake that allowed for faster queries of both historical and real-time data to analyze customer loan repayment patterns, uh, to up to the minute transaction records, um, Robert bank and, and its customers could now immediately access, uh, the valuable data needed to help them understand, um, the status of their financial situation, this enabled, uh, rebel bank to spot financial disasters before they happened, enabling them to gain deep and timely insights into which customers were at risk of defaulting on loans. Um, having established the foundation of a modern data architecture Rabobank is now able to run sophisticated machine learning algorithms and, uh, financial models, uh, to help customers manage, um, financial, uh, obligations, um, including, uh, loan repayments, and are able to generate accurate, uh, current liquidity overviews, uh, no next, uh, let's, uh, speak about, um, uh, OVO. >>Uh, so OVO is the leading digital payment rewards and financial services platform in Indonesia, and is present in 115 million devices across the company across the country. Excuse me. Um, as the volume of, of products, uh, within Obos ecosystem increases, the ability to ensure marketing effectiveness is critical to avoid unnecessary waste of time and resources, unlike competitors, uh, banks, w which use traditional mass marketing, uh, to reach customers over, oh, decided to embark on a, on a bold new approach to connect with customers via a ultra personalized marketing, uh, using the stack, the team at OVO were able to implement a change point detection algorithm, uh, to discover customer life stage changes. This allowed OVO, uh, to, uh, build a segmentation model of one, uh, the contextual offer engine Bill's recommendation algorithms on top of the product, uh, including collaborative and context-based filters, uh, to detect changes in consumer consumption >>Patterns. >>As a result, OVO has achieved a 15% increase in revenue, thanks to this, to this project, um, significant time savings through automation and eliminating the chance of human error and have reduced engineers workloads by, by 30%. Uh, next let's talk about, uh, bank Bri, uh, bank Bri is one of the largest and oldest, uh, banks in Indonesia, um, engaging in, in general banking services, uh, for its customers. Uh, they are headquartered in, in Jakarta Indonesia, uh, BR is a well-known, uh, for its, uh, focused on financing initiative initiatives and serves over 75 million customers through its more than 11,000 offices and rural outposts, >>Um, Bri >>Needed to gain better understanding of their customers and market, uh, to improve the efficiency of its operations, uh, reduce losses from non-performing loans and address the rising concern around data security from regulators and consumers, uh, through enhanced fraud detection. This would require the ability to analyze vast amounts of, uh, historical financial data and use those insights, uh, to enhance operations and, uh, deliver better service. Um, Bri used Cloudera's enterprise data platform to build an agile and reliable, uh, predictive augmented intelligence solution. Uh, Bri was now able to analyze 124 years worth of historical financial data and use those insights to enhance its operations and deliver better services. Um, they were able to, uh, enhance their credit scoring system, um, the solution analyzes customer transaction data, and predicts the probability of a customer defaulting on, on payments. Um, the following month, it also alerts Bri's loan officers, um, to at-risk customers, prompting them to take the necessary action to reduce the likelihood of a Vanette profit lost. Uh, this resulted in improved credits in, in improved, uh, credit scoring system, uh, that cut down the approval of micro financing loans, uh, from two weeks to two days to two minutes and, uh, enhanced, uh, fraud detector. >>All right. Uh, this example shows a tabular representation, uh, the evolution of a customer retention use case, um, the evolution of data and analytics, uh, journey that, uh, that for that use case, uh, from aware, uh, text flirtation, uh, to optimization, to being transformative, uh, with every level, uh, data sources increase. And, uh, for the most part, uh, are, are less, less standard, more dynamic and less structured, but always adding more value, more insights into the customer, uh, allowing us to continuously improve our analytics, increase the velocity of the data we ingest, uh, from, from batch, uh, to, uh, near real time, uh, to real-time streaming, uh, the volume of data we ingest continually increases and we progress, uh, the value of the data on our customers, uh, is continuously improving, allowing us to interact more proactively and more efficiently. And, and with that, um, I would, uh, you know, ask you to consider an assess if you are using all the, uh, the data available to understand, uh, and service your customers, and to learn more about, about this, um, you know, visit cloudera.com and schedule a meeting with Cloudera to learn more. And with that, thank you for your time. And thank you for listening.

>>Uh, hi, I'm Joe Rodriguez, managing director of financial services at Cloudera. Uh, welcome to the fight fraud with a data session, uh, at Cloudera, we believe that fighting fraud with, uh, uh, begins with data. Um, so financial services is Cloudera's largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five top, uh, stock exchanges, uh, eight out of the top 10 wealth management firms and all four of the top credit card networks. So as you can see most financial services institutions, uh, utilize Cloudera for data analytics and machine learning, uh, we also have over 20 central banks and a dozen or so financial regulators. So it's an incredible footprint which gives Cloudera lots of insight into the many innovations, uh, that our customers are coming up with. Uh, criminals can steal thousands of dollars before a fraudulent transaction is detected. So the cost of, uh, to purchase a, your account data is well worth the price to fraudsters. Uh, according to Experian credit and debit card account information sells on the dark web for a mere $5 with the CVV number and up to $110. If it comes with all the bank information, including your name, social security number, date of birth, uh, complete account numbers and, and other personal data. >>Um, our customers have several key data and analytics challenges when it comes to fighting financial crime. The volume of data that they need to deal with is, is huge and growing exponentially. Uh, all this data needs to be evaluated in real time. Uh, there is, uh, there are new sources of, of streaming data that need to be integrated with existing, uh, legacy data sources. This includes, um, biometrics data and enhanced, uh, authentication, uh, video surveillance call center data. And of course all that needs to be integrated with existing legacy data sources. Um, there is an analytics arms race between the banks and the criminals and the criminal networks never stop innovating. They also we'll have to deal with, uh, disjointed security and governance, security and governance policies are often set per data source, uh, or application requiring redundant work, work across workloads. And, and they have to deal with siloed environments, um, the specialized nature of platforms and people results in disparate data sources and data management processes, uh, this duplicates efforts and, uh, divides the, the business risk and crime teams, limiting collaboration opportunities between CDP enhances financial crime solutions, uh, to be holistic by eliminating data gaps between siloed solutions with, uh, an enterprise data approach, uh, advanced, uh, data analytics and machine learning, uh, by deploying an enterprise wide data platform, you reduce siloed divisions between business risk and crime teams and enable better collaboration through industrialized machine learning. >>Uh, you tighten up the loop between, uh, detection and new fraud patterns. Cloudera provides the data platform on which a best of breed applications can run and leverage integrated machine learning cloud Derrick stands rather than replaces your existing fraud modeling applications. So Oracle SAS Actimize to, to name a few, uh, integrate with an enterprise data hub to scale the data increased speed and flexibility and improve efficacy of your entire fraud system. It also centralizes the fraud workload on data that can be used for other use cases in applications like enhanced KYC and a customer 360 4 example. >>I just, I wanted to highlight a couple of our partners in financial crime prevention, uh, semi dine, and Quintex, uh, uh, so send me nine provides fraud simulation using agent-based modeling, uh, machine learning techniques, uh, to generate synthetic transaction data. This data simulates potential fraud scenarios in a cost-effective, uh, GDPR compliant, virtual environment, significantly improved financial crime detection systems, semi dine identifies future fraud topologies, uh, from millions of, of simulations that can be used to dynamically train, uh, new machine learning algorithms for enhanced fraud identification and context, um, uh, connects the dots within your data, using dynamic entity resolution, and advanced network analytics to create context around your customers. Um, this enables you to see the bigger picture and automatically assesses potential criminal beads behavior. >>Now let's go some of our, uh, customers, uh, and how they're using cloud caldera. Uh, first we'll talk about, uh, United overseas bank, or you will be, um, you'll be, is a leading full service bank in, uh, in Asia. It, uh, with, uh, a network of more than 500 offices in, in 19 countries and territories in Asia, Pacific, Western Europe and north America UA, um, UOB built a modern data platform on Cloudera that gives it the flexibility and speed to develop new AI and machine learning solutions and to create a data-driven enterprise. Um, you'll be set up, uh, set up it's big data analytics center in 2017. Uh, it was Singapore's first centralized big data unit, uh, within a bank to deepen the bank's data analytic capabilities and to use data insights to enhance, uh, the banks, uh, uh, performance essential to this work was implementing a platform that could cost efficiently, bring together data from dozens of separate systems and incorporate a range of unstructured data, including, uh, voice and text, um, using Cloudera CDP and machine learning. >>UOB gained a richer understanding of its customer preferences, uh, to help make their, their banking experience simpler, safer, and more reliable. Working with Cloudera UOB has a big data platform that gives business staff and data scientists faster access to relevant and quality data for, for self-service analytics, machine learning and, uh, emerging artificial intelligence solutions. Um, with new self-service analytics and machine learning driven insights, you'll be, uh, has realized improvements in, in digital banking, asset management, compliance, AML, and more, uh, advanced AML detection capabilities, help analysts detect suspicious transactions either based on hidden relationships of shell companies and, uh, high risk individuals, uh, with, uh, Cloudera and machine learning, uh, technologies. You you'll be, uh, was able to enhance AML detection and reduce the time to identify new links from months 2, 3, 3 weeks. >>Excellent mass let's speak about MasterCard. So MasterCard's principle businesses to process payments between banks and merchants and the credit issuing banks and credit unions of the purchasers who use the MasterCard brand debit and credit cards to make purchases MasterCard chose Cloudera enterprise for fraud detection, and to optimize their DW infrastructure, delivering deepens insights and best practices in big data security and compliance. Uh, next let's speak about, uh, bank Rakka yet, uh, in Indonesia or Bri. Um, it, VRI is one of the largest and oldest banks in Indonesia and engages in the provision of general banking services. Uh, it's headquartered in Jakarta Indonesia. Uh, Bri is well known for its focus on financing initiatives and serves over 75 million customers through it's more than 11,000 offices and rural service outposts. Uh, Bri required better insight to understand customer activity and identify fraudulent transactions. Uh, the bank needed a solid foundation that allowed it to leverage the power of advanced analytics, artificial intelligence, and machine learning to gain better understanding of customers and the market. >>Uh, Bri used, uh, Cloudera enterprise data platform to build an agile and reliable, predictive augmented intelligence solution, uh, to enhance its credit scoring system and to address the rising concern around data security from regulators, uh, and customers, uh, Bri developed a real-time fraud detection service, uh, powered by Cloudera and Kafka. Uh, Bri's data scientists developed a machine learning model for fraud detection by creating a behavioral scoring model based on customer savings, uh, loan transactions, deposits, payroll and other financial, um, uh, real-time time data. Uh, this led to improvements in its fraud detection and credit scoring capabilities, as well as the development of a, of a new digital microfinancing product, uh, with the enablement of real-time fraud detection, VRI was able to reduce the rate of fraud by 40%. Uh, it improved, uh, relationship manager productivity by two and a half fold. Uh, it improved the credit score scoring system to cut down on micro-financing loan processing times from two weeks to two days to now two minutes. So fraud prevention is a good area to start with a data focus. If you haven't already, it offers a quick return on investment, uh, and it's a focused area. That's not too entrenched across the company, uh, to learn more about fraud prevention, uh, go to kroger.com and to schedule, and you should schedule a meeting with Cloudera, uh, to learn even more. Uh, and with that, thank you for listening and thank you for your time. >>Welcome to the customer. Obsession begins with data session. Uh, thank you for, for attending. Um, at Cloudera, we believe that a custom session begins with, uh, with, with data, um, and, uh, you know, financial services is Cloudera is largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five top stock exchanges, eight out of the 10 top wealth management firms and all four of the top credit card networks. Uh, so as you can see most financial services institutions utilize Cloudera for data analytics and machine learning. Uh, we also have over 20 central banks and it doesn't or so financial regulators. So it's an incredible footprint, which glimpse Cloudera, lots of insight into the many innovations that our customers are coming up with. >>Customers have grown more independent and demanding. Uh, they want the ability to perform many functions on their own and, uh, be able to do it. Uh, he do them on their mobile devices, uh, in a recent Accenture study, more than 50% of customers, uh, are focused on, uh, improving their customer experience through more personalized offers and advice. The study found that 75% of people are actually willing to share their data for better personalized offers and more efficient and intuitive services to get it better, better understanding of your customers, use all the data available to develop a complete view of your customer and, uh, and better serve them. Uh, this also breaks down, uh, costly silos, uh, shares data in, in accordance with privacy laws and assists with regulatory advice. It's so different organizations are going to be at different points in their data analytics and AI journey. >>Uh, there are several degrees of streaming and batch data, both structured and unstructured. Uh, you need a platform that can handle both, uh, with common, with a common governance layer, um, near real time. And, uh, real-time sources help make data more relevant. So if you look at this graphic, looking at it from left to right, uh, normal streaming and batch data comes from core banking and, uh, and lending operations data in pretty much a structured format as financial institutions start to evolve. Uh, they start to ingest near real-time streaming data that comes not only from customers, but also from, from newsfeeds for example, and they start to capture more behavioral data that they can use to evolve their models, uh, and customer experience. Uh, ultimately they start to ingest more real time streaming data, not only, um, standard, uh, sources like market and transaction data, but also alternative sources such as social media and connected sources, such as wearable devices, uh, giving them more, more data, better data, uh, to extract intelligence and drive personalized actions based on data in real time at the right time, um, and use machine learning and AI, uh, to drive anomaly detection and protect and predict, uh, present potential outcomes. >>So this is another way to look at it. Um, this slide shows the progression of the big data journey as it relates to a customer experience example, um, the dark blue represents, um, visibility or understanding your customer. So we have a data warehouse and are starting to develop some analytics, uh, to know your customer and start to provide a better customer 360 experience. Uh, the medium blue area, uh, is a customer centric or where we learn, uh, the customer's behavior. Uh, at this point we're improving our analytics, uh, gathering more customer centric information to perform, uh, some more exploratory, uh, data sciences. And we can start to do things like cross sell or upsell based on the customer's behavior, which should improve, uh, customer retention. The light blue area is, uh, is proactive customer inter interactions, or where we now have the ability, uh, to predict customers needs and wants and improve our interaction with the customer, uh, using applied machine learning and, and AI, uh, the Cloudera data platform, um, you know, business use cases require enabling, uh, the end-to-end journey, which we referred to as the data life cycle, uh, what the data life cycle, what is the data life cycle that our customers want, uh, to take their data through, to enable the end to end data journey. >>If you ask our customers, they want different types of analytics, uh, for their diverse user bases to help them implement their, their, their use cases while managed by a centralized security and governance later layer. Uh, in other words, um, the data life cycle to them provides multifunction analytics, uh, at each stage, uh, within the data journey, uh, that, uh, integrated and centralized, uh, security, uh, and governance, for example, uh, enterprise data consists of real time and transactional type type data. Examples include, uh, click stream data, web logs, um, machine generated, data chat bots, um, call center interactions, uh, transactions, uh, within legacy applications, market data, et cetera. We need to manage, uh, that data life cycle, uh, to provide real enterprise data insights, uh, for use cases around enhanced them, personalized customer experience, um, customer journey analytics next best action, uh, sentiment and churn analytics market, uh, campaign optimization, uh, mortgage, uh, processing optimization and so on. >>Um, we bring a diverse set of data then, um, and then enrich it with other data about our customers and products, uh, provide reports and dashboards such as customer 360 and use predictions from machine models to provide, uh, business decisions and, and offers of, uh, different products and services to customers and maintain customer satisfaction, um, by using, um, sentiment and churn analytics. These examples show that, um, the whole data life cycle is involved, um, and, uh, is in continuous fashion in order to meet these types of use cases, uh, using a single cohesive platform that can be, uh, that can be served by CDP, uh, the data, the Cloudera data platform. >>Okay. Uh, let's talk about, uh, some of the experiences, uh, from our customers. Uh, first we'll talk about Bunco suntan there. Um, is a major global bank headquartered in Spain, uh, with, uh, major operations and subsidiaries all over Europe and north and, and south America. Uh, one of its subsidiaries, something there UK wanted to revolutionize the customer experience with the use of real time data and, uh, in app analytics, uh, for mobile users, however, like many financial institutions send them there had a, he had a, had a large number of legacy data warehouses spread across many business use, and it's within consistent data and different ways of calculating the same metrics, uh, leading to different results. As a result, the company couldn't get the comprehensive customer insights it needed. And, uh, and business staff often worked on multiple versions of the truth. Sometime there worked with Cloudera to improve a single data platform that could support all its workloads, including self-service analytics, uh, operational analytics and data science processes, processing processing, 10 million transactions daily or 30,000 transactions per second at peak times. >>And, uh, bringing together really, uh, nearly two to two petabytes of data. The platform provides unprecedented, uh, customer insight and business value across the organization, uh, over 80 cents. And there has realized impressive, uh, benefits spanning, uh, new revenues, cost savings and risk reductions, including creating analytics for, for corporate customers with near real-time shopping behavior, um, and, and helping identify 7,000 new corporate, uh, customer prospects, uh, reducing capital expenditures by, uh, 3.2 million annually and decreasing operating expenses by, uh, 650,000, um, enabling marketing to realize, uh, 2.4 million in annual savings on, on cash, on commercial transactions, um, and protecting 3.7 million customers from financial crime impacts through 95, new proactive control alerts, improving risk and capital calculations to reduce the amount of money. It must set aside, uh, as part of a, as part of risk mandates. Uh, for example, in one instance, the risk team was able to release a $5.2 million that it had withheld for non-performing credit card loans by properly identifying healthy accounts miscategorized as high risk next, uh, let's uh, talk about, uh, Rabobank. >>Um, Rabobank is one of the largest banks in the Netherlands, uh, with approximately 8.3 million customers. Uh, it was founded by farmers in the late 19th century and specializes in agricultural financing and sustainability oriented banking, uh, in order to help its customers become more self-sufficient and, uh, improve their financial situations such as debt settlement, uh, rebel bank needed to access, uh, to a varied mix of high quality, accurate, and timely customer data, the talent, uh, to provide this insight, however, was the ability to execute sophisticated and timely data analytics at scale Rabobank was also faced with the challenge of, uh, shortening time to market. Uh, it needed easier access to customer data sets to ensure that they were using and receiving the right financial support at the right time with, with, uh, data quality and speed of processing. Um, highlighted as two vital areas of improvement, Rabobank was looking to incorporate, um, or create new data in an environment that would not only allow the organization to create a centralized repository of high quality data, but also allow them to stream and, uh, conduct data analytics on the fly, uh, to create actionable insights and deliver a strong customer experience bank level Cloudera due to its ability to cope with heavy pressures on data processing and its capability of ingesting large quantities of real time streaming data. >>They were able to quickly create a new data lake that allowed for faster queries of both historical and real time data to analyze customer loan repayment patterns, uh, to up to the minute transaction records, um, Robert bank and, and its customers could now immediately access, uh, the valuable data needed to help them understand, um, the status of their financial situation in this enabled, uh, rebel bank to spot financial disasters before they happened, enabling them to gain deep and timely insights into which customers were at risk of defaulting on loans. Um, having established the foundation of a modern data architecture Rabobank is now able to run sophisticated machine learning algorithms and, uh, financial models, uh, to help customers manage, um, financial, uh, obligations, um, including, uh, long repayments and are able to generate accurate, uh, current real liquidity. I refuse, uh, next, uh, let's uh, speak about, um, uh, OVO. >>Uh, so OVO is the leading digital payment rewards and financial services platform in Indonesia, and is present in 115 million devices across the company across the country. Excuse me. Um, as the volume of, of products within Obos ecosystem increases, the ability to ensure marketing effectiveness is critical to avoid unnecessary waste of time and resources, unlike competitors, uh, banks, w which use traditional mass marketing, uh, to reach customers over, oh, decided to embark on a, on a bold new approach to connect with customers via, uh, ultra personalized marketing, uh, using the Cloudera stack. The team at OVO were able to implement a change point detection algorithm, uh, to discover customer life stage changes. This allowed OVO, uh, to, uh, build a segmentation model of one, uh, the contextual offer engine Bill's recommendation algorithms on top of the product, uh, including collaborative and context-based filters, uh, to detect changes in consumer consumption patterns. >>As a result, OVO has achieved a 15% increase in revenue, thanks to this, to this project, um, significant time savings through automation and eliminating the chance of human error and have reduced engineers workloads by, by 30%. Uh, next let's talk about, uh, bank Bri, uh, bank Bri is one of the largest and oldest, uh, banks in Indonesia, um, engaging in, in general banking services, uh, for its customers. Uh, they are headquartered in, in Jakarta Indonesia, uh, PR is a well-known, uh, for its, uh, focused on micro-financing initiative initiatives and serves over 75 million customers through more than 11,000 offices and rural outposts, um, Bri needed to gain better understanding of their customers and market, uh, to improve the efficiency of its operations, uh, reduce losses from non-performing loans and address the rising concern around data security from regulators and consumers, uh, through enhanced fraud detection. This would require the ability to analyze the vast amounts of, uh, historical financial data and use those insights, uh, to enhance operations and, uh, deliver better service. >>Um, Bri used Cloudera's enterprise data platform to build an agile and reliable, uh, predictive augmented intelligence solution. Uh, Bri was now able to analyze 124 years worth of historical financial data and use those insights to enhance its operations and deliver better services. Um, they were able to, uh, enhance their credit scoring system, um, the solution analyzes customer transaction data, and predicts the probability of a customer defaulting on, on payments. Um, the following month, it also alerts Bri's loan officers, um, to at-risk customers, prompting them to take the necessary action to reduce the likelihood of the net profit lost, uh, this resulted in improved credit, improved credit scoring system, uh, that cut down the approval of micro financing loans, uh, from two weeks to two days to, to two minutes and, uh, enhanced fraud detection. >>All right. Uh, this example shows a tabular representation, uh, the evolution of a customer retention use case, um, the evolution of data and analytics, uh, journey that, uh, that for that use case, uh, from aware, uh, text flirtation, uh, to optimization, to being transformative, uh, with every level, uh, data sources increase. And, uh, for the most part, uh, are, are less, less standard, more dynamic and less structured, but always adding more value, more insights into the customer, uh, allowing us to continuously improve our analytics, increase the velocity of the data we ingest, uh, from, from batch, uh, to, uh, near real time, uh, to real-time streaming, uh, the volume of data we ingest continually increases and we progress, uh, the value of the data on our customers, uh, is continuously improving, allowing us to interact more proactively and more efficiently. And, and with that, um, I would, uh, you know, ask you to consider and assess if you are using all the, uh, the data available to understand, uh, and service your customers, and to learn more about, about this, um, you know, visit cloudera.com and schedule a meeting with Cloudera to learn more. And with that, thank you for your time. And thank you for listening.

>> Hi, I'm Peter Burroughs. And welcome to another cube conversation. This one is part of a very, very special digital community event sponsored by day trip. What we're going to be talking about today. Well, date comes here with a special product announcement that's intended to help customers do a better job of matching their technology needs with the speed and opportunities to use their data differently within their business. This is a problem that every single customer faces every single enterprise faces, and it's one that's become especially acute as those digital natives increasingly hunt down and take out some of those traditional businesses that are trying to better understand how to use their data. Now, as we have with all digital community events at the end of this one, we're gonna be running a crowd chat, so stay with us, will go through a couple of day trim and datum customer conversations, and then it'LL be your turn toe. Weigh in on what you think is important. Ask the questions of Data Room and others in the community that you think need to be addressed. Let's hear what you have to say about this increasingly special relationship between data technology and storage services. So without further ado, let's get it kicked off. Tim Page is the CEO of Datum. Tim, Welcome to the Cube. Thank you, Peter. So data give us a quick take on where you guys are. >> Yeah, Day tree ums formulated as a software to find converged infrastructure company that takes that converges to the next level. And the purpose of us is to give the user the same experience, whether you're working on Prem or across multi cloud. >> Great. So let's start by saying, that's the vision, but you've been talking a lot of customers. What's the problem that you keep hearing over and over that you're pointing towards? >> Yeah, it's funny. So it's so meet with the number CEOs over the years and specifically is related to a tree, and they'LL tell you they were on an on demand economy that expects instant outcomes, which means you have to digitally transform. And to do that, you've got to transform it, which means it's got to be easy. It's got to be consistent. You've got to get rid of a lot of the management issues, and it's got a feel and take advantage of the services that cloud has to offer. >> All right, so that's the nature of the problem. You've also done a fair amount of research looking into the specifics of what they're asking for. Give us some insight into what day terms discovering as you talk to customers about what the solutions are going to look like. >> It's interesting. So if you look at how to resolve that, you've got to conf urged to transform in some form or fashion. If you look at the first level of convergence a lot of people have done, it's been directly as relates the hardware architecture. We've taken that to a whole new level until Point were saying, How do you actually automate those mundane task that take multiple groups to solve specifically primary backup disaster recovery? All the policies involved in that is a lot of work that goes into that across multiple groups, and we set out to solve those issues, >> so there's still a need for performance. There's still the need for capacity to reduce management time and overhead etcetera. But Tim, as we move forward, how our customers responding this you're getting some sense of what percentage of them are going, Teo say Yeah, that's it >> s so interesting. So we could start a survey and got over five hundred people leaders to respond to it. It's interesting is they talk about performance management security, but they're also talking about consistency of that experience. And specifically, we asked how many of you is important to have your platform have built in backup and policy services with encryption built in et cetera. We got a seventy percent rate of those applicants of those those people interviewed saying is really important for that to be part of a plan. >> So it sounds like you're really talking about something Mohr than just a couple of products. You really talking about forcing customers or you're not forcing. Customers are starting the process of rethinking your data infrastructure, and I got that right. >> That's right. If you look at how infrastructure is grown in the last twenty years, right? Twenty years ago, san technology was related, and every time you throw open app, you had to put different policies that Apple put different one tight management to how much of my resources and go to certain things. We set out to actually automate that which is why it took us four years. To build this platform with a hundred programmers is, Well, how do we actually make you not think about how you're going to back up? How do you set a policy and no disaster recovery is going to run? And to do that, you've gotta have it one code base and we know we're on to something, even based on our survey, because the old array vendors are all buying Bolton's because they know users want an experience. But you can't have that experience with the ball time. You have to have it your fundamental platform. >> Well, let me let me step in here. So I've been around for a long time him and heard a lot of people talk about platforms. And if I have kind of one rule companies and introduce platforms that just expand, typically failed companies that bring an opinion and converge more things so it's simpler tend to be more successful. Which direction's date >> going? So we definitely That's why we took time, right? If you want to be an enterprise class company, you can't build a cheap platform in eighteen months and hit the market because were you, architect, you stay. So our purpose from the beginning was purposefully to spend four years building an enterprise clap platform that did away with a lot of the mundane task seeing management That's twenty years old. Technology right? One management. So if you're buying your multi cloud type technology experience in cages, you're just buying old stuff. We took an approach saying, We want that consistent approach that whether you're running your services on from or in any type of cloud, you could instantly take advantage of that, and it feels the same. That's a big task because you're looking to run the speed of storage with the resiliency of backup right, which is a whole different type of technology. Which is how our founders, who have built the first words in this went to the second, almost third version of that type of oven. Stan she ation of a platform. >> All right, so we know what the solution is going to look like. It's going to look like a data platform that's rethought to support the needs of data assets and introduces a set of converge services that really focus the value proposition to what the enterprise needs So what do you guys announcing? >> That's exactly right. So we've finalized what we call our auto matrix platform. So auto matrix in inherently In it we'LL have primary backup Disaster recovery D Our solution All the policies within that an encryption built in from the very beginning. Soto have those five things we believe toe actually have on the next generation experience across true multi cloud. You're not bolting on hardware technologies. You're bolting on software technologies that operate in the same manner. Those five things have to be an errand or you're a bolt on type company. >> So you're not building a platform out by acquisition. You build a platform out by architecture and development. >> That's right. And we took four years to do it with one hundred guys building this thing out. It's released, it's out and it's ready to go. So our first we're announcing is that first in Stan she ation of that as a product we're calling control shift, which is really a data mobility orchestrator. True sas based you could orchestrate from the prime from the cloud cloud to cloud, and our first generation of that is disaster recovery so truly to be able to set up your policies, check those policies and make sure you're going to have true disaster recovery with an Rto zero. It's a tough thing we've done it. >> That's upstanding. Great to hear Tim Page, CEO Data Room, talking about some of the announcement that were here more about in the second. Let's now turn our attention, Teo. A short video. Let's hear more about it. >> The bank is focused on small businesses and helping them achieve their success. We want to redesign the customer engagement in defining the bank of the future. This office is our first implementation of that concept, as you can see is a much more open floor plan design that increases the interaction between our lead bank associates and our clients with day tree and split provisioning. Oliver Data is now on the host, so we have seen eighty times lower application. Leighton. See, this gives our associates instant responses to their queries so they can answer client questions in real time. That time is always expensive in our business. In the past we had a forty eight hour recovery plan, but with the atrium we were able to far exceed that plan we've been able to recover systems in minutes now instead of backing at once per day with that backup time taking eighteen hours. Now we're doing full system snapshots our league, and we're replicating those offsite stay trim is the only vendor I know of that could provide this end to end encryption. So any cyber attacks that get into our system are neutralized with the data absolution. We don't have to have storage consultants anymore. We don't have to be stored. Experts were able to manage everything from a storage perceptive through the center, obviously spending less time and money on infrastructure. We continue to leverage new technologies to improve application performance and lower costs. We also want to animate RDR fail over. So we're looking forward to implementing daydreams. Product deloused orchestrate an automaton. RDR fell over process. >> It is always great to hear from a customer. Want to get on Peterborough's? This's a Cube conversation, part of a digital community event sponsored by Data Room. We were talking about how the relationship between the new digital business outcomes highly dependent upon data and the mismatch of technology to be able to support those new classes of outcomes is causing problems in so many different enterprises. So let's dig a little bit more deeply into someone. Tatum's announcements to try to find ways to close those gaps. We've got his already who's the CTO of data on with today, says all are welcome to the Cube, >> that being a good to see you again. >> So automate tricks give us a little bit more toe tail and how it's creating value for customers. >> So if you go to any data center today, you notice that for the amount of data they have their five different vendors and five different parts to manage the data. There is the primary storage. There is the backup on DH. There is the D R. And then there's mobility. And then there is the security or think about so this five different products, our kind of causing friction for you if you want to move, if you want to be in the undermanned economy and move fast in your business, these things are causing friction. You cannot move that fast. And so what we have done is that we took. We took a step back and built this automatics platform. It's provides this data services. We shall kind of quality that autonomous data services. The idea is that you don't have to really do much for it by converging all this functions into one simple platform that we let him with all the friction you need to manage all your data. And that's kind of what we call auto metrics that >> platform. So as a consequence, I gotta believe, Then your customers are discovering that not only is it simpler, easy to use perhaps a little bit less expertise required, but they also are more likely to be operationally successful with some of the core functions like D are that they have to work with. >> Yeah, So the other thing about these five five grandpre functions and products you need is that if you want to imagine a future, where you going, you know, leverage the cloud For a simple thing like the R, for example, the thing is that if you want to move this data to a different place with five different products, how does it move? Because, you know, all these five products must move together to some of the place. That's not how it's gonna operate for you. So by having these five different functions converge into one platform is that when the data moves between the other place, the functions move with it giving the same exist same exact, consistent view for your data. That's kind of what we were built. And on top of all the stuff is something we have this global data management applications to control the all the data you have your enterprise. >> So how are customers responding to this new architecture of autumn matrix converge services and a platform for building data applications? >> Yeah, so our customers consistently Teyla's one simple thing is that it's the most easiest platform there ever used in their entire enterprise life. So that's what we do aimed for simplicity for the customer experience. Autonomous data services give you exactly that experience. So as an example, last quarter we had about forty proof of concept sort in the field out of them, about thirty of adopted already, and we're waiting for the ten of the results to come out this quarter. So generally we found that a proof of concept don't come back because once you touch it, experience simplicity offered and how how do you get all this service is simple, then people don't tend to descend it back. They like to keep it and could have operated that way. >> So you mentioned earlier, and I kind of summarizing notion of applications, Data services, applications tell us a little bit about those and how they really toward a matrix. >> Right? So once you have data in multiple places, people have not up not a cloud. And we're going to also being all these different clouds and report that uniform experience you need this date. You need this global data management applications to extract value out off your data. And that's kind of the reason why we built some global data management applications. I SAS products, I think, install nothing to manage them. Then they sit outside and then they help you manage globally. All the data you have. >> So as a result, the I and O people, the destruction operations administrators, I can think of terms of automata whose platform the rest of the business could look at in terms of services and applications that through using and support, >> that's exactly right. So you get the single dashboard to manage all the data. You have an enterprise >> now I know you're introducing some of these applications today. Can you give us a little peek into? Yeah. >> Firstly, our automatics platform is a soft is available on prime as a software defined converge infrastructure, and you can get that we call it D V X. And then we also offer in the cloud our services. It's called Cloud Devi Ex. You could get these and we're also about kind off announcing the release ofthe control shift. It's over for one of our first date. Imagine applications, which kind of helps you manage data in a two different locations. >> So go over more specific and detail in the control shift. Specifically is which of those five data services you talk about is control shift most clearly associating with >> right. So if you go toe again back to this question about like five different services, if you have to think about B r o D. R. Is a necessity for every business, it's official protection. You need it. But the challenge is that you know that three four challenges you gently round into the most common people talk about is that one is that you have a plan. You'LL have a proper plan. It's challenging to plan something, and then you think about the fire drill. We have to run when there's a problem. And then last leaving actually pushed the button. Tofail over doesn't really work for you. Like how fast is it going to come up? So those three problems we can have one to solve really like really solidly So we call our service is a dear services fail proof tr that's actually takes a little courage to say fail proof. So control shift is our service, which actually does this. The orchestration does mobility across the two different places from could be on prime time on Prem on prompted the cloud. And because we have this into end data services ourselves, the it's easy to then to compliance checks all the time so we could do compliance checks every few minutes. So what that gives you that? Is that the confidence that that your dear plan's going to work for you when you need it? And then secondly, when you push the button because you also prime restoration back up, it's then easy to bring upon your services at once like that, and the last one is that because we are able to then work across the clouds and pride, the seamless experience. So when you move the data to the cloud, have some backups there. When you push a button to fail over, we'LL bring up your services in via MacLeod so that the idea is that it look exactly the same no matter where you are in the D. R or North India and then, you know, you watch the video, watch the demos. I think they look and see that you can tell the difference. >> Well, that's great. So give us a little bit of visibility into how day Truman intends to extend these capabilities, which give us a little visibility in the road map. Next. >> So we are already on Amazon with the cloud. The next time you're gonna be delivering his azure, that's the next step. But But if I step back a little bit and how do we think about our ourselves? Like if you look at his example Google, Google, you know, fairies, all the data and Internet data and prizes that instant search for that instant like an access to all the data you know, at your finger finger tips. So we wanted something similar for enterprise data. How do we Federated? How do we aggregate data and the property? The customer, the instant management they can get from all the data. They have already extract value from the data. So those things are set off application We're building towards organic scum. Examples are we're building, like, deep search. How do you find the things you want to find? You know, I've been a very nice into to weigh. And how do you do Compliance? GPR. And also, how do you think about you know, some dependent addicts on the data? And so we also extend our control shift not to just manage the data on all platforms. Brawls hardly manage data across different platforms. So those kind of things they're thinking about as a future >> excellent stuff is already CTO daydream. Thanks very much for talking to us about auto matrix control shift and the direction that you're taking with this very, very extreme new vision about data on business come more easily be bought together. So, you know, I'll tell you what. Let's take a look at a demo >> in today's enterprise data centers. You want a simple way to deal with your data, whether in the private or public cloud, and ensure that dealing with disaster recovery is easy to set up. Always complied and in sync with the sites they address and ready to run as the situations require built on consistent backups, allowing you to leverage any current or previous recovery point in time with near zero rto as the data does not have to be moved in order to use it. Automated orchestration lets you easily test or execute recovery plans you have constructed with greater confidence, all while monitoring actions and progress from essential resource. This, along with maintaining comprehensive run books of these actions, automatically from the orchestration framework. Managing your Systems Day Tree in autumn matrix provides this solution. Run on local host flash and get the benefits of better performance and lower. Leighton sees back up and protect your data on the same converged platform without extracting it to another system while securing the data in your enterprise with end and encryption automating salas desired for your business needs with policy driven methods. The capture the what, when and where aspects of protecting your data, keeping copies locally or at other sites efficiently Move the data from one location to another weather in your private or public cloud. This is the power of the software defined converged infrastructure with cloudy are from day tree, um, that we call Oughta Matrix. >> Hi. And welcome back to another cube conversation once again on Peter Births. And one of the biggest challenges that every user faces is How did they get Mohr out of their technology suppliers, especially during periods of significant transformation? Soto have that conversation. We've got Brian Bond, who's the director of infrastructure? The meter A seaman's business. Brian, welcome to the Cube. >> Thanks for having me. >> So tell us a little about the meteor and what you do there. >> So E Meter is a developer and supplier of smart grid infrastructure software for enterprise level clients. Utilities, water, power, energy and, ah, my team was charged with managing infrastructure for that entire business units. Everything from Deb Test Q and sales. >> Well, the you know, the intelligent infrastructure as it pertains to electronica rid. That's not a small set of applications of small city use cases. What kinds of pressure is that putting on your infrastructure >> A lot of it is the typical pressures that you would see with do more with less doom or faster. But a lot of it is wrapped around our customers and our our other end users in needing more storage, needing Mohr at performance and needing things delivered faster on a daily basis. Things change, and keeping up with the Joneses gets harder and harder to do as time moves on. >> So as you think about day trims Auto Matrix. How is it creating value for you today? Give us kind of, Ah, peek into what it's doing to alleviate some of these scaling and older and researcher pressures, >> So the first thing it does is it does allow us to do a lot more with less. We get two times the performance five times the capacity, and we spend zero time managing our storage infrastructure. And when I say zero time I mean zero time, we do not manage storage anymore. With the data in product, we can deploy thanks faster. We can recover things faster are Rto and R R P. O matrix is down two seconds instead of minutes or hours, and those types of things really allow us to provide, Ah much better level of service to our customers. >> And it's especially critical. Infrastructure like electronic grid is good to hear. That the Rto Harpo is getting is close to zero as possible. But that's the baseline today. Look out and is you and vision where the needs of these technologies are going for improving protection, consolidating converging gated services and overall, providing a better experience from a business uses data. How do you anticipate that you're goingto evolve your use of autumn matrix and related data from technologies? >> Well, we fully intend to to expand our use of the existing piece that we have. But then this new autumn matrix piece is going to help us, not witches deployments. But it's also going to help us with compliance testing, data recovery, disaster recovery, um, and also being able to deploy into any type of cloud or any type of location without having to change what we do in the back in being able to use one tool across the entire set of the infrastructure they were using. >> So what about the tool set? You're using the whole thing consistently, but what about the tool set when in easiest for you within your shop, >> installing the infrastructure pieces themselves in its entirety. We're very, very easy. So putting that into what we had already and where we were headed was very, very simple. We were able to do that on the fly in production and not have to do a whole lot of changes with the environments that were doing at the time. The the operational pieces within the D. V X, which is this the storage part of the platform were seamless as far as V Center and other tools that we're using went and allowed us to just extend what we were doing already and be able to just apply that as we went forward. And we immediately found that again, we just didn't manage storage anymore. And that wasn't something we were intending and that made our r I just go through the roof. >> So it sounds like time to value for the platform was reserved for quick and also it fit into your overall operational practices. So you have to do a whole bunch of unnatural acts to get >> right. We did not have to change a lot of policies. We didn't have to change a lot of procedures, a lot of sounds. We just shortened. We took a few steps out on a lot of cases. >> So how is it changing being able to do things like that, changing your conversation with your communities that you're serving a CZ? They asked for more stores where they ask for more capabilities. >> First off, it's making me say no, a lot less, and that makes them very, very happy. The answer usually is less. And then the answer to the question of how long will it take changes from? Oh, we can get that done in a couple of days or, oh, we can get that done in a couple hours to I did that while I was sitting here in the meeting with you, and it's it's been handled and you're off to the races. >> So it sounds like you're police in a pretty big bed and a true, uh, what's it like? Working with them is a company. >> It's been a great experience from from the start, in the initial piece of talking to them and going through the POC process. They were very helpful, very knowledgeable SCS on DH, and since then They've been very, very helpful in allowing us to tell them what our needs are, rather than them telling us what our needs are and going through and working through the new processes and the and the new procedures within our environments. They've been very instrumental and performance testing and deployment testing with things, uh, that a lot of other storage providers didn't have any interest in talking with us about so they've been very, very helpful with that and very, very knowledgeable people that air there are actually really smart, which is not surprising. But the fact that they can relay that into solutions to what my actual problems are and give me something that I can push forward on to my business and have ah, positive impact from Day one has been absolutely, without question, one of the better things. >> Well, it's always one of the big, biggest challenge when working with a company that just getting going is how do you get the smarts of that organization into the business outcomes that really succeeded? Sounds like it's working well. Absolutely. All right. Brian Bond, director Vital infrastructure demeanor, Seaman's business Thanks again for being on the Cube >> has been great >> on. Once again, this has been a cube conversation, and now what we'd like to do is don't forget this is your opportunity to participate in the crowd. Chat immediately after this video ends and let's hear your thoughts. What's important in your world is you think about new classes of data platforms, new rules of data, new approaches to taking great advantage of the data assets that air differentiating your business. Have those conversations make those comments? Asked those questions. We're here to help. Once again, Peter Bourjos, Let's go out yet.