(soft upbeat music) >> Okay, we're kicking things off with Brian Gilmore. He's the director of IoT, an emerging technology at InfluxData. Brian, welcome to the program. Thanks for coming on. >> Thanks, Dave, great to be here. I appreciate the time. >> Hey, explain why InfluxDB, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >> No, no, not at all. I mean, I think, for us it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like SQL query support, things like that, we have to figure out a way to execute those for them in a way that will scale long term. And then we also want to make sure we're innovating, we're sort of staying ahead of the market as well, and sort of anticipating those future needs. So, you know, this is really a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine. But, you know, initially, the customers who are using us are going to see just great improvements in performance, you know, especially those that are working at the top end of the workload scale, you know, the massive data volumes and things like that. >> Yeah, and we're going to get into that today and the architecture and the like. But what was the catalyst for the enhancements? I mean, when and how did this all come about? >> Well, I mean, like three years ago, we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product. And sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was a long journey. (chuckles) I guess, you know, phase one was, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to optimize for like multi-tenant, multi-cloud, be able to host it in a truly like SAS manner where we could use, you know, some type of customer activity or consumption as the pricing vector. And that was sort of the birth of the real first InfluxDB cloud, you know, which has been really successful. We've seen, I think, like 60,000 people sign up. And we've got tons and tons of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a daily basis. And having that sort of big pool of very diverse and varied customers to chat with as they're using the product, as they're giving us feedback, et cetera, has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that, and then also making these big leaps as we're doing with this new engine. >> All right, so you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really want to understand how much of a pivot this is, and what does it take to make that shift from, you know, time series specialist to real time analytics and being able to support both? >> Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. Time series data is always going to be fundamental in sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. The time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics. If we're being honest though, I think our user base is well aware that the way we were architected was much more towards those sort of like backwards-looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a time to response on the queries, and can we get that to the point where the result sets are coming back so quickly from the time of query that we can like, limit that window down to minutes and then seconds? And now with this new engine, we're really starting to talk about a query window that could be like returning results in, you know, milliseconds of time since it hit the ingest queue. And that's really getting to the point where, as your data is available, you can use it and you can query it, you can visualize it, you can do all those sort of magical things with it. And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the real time queries, the multiple language query support. But, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a limited number of customers, strategic customers and strategic availabilities zones to start, but, you know, everybody over time. >> So you're basically going from what happened to, and you can still do that, obviously, but to what's happening now in the moment? >> Yeah. Yeah. I mean, if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the underlying data collection, the architecture, the infrastructure, the devices, and you know, the sort of highly distributed nature of all of this. So, yeah, I mean, getting a customer or a user to be able to use the data as soon as it is available, is what we're after here. I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >> Yeah, I mean, it is operationally, or operational real time is different. And that's one of the things that really triggered us to know that we were heading in the right direction is just how many sort of operational customers we have, you know, everything from like aerospace and defense. We've got companies monitoring satellites. We've got tons of industrial users using us as a process historian on the plant floor. And if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're going to do here is we're going to start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their historians and databases. >> Is this available, these innovations to InfluxDB cloud customers, only who can access this capability? >> Yeah, I mean, commercially and today, yes. I think we want to emphasize that for now our goal is to get our latest and greatest and our best to everybody over time of course. You know, one of the things we had to do here was like we doubled down on sort of our commitment to open source and availability. So, like, anybody today can take a look at the libraries on our GitHub and can inspect it and even can try to implement or execute some of it themselves in their own infrastructure. We are committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. And so just, you know, being careful, maybe a little cautious in terms of how big we go with this right away. Just sort of both limits, you know, the risk of any issues that can come with new software roll outs, we haven't seen anything so far. But also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products. But once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's going to be exciting time for the whole ecosystem. >> Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are going to help deliver on this vision. What should we know there? >> Well, I mean, I think, foundationally, we built the new core on Rust. This is a new very sort of popular systems language. It's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well, and if it does find error conditions. I mean, we've loved working with Go, and a lot of our libraries will continue to be sort of implemented in Go, but when it came to this particular new engine, that power performance and stability of Rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parquet for persistence. I think, for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our time series merge trees, this is a big break from that. You know, Arrow on the sort of in mem side and then Parquet in the on disk side. It allows us to present, you know, a unified set of APIs for those really fast real time queries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that Parquet format, which is also cool because there's an entire ecosystem sort of popping up around Parquet in terms of the machine learning community. And getting that all to work, we had to glue it together with Arrow Flight. That's sort of what we're using as our RPC component. It handles the orchestration and the transportation of the columnar data now, we're moving to like a true columnar database model for this version of the engine. You know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like, blurring that line between real time and historical data, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >> Yeah, again, I mean, it's funny. You mentioned Rust. It's been around for a long time but it's popularity is, you know, really starting to hit that steep part of the S-curve. And we're going to dig into more of that, but give us, is there anything else that we should know about, Brian? Give us the last word. >> Well, I mean, I think first, I'd like everybody sort of watching, just to like, take a look at what we're offering in terms of early access in beta programs. I mean, if you want to participate or if you want to work sort of in terms of early access with the new engine, please reach out to the team. I'm sure, you know, there's a lot of communications going out and it'll be highly featured on our website. But reach out to the team. Believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to, because we can flip a lot of stuff on, especially in cloud through feature flags. But if there's something new that you want to try out, we'd just love to hear from you. And then, you know, our goal would be, that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to sort of build the next versions of your business. Because, you know, the whole database, the ecosystem as it expands out into this vertically-oriented stack of cloud services, and enterprise databases, and edge databases, you know, it's going to be what we all make it together, not just those of us who are employed by InfluxDB. And then finally, I would just say, please, like, watch and Anais' and Tim's sessions. Like, these are two of our best and brightest. They're totally brilliant, completely pragmatic, and they are most of all customer-obsessed, which is amazing. And there's no better takes, like honestly, on the sort of technical details of this than theirs, especially when it comes to the value that these investments will bring to our customers and our communities. So, encourage you to, you know, pay more attention to them than you did to me, for sure. >> Brian Gilmore, great stuff. Really appreciate your time. Thank you. >> Yeah, thanks David, it was awesome. Looking forward to it. >> Yeah, me too. I'm looking forward to see how the community actually applies these new innovations and goes beyond just the historical into the real time. Really hot area. As Brian said, in a moment, I'll be right back with Anais Dotis-Georgiou to dig into the critical aspects of key open source components of the InfluxDB engine, including Rust, Arrow, Parquet, Data Fusion. Keep it right there. You don't want to miss this. (soft upbeat music)

>>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.

(upbeat music) >> Hi, I'm Joe Rodriguez, Managing Director of Financial Services at Cloudera. Welcome to the Fight Fraud with Data session. At Cloudera we believe that fighting fraud begins with data. So financial services is Cloudera's largest industry vertical. We have approximately 425 global financial services customers, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks. Four out of the five top stock exchanges, 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 utilize Cloudera for data analytics and machine learning. 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 that our customers are coming up with. Criminals can steal thousands of dollars before a fraudulent transaction is detected. So the cost to purchase your account data is well worth the price to fraudsters. According to Experian, credit and a 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, complete account numbers, and other personal data. 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 huge and growing exponentially. All this data needs to be evaluated in real time. There are new sources of streaming data that need to be integrated with existing legacy data sources. This includes biometrics data and enhanced authentication video surveillance, call center data, and of course all that needs to be integrated with existing legacy data sources. There is an analytics Arms Race between the banks and the criminals, and the criminal networks never stop innovating. They also have to deal with disjointed security and governance. Security and governance policies are often set per data source or application requiring redundant work across workloads. And they have to deal with siloed environments. The specialized nature of platforms and people results in disparate data sources and data management processes. This duplicates efforts and divides the business risk and crime teams, limiting collaboration opportunities between them. CDP enhances financial crime solutions to be holistic by eliminating data gaps between siloed solutions, with an enterprise data approach, advanced data analytics and machine learning. 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, you tighten up the loop between detection and new fraud patterns. Cloudera provides the data platform on which a best of breed applications can run and leverage integrated machine learning. Cloudera stands rather than replaces your existing fraud modeling applications. So Oracle, SAS, Actimize, to name a few, integrate with an enterprise data hub to scale the data, increase 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 Customer 360 for example. I just wanted to highlight a couple of our partners in financial crime prevention, Simudyne and Quantexa. So Simudyne provides fraud simulation using agent-based modeling machine learning techniques to generate synthetic transaction data. This data simulates potential fraud scenarios in a cost-effective GDPR-compliant virtual environment to significantly improve financial crime detection systems. Simudyne identifies future fraud topologies for millions of simulations that can be used to dynamically train new machine learning algorithms for enhanced identification. And Quantexa connects the dots within your data using dynamic entity resolution, and advanced network analytics to create context around your customers. This enables you to see the bigger picture and automatically assesses potential criminal behavior. Now let's go over some of our customers and how they're using Cloudera. First, we'll talk about United Overseas Bank or UOB. UOB is a leading full service bank in Asia with a network of more than 500 offices in 19 countries and territories, in Asia Pacific, Western Europe and North America. 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. UOB set up it's big data analytics center in 2017. It was Singapore's first centralized big data unit within a bank to deepen the bank's data analytic capabilities and to use data insights to enhance the bank's 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 voice and text. Using Cloudera CDP and machine learning, UOB gained a richer understanding of its customer preferences to help make 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 self-service analytics, machine learning and emerging artificial intelligence solutions. With new self-service analytics and machine learning driven insights, UOB has realized improvements in digital banking, asset management, compliance, AML, and more. Advanced AML detection capabilities, help analysts detect suspicious transactions either based on hidden relationships of shell companies and high risk individuals with Cloudera and machine learning technologies, UOB was able to enhance AML detection and reduce the time to identify new links from months to three weeks. Next, let's speak about MasterCard. So MasterCard's principle business is 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 deep insights and best practices and big data security and compliance. Next, let's speak about Bank Rakyat in Indonesia or BRI. BRI is one of the largest and oldest banks in Indonesia and engages in the provision of general banking services. It's headquartered in Jakarta, Indonesia. BRI is well-known for its focus on microfinancing initiatives and serves over 75 million customers through its more than 11,000 offices and rural service outposts. BRI required better insight to understand customer activity and identify fraudulent transactions. 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. BRI used Cloudera Enterprise data platform to build an agile and reliable, predictive augmented intelligence solution to enhance its credit scoring system. And to address the rising concern around data security from regulators and customers, BRI developed a real-time fraud detection service powered by Cloudera and Kafka, BRI's data scientists developed a machine learning model for fraud detection by creating a behavioral scoring model based on customer savings, loan transactions, deposits, payroll and other financial real-time data. This led to improvements in its fraud detection and credit scoring capabilities, as well as the development of a new digital microfinancing product. With the enablement of real-time fraud detection, BRI was able to reduce the rate of fraud by 40%. It improved relationship manager productivity by two and a half fold. It improved the credit 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 data focus if you haven't already. It offers a quick return on investment and it's a focused area that's not too entrenched across the company. To learn more about fraud prevention, go to www.cloudera.com, and you should schedule a meeting with Cloudera to learn even more. And with that, thank you for listening and thank you for your time. (upbeat music)

>>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.

>>from >>around the globe. It's the Cube with digital coverage of AWS reinvent >>2020 sponsored >>by Intel, AWS and our community >>partners. >>Hi. And welcome to the cubes coverage of AWS reinvent 2020. I'm your host, Justin Warren. And today I am joined by two lovely gentlemen. We have Brian Cahill from a company called Frog Slur, which is interesting. And we also have Chad Kenny from Clooney. Oh, gentlemen, welcome to AWS reinvent 2020 Chad, It's bean about what A year since I think we last spoke at at reinvent last year. Why don't you catch us up on what's been happening in the last year of the Korean Times >>s? Um we're excited to be here. Justin, thanks so much for the introduction and hosting us. So it's been an exciting action back here. I will say we've had a bunch of new innovations. I think last time we talked, we were just getting our first native solution inside of AWS for EBS. And since then we've evolved the dissolution dramatically. Claudio is ah, secure backup is a service offering for the enterprise, and this allowed us to be able to scale from just EBS into being the industry's first platform to go across public, private and SAS all in one service, >>and >>we innovated within AWS a ton. So we expanded from CBS Thio, Easy to and RDS. We brought in one of the most native services Outside of snapshots. We kind of progress the enterprise from the traditional snapshot primitive into a true enterprise class Back up on built in a time series Data Lake that allows, you know, enterprises to decouple their data from the infrastructure and really be able to provide tons of value into the future. So it's an exciting time for us. Toe, you know, really bring new innovative solutions to the market. >>That's an impressive amount of work given whatever else has been going on in the last 12 months, Teoh be able to ship that much stuff. You've been really, really busy. Um, brought Brian on now. Brian Frog Slayer. Tell me. Tell me a bit about the background for the name of the company they >>frogs layer. The name actually came from a initial founder who, you know, was trying to protect the animals, wanted to take care of nature and stuff and actually stepped on frog. So you got nicknamed by his buddies frogs here and that, then became the company name. >>So tell us about frogs layer. What is it that and your role there. What is it the Frog Slayer does? And what's your role there? >>Frogs there does business consulting. And then we developed custom software star goals to help businesses get past ah, hurdle. So a growth business that's that's kind of stuck make them more efficient, more productive thing kind of move to the next level. And my role here is the head of I t. That custom software rebuild we host for our clients. And so we try to offer to them is a SAS solution. So it's not only a custom software, but it's kind of offered a SAS solution them to consume. >>Terrific. So >>how long has >>the relationship with Clooney I've been going on? >>It's been about four months now, >>all right. And how did you get introduced Thio chat on the team in Colombia? >>Um, we started with AWS writing our own backup scripts and as we started to move more of their past services like RDS and then RDS went to serve Earless and Aurora the You just have to keep upgrading and changing and tweaking your scripts. And so we started looking round to say, Is there, uh is there a software we could use instead of doing this ourselves? And so through a bar, we got connected with Clooney? Oh, we're checking out a whole bunch of solutions. And most of them were snapshot managers just using the a p i s to do the same things we were doing. Whereas Clooney I was doing it totally differently where they would actually take a snapshot and then rehydrate it, take that data and then make it more like a traditional backup where you could d duplicate it and save on costs and stuff. >>Right? Okay, so, Chad, is that something that you've been? Is that one of the many features that you've added in the last 12 months? Or is this something that a little more fundamental to the way Columbia works? It's >>very fundamental. I think what we're doing is both doing efficiencies around the data itself. So do you do compression and, of course, security around encryption. But we ingest the data index and catalog it on, then make it so that customers could get fine grained granularity for how they restore even down to the database record. And so one of the big things that we've seen, especially in Cloud First customers such as frogs Layer is they're really trying to use either the native tools to start with or build your own type. Models on the costs increased dramatically. The complexity of not having a catalog and index make restores incredibly hard. Andi. It just becomes, ah, much more painful model of hidden costs, left and right. And so what we wanted to do was really provide unique simplicity to be able to protect all of the AWS accounts and even all of the data assets across clouds in one single pane of glass and give a user experience that was dramatically different than having to run very scripts or build your own or have a tool on prim and have a different tool for this cloud versus another cloud. And by having this consolidated index obviously drive a ton of value around leverage from the data, >>Hmm, >>interesting. So, Brian, you mentioned that this is your relationship with Colombia has Bean only about sort of four months that sort of smack in the middle of the pandemic that's been going on here was Was that a trigger for you looking at alternate options? Or were Or is this something that you've been planning for a while? >>No. This has been on a road map for a little while. Um, just as we start using more AWS services and trying to figure out how do we scale what we're doing? Um, we're looking for Mormon Enterprise Backup. But then, as we looked around most the backup solutions, you end up hosting the software upgrade in the software and maintaining things on. >>Have you noticed a major change since you've been using Colombia? >>Yes, What Cuneo offered was the ability to because it's a fast solution. It's a There's an air gap between us and the backup, so I'm not hosting the backups or the data. It's in a separate account, and I can't even delete it. So there's kind of a protection level that someone who are and can't accidentally delete the stuff we're backing up >>right? And one thing that I've noticed is in the news a lot more over the last couple of days. But it's certainly been hitting a lot this year is the idea of ransomware. So a lot of customers that certainly that I speak to have been quite concerned that what's going on with that? So how are you Brian addressing that within your organization? Do you feel comfortable that you're well protected and what else are you looking at? But you're trying to protect yourselves from >>right when it comes to ransom, where we try to have our client data in such a way that no one person can access or delete all of it. And so that's where we initially had separate AWS accounts for every client and with Columbia we now have Colonial maintains that separation. So they're keeping that air gap for us. And then, you know, we're doing our own stuff internally. Just make sure we don't get something. But the backups, including our kind of that second step for say something, gets past all of our safeguards. We've got another safeguard in place that >>sounds pretty prudent. So, Chad, is that is that something that you're hearing from a lot of customers? The need for this separation of powers within the system? >>Yeah, it's coming up quite often. And I think one of the big challenges here is to deliver an air gap solution with other types of data protection products. Whether it's on primer in the cloud have a ton of complexity to it, whether you're buying a separate appliance and you have to create a network air gap or whether you're actually replicating from one AWS account into another AWS account, the cost just double. And so what we built in was a system that not only is immutable, but as Brian mentioned, there's no ability to actually delete the data because the timeto live for the data that's persisted is defined by the policy. And so if a bad actor was to get into the environment, there's no way that they could potentially go into our system and actually delete anything. But if you look at like AWS as an example, if most customers they're storing snapshots inside their account as a hole on theirs, vulnerabilities even beyond, you know, ran somewhere and just on accident or a bad actor even inside the environment that's not even ran somewhere. And so protecting that is one of the key capabilities of the platform where We're outside of the service outside of the cloud, in many cases to protect the customer's data on make sure that they can restore it to any account in the event that even a bad actor gets access to it. Yeah. So, Brian, one thing >>that I like to ask customers about, particularly and cloud services is they've changed the way that we do things. And why Why we started using cloud is often not what we're actually using it for today. So with respect to Cuneo and your services that you're running in cloud, what's something that you've noticed that you're now doing? That surprises you? One of those added bonus is that you weren't really expecting. Have you seen anything like that? As you've managed Thio to start using Clooney Oh, that did everything that you wanted it to do. And now you're finding there's these new opportunities. >>Yeah. One of the big advantages of Colombia was when we took snapshots and replicated them out of the source AWS account. It's like in the source account. There was d duplication enabled. Once you replicated to another AWS account, it re hydrates the snapshot. So everyone takes up the full amount of space And to start hitting this like, how much data do I retain versus like, Oh, this is really expensive. I should like, you know, lower my retention. And we just that totally went away with Clooney. Oh, and then as far as the cloud is, the whole what's cool is that they're kind of more past services. So rds where I don't maintain, you know, patches on the O. S or on the sequel or yours, um, application service where you're not maintaining the OS. That's kind of moving at the next level up faras less less that you're maintaining your more maintaining your code in your application, >>right? And how important is the cloud native capability of Columbia? There's plenty of backup solutions around, and we've We've had them for many years because data protection is not a new idea. Ah, lot of a lot of what other side now cloud native. We try to put things into the cloud first. How important is it? Toe have something which understands cloud native >>and it basically means they're totally aware of what we're doing. And so they're not trying to take an old solution and make it fit in the cloud. They built it for the cloud from the ground up. So when you get in there user interface, there's not all of these old buttons and knobs and stuff. It's very simple. It's a policy, a tag. And then inside the account, the tag grabs objects. So they've made a very simple user interface that's saves a lot of time on implementation. >>Excellent. What are some of the things that you're looking to do in the future now that you've better things in and you've now got four months of solid experience with the product? What are you anticipating that you're going to be doing next? >>Um, we're excited about We're starting. But some are customers in a jurors cloud with Clooney was developing capabilities for that, and then Colombia is also working on capabilities for some of our business applications. So the idea of having all of our kind of backups in one place and less separate buckets you've got to go manages exciting. >>Yes, so Chad multi cloud hybrid cloud. Their words sort of called to be the controversy over the over the years. It does certainly sound like a lot of customers they're using, or at least exploring multiple, different options on Certainly for yourselves, you'll have customers who exist in in one cloud and others that will be in a different one. So how are you addressing the idea of of hybrid cloud and multi cloud? >>Great question. So our belief is that data is going to disperse itself Mawr and Mawr, especially as time goes on and there's multiple faces, this kind of cloud adoption that we see we see kind of, you know, the initial lift into Public Cloud, which kind of created that first hybrid example than theirs. You know the optimization within the clouds, so they're looking for cost reduction and operational izing. And then it's kind of like looking at ways of how doe I utilize different clouds for different things that may be mawr operationalized arm or optimized than others. And so we really believe in this world of creating a single platform or fabric that goes and expands across all clouds, consolidates and index and catalog into one view for the end user, and allows them to be able to push data to any cloud that they need to longer term. And at the same time, protect it. The fun part about migrations is yeah, you could move data, but when you're protecting it at the same time to it allows you to actually keep your production up and running, restore a dev environment somewhere else to play around with it and do it in multiple different potential clouds on then have that initial data that's still fully protected in your environment. And so I'd say that the protection side is a really cool on. The second one is Brian mentioned was the whole Data Lake concept that sits behind where we decouple the data from the infrastructure and with past services. This is incredibly important because, let's say, a year and a half from now, the database engines not even supported with the snapshot that you have left over in your account you've been retaining, you've not got to go through the process of upgrading and getting it up to the rev toe actually even get it working in our world, we create logical backups of those data sets, and they're instantaneously available for direct query access, even right in the gooey. And so now this decoupling of infrastructure brings significant value, right now but into the future. This opens up opportunities to be able to do et al pipelines and actually levers the data well beyond back up into other use cases, >>sort of to finish up looking forward. Always, like Thio have a bit of a view of what the future future holds. Its one of my favorite parts of being at reinvent is we get to see the new technology and and what the possibilities are for for what we could use. It takes something, take it home, have a bit of a play with it and and see what we could do for next year. So but if you Brian, we'll start with you. What are you looking forward to in 2021? What do your your future plans? >>Looking forward to migrating mawr of our stuff toe platform as a service offerings where we're taking advantage of the fact that the cloud has built some of the base layers and we could just build on top of that and then the second one that's exciting is the scalability. So with a B, A s, a server lists and the other land and different things that they're running out where we don't need to run physically. See two instances, air always on databases, but things that can scale up and down based on our client workload. That's just exciting as far as our infrastructure and and just the ability for cost savings, but also that just just in time, scaling for our customer demand >>and chad yourselves at Columbia What what can we Can you give us a hint of what we we might see in 2021 from Clooney? Oh, >>yes. So the first thing I'd say that I'm most excited about any New Year is just seeing the advantages customers get with the platform, right? Like we did a lot of innovation during this time. I'd say Cove, it had, you know, some benefits and some downsides from just company growth and, you know, not being close together and having that feeling. But we innovated incredibly quickly, and we were heads down and highly efficient, and eso I'm excited about really showcasing a lot of the innovation that we built during this year, and I think our customers are moving to the cloud faster than ever. And so I'm excited toe to see a lot of that. What you'll see from us is more and more innovation outside of just, you know, the traditional realm. Changing the user experience dramatically with new innovations, which sounds kind of broad. But think of it as creating more and more of that fabric. We're going to get into new public clouds. We're going to get into new SAS services. We're going to expand the user experience in the core platform for recover ability, for security, for enabling easy work flows for various different use cases. And so I'm excited about taking the data and really leveraging it into multiple different use cases outside of data protection on into the future. >>Well, it sounds like we have a lot to look forward to from Cuneo. I I personally look forward to hearing more about it. Hopefully we get to catch up. Ah, little bit earlier, Not not quite. Wait a full 12 months between reinvents, but if not, we'll definitely be seeing you again next year and and hearing about all of the new innovations that you've managed to come up with. You've got 12 months. There's plenty of time. Yeah, definitely Awesome. Sorry. Thank you very much. Brian Brian Kale from Frogs Layer and Pritchard, Kenny from Clooney. Oh, did my guest today. I've been Justin Warren for the Cube and all of our coverage here for AWS reinvent 2020. Do check out all the rest of the videos on. We will see you next time. >>Take care, Yeah.

>>Live from Washington, D C it's the cube covering Boomi world 19 how to bide booming. >>Welcome to the cube at Lisa Martin at Boomi world 19 in Washington DC and with John furrier and John and I are pleased to welcome to the cube Jason Maynard, the SVP of global field operations from NetSuite. Jason, welcome. Thanks for having me. It's great to be in D C and on the cube. It is. We were just talking about baseball, so we'll have to park that for a second and talk about some other sexy stuff besides baseball, ERP. So nets we, I saw you on stage this morning. You guys have been a partner of the first Alliance partner with Boomi for about 12 years. Thousands of joint customers. candy.com is one of them. Yep. They're going to be on later today. So I'm excited to have my afternoon sugar rush. Make sure he brings a big bag. You got it. So talk to us about you guys. We're also, I noticed Boomie's 2019 Alliance partner of the year. Lots of innovations going on. Give our audience a little bit of an overview of what NetSuite is doing with Boomi. >>Great. So Boomi is, has been one of our longest partners. I said I think we, we first inked the partnership in 2007 so it goes back 12, 13 years. Um, we, we, when we sell ERP, you always end up having to connect to a legacy on prem system, right? Or you may have to connect to new marketplaces to sell and so there's always need for integration. And so from day one, Boomi wanted to really kind of push the envelope work with cloud players. You know, when we started NetSuite 20 years ago, it was kinda crazy to put business applications on the internet and they'd been there from day one with us really on this journey. And so they've been a great partner to sort of help all those customers migrate and move their business to the cloud. >> You guys had success with Boomi on the customer front. >>Can you unpack that a little bit? Because the customer equation around data is interesting. You guys have turned this into an opportunity with nets. We talk about how that works. Yeah, I mean look EV every customer needs to get more insight out of their data. And you know, the ERP system is one of the major hubs in any organization, right? You've got a handful of system of records, right? And core financials is one of the main systems of record and inevitably every customer will have probably 1520 legacy data sources, right? That are going to be necessary for an ERP. And so for us, working with Boomi across not just the U S but across the globe with a lot of different international customers, it's a natural fit because we're not obviously going to be connecting with all of the systems that they're touching today. It brings a lot more value of data into NetSuite, which obviously then helps our customer out. >>So you guys were at, you said an early partner of Boomi back in 2007 when they were founded. We got to speak with Rick Nucci yesterday. So one of the interesting things that we talk about, and John even pointed out yesterday is you know, they took a big bet, Boomi dead way back then with building this architecture that's pretty unique to this day. This single instance, multi-tenant cloud application. Take us back to, because obviously NetSuite's been around longer, you a lot of choice, there are more iPods vendors out there. What is it about the way that Boomi is architected that is enabling your customers to achieve so much success but also really that you buy saw back in Oh seven I think this is something that's going to be a real big opportunity for NetSuite. >>You know, it's, it's, it's been an interesting ride because if you go back even to Oh seven and didn't even maybe eight or nine years ago, it was not a foregone conclusion with a lot of technology vendors that the world was going to shift to the cloud. Yeah, right. There were a lot of server huggers out there. There still are. They still want to hug this, they still want to hug the machine. Right. And so it's important, I think that we work with partners who have the same true North in terms of where we think that the technology is going. And I think that alignment, which is, you know, we're 100% in the cloud, always have been, always will be. Boomi shared that vision early on. So it was easier to make a bet then right, with a vendor who was going to have that commitment. >>And so that's been, to their credit, the vision that they've had for obviously years now. And I think that's what's helped them grow so quickly. And one of the things that you observed obviously is that the customers have choices, but the world software's changing, right? I mean cloud has changed the software development life cycle. I mean just in the past decade alone, the business of change, you still going to have the system of records. Okay. But with containers and Kubernetes and some of these cloud native opportunities, there's more flexibility in how people are deploying legacy and or core apps. Yeah. So they're not getting thrown away as everyone had predicted. So, I mean, there was some funded saying, well, everyone's going to move to the cloud and not really. Yeah, well I look at it, it's a good point because there's no packaged applications. They're not the entirety of the application market as you know. >>Right? Custom application development will never go away. You will always have, you know, things that are custom. People build apps on NetSuite, right? Things that are very close to ERP you'll build on the NetSuite platform. But there are things that are not, you know, native to our platform that need to connect to NetSuite. And there are customers that we share who are, have legacy COBOL applications for example. Right? And they may need to put a wrapper around that and get certain forms into NetSuite. So it really does run the gamut. And so it'll never be one thing, right? We just sort of, in the technology industry, we never go from, you know, 100 to zero in terms of what's deployed in the legacy. We sort of layer in compost technology. And I think that's what's happening. And so, you know, we'll replace certain systems. We go in and we pretty much always replace a an on prem system but there are a lot of on-prem technologies that a will never, never go away. >> I was digging around about Boomi and you guys net suite looking at some of the use cases. One thing that caught my eye was, you know, the growth startup for instance, might be born in the cloud. Yup. Never have an it department. Um, they have kind of a um, hacked together system of record at HR and ERP kind of things, but at some point they've got to grow and they hit a growth spurt and they just become rapid growth. Eventually goes public. You guys have had good success with Boomi in these kinds of startups. It's pretty normal. You've seen this before. Can you talk about that dynamic because at some point people got to start establishing formal, is this the systems applications? You're gonna need payroll, you're gonna need HR. I mean this is blocking and tackling. You guys have been successful there. >> Well, you know, we, we like to think about we can be the first system that you'll ever need and hopefully we'll be the last system that you'll ever need. Right? And what ends up happening is we've architected NetSuite to let you start small and then add more functionality as you grow. So you may start with just basic financials. You may add order management, move into full fledged ERP, maybe you're going to use our HR system down the road. And so we kind of, we kind of stairway a customer through their journey. Boomi does the same thing. Maybe you start with two connectors, right? You're just connecting two basic applications and, and that's sweet. And then you evolve into something more sophisticated, right? Where as you saw today and some of the technology demos where, you know, they're tapping into all sorts of different systems that are not even ERP or CRM, it's, you know, IOT and just all sorts of different insights that they can bring from the different technologies. >>Better together message is legit and this works. Yeah. You know, we look at, technology is all about coopertition these days, right? Is every vendor, right? In some way we overlap, you know, Boomie's owned by Dell, NetSuite's owned by Oracle, right? We're, we're all sort of inner inner locked in one way or another. But ultimately we have to work together because we share so many customers and so customers don't have the patience and nor should they for any of the sort of the, the vendor warfare. And I think that's the cool thing that's evolved with technology standards. It's easy for us to work together and we have to do it and we want to do it because it's what's the right thing for the customer. >>Let's talk about net suite as a launching pad for a lot of tech IPOs in the last few years. Give us your perspectives on what you guys started to recognize as a lot of these tech companies have kind of, that's why it just seems to me like net suite has been this sort of launchpad for that. Talk to us about what you've achieved there. >>Yeah, no, it's, we're, we're really humbled by the fact that more companies go, Poe tech companies go public on NetSuite than frankly you need any other ERP system. Um, you know, we help invent the industry. Early on, 20 years ago, Evan Goldberg and Larry had the famous four minute phone call to, you know, kind of crazily idea to put business apps on the web. Um, and so we've been, you know, at the forefront of this, but it's not just technology. It's, you know, we, we're a subscription business right from day one. Like we didn't sell a license with maintenance. We sold a subscription. So I think a lot of customers look at us and say, okay, they've been through the journey that we have. You know, we went public 12 years ago, you know, we past $1 billion in sales, you know, we got acquired. So the journey that we've been on, most of our customers are going to be on that journey in one form or another. >>We're going to, we've made acquisitions. Our customers make acquisitions, right? So we tried it and this was sort of the genius of what Evan and the team built is a system that can handle any business model. So whether you're selling time as a service, whether you're selling time or you're selling a subscription, you're selling a widget, maybe you're going to sell a widget as a service in the future. We can kind of handle any of the business models and most of the IPS are innovative companies that innovate not just with what they sell, but in how they sell it. >> Show about some stories from the field that you've seen out there. Anecdotally, share some turn situation. What are customers going through right now? Enterprises as they go through their journeys, they realize cloud's there. They got some stuff on premise is going to keep there. >>There's obviously certain reasons you're gonna run payroll in the cloud. You're going to have to have multitenancy is allows it news cases and clouds, not that straightforward. When you start thinking about having an enterprise and the hybrid mode of operations, what are some of the customers feeling? What's a, what's the mindset? What's their architecture look like? What are some of the examples? Can you share? Yeah. You know, I'd say three things come to mind. So first off, it's this business model innovation, right? The, the on prem systems tend to lock you into a model, right? And there's nothing, and when they were built, they were innovative 1520, 30 years ago. Most companies, business models have outgrown that legacy system. So they need to move off that to enable some new thing that they want to do. So that's a big driver. I think the other thing is, is globalization is here to stay. >>Um, you know, whether you're in the United States or you're in the UK or you're in Asia, right? We're one interconnected global economy. And so you may, you know, source from Asia, you may design in California, you may do nearshore assembly in Mexico and then you do omni-channel distribution. So you have to be global. And I would say the thing that's changed in the last 10 years is companies are being global from day one. It's not just something you add on five, seven, eight years down the road. You see companies designed for being global. And that I think those two things, business model, innovation global are our big catalyst right now. I mean we had, Oh one more thing real quick. So we have a Cuba alumni set on the cube data's the new software. Yeah. So if you've got a global business, data's critical as the data needs to be acted upon, you've got policy, you got regulations, regulatory issues, personal privacy stuff, company policy. >>As you have this global layer of data, making it available, addressable across multiple systems is a huge task. What's your view on that? Well it's, it's, it's an interesting question cause we think of it and kind of three pillars. It's we give you visibility, we give you control and then we give you the agility, right? So you've got to, first off, you've got to have visibility into the data, right? You need to know what's happening. Like how much did we sell in the Australian subsidiary yesterday, right? You need to have controls. If your CFO, you need to have global financial controls. You may have sold a lot in Australia. You've got to make sure you're spending too much. Right? How do you manage that? And then ultimately the agility is how do you make a decision on that? Right. And so that's those three things I think all play into it. >>And how does the consumerization effect impact it? Visibility, control, agility. Because as consumers we have this expectation whether you know in our personal lives we can get anything that we want within a couple of clicks. So when you're talking to a tech, whether it's a young tech company or even not a tech company like candy.com which is seems like a mixture. You and I were talking before of a number of different industries, all, all in one. How does, has NetSuite evolved to enable that consumer to go from their personal life to being able to interact with ERP next, struck the value from it in the ways that they want? Anywhere, anytime. >>Let's, let's be honest, for a second, ERP kinda got a dirty reputation. You know, in the nineties nobody loved their ERP implementations. Books had been written on this, right? ERP was like, it was like going like a bad trip to the dentist office in the 90s and that was sort of the catalyst for our company. But that's not enough just to be in the cloud. It's you have to make your user experience consumer grade, right? We always talk about enterprise grade. It's all the, reliability, scalability, all that kind of stuff. That's sort of a given, like you have to do that, but I think you have to, you have to adopt the consumer grade. So we spent a lot of time and we're doing a lot more and we're going to be rolling out some new stuff around user interface and just how easy is it to have a dashboard on your phone so that you can run your business from your smartphone versus actually having to be tethered to the desktop because we're all mobile, we're all traveling. You're a business owner, you're a CFO, you're CEO. You need to be connected. Maybe you're too connected. Maybe that's part, maybe we have screen-time problems. We do business. If we, if we can give our customers Screentime addiction to watch their business in real time, I guess that's a good thing. Right? And so we want to be able to make sure that they can have all that insight at their fingertips, whether they're in the office or at the beach. >>And speaking of insight, talk to us about brain yard. What that is, why you developed it and what it's enabling. >>Yeah. Thank you. That's like my, I was hoping you were gonna ask me. It's my secret, but not so secret anymore. Pet project. So one of the things being in the cloud, we have 18,000 customers, right? We have a single instance of NetSuite and so we've had the unique seat at the table to see all of these different companies grow in all these different industries. We evolved into selling by industry. So we have a retail version of software version of manufacturing, nonprofit, 1213 different industries. What we had in that is we had all these insights by industry. What is the right DSO number for a software company, right? What is the thing that a nonprofit needs to look at? And so we had trapped inside of NetSuite, all these brains sitting in all this information and PowerPoint and word docs and just everywhere. And so we decided to crack the hood open and literally open source that information and put it on the website. >>And so there's a subtle message here is that we have to do more than just sell bits. We, we're ultimately selling customer success or a business outcome, whatever you want to call it. So we need to transfer that knowledge to our customers so they can run their business better. So it's our investment back into the customer saying, Hey, you know what, if you're a software company and your DSO is at this level, you know, best in class is actually, you know, five days lower on a day sale, outstanding. How do you get your business to close that gap? And that's where we can really add value comms. People love comparables and best practices. You're essentially taking that heavy lifting work. It's giving it up there. It's benchmarking, it's analysis. You know, I was a former wall street analyst, so this one's near and dear to my heart, which is comparison, you know, how is this company doing versus that company? >>And so we have lots of data, um, that we've gleaned over the years. Lots of insights. So we kind of know what those best practices are. This is just the first phase of what we're doing. We're working with a lot of partners across the industry to give us some of their industry data so we kind of mash it up and come up with the insights. So it wasn't as an analyst, I'd love to get your thoughts real quick and take the, take the net suite hat off, put your industry participants hat on. Lot of wall street challenges around we worked, pulled their IPO, their GP gross profit was down. Other SAS businesses have huge margins. Their successful zooms public. There's a new formula developing in this cloud 2.0 world software world where the dynamic between classic software and software economics in the cloud are changing. What's your thoughts on this? >>If a startups out there and growing companies that are really looking to crack the code by at all costs and then monetize, get the margins that would, what's your, what's your analysis? No, it's, I, this is an area that I think a lot of companies raise too much, too much capital. Right? And they, we've been in this very unique environment over the last kind of eight or nine years where I'd argue a lot of startups who've been overfunded and when you have overfunding you chase growth at really no, you know, at without any limit on terms of the cost and what you see as you sort of distort the reality of what's happening in the business. And so I would argue that we've had, you know, zero in basically free money in terms of access to capital and we've lost track of some of the basics that you need to build a profitable, sustainable business. >>So, you know, when I was working on wall street, you couldn't go public, you know, if you were within say four quarters of cashflow break even, right? Those are some of the things that we used to have. But you've seen, you know, business fundamentals. Yeah, I need, and so what's happening right now? It's just a little bit of her. I think it's mean reversion. Honestly. I think you're seeing, you know, the public markets, you know, if you will veto some of the frothiness that's been in the private markets. And so this is, I think companies, some marketplaces do. That's what they, that's there. It's fantastic. It's a self correcting mechanism, right? I mean it's, you know, just cause you marked up your last round when you were private to a good Jillian dollars doesn't mean that the buy side on, you know, the pension fund is going to want to pay that and we work so you can't be high and run a business. You know, as we were saying, you know, trying, you know, God bless them, they're trying, but it's probably not the best practice I would not have. I would not recommend that. It's not a good look for wall street. How a good luck, you know, you can get on the Joe Rogan show there, knock yourself out. If you're a Ilan, you can do it. But you know, he's the, he's the only one we're going to let, don't know. >>Probably shouldn't be publicly. Air's too much unless you want something to laugh at and you know what, in this economy, I think we all need that. Jason, thank you for sharing with us what you're doing at NetSuite with Boomi, the insights that you guys are opening up with brain yard. So from brain yard, let's go back to the other yard that I promised. The baseball yard, your Dodger fan giants fan. Hats off. You guys are there. We are not. So I will say good luck to your team. We appreciate your time and what can I say, Bri? I'll give it to ya. All right, well it's been a pleasure talking to you and thank you for your time. Thanks for John furrier. I'm Lisa Martin. You're watching the cube from booby world 19 thanks for watching.