(upbeat music) >> When Oracle acquired Sun in 2009, it paid $5.6 billion net of Sun's cash and debt. Now I argued at the time that Oracle got one of the best deals in the history of enterprise tech, and I got a lot of grief for saying that because Sun had a declining business, it was losing money, and its revenue was under serious pressure as it tried to hang on for dear life. But Safra Catz understood that Oracle could pay Sun's lower profit and lagging businesses, like its low index 86 product lines, and even if Sun's revenue was cut in half, because Oracle has such a high revenue multiple as a software company, it could almost instantly generate $25 to $30 billion in shareholder value on paper. In addition, it was a catalyst for Oracle to initiate its highly differentiated engineering systems business, and was actually the precursor to Oracle's Cloud. Oracle saw that it could capture high margin dollars that used to go to partners like HP, it's original exit data partner, and get paid for the full stack across infrastructure, middleware, database, and application software, when eventually got really serious about cloud. Now there was also a major technology angle to this story. Remember Sun's tagline, "the network is the computer"? Well, they should have just called it cloud. Through the Sun acquisition. Oracle also got a couple of key technologies, Java, the number one programming language in the world, and MySQL, a key ingredient of the LAMP stack, that's Linux, Apache, MySQL and PHP, Perl or Python, on which the internet is basically built, and is used by many cloud services like Facebook, Twitter, WordPress, Flicker, Amazon, Aurora, and many other examples, including, by the way, Maria DB, which is a fork of MySQL created by MySQL's creator, basically in protest to Oracle's acquisition; the drama is Oscar worthy. It gets even better. In 2020, Oracle began introducing a new version of MySQL called MySQL HeatWave, and since late 2020 it's been in sort of a super cycle rolling, out three new releases in less than a year and a half in an attempt to expand its Tam and compete in new markets. Now we covered the release of MySQL Autopilot, which uses machine learning to automate management functions. And we also covered the bench marketing that Oracle produced against Snowflake, AWS, Azure, and Google. And Oracle's at it again with HeatWave, adding machine learning into its database capabilities, along with previously available integrations of OLAP and OLTP. This, of course, is in line with Oracle's converged database philosophy, which, as we've reported, is different from other cloud database providers, most notably Amazon, which takes the right tool for the right job approach and chooses database specialization over a one size fits all strategy. Now we've asked Oracle to come on theCUBE and explain these moves, and I'm pleased to welcome back Nipun Agarwal, who's the senior vice president for MySQL Database and HeatWave at Oracle. And today, in this video exclusive, we'll discuss machine learning, other new capabilities around elasticity and compression, and then any benchmark data that Nipun wants to share. Nipun's been a leading advocate of the HeatWave program. He's led engineering in that team for over 10 years, and he has over 185 patents in database technologies. Welcome back to the show Nipun. Great to see you again. Thanks for coming on. >> Thank you, Dave. Very happy to be back. >> Yeah, now for those who may not have kept up with the news, maybe to kick things off you could give us an overview of what MySQL HeatWave actually is so that we're all on the same page. >> Sure, Dave, MySQL HeatWave is a fully managed MySQL database service from Oracle, and it has a builtin query accelerator called HeatWave, and that's the part which is unique. So with MySQL HeatWave, customers of MySQL get a single database which they can use for transactional processing, for analytics, and for mixed workloads because traditionally MySQL has been designed and optimized for transaction processing. So in the past, when customers had to run analytics with the MySQL based service, they would need to move the data out of MySQL into some other database for running analytics. So they would end up with two different databases and it would take some time to move the data out of MySQL into this other system. With MySQL HeatWave, we have solved this problem and customers now have a single MySQL database for all their applications, and they can get the good performance of analytics without any changes to their MySQL application. >> Now it's no secret that a lot of times, you know, queries are not, you know, most efficiently written, and critics of MySQL HeatWave will claim that this product is very memory and cluster intensive, it has a heavy footprint that adds to cost. How do you answer that, Nipun? >> Right, so for offering any database service in the cloud there are two dimensions, performance and cost, and we have been very cognizant of both of them. So it is indeed the case that HeatWave is a, in-memory query accelerator, which is why we get very good performance, but it is also the case that we have optimized HeatWave for commodity cloud services. So for instance, we use the least expensive compute. We use the least expensive storage. So what I would suggest is for the customers who kind of would like to know what is the price performance advantage of HeatWave compared to any database we have benchmark against, Redshift, Snowflake, Google BigQuery, Azure Synapse, HeatWave is significantly faster and significantly lower price on a multitude of workloads. So not only is it in-memory database and optimized for that, but we have also optimized it for commodity cloud services, which makes it much lower price than the competition. >> Well, at the end of the day, it's customers that sort of decide what the truth is. So to date, what's been the customer reaction? Are they moving from other clouds from on-prem environments? Both why, you know, what are you seeing? >> Right, so we are definitely a whole bunch of migrations of customers who are running MySQL on-premise to the cloud, to MySQL HeatWave. That's definitely happening. What is also very interesting is we are seeing that a very large percentage of customers, more than half the customers who are coming to MySQL HeatWave, are migrating from other clouds. We have a lot of migrations coming from AWS Aurora, migrations from RedShift, migrations from RDS MySQL, TerriData, SAP HANA, right. So we are seeing migrations from a whole bunch of other databases and other cloud services to MySQL HeatWave. And the main reason we are told why customers are migrating from other databases to MySQL HeatWave are lower cost, better performance, and no change to their application because many of these services, like AWS Aurora are ETL compatible with MySQL. So when customers try MySQL HeatWave, not only do they get better performance at a lower cost, but they find that they can migrate their application without any changes, and that's a big incentive for them. >> Great, thank you, Nipun. So can you give us some names? Are there some real world examples of these customers that have migrated to MySQL HeatWave that you can share? >> Oh, absolutely, I'll give you a few names. Stutor.com, this is an educational SaaS provider raised out of Brazil. They were using Google BigQuery, and when they migrated to MySQL HeatWave, they found a 300X, right, 300 times improvement in performance, and it lowered their cost by 85 (audio cut out). Another example is Neovera. They offer cybersecurity solutions and they were running their application on an on-premise version of MySQL when they migrated to MySQL HeatWave, their application improved in performance by 300 times and their cost reduced by 80%, right. So by going from on-premise to MySQL HeatWave, they reduced the cost by 80%, improved performance by 300 times. We are Glass, another customer based out of Brazil. They were running on AWS EC2, and when they migrated, within hours they found that there was a significant improvement, like, you know, over 5X improvement in database performance, and they were able to accommodate a very large virtual event, which had more than a million visitors. Another example, Genius Senority. They are a game designer in Japan, and when they moved to MySQL HeatWave, they found a 90 times percent improvement in performance. And there many, many more like a lot of migrations, again, from like, you know, Aurora, RedShift and many other databases as well. And consistently what we hear is (audio cut out) getting much better performance at a much lower cost without any change to their application. >> Great, thank you. You know, when I ask that question, a lot of times I get, "Well, I can't name the customer name," but I got to give Oracle credit, a lot of times you guys have at your fingertips. So you're not the only one, but it's somewhat rare in this industry. So, okay, so you got some good feedback from those customers that did migrate to MySQL HeatWave. What else did they tell you that they wanted? Did they, you know, kind of share a wishlist and some of the white space that you guys should be working on? What'd they tell you? >> Right, so as customers are moving more data into MySQL HeatWave, as they're consolidating more data into MySQL HeatWave, customers want to run other kinds of processing with this data. A very popular one is (audio cut out) So we have had multiple customers who told us that they wanted to run machine learning with data which is stored in MySQL HeatWave, and for that they have to extract the data out of MySQL (audio cut out). So that was the first feedback we got. Second thing is MySQL HeatWave is a highly scalable system. What that means is that as you add more nodes to a HeatWave cluster, the performance of the system improves almost linearly. But currently customers need to perform some manual steps to add most to a cluster or to reduce the cluster size. So that was other feedback we got that people wanted this thing to be automated. Third thing is that we have shown in the previous results, that HeatWave is significantly faster and significantly lower price compared to competitive services. So we got feedback from customers that can we trade off some performance to get even lower cost, and that's what we have looked at. And then finally, like we have some results on various data sizes with TPC-H. Customers wanted to see if we can offer some more data points as to how does HeatWave perform on other kinds of workloads. And that's what we've been working on for the several months. >> Okay, Nipun, we're going to get into some of that, but, so how did you go about addressing these requirements? >> Right, so the first thing is we are announcing support for in-database machine learning, meaning that customers who have their data inside MySQL HeatWave can now run training, inference, and prediction all inside the database without the data or the model ever having to leave the database. So that's how we address the first one. Second thing is we are offering support for real time elasticity, meaning that customers can scale up or scale down to any number of nodes. This requires no manual intervention on part of the user, and for the entire duration of the resize operation, the system is fully available. The third, in terms of the costs, we have double the amount of data that can be processed per node. So if you look at a HeatWave cluster, the size of the cluster determines the cost. So by doubling the amount of data that can be processed per node, we have effectively reduced the cluster size which is required for planning a given workload to have, which means it reduces the cost to the customer by half. And finally, we have also run the TPC-DS workload on HeatWave and compared it with other vendors. So now customers can have another data point in terms of the performance and the cost comparison of HeatWave with other services. >> All right, and I promise, I'm going to ask you about the benchmarks, but I want to come back and drill into these a bit. How is HeatWave ML different from competitive offerings? Take for instance, Redshift ML, for example. >> Sure, okay, so this is a good comparison. Let's start with, let's say RedShift ML, like there are some systems like, you know, Snowflake, which don't even offer any, like, processing of machine learning inside the database, and they expect customers to write a whole bunch of code, in say Python or Java, to do machine learning. RedShift ML does have integration with SQL. That's a good start. However, when customers of Redshift need to run machine learning, and they invoke Redshift ML, it makes a call to another service, SageMaker, right, where so the data needs to be exported to a different service. The model is generated, and the model is also outside RedShift. With HeatWave ML, the data resides always inside the MySQL database service. We are able to generate models. We are able to train the models, run inference, run explanations, all inside the MySQL HeatWave service. So the data, or the model, never have to leave the database, which means that both the data and the models can now be secured by the same access control mechanisms as the rest of the data. So that's the first part, that there is no need for any ETL. The second aspect is the automation. Training is a very important part of machine learning, right, and it impacts the quality of the predictions and such. So traditionally, customers would employ data scientists to influence the training process so that it's done right. And even in the case of Redshift ML, the users are expected to provide a lot of parameters to the training process. So the second thing which we have worked on with HeatWave ML is that it is fully automated. There is absolutely no user intervention required for training. Third is in terms of performance. So one of the things we are very, very sensitive to is performance because performance determines the eventual cost to the customer. So again, in some benchmarks, which we have published, and these are all available on GitHub, we are showing how HeatWave ML is 25 times faster than Redshift ML, and here's the kicker, at 1% of the cost. So four benefits, the data all remain secure inside the database service, it's fully automated, much faster, much lower cost than the competition. >> All right, thank you Nipun. Now, so there's a lot of talk these days about explainability and AI. You know, the system can very accurately tell you that it's a cat, you know, or for you Silicon Valley fans, it's a hot dog or not a hot dog, but they can't tell you how the system got there. So what is explainability, and why should people care about it? >> Right, so when we were talking to customers about what they would like from a machine learning based solution, one of the feedbacks we got is that enterprise is a little slow or averse to uptaking machine learning, because it seems to be, you know, like magic, right? And enterprises have the obligation to be able to explain, or to provide a answer to their customers as to why did the database make a certain choice. With a rule based solution it's simple, it's a rule based thing, and you know what the logic was. So the reason explanations are important is because customers want to know why did the system make a certain prediction? One of the important characteristics of HeatWave ML is that any model which is generated by HeatWave ML can be explained, and we can do both global explanations or model explanations as well as we can also do local explanations. So when the system makes a specific prediction using HeatWave ML, the user can find out why did the system make such a prediction? So for instance, if someone is being denied a loan, the user can figure out what were the attribute, what were the features which led to that decision? So this ensures, like, you know, fairness, and many of the times there is also like a need for regulatory compliance where users have a right to know. So we feel that explanations are very important for enterprise workload, and that's why every model which is generated by HeatWave ML can be explained. >> Now I got to give Snowflakes some props, you know, this whole idea of separating compute from storage, but also bringing the database to the cloud and driving elasticity. So that's been a key enabler and has solved a lot of problems, in particular the snake swallowing the basketball problem, as I often say. But what about elasticity and elasticity in real time? How is your version, and there's a lot of companies chasing this, how is your approach to an elastic cloud database service different from what others are promoting these days? >> Right, so a couple of characteristics. One is that we have now fully automated the process of elasticity, meaning that if a user wants to scale up or scale down, the only thing they need to specify is the eventual size of the cluster and the system completely takes care of it transparently. But then there are a few characteristics which are very unique. So for instance, we can scale up or scale down to any number of nodes. Whereas in the case of Snowflake, the number of nodes someone can scale up or scale down to are the powers of two. So if a user needs 70 CPUs, well, their choice is either 64 or 128. So by providing this flexibly with MySQL HeatWave, customers get a custom fit. So they can get a cluster which is optimized for their specific portal. So that's the first thing, flexibility of scaling up or down to any number of nodes. The second thing is that after the operation is completed, the system is fully balanced, meaning the data across the various nodes is fully balanced. That is not the case with many solutions. So for instance, in the case of Redshift, after the resize operation is done, the user is expected to manually balance the data, which can be very cumbersome. And the third aspect is that while the resize operation is going on, the HeatWave cluster is completely available for queries, for DMLS, for loading more data. That is, again, not the case with Redshift. Redshift, suppose the operation takes 10 to 15 minutes, during that window of time, the system is not available for writes, and for a big part of that chunk of time, the system is not even available for queries, which is very limiting. So the advantages we have are fully flexible, the system is in a balanced state, and the system is completely available for the entire duration operation. >> Yeah, I guess you got that hypergranularity, which, you know, sometimes they say, "Well, t-shirt sizes are good enough," but then I think of myself, some t-shirts fit me better than others, so. Okay, I saw on the announcement that you have this lower price point for customers. How did you actually achieve this? Could you give us some details around that please? >> Sure, so there are two things for announcing this service, which lower the cost for the customers. The first thing is that we have doubled the amount of data that can be processed by a HeatWave node. So if we have doubled the amount of data, which can be a process by a node, the cluster size which is required by customers reduces to half, and that's why the cost drops to half. The way we have managed to do this is by two things. One is support for Bloom filters, which reduces the amount of intermediate memory. And second is we compress the base data. So these are the two techniques we have used to process more data per node. The second way by which we are lowering the cost for the customers is by supporting pause and resume of HeatWave. And many times you find customers of like HeatWave and other services that they want to run some other queries or some other workloads for some duration of time, but then they don't need the cluster for a few hours. Now with the support for pause and resume, customers can pause the cluster and the HeatWave cluster instantaneously stops. And when they resume, not only do we fetch the data, in a very, like, you know, a quick pace from the object store, but we also preserve all the statistics, which are used by Autopilot. So both the data and the metadata are fetched, extremely fast from the object store. So with these two capabilities we feel that it'll drive down the cost to our customers even more. >> Got it, thank you. Okay, I promised I was going to get to the benchmarks. Let's have it. How do you compare with others but specifically cloud databases? I mean, and how do we know these benchmarks are real? My friends at EMC, they were back in the day, they were brilliant at doing benchmarks. They would produce these beautiful PowerPoints charts, but it was kind of opaque, but what do you say to that? >> Right, so there are multiple things I would say. The first thing is that this time we have published two benchmarks, one is for machine learning and other is for SQL analytics. All the benchmarks, including the scripts which we have used are available on GitHub. So we have full transparency, and we invite and encourage customers or other service providers to download the scripts, to download the benchmarks and see if they get any different results, right. So what we are seeing, we have published it for other people to try and validate. That's the first part. Now for machine learning, there hasn't been a precedence for enterprise benchmarks so we talk about aiding open data sets and we have published benchmarks for those, right? So both for classification, as well as for aggression, we have run the training times, and that's where we find that HeatWave MLS is 25 times faster than RedShift ML at one percent of the cost. So fully transparent, available. For SQL analytics, in the past we have shown comparisons with TPC-H. So we would show TPC-H across various databases, across various data sizes. This time we decided to use TPC-DS. the advantage of TPC-DS over TPC-H is that it has more number of queries, the queries are more complex, the schema is more complex, and there is a lot more data skew. So it represents a different class of workloads, and which is very interesting. So these are queries derived from the TPC-DS benchmark. So the numbers we have are published this time are for 10 terabyte TPC-DS, and we are comparing with all the four majors services, Redshift, Snowflake, Google BigQuery, Azure Synapse. And in all the cases, HeatWave is significantly faster and significantly lower priced. Now one of the things I want to point out is that when we are doing the cost comparison with other vendors, we are being overly fair. For instance, the cost of HeatWave includes the cost of both the MySQL node as well as the HeatWave node, and with this setup, customers can run transaction processing analytics as well as machine learning. So the price captures all of it. Whereas with the other vendors, the comparison is only for the analytic queries, right? So if customers wanted to run RDP, you would need to add the cost of that database. Or if customers wanted to run machine learning, you would need to add the cost of that service. Furthermore, with the case of HeatWave, we are quoting pay as you go price, whereas for other vendors like, you know, RedShift, and like, you know, where applicable, we are quoting one year, fully paid upfront cost rate. So it's like, you know, very fair comparison. So in terms of the numbers though, price performance for TPC-DS, we are about 4.8 times better price performance compared to RedShift We are 14.4 times better price performance compared to Snowflake, 13 times better than Google BigQuery, and 15 times better than Synapse. So across the board, we are significantly faster and significantly lower price. And as I said, all of these scripts are available in GitHub for people to drive for themselves. >> Okay, all right, I get it. So I think what you're saying is, you could have said this is what it's going to cost for you to do both analytics and transaction processing on a competitive platform versus what it takes to do that on Oracle MySQL HeatWave, but you're not doing that. You're saying, let's take them head on in their sweet spot of analytics, or OLTP separately and you're saying you still beat them. Okay, so you got this one database service in your cloud that supports transactions and analytics and machine learning. How much do you estimate your saving companies with this integrated approach versus the alternative of kind of what I called upfront, the right tool for the right job, and admittedly having to ETL tools. How can you quantify that? >> Right, so, okay. The numbers I call it, right, at the end of the day in a cloud service price performance is the metric which gives a sense as to how much the customers are going to save. So for instance, for like a TPC-DS workload, if we are 14 times better price performance than Snowflake, it means that our cost is going to be 1/14th for what customers would pay for Snowflake. Now, in addition, in other costs, in terms of migrating the data, having to manage two different databases, having to pay for other service for like, you know, machine learning, that's all extra and that depends upon what tools customers are using or what other services they're using for transaction processing or for machine learning. But these numbers themselves, right, like they're very, very compelling. If we are 1/5th the cost of Redshift, right, or 1/14th of Snowflake, these numbers, like, themselves are very, very compelling. And that's the reason we are seeing so many of these migrations from these databases to MySQL HeatWave. >> Okay, great, thank you. Our last question, in the Q3 earnings call for fiscal 22, Larry Ellison said that "MySQL HeatWave is coming soon on AWS," and that caught a lot of people's attention. That's not like Oracle. I mean, people might say maybe that's an indication that you're not having success moving customers to OCI. So you got to go to other clouds, which by the way I applaud, but any comments on that? >> Yep, this is very much like Oracle. So if you look at one of the big reasons for success of the Oracle database and why Oracle database is the most popular database is because Oracle database runs on all the platforms, and that has been the case from day one. So very akin to that, the idea is that there's a lot of value in MySQL HeatWave, and we want to make sure that we can offer same value to the customers of MySQL running on any cloud, whether it's OCI, whether it's the AWS, or any other cloud. So this shows how confident we are in our offering, and we believe that in other clouds as well, customers will find significant advantage by having a single database, which is much faster and much lower price then what alternatives they currently have. So this shows how confident we are about our products and services. >> Well, that's great, I mean, obviously for you, you're in MySQL group. You love that, right? The more places you can run, the better it is for you, of course, and your customers. Okay, Nipun, we got to leave it there. As always it's great to have you on theCUBE, really appreciate your time. Thanks for coming on and sharing the new innovations. Congratulations on all the progress you're making here. You're doing a great job. >> Thank you, Dave, and thank you for the opportunity. >> All right, and thank you for watching this CUBE conversation with Dave Vellante for theCUBE, your leader in enterprise tech coverage. We'll see you next time. (upbeat music)

(upbeat music) >> Welcome to this BizOps Manifesto Power Panel, Data Lake or Data Landfill. We're going to be talking about that today. I've got three guests joining me. We're going to dive through that. Kieran Taylor is here the CMO of Broadcom's Enterprise Software Division. Kieran, great to have you on the program. >> Thank you, Lisa. >> Kevin Surace is here as well. Chairman and CTO of Appvance, hey Kevin. >> Hey Lisa. >> And Isaac Sacolick Author and CEO of StarCIO. Isaac, welcome. >> Hi Lisa, thanks for having me. >> So we're going to spend the next 25 to 30 minutes talking about the challenges and the opportunities that data brings to organizations. You guys are going to share some of your best practices for how organizations can actually sort through all this data to make data-driven decisions. We're also going to be citing some statistics from the Inaugural BizOps Industry Survey of the State of Digital Business in which 519 business and technology folks were surveyed across five nations. Let's go ahead and jump right in and the first one in that server that I just mentioned 97% of organizations say we've got data related challenges, limiting the amount of information that we actually have available to the business. Big conundrum there. How do organizations get out of that conundrum? Kieran, we're going to start with you. >> Thanks Lisa. You know, I think, I don't know if it's so much limiting information as it is limiting answers. There's no real shortage of data I don't think being captured, recently met with a unnamed auto manufacturer Who's collecting petabytes of data from their connected cars and they're doing that because they don't really yet know what questions they have of the data. So I think you get out of this Data Landfill conundrum by first understanding what questions to ask. It's not algorithms, it's not analytics. It's not, you know, math that's going to solve this problem. It's really, really understanding your customer's issues and what questions to ask of the data >> Understanding what questions to ask of the data. Kevin, what are your thoughts? >> Yeah, look, I think it gets down to what questions you want to ask and what you want out of it, right? So there's questions you want to ask but what are the business outcomes you're looking for, which is the core of BizOps anyway, right? What are the business outcomes and what business outcomes can I act upon? So there are so many business outcomes you can get from data and you go, well, I can't legally act upon that. I can't practically act upon that. I can't, whether it's lay off people or hire people or whatever it is, right? So what are the actionable items? There is plenty of data. We would argue too much data. Now we could say, is the data good? Is the data bad? Is it poorly organized? Is it, noisy? There's all other problems, right? There's plenty of data. What do I do with it? What can I do that's actionable? If I was an automaker and I had lots of sensors on the road, I had petabytes, as Kieran says and I'd probably bringing in petabytes potentially every day. Well, I could make myself driving systems better. That's an obvious place to start or that's what I would do but I could also potentially use that to change people's insurance and say, if you drive in a certain way something we've never been able to do. If you drive in a certain way, based on the sensors you get a lower insurance rate, then nobody's done that. But now there's interesting business opportunities for that data that you didn't have one minute ago and I just gave away. So, (laughs) it's all about the actionable items in the data. How do you drive something to the top line and the bottom line? 'Cause in the end, that's how we're all measured. >> And Isaac, I know you say data is the lifeblood. What are your thoughts on this conundrum? >> Well, I think, you know, they gave you the start and the end of the equation, start with a question. What are you really trying to answer? What you don't understand that you want to learn about your business connect it to an outcome that is valuable to you. And really what most organizations struggle with is a process that goes through discovery, learning what's in the data, addressing data, quality issues, loading new data sources if required and really doing that iteratively and we're all agile people here at BizOps, right? So doing it iteratively, getting some answers out and understanding what the issues are with the underlying data and then going back and revisiting and reprioritizing what you want to do next. Do you want to go look at another question? Is the answer heading down a path that you can drive outcomes? Do you got to go cleanse some data? So it's really that, how do you put it together so that you can peel the onion back and start looking at data and getting insights out of it. >> Great advice, another challenge though, that the survey identified was that nearly 70% of the respondents and again, 519 business and technology professionals from five countries said, we are struggling to create business metrics from our data with so much data, so much that we can't access. Can you guys share best practices for how organizations would sort through and identify the best data sources from which they can identify the ideal business metrics? Kieran, take it away. >> Sure thing, I guess I'll build on Isaac's statements. Every company has some gap in data, right? And so when you do that, that data gap analysis I think you really, I don't know. It's like Alice in Wonderland, begin at the beginning, right? You start with that question like Isaac said, And I think the best questions are really born from an understanding of what your customers value. And if you dig into that, you understand what the customers value, you build it off of actual customer feedback, market research then you know what questions to ask and then from that, hey, what inputs do I need to really understand how to solve that particular business issue or problem. >> Kevin, what are your thoughts? >> Yeah, I'm going to add to that, completely agree but, look, let's start with sales data, right? So sales data is something, everybody watching this understands, even if they're not in sales, they go well, okay, I understand sales data. What's interesting there is we know who our customers are. We could probably figure out if we have enough data, why they buy, are they buying because of a certain sales person? Are they buying because it's a certain region? Are they buying because of some demographic that we don't understand, but AI can pull out, right? So I would like to know, who's buying and why they're buying. Because if I know that I might make more of what more of those people want whatever that is, certain fundamental sales changes or product changes or whatever it is. So if you could certainly start there, if you start nowhere else, say I sell X today. I'd like to sell X times 1.2 by next year. Okay, great. Can I learn from the last five years of sales, millions of units or million or whatever it is, how to do that better and the answer is for sure yes and yes there's problems with the data and there's holes in the data as Kieran said and there's missing data. It doesn't matter, there's a lot of data around sales. So you can just start there and probably drive some top line growth, just doing what you're already doing but doing it better and learning how to do it better. >> Learning how to do it better. Isaac, talk to us about what your thoughts are here with respect to this challenge. >> Well, when you look at that percentage 70% struggling with business metrics, you know what I see is some companies struggling when they have too few metrics and you know, their KPIs, it really doesn't translate well to people doing work for a customer for an application, responding to an issue. So when you have too few in there too disconnected from the work, people don't understand how to use them and then on the flip side I see other organizations trying to create metrics around every single part of the operation, you know, dozens of different ways of measuring user experience and so forth. And that doesn't work because now we don't know what to prioritize. So I think the art of this is management coming back and saying, what are the metrics? Do we want to see impact and changes over in a short amount of time, over the next quarter, over the next six months and to pick a couple in each category, certainly starting with the customer, certainly looking at sales but then also looking at operations and looking at quality and looking at risk and say to the organization, these are the two or three we're going to focus on in the next six months and then I think that's what simplifies it for organizations. >> Thanks, Isaac. So something that I found interesting, it's not surprising in that the survey found that too much data is one of the biggest challenges that organizations have followed by the limitations that we just talked about in terms of identifying what are the ideal business metrics, but a whopping 74% of survey respondents said we failed to have key data available in real time, which is a big inhibitor for data-driven decision-making. Can you guys offer some advice to organizations? How can they harness this data and glean insights from it faster, Kieran, take it away. >> Yeah, I think there are probably five steps to establishing business KPIs and Lisa your first two questions and these gentleman's answers laid out the first two that is define the questions that you want answers for and then identify what those data inputs would be. You know, if you've got a formula in mind, what data inputs do do you need? The remaining three steps. One is, you know, to evaluate the data you've got and then identify what's missing, you know what do you need to then fetch? And then that fetching, you need to think about the measurement method, the frequency I think Isaac mentioned, you know this concept of tools for all. We have too many tools to collect data. So, the measurement method and frequency is important standardizing on tools and automating that collection wherever possible. And then the last step, this is really the people component of the formula. You need to identify stakeholders that will own those business KPIs and even communicate them within the organization. That human element is sometimes forgotten and it's really important. >> It is important, it's one of the challenges as well. Kevin, talk to us about your thoughts here. >> Yeah, again I mean, for sure you've got in the end you've got the human element. You can give people all kinds of KPIs as Isaac said, often it's too many. You have now KPI the business to death and nobody can get out and do anything that doesn't work. Obviously you can't improve things until you measure them. So you have to measure, we get that. But this question of live data is interesting. My personal view is only certain kinds of data are interesting, absolutely live in the moment. So I think people get in their mind, oh, well if I could deploy IOT everywhere and get instantaneous access within one second to the amalgam of that data, I'm making up words too. That would be interesting. Are you sure that'd be interesting? I might rather analyze the last week of real, real data, really deep analysis, right? Build you know, a real model around that and say, okay for the next week, you ought to do the following. Now I get that if you're in the high-frequency stock trading business you know, every millisecond counts, okay? But most of our businesses do not run by the millisecond and we're not going to make a business decision especially humans involved in a millisecond anyway. We make business decisions based on a fair bit of data, days and weeks. So this is just my own personal opinion. I think people get hung up on this. I've got to have all this live data. No, you want great data analysis using AI and machine learning to evaluate as much data as you can get over whatever period of time that is a week, a month a year and start making some rational decisions off of that information. I think that is how you run a business that's going to crush your competition. >> Good advice, Isaac what are your thoughts on these comments? >> Yeah, I'm going to pair off of Kevin's comments. You know, how do you chip away at this problem at getting more real time data? And I'll share two insights first, from the top down, you know, when StarCIO works with a group of CEO and their executive group, you know how are they getting their data? Well, they're getting it in a boardroom with PowerPoints with spreadsheets behind those PowerPoints, with analysts doing a lot of number crunching and behind all that are all the systems of record around the CRM and the ERP and all the other systems that are telling them how they're performing. And I suggest to them for a month, leave the world of PowerPoint and Excel and bring your analysts in to show you the data live in the systems, ask questions and see what it's like to work with real time data. That first changes the perspective in terms of all the manual work that goes into homogenizing that data for them. But then they start getting used to looking at the tools where the data is actually living. So that's an exercise from the top down from the bottom up when we talk to the it groups, you know so much of our data technologies were built at a time when batch processing in our data centers was the only way to go. We ran these things overnight to move data from point a to point B and with the Cloud, with data streaming technologies it's really a new game in town. And so it's really time for many organizations to modernize and thinking about how they're streaming data. Doesn't necessarily have to be real time. It's not really IOT but it's really saying, I need to have my data updated on a regular basis with an SLA against it so that my teams and my businesses can make good decisions around things. >> So let's talk now about digital transformation. We've been talking about that for years. We talked a lot about in 2020, the acceleration of digital transformation for obvious reasons. But when organizations are facing this data conundrum that we talked about, this sort of data disconnect too much can't get what we need right away. Do we need it right away? How did they flip the script on that so that it doesn't become an impediment to digital transformation but it becomes an accelerant. Kieran >> You know, a lot of times you'll hear vendors talk about technology as being the answer, right? So MI, ML, my math is better than your math, et cetera. And technology is important but it's only effective to the point that which people can actually interpret understand and use the data. And so I would put forth this notion of having data at all levels throughout an organization too often. What you'll see is that I think Isaac mentioned it, you know the data is delivered to the C-suite via PowerPoint and it's been sanitized and scrubbed, et cetera. But heck, by the time it gets to the C-suite it's three weeks old. Data at all levels is making sure that throughout organization, the right people have real-time access to data and can make actionable decisions based upon that. So I think that's a real vital ingredient to successful digital transformation. >> Kevin. >> Well, I like to think of digital transformation as looking at all of your relatively manual or paper-based or other processes whatever they are throughout the organization and saying is this something that can now be done for lack of a better word by a machine, right? And that machine could be algorithms. It could be computers, it could be humans it could be Cloud, it could be AI it could be IOT doesn't really matter. (clears throat) And so there's a reason to do that and of course, the basis of that is the data. You've got to collect data to say, this is how we've been performing. This is what we've been doing. So an example, a simple example of digitalization is people doing RPA around customer support. Now you collect a lot of data on how customer support has been supporting customers. You break that into tiers and you say, here's the easiest, lowest tier. I had farmed that out to probably some other country 20 years ago or 10 years ago. Can I even with the systems in place, can I automate that with a set of processes, Robotic Process Automation that digitizes that process now, Now there still might be, you know 20 different screens that click on all different kinds of things, whatever it is, but can I do that? Can I do it with some Chatbots? Can I do it with it? No, I'm not going to do all the customer support that way but I could probably do a fair bit. Can I digitize that process? Can I digitize the process? Great example we all know is insurance companies taking claims. Okay, I have a phone. Can, I take a picture of my car that just got smashed send it in, let AI analyze it and frankly, do an ACH transfer within the hour, because if it costs them insurance company on average 300 to $500 depending on who they are to process a claim, it's cheaper to just send me the $500 then even question it. And if I did it two or three times, well then I'm trying to steal their money and I should go to jail, right? So these are just, I'm giving these as examples 'cause they're examples that everyone who is watching this would go, oh I understand you're digitizing a process. So now when we get to much more complex processes that we're digitizing in data or hiring or whatever, those are a little harder to understand but I just tried to give those as like everyone understands yes, you should digitize those. Those are obvious, right? >> Now those are great examples, you're right. They're relatable across the board here. Isaac, talk to me about what your thoughts are about. Okay, let's do the conundrum. How do we flip the script and leverage data, access to it insights to drive and facilitate digital transformation rather than impede it. >> Well remember, you know, digital transformation is really about changing the business model, changing how you're working with customers and what markets you're going after. You're being forced to do that because of the pace digital technologies are enabling competitors to outpace you. And so we really like starting digital transformations with a vision. What does this business need to do better, differently more of what markets are we going to go after? What types of technologies are important? And we're going to create that vision but we know long-term planning, doesn't work. We know multi-year planning, doesn't work. So we're going to send our teams out on an agile journey over the next sprint, over the next quarter and we're going to use data to give us information about whether we're heading in the right direction. Should we do more of something? Is this feature higher priority? Is there a certain customer segment that we need to pay attention to more? Is there a set of defects happening in our technology that we have to address? Is there a new competitor stealing market share all that kind of data is what the organization needs to be looking at on a very regular basis to say, do we need to pivot, what we're doing? Do we need to accelerate something? Are we heading in the right direction? Should we give ourselves high fives and celebrate a quick win? Because we've accomplished something 'cause so much of transformation is what we're doing today. We're going to change what we're doing over the next three years, and then guess what? There's going to be a new set of technologies. There's going to be another disruption that we can't anticipate and we want our teams sitting on their toes waiting to look at data and saying, what should we do next? >> That's a great segue Isaac into our last question, which is around culture that's always one of those elephants in the room, right? Because so much cultural transformation is necessary but it's incredibly difficult. So question for you guys, Kieran we'll start with you is, should you advise leadership, should really create a culture, a company-wide culture around data? What do you think? >> Absolutely. I mean, this reminds me of DevOps in many ways and you know, the data has to be shared at all levels and has to empower people to make decisions at their respective levels so that we're not, you know kind of siloed in our knowledge or our decision-making, it's through that collective intelligence that I think organizations can move forward more quickly but they do have to change the culture and they've got to have everyone in the room. Everyone's got a stake in driving business success from the C-suite down to the individual contributor >> Right, Kevin, your thoughts >> You know what? Kieran's right. Data silos, one of the biggest brick walls in all of our way, all the time, you know SecOps says there is no way I'm going to share that database because it's got PII. Okay, well, how about if we strip the PII? Well, then that won't be good for something else and you're getting these huge arguments and if you're not driving it from the top, certainly the CIO, maybe the CFO, maybe the CEO I would argue the CEO, drives it from the top. 'Cause the CEO drives company culture and you know, we talk BizOps and the first word of that is Biz. It's the business, right? It's Ops being driven by business goals and the CEO has to set the business goals. It's not really up to the CIO to set business goals. They're setting operational goals, it's up to the CEO. So when the CEO comes out and says our business goals are to drive up sales by this drive down cost by this drive up speed of product development, whatever it is and we're going to digitize all of our processes to do that. We're going to set in KPIs. We're going to measure everything that we do and everybody's going to work around this table. By the way just like we did with DevOps a decade ago, right? And said, Dev, you actually have to work with Ops now and they go, those dangerous guys way over in that other building, we don't even know who they are but in time people realize that we're all on the same team and that if developers develop something that operations can't host and support and keep alive, it's junk right? And we used to do that and now we're much better at it. And whether it's Dev, SecOps or Dev two-way Ops, whatever all those teams working together. Now we're going to spread that out and make it a bigger pyre on the company and it starts with the CEO. And when the CEO makes it a directive for the company I think we're all going to be successful. >> Isaac, what are your thoughts? >> I think we're really talking about a culture of transformation and a culture of collaboration. I mean, again, everything that we're doing now we're going to build, we're going to learn. We're going to use data to pivot what we're doing. We're going to release a product to customers. We're going to get feedback. We're going to continue to iterate over those things. Same thing when it comes to sales, same things that you know, the experiments that we do for marketing, what we're doing today, we're constantly learning. We're constantly challenging our assumptions. We're trying to throw out the sacred cows with status quo, 'cause we know there's going to be another Island that we have to go after and that's the transformation part. The collaboration part is really you know, what you're hearing. Multiple teams, not just Dev and Ops and not just data and Dev, but really the spectrum of business of product, of stakeholders, of marketing and sales, working with technologists and saying, look this is the things that we need to go after over these time periods and work collaboratively and iteratively around them. And again, the data is the foundation for this, right? And we talk about a learning culture as part of that, the data is a big part of that learning, learning new skills and what new skills to learn is as part of that. But when I think about culture, you know the things that slow down organizations is when they're not transforming fast enough, or they're going in five or six different directions, they're not collaborative enough and the data is the element in there that is an equalizer. It's what you show everybody to say, look what we're doing today is not going to make us survive over the next three years. >> The data equalizer, that sounds like it could be movie coming out in 2021. (laughing) Gentlemen, thank you for walking us through some of those interesting metrics coming out of the BizOps Inaugural Survey. Yes, there are challenges with data. Many of them aren't surprising but there's also a lot of tremendous opportunity and I liked how you kind of brought it around to from a cultural perspective. It's got to start from that C-suite to Kieran's point all the way down. I know we could keep talking, we're out of time, but we'll have to keep following, this as a very interesting topic. One that is certainly pervasive across industries. Thanks guys for sharing your insights. >> Than you. >> Thank you, Lisa. >> Thank you, Lisa. >> For Kieran Taylor, Kevin Surace and Isaac Sacolick. I'm Lisa Martin. Thanks for watching. (upbeat music)

>> Narrator: Live from San Francisco It's theCUBE. Covering RSA Conference 2020, San Francisco. Brought to you by Silicon Angled Media >> Hi everyone, welcome to theCUBE's coverage here at RSA Conference 2020. I'm John Furrier, host of theCUBE We're on the floor getting all the data, sharing it with you here, Cube coverage. Got the best new generation shift happening as cloud computing goes to the whole other level. Multi-cloud, hybrid cloud changing the game. You're seeing the companies transition from an on-premises to cloud architecture. This is forcing all the companies to change. So a new generation of security is here and we've got a great guest, so a hot start-up. Masha Sedova, co-founder of Elevate Security. Welcome to theCUBE, thanks for joining us. >> Thank you so much for having me, John. >> So the next generation in what will be a multi-generational security paradigm, is kind of happening right now with the beginning of, we're seeing the transition, Palo Alto Networks announced earnings yesterday down 13% after hours because of the shift to the cloud. Now I think they're going to do well, they're well positioned, but it highlights this next generation security. You guys are a hot start-up, Elevate Security. What is the sea change? What is going on with security? What is this next generation paradigm about? >> Yeah, so it's interesting that you talk about this as next generation. In some ways, I see this as a two-prong move between, yes, we're moving more into the cloud but we're also going back to our roots. We're figuring out how to do asset management right, we're figuring out how to do patching right, and for the first time, we're figuring how to do the human element right. And that's what where we come in. >> You know, the disruption of these new shifts, it also kind of hits like this, the old expression, 'same wine, new bottle', all this, but it's a data problem. Security has always been a data problem, and we've seen some learnings around data. Visualization, wrangling, there's a lot of best practices around there. You guys are trying to change the security paradigm by incorporating a data-centric view with changing the behavior of the humans and the machines and kind of making it easier to manage. Could you share what you guys are doing? What's the vision for Elevate? >> Yeah, so we believe and we've seen, from our experience being practitioners, you can't change what you can't measure. If you don't have visibility, you don't know where you're going. And that's probably been one of the biggest pain-point in the security awareness space traditionally. We just roll out training and hope it works. And it doesn't, which is why human error is a huge source of our breaches. But we keep rolling out the same one-size fits all approach without wanting to measure or, being able to. So, we've decided to turn the problem on its head and we use existing data sets that most organizations who have a baseline level of maturity already have in place. Your end point protections, your DLP solutions, your proxies, your email security gateways and using that to understand what your employees are doing on the network to see if user generated incidents are getting better over time or getting worse. And using that as the instrumentation and the level of visibility into understanding how you should be orchestrating your program in this space. >> You know, that's a great point. I was just having a conversation last night at one of the cocktail parties here around RSA and we were debating on, we talk about the kind of breaches, you mentioned breaches, well there's the pure breach where I'm going to attack and penetrate the well fortified network. But then there's just human error, an S3 bucket laying open or some configuration problem. I guess it's not really a breach, it's kind of an open door so the kind of notion of a breach is multifold. How do you see that, because again, human error, insider threats or human error, these are enabling the hackers. >> Yeah >> This is not new. >> Yeah. >> How bad is the problem? >> It depends on what report you read. The biggest number I've seen so far is something like 95% of breaches have human error. But I honestly, I couldn't tell you what the 5% that don't include it because if you go far enough back, it's because a patch wasn't applied and there is a human being involved there because there is vulnerability in code, that's probably a secure coding practice when you're a development organization. Maybe it's a process that wasn't followed or even created in the first place. There's a human being at the core of every one of these breaches and, it needs to be addressed as holistically as our technologies and our processes right now in the space. >> The evolution of human intelligence augmented by machines will certainly help. >> That's it, yeah. >> I mean, I've got to ask you, obviously you're well-funded. Costanova Ventures well known in the enterprise space, Greg Sands and the team there, really strong, but you guys entered the market, why? I mean you guys, you and your founder both at Salesforce.com. Salesforce gurus doing a lot of work there. Obviously you've seen the large scale, first wave of the cloud. >> Yeah >> Why do the start-up? What was the problem statement you guys were going after? >> So, my co-founder and I both came from the world of being practitioners and we saw how limited the space was and actually changing human behavior, I was given some animated PowerPoints, said use this to keep the Russians out of your network, which is a practical joke unless your job is on the line, so I took a huge step back and I said, there are other fields that have figured this out. Behavioral science being one of them, they use positive reinforcement, gamification, marketing and advertisements have figured out how to engage the human element, just look around the RSA floor, and there's so many learnings of how we make decisions as human beings that can be applied into changing people's behaviors in security. So that's what we did. >> And what was the behavior you're trying to change? >> Yeah, so the top one's always that our attackers are getting into organizations, so, reducing phishing click-throughs an obvious one, increasing reporting rates, reducing malware infection rates, improving sensitive data handling, all of which have ties back to, as I was mentioning earlier, security data sources. So, we get to map those and use that data to then drive behavior change that's rooted in concepts like social proof, how are you doing compared to your peers? We make dinner decisions on that and Amazon buying decisions on that, why not influence security like that? >> So building some intelligence into the system, is there a particular market you're targeting? I mean, here people like to talk in segments, is there a certain market that you guys are targeting? >> Yeah, so the amazing thing about this is, and probably no surprise, the human element is a ubiquitous problem. We are in over a dozen different industries and we've seen this approach work across all of those industries because human beings make the same mistakes, no matter what kind of company they're in. We really work well with larger enterprises. We work well with larger enterprises because they tend to have the data sets that really provides insights into human behavior. >> And what's the business model you guys envision happening with your service product? >> We sell to enterprises and security, the CISO and the package as a whole, gives them the tools to have the voice internally in their organization We sell to Fortune 1000 companies, >> So it's a SAAS service? >> Yeah, SAAS service, yeah. >> And so what's the technology secret sauce? (laughing) >> Um, that's a great question but really, our expertise is understanding what information people need at what time and under what circumstances, that best changes their behavior. So we really are content diagnostic, we are much more about the engine that understands what content needs to be presented to whom and why. So that everyone is getting only the information they need, they understand why they need it and they don't need anything extra-superfluous to their... >> Okay, so I was saying on theCUBE, my last event was at, CIO's can have good days and bad days. They have good days, CISOs really have good days, many will say bad days, >> Masha: Yeah, it's a hard job. >> So how do I know I need the Elevate Solution? What problem do I have, what's in it for me? What do I get out of it? When do I know when to engage with you guys? >> I take a look at how many user generated incidents your (mumbles) responding to, and I would imagine it is a large majority of them. We've seen, while we were working at Salesforce and across our current customers, close to a 40% reduction rate in user generated incidents, which clearly correlates to time spent on much more useful things than cleaning up mistakes. It's also one of the biggest ROI's you can get for the cheapest investment. By investing a little bit in your organization now, the impact you have in your culture and investing in the future decision, the future mistakes that never get made, are actually untold, the benefit of that is untold. >> So you're really kind of coming in as a holistic, kind of a security data plane if you will, aggregating the data points, making a visualization in human component. >> You've got it. >> Now, what's the human touchpoint? Is it a dashboard? Is it notifications? Personalization? How is the benefit rendered for the customer? >> So we give security teams and CSOs a dashboard that maps their organization's strengths and weaknesses. But for every employee, we give personalized, tailored feedback. Right now it shows up in an email that they get on an ongoing basis. We also have one that we tailor for executives, so the executive gets one for their department and we create an executive leaderboard that compares their performance to fellow peers and I'll tell you, execs love to win, so we've seen immense change from that move alone. >> Well, impressive pedigree on your entrepreneurial background, I see Salesforce has really kind of, I consider real first generation cloud before cloud actually happened, and there's a lot of learn, it was always an Apple case, now it's AWS, but it's it's own cloud as we all know, what are the learnings that you saw from Salesforce that you said hey, I'm going to connect those dots to the new opportunity? What's the real key there? >> So, I had two major aha's that I've been sharing with my work since. One, it's not what people know, but it's what they do that matters, and if you can sit with a moment and think about that, you realize it's not more training, because people might actually know the information, but they just choose not to do it. How many people smoke, and they still know it kills them? They think that it doesn't apply to them, same thing with security. I know what I need to do, I'm just not incentivized to do it, so there's a huge motivation factor that needs to be addressed. That's one thing that I don't see a lot of other players on the market doing and one thing we just really wanted to do as well. >> So it sounds like you guys are providing a vision around using sheet learning and AI and data synthesis wrangling and all that good stuff, to be an assistant, a personal assistant to security folks, because it sounds like you're trying to make their life easier, make better decisions. Sounds like you guys are trying to distract away all these signals, >> You're right. >> See what to pay attention to. >> And make it more relevant, yeah. Well think about what Fitbit did for your own personal fitness. It curates a personal relationship based on a whole bunch of data. How you're doing, goals you've set, and all of a sudden, a couple of miles walk leads to an immense lifestyle change. Same thing with security, yeah. >> That's interesting, I love the Fitbit analogy because if you think about the digital ecosystem of an enterprise, it used to be siloed, IT driven, now with digital, everything's connected so technically, you're instrumenting a lot of things for everything. >> Yeah. >> So the question's not so much instrumentation, it's what's happening when and contextually why. >> That's it, why, that's exactly it. Yeah, you totally got it. >> Okay. I got it. >> Yeah, I can see the light bulb. >> Okay, aha, ding ding. All right, so back to the customer pain point. You mentioned some data points around KPI's that they might or things that they might want to call you so it's incidents, what kind of incidents? When do I know I need to get you involved? Will you repeat those again? >> There's two places where it's a great time to involve. Now, because of the human element is, or think about this as an investment. If you do non-investor security culture, one way or another, you have security culture. It's either hurting you or it's helping you and by hurting you, people are choosing to forego investing security processes or secure cultures and you are just increasing your security debt. By stepping in to address that now, you are actually paying it forward. The second best time, is after you realize you should have done that. Post-breaches or post incidents, is a really great time to come in and look at your culture because people are willing to suspend their beliefs of what good behavior looks like, what's acceptable and when you look at an organization and their culture, it is most valuable after a time of crisis, public or otherwise, and that is a really great time to consider it. >> I think that human error is a huge thing, whether it's as trivial as leaving an S3 bucket open or whatever, I think it's going to get more acute with service meshes and cloud-native microservices. It's going to get much more dynamic and sometimes services can be stood up and torn down without any human knowledge, so there's a lot of blind spots potentially. This brings up the question of how does the collaboration piece, because one of the things about the security industry is, it's a community. Sharing data's important, having access to data, how do you think about that as the founder of a start-up that has a 20 mile steer to the future around data access, data diversity, blind spots, how do you look at that and how do you advise your clients to think about that? >> I've always been really pro data sharing. I think it's one of the things that has held us back as an industry, we're very siloed in this space, especially as it relates to human behavior. I have no idea, as a regular CISO of a company, if I am doing enough to protect my employees, is my phishing click (mumbles), are my malware download rates above normal, below or should I invest more, am I doing enough? How do I do compared to my peers and without sharing industry stats, we have no idea if we're investing enough or quite honestly, not enough in this space. And the second thing is, what are approaches that are most effective? So let's say I have a malware infection problem, which approach, is it this training? Is it a communication? Is it positive reinforcement, is it punishment? What is the most effective to leverage this type of output? What's the input output relation? And we're real excited to have shared data with Horizon Data Breach Report for the first time this year, to start giving back to the communities, specifically to help answer some of these questions. >> Well, I think you're onto something with this behavioral science intersection with human behavior and executive around security practices. I think it's going to be an awesome, thanks for sharing the insights, Miss Masha on theCUBE here. A quick plug for your company, (mumbles) you're funded, Series A funding, take us through the stats, you're hiring what kind of positions, give a plug to the company. >> So, Elevate Security, we're three years old. We have raised ten million to date. We're based in both Berkeley and Montreal and we're hiring sales reps on the west coast, a security product manager and any engineering talent really focused on building an awesome data warehouse infrastructure. So, please check out our website, www.elevatesecurity.com/careers for jobs. >> Two hot engineering markets, Berkeley I see poaching out of Cal, and also Montreal, >> Montreal, McGill and Monterey. >> You got that whole top belt of computer science up in Canada. >> Yeah. >> Well, congratulations. Thanks for coming on theCUBE, sharing your story. >> Thank you. >> Security kind of giving the next generation all kinds of new opportunities to make security better. Some CUBE coverage here in San Francisco, at the Moscone Center. I'm John Furrier, we'll be right back after this break. (upbeat music)

(upbeat tech music) >> Announcer: From London, England it's theCUBE, covering Coupa Insp!re 19 Emea. Brought to you by Coupa. >> Hey, welcome to theCUBE, Lisa Martin on the ground in London at Coupa Insp!re 19. Very pleased to welcome back to theCUBE the CEO and Chairman of Coupa, Rob Bernshteyn. Rob, welcome back. >> Thank you so much, thank you for being with me. >> It's great to be here, so we are in with all of these customers and partners, this has been busy all day. You started things off today with a great keynote. I was telling you before we went live, I lost count of how many big customer examples were sprinkled, and I think infused throughout your keynote. I was looking at some numbers, Coupa just keeps doing this. 5x increase in spend under management since 2016, that's only three years. You guys have thousands of customers, five million suppliers on the platform, lot of growth. What are some of the key drivers to this great growth that you're seeing? Well a couple of things, I mean first of all, this is a huge total addressable market. Every company in the world could do a better job of the way they manage their business spending, and they could use information technology, hopefully from Coupa to help make that happen, and we are so proud to cultivate this community of like minded, thoughtful professionals that want to apply best practices, best in-class modern technology solutions like the ones we offer obviously, to drive quantifiable, measurable, outcomes for the companies that they work for. So in many ways, this is a celebration of our customer community and it's a wonderful opportunity to be with our customers here like this every year in Europe and every year in the United States, and now frankly in lots of other places around the world. >> So one of the themes that was also expressed during the keynote was Rachel Botsman's theme of trust and I think about the open community, the open platform and the community that Coupa is building, there's a lot of earned trust there that Coupa has earned from this growing community. Talk to me about what that means to you and the whole team and how it's influencing the direction that Coupa is going in. >> It means a lot to me personally frankly. The O in Coupa stands for Open, and that means not only technically open in terms of APIs and integrations, but it means open in spirit, open in dialogue, honest, transparent communications. I feel that our industry in enterprise software has a legacy or a history of a lot of PowerPoints, and a lot of demos, but frankly, quite a few failures of large scale deployments and a whole host of sectors. And we want to be part of the solution, we want to have an open, authentic, honest communication with our customers, with our prospective customers in the sales process, with our partners, with all of my Coupa colleagues, so we can avoid the friction and nonsense of politics that often gets in the way of driving measurable, meaningful value for every constituent. It's a very, very important thing to me, it's important to my team, and that's something we're doing our very best to cultivate in this Coupa community that we're creating. >> Speaking of cultivation, Coupa is cultivating this category of Business Spend Management. Tell us a little bit more about that and where you are with that. >> Sure, Business Spend Management is a pretty straightforward three words to describe the fact that our buyers and our customers are responsible for literally trillions of dollars and pounds and dollars and euros of spend all over the world. And as information becomes more and more transparent, the buyer, the one who's repsonsible for that spend becomes more and more powerful. So we sit on the side of that buyer, we give them information technology solutions from sourcing, to inventory management, to spend analytics, to procurement, to expensing, to invoicing, to payments, to supplier performance. All the capabilities needed to help them make the best purchasing decisions for their organizations, and help their companies become more profitable so that every one of these Coupa community customers we have here could get more bang for their buck and be that much more operationally efficient frankly in driving their own company's visions and missions and whatever it is that they bring to the world. And that's very aspirational for us and we're excited that so many have come on board with this establishment of the Business Spend Management category with us. >> So if we look at the PIPE, as you were calling it this morning, P-I-P-E, procure, invoice, pay, expense, I memorized that, you've got this one platform that can deliver all of that to this growing community of users who have the ability to get that visibility. That is one of the biggest challenges, I was reading some stats recently about the number of businesses, they were the percentages of businesses that don't have complete visibility over their spend, it's high. >> It's very high, we just did a study of 250 or so CFOs in the UK, and they're doing a great job at budgeting and reporting, but they have minimal visibility into their supply relationships, especially with what's happening here with Brexit. They have minimal visibility in supply risks, supply chain risks, and one of the ingredients that I think we're very special at and I'm proud of is the U in Coupa, the user centricity. In order to have visibility into your spend, you have to have adoption, you have to have people purchasing, spending, expensing, paying, processing invoices, everything that you just mentioned through this pipe on one centralized platform with a common UI layer, User Experience layer or User Interface, common business logic layer, common data model, use of community intelligence to help you make the best purchasing decisions, spend decisions. So we're really on the forefront of something very, very exciting because this adoption level is happening through this user centricity, and it's given these companies control and visibility of spend, and what could be more important to driving profitability, sustained business development? I think we're in a very unique position to help these customers. >> So is one of the biggest challenges for those, think it was 96% of those UK financial decision makers that you guys surveyed said, "We don't have complete visibility." Is it because they have legacy siloed solutions that don't give them that common layer? Or is it because maybe that and a mixture of users just not adopting it because it's not as intuitive to use? >> It's a number of things. First of all, for every process, whether it's procurement, expenses, invoicing, or payments, they have seperate systems to your point. Some cases, they don't even have systems. They're calling in orders, they're handling paper invoices, so there are different levels of maturity in each of those four areas. So one is getting them on to a common platform where all of those are orchestrating together. Secondarily, there's an opportunity to create synergy between those areas, so a lot of things that are getting expensed really should be preapproved and should be routed toward preferential pricing that procurement can negotiate on behalf of the user. Many times invoices are duplicate coming in from suppliers and AP departments are so excited that they pay quickly, but they're not necessarily sure whether they received the goods and services that the invoice is for. So having one common platform, that's the C in Coupa, Comprehensive. One common comprehensive platform for all these business processes is critical, leveraging the synergy of all them working together is critical, and getting that widespread user adoption is part of the secret formula here. >> Let's talk about the community. It's big, it's growing, 1.3 trillion in spend managed, and I watched our video back that you and I did a few months ago, it was 1.2. So that was four months ago, and you showed a bar chart today of just the last 12 months, had to look up this way to see that, so this community that has the ability to help derive and leverage the insights, talk to me about the insights and being able to help businesses go from reactive to predictive as a game changer for Coupa. >> Sure, it's a huge game changer and we really aspire to be, if you will, the tail that wags the dog in the enterprise software industry overall because the enterprise software industry, in effect, every customer is on their own island using information technology for a certain business process. What we've done with community intelligence is we've aggregated, anonymized, and sanitized data from the customer base and then are distilling insights that we could be prescriptive about. So we could tell our customers and we're telling them, "Hey, our community is having challenges with such "and such supplier based on literally perhaps millions "of dollars and millions of pounds in transactional spend. "We recommend you consider this supplier in "that same category because our community is having "great success with them. "The products are being shipped on time, "there's no war over invoicing, there's no breakage in "what's delivered." Those are just some examples, we're helping them think through commodities. A lot of our customers forgiven commodity, they have 20, 30 different suppliers. We're helping them think through in their industry. How can they do supply consolidation that makes sense based on benchmarking across the entire industry? We're helping them avoid supplier risk, we're helping them avoid fraud, we're identifying employees that may be expensing things or doing things that are fraudulent based on the collective intelligence of what we're seeing around the entire world in real time and we're prescribing actions to be taken before payments go out. So these are just some examples of what we're doing, we're doing things in benchmarking based on community intelligence, we're really just at the tip of the sphere of what's possible and we've prescribed tens of thousands of prescriptions in our platform to our customers. Many of them are taking those prescriptions and are making their businesses more operationally fit, and more agile, which is something we're very, very proud of. >> Speaking of those prescriptions, I think the number you shared this morning was 22,000 prescriptions delivered in one year? >> In the last 12 months, that's right. >> So we've got to talk about acceleration 'cause we've talked about the COUP, the acceleration, that is one example of that. I also saw that you guys have gotten, customers are doing approvals 30% faster than they were a year ago. You're getting mid-market customers up and running in four months, large enterprises up in eight months, talk to me about that acceleration that you guys are achieving. >> Absolutely, the A in Coupa is about Accelerated, it's about learning from our entire customer base and taking those learnings and making them part of best practices-based appointments so we could go faster and faster and faster. We look at retail customer, we've done dozens of retail customers, large and small. We know how to set up catalogs, we know how to set up workflow, we know how to think through the analytics that they need. So when they get going with the deployment from Coupa, they can get up and running way faster than with going back to five or six years ago where you have to think about it from scratch and a blueprint. They could leverage the insight from the community with doing that in mid-market, with doing that in subverticals like credit unions, for example. Biotechs, we're doing it in insurance, we're doing it in pharma, all hosts of industries, and I think as we learn from every deployment and collect those insights, we're going to be able to drive value faster and faster to our customers. And the other element that's important here is it's not just taking the customer live, all of our customers grow with us. They get more and more value every year, this is why our renewal rate is so strong and customers add more business with us because they're getting value and that value continues to grow, and that's really what value as a service is about. We're not a software company, and we're not a software as a service company. We're truly a value as a service company, which is a very different concept and one that we're cultivating in this marketplace. >> What are some of your favorite, I know you love being in front of the customers, what are some of your favorite examples that really show the value that Coupa is delivering to the changing role of procurement, making that girl or guy much more strategic and much more of a partner to the business? >> Sure, I shared some examples this morning that I really loved and appreciated celebrating some of our trendsetters, or what we call spendsetters. You look at Zalando, our retailer where they weren't necessarily going to take them so seriously about savings, but when they went to marketing and said, "We can give you much more bang "for your marketing budget "so you could reach more potential consumers," well of course they embraced that. And we gave them a usable opportunity, a usable platform for doing that as similar Zalando, they engaged. Now they have something like 85% spender management. When we started working with them, they had zero purchase orders, everything was the wild west. You look at, I was just speaking to one of our customers at Procter & Gamble just five minutes ago here at the expo. They've run more than 50 billion pounds of spend through the Coupa platform, 50 billion. That's not easy, but they've done that in just a couple of years with us, and not only did they have visibility spent, but they're saving, they're routing purchases to preferred suppliers, so the list just goes on and on and on our website, at Coupa.com on the Customers tab, you'll see obviously dozens of customers holding up signs of the real measurable value they're getting from working with us and that's something that we really take a lot of pride in. >> That speaks for itself. Last question for you Rob, talk to me about those strategic partnerships that Coupa has. I know some news coming out today with what you guys are doing with American Express. >> Sure, we've entered the payment space and we entered it because our customer community asked us for it. They said, "Look, if we're procuring goods "and services through you, why wouldn't we all, "and we're doing invoice and we're doing all "of the components of the pipe, "why wouldn't we also go deeper into payments, help us pay." Because many now have to log in to all these different ERP systems and kick off batch process, so we went into payments. And in payments, we have a host of partnerships. Now, today we announced the relationship with American Express in the UK and Australia for virtual credit card payments. Now it's very simple in Coupa, someone needs a good or service, it gets routed through workflow for approval. Once approved, a dynamic credit card number is generated by American Express, the individual makes the purchase, and all the reconciliation, the back-end is handled by Coupa. All the reporting, the visibility, the insights to price points and category assessments are there and visible and the company's in a position to fine tune their spend profile. So that's just one example, and we're doing things in dynamic discounting and accelerating payments. We've just launched today in general availability and Robby will be discussing it tomorrow ahead of business acceleration. We launched our batch payments capability, the ability to do invoice payments in batch along any rail, whether it be banking relationships, whether it be eCheck, whether it be credit card, going into one environment and kicking off batch payments without having to wait for all these different ERP systems to take hold. So we're really at the, in my mind, at the very beginning of addressing a huge market opportunity, we're proud of what we've achieved so far. I'm particularly proud of the customer community developing around us, and we're excited about the days, weeks, months, quarters, and years to come. >> So you talked about, last question, the big TAM, in this total adjustable market. What are some of the core elements to Coupa's path to a billion in revenue? >> We're not as exciting to many investors as a hot startup that grows really quickly and maybe has some sort of viral component to it. We've been at this for over 10 years, we've grown thoughtfully, we've grown carefully. The growth is fast 30, 40 plus percent, but it's thoughtful and careful, it's one customer at a time. We're careful in how much we spend on sales and marketing, especially want customers to choose us rather than us hard-selling them on everything, we want the offering to sell itself. We have an ecosystem of systems integrators, now more than 3,000, Centric, APMG, Deloitte, and others that are certified on deploying Coupa. We're expanding our product footprint, our customers now use on average 4.7 applications from us and they're consuming those applications rather than us pushing them on them. We're expanding globally, we're expanding in terms of the enterprise business and the mid-market business. Our mid-market business is now really at scale and scaling beautifully, it's a beautiful business model. So those are just some of the vectors in which we'll continue to expand, but I think the path to $1 billion for us is very clear, and ultimately comes down to execution, delivering for every customer, making sure they're getting value from working with us year in and year out, and I think before you know it, we'll be on the doorstep of that $1 billion. >> Excellent. Rob, it's been a pleasure having you back on theCUBE. Thank you for having theCUBE out here in London, we appreciate your time. >> Thank you. >> For Rob Bernshteyn, I am Lisa Martin, you're watching theCUBE from Coupa Insp!re 19. Thanks for watching. (upbeat tech music)

>> Announcer: Live from San Francisco, celebrating 10 years of high tech coverage, it's theCUBE, covering VMworld 2019. Brought to you by VMware and its ecosystem partners. >> Welcome back, this is theCUBE at VMworld 2019. I'm Stu Miniman, my cohost is John Troyer. It's our 10th year at the show, we've been going three days, wall to wall, on two sets and really happy to welcome to the program, a first time guest, Geoff Tudor is the vice president, general manager of Vision.AI, inside Panzura. Thanks so much for joining us. >> Yes, you best suit things John. >> All right, so, we've known Panzura for quite a number of years, the founder of the company's someone we've talked to a bit. I believe this is the first time we've talked about Vision.ai, so maybe set the table with us, of Panzura Today, and the value of the sharing app. >> Sure, absolutely, so, Panzura is known predominantly for its file services, of which we can provide a global collaborative name space, across multiple different locations. So, entities that are in the design, engineering, manufacturing, anything where you're working with a lot of distributed groups that need access to the same kind of working set files, and big data files have been using Panzura for file services for a number of years. We're in 33 countries, 7,000 deployments, and largely in the Fortune 100. And that's kind of where we started to see that the growth of data is not only in user-generated content like PowerPoints or data-based backups, but it's the machine-generated data, and that's what brought us to Vision.ai. >> Okay, so great. The layout, that was an internally created product. How long has it been available, what's the key IP in there that differentiates from others in the marketplace? >> So, great question. Well, the thing with machine-generated data is there's a lot of it, right? And as you know, it's growing at 60% compounding growth rate, all these great statistics, but in order to drive value of machine data, especially when you're looking at ML and AI, the larger the data set, the larger the training data set, the better the prediction models, and one of the problems with today's storage platforms for machine data, is that you're taking data, you're indexing it, then you're putting it on Flash, which is a phenomenal storage platform, but if you're looking for petabytes of Flash for just retaining a couple months worth of data, it becomes very expensive, very fast so a couple of years ago, we took some of the core IP that we had and creating file to object mapping, and said, look, let's build a new cloud native architecture to manage cloud native digital machine-generated data, and be able to transfer that not only for the block storage to put in the object storage. So we created something called VBOS, Vision Block Object Storage, that allows us to take, index this data, and then write it to object but still, while it's an object, have it still searchable, and that really unlocks a value of these very large data sets, so you no longer have to push this off on a tape, or push it off into object storage where it's no longer available, it sits in object storage, but it's always on, it's always available. >> And is this a software offering, does it sit in my bucket somewhere, or does it sit in yours, and then actually are we, machine-generated data, that's a pretty wide term. Are we talking log files, or? >> Well, certainly log files is a core starting point because that's something everyone here at VMWorld, you know, has in common, right? As our systems and records are creating and running virtual machines, it's generating digital data about who accessed what, when, where, when and how, for IP address security information, dashboards, et cetera. So, we've created this as a service because, in a multicloud world, you need one platform where you can ingest these data feeds and these log feeds, and then store that and search it. People have been generating and deploying on-site log files for some time, but we've seen a large interest among our customer base, in a hosted service that can securely store and make their logs accessible. >> All right, Geoff, bring us inside a customer. What is some of the typical use cases, outcomes, that customers, if you have any example that might illustrate it, I'd love that. >> Sure, so we'll take a customer that is in the publishing business and as you know, in the publishing busines, we were going from paper into digital right? So it's just digital transformation and as their industry changed, they became now a web hoster, so the sites and their papers that used to advertise and their classifieds and by-print ads, they're now managing their digital experience, well, as they were doing that, they came into a situation where some of their sites were having unpredictable performance lows, and they're just sophisticated enough to have one IT person managing, you know, 50 different, to about 50 different servers, virtual machines, running these, hosting these sites. So they needed help, like is there a platform that can come help me create dashboards so I can visualize this log data that came in to us so our partner, one of our key partners here is phoenixNAP at the show, and Intel's demonstrating our Octane Accelerated Technology, so we went into this particular customer, onboarded him in five minutes, created the dashboards for him, and now their logs are coming in a number of gigabytes per day, and that can visualize and find out any points of their operations that are creating problems and slow access time for their customers. >> You know, I love the storage data aspect of it, right? The searchable object store sounds very neat, I bet there's some very cool computer science there, storage and data geeks love that. It's also got AI in the name, and we talked about ML and AI, so where does that come into the picture? >> Absolutely, sir, great question. The AI and ML aspect of this is because as you get primarily the large data set sizes, then you can start putting machine generated algorithms on top of it, right, so creating large data stores, and then the first machine generated analytics that we've run on top of it, are things such as storage prediction costs, it's actually one where we've saved one of our customers in financial services, tens of thousands of dollars a month, because we can look at their bucket, their bucket sizes and the access times to their S3 buckets and say look, you know, you're actually not accessing it. You can drop it down in the infrequent access and you're not going to get a higher bill, so we can run these analytics for them, provide that data to them. >> Geoff, we're here at VMWorld. >> Yes. >> VMworld's talking a lot about multicloud, and microservices, cloud native, VMware cloud pieces, help us understand the intersection between what you're doing, and how that ties into VMware and their customers. >> Absolutely, well, in a multicloud world, VMware is obviously one very important component of it. But there's also components that are non-Vsphere based, right, and so, we have to be cognizant of this, and need a platform that can support any data feed from any data source, so that's certainly one of them. But number two, as you mention it, microservice. Traditional log platforms or machine data platforms such as Elastix, or Archer Splunk or things like that, is where you go and you create your architecture, and your infrastructure, and you manage that infrastructure as you're putting that data into it so it puts operational burden on the customer to go manage all this. In our view of the world, it needs to be completely serverless. You need to be able to consume machine data, log data, as a microservice in a complete service mythology, so you send your data into this URL, it goes into your bucket, it's encrypted, it's dropped into your object service where it's searchable. >> Yeah, it's funny, I've been looking at the serverless space for a couple of years now, functions, really interesting stuff, Kelsey Hightower actually put out, he said, isn't most of networking serverless by definition? Maybe just clarify that for us. >> Yeah, so serverless is just like the cloud. It's just somebody else's data center, okay? >> I actually have the T-shirt for serverless, it updates that there is no cloud, it's a computer, it's just a microservice that you pay a little bit for when you need it, things like that. >> Right, when you need it, but really, it gets into if I want to spin up elastic searches, talk about that, because that is one of the key workloads that's running in our platform, when you talk about elastic search service, if you want to spin that up, you need to go literally spin up virtual machines, assign block storage to those virtual machines, and hope that you assign enough storage for your data ingestion, otherwise your performance is going to go down, your data is going to become blocked, then you're going to need to assign storage. So, you're still managing stuff, even though it's in the cloud, in our world, we're kind of trying to turn machine generated data and democratize it into simple as a Gmail account right? I create, I request a microservice endpoint, then you write to that endpoint. Now, of course, we're managing servers, and we're managing clusters and virtual machines, and all that funness, but it's transparent to you, and most importantly, you're not hit with any cost for the infrastructure. You're only charged for what you're consuming, and that means it's a complete consumable base bottle from that standpoint, which saves customers a lot money from otherwise having to buy and host a lot of infrastructure. >> So, Geoff, you have a big presence here at the show, A nice booth, I hope it's been a good week, I'm curious about what you thought the energy was, I think you all had an announcement, talk a little about that and how that works with the ecosystem in the audience here. >> Yeah, we actually have two announcements, and let's take the first from the file services, because from our file service platform, we're announcing Vsense certification, which is coming in the fall, we've gone through that process, so that anywhere you're running VMware, on any of the cloud providers on top of SAN, vSAN, you now have a file services platform on top of that that can expand beyond just your NVMe, and also leverage that object storage for this kind of infinite filer, if you will, for that, but the other announcement we have is the log analytics service. >> All right, yeah, tell us a little bit about customer meetings you're having. What are the things that are bringing customers to you, is there a certain thing that, you know, when you hear it, you're like ah, this is a perfect Panzura customer. >> Well, yeah, certainly, I would think that any data and storage is just a universal problem, and people can't get enough of it, and ultimately they want to get out of the business of managing storage a lot, in this case so in that particular instance, being able to offer them a software defined file system platform for our traditional filer environment, is something that's going to, it's just a evergreen forest, right, it's going to continue to grow, you know, the performance of file and flash at the price of object, that's a pretty clear value proposition. In the machine generated data analytics space with Vision.ai, it is how do I make sense of my data? I need to take all of these data streams, and actually put some intelligence on it, and create alerts, visualize this data, so our big proposition here is five minutes to visualize your data, and that resonates. I can walk these customers that are traditionally having to go build their own log service environments, and I'm saying here, let me onboard you. We can actually start sending their data up and having visualizations and alerts in five minutes, and that's revolutionary to them, right? The simplicity of it is key, and I think that's making IT simple to consume and democratizing is something we're focused on doing. >> Geoff, last thing I have, tell us a little bit about what we should expect going forward. Obviously, the AI and ML stuff is continuing to grow, what should customers be looking for from Panzura in the near future? >> No matter how sophisticated a customer in an enterprise is, they don't have enough smart people, right? And data scientists are very expensive, and they're very scarce so what we're doing and focused on doing and we will be doing more of, is we've built a marketplace, a marketplace where data applications, data analytics applications, can be created by the community, can be published into and be consumed by an enterprise, so that they have their account, they add in this application, they can immediately start utilizing and experiencing and unlocking the power of their data. >> Geoff Tudor, really appreciate the update on Panzura. Congrats on the progress of Vision.ai, and hope to catch up with you in the near future. >> Great, thanks so much, I look forward to next year. >> For John Troyer, I'm Stu Miniman, getting towards the end of our coverage from Vmworld 2019, but as always, thanks for watching theCUBE. (gentle music)

>> From Cambridge, Massachusetts. It's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts, everybody. We're here at MIT, sweltering Cambridge, Massachusetts. You're watching theCUBE, the leader in live tech coverage, my name is Dave Vellante. I'm here with my co-host, Paul Gillin. Special coverage of the MITCDOIQ. The Chief Data Officer event, this is the 13th year of the event, we started seven years ago covering it, Mark Ramsey is here. He's the Chief Data and Analytics Officer Advisor at Ramsey International, LLC and former Chief Data Officer of GlaxoSmithKline. Big pharma, Mark, thanks for coming onto theCUBE. >> Thanks for having me. >> You're very welcome, fresh off the keynote. Fascinating keynote this evening, or this morning. Lot of interest here, tons of questions. And we have some as well, but let's start with your history in data. I sat down after 10 years, but I could have I could have stretched it to 20. I'll sit down with the young guns. But there was some folks in there with 30 plus year careers. How about you, what does your data journey look like? >> Well, my data journey, of course I was able to stand up for the whole time because I was in the front, but I actually started about 32, a little over 32 years ago and I was involved with building. What I always tell folks is that Data and Analytics has been a long journey, and the name has changed over the years, but we've been really trying to tackle the same problems of using data as a strategic asset. So when I started I was with an insurance and financial services company, building one of the first data warehouse environments in the insurance industry, and that was in the 87, 88 range, and then once I was able to deliver that, I ended up transitioning into being in consulting for IBM and basically spent 18 years with IBM in consulting and services. When I joined, the name had evolved from Data Warehousing to Business Intelligence and then over the years it was Master Data Management, Customer 360. Analytics and Optimization, Big Data. And then in 2013, I joined Samsung Mobile as their first Chief Data Officer. So, moving out of consulting, I really wanted to own the end-to-end delivery of advanced solutions in the Data Analytics space and so that made the transition to Samsung quite interesting, very much into consumer electronics, mobile phones, tablets and things of that nature, and then in 2015 I joined GSK as their first Chief Data Officer to deliver a Data Analytics solution. >> So you have long data history and Paul, Mark took us through. And you're right, Mark-o, it's a lot of the same narrative, same wine, new bottle but the technology's obviously changed. The opportunities are greater today. But you took us through Enterprise Data Warehouse which was ETL and then MAP and then Master Data Management which is kind of this mapping and abstraction layer, then an Enterprise Data Model, top-down. And then that all failed, so we turned to Governance which has been very very difficult and then you came up with another solution that we're going to dig into, but is it the same wine, new bottle from the industry? >> I think it has been over the last 20, 30 years, which is why I kind of did the experiment at the beginning of how long folks have been in the industry. I think that certainly, the technology has advanced, moving to reduction in the amount of schema that's required to move data so you can kind of move away from the map and move type of an approach of a data warehouse but it is tackling the same type of problems and like I said in the session it's a little bit like Einstein's phrase of doing the same thing over and over again and expecting a different answer is certainly the definition of insanity and what I really proposed at the session was let's come at this from a very different perspective. Let's actually use Data Analytics on the data to make it available for these purposes, and I do think I think it's a different wine now and so I think it's just now a matter of if folks can really take off and head that direction. >> What struck me about, you were ticking off some of the issues that have failed like Data Warehouses, I was surprised to hear you say Data Governance really hasn't worked because there's a lot of talk around that right now, but all of those are top-down initiatives, and what you did at GSK was really invert that model and go from the bottom up. What were some of the barriers that you had to face organizationally to get the cooperation of all these people in this different approach? >> Yeah, I think it's still key. It's not a complete bottoms up because then you do end up really just doing data for the sake of data, which is also something that's been tried and does not work. I think it has to be a balance and that's really striking that right balance of really tackling the data at full perspective but also making sure that you have very definitive use cases to deliver value for the organization and then striking the balance of how you do that and I think of the things that becomes a struggle is you're talking about very large breadth and any time you're covering multiple functions within a business it's getting the support of those different business functions and I think part of that is really around executive support and what that means, I did mention it in the session, that executive support to me is really stepping up and saying that the data across the organization is the organization's data. It isn't owned by a particular person or a particular scientist, and I think in a lot of organization, that gatekeeper mentality really does put barriers up to really tackling the full breadth of the data. >> So I had a question around digital initiatives. Everywhere you go, every C-level Executive is trying to get digital right, and a lot of this is top-down, a lot of it is big ideas and it's kind of the North Star. Do you think that that's the wrong approach? That maybe there should be a more tactical line of business alignment with that threaded leader as opposed to this big picture. We're going to change and transform our company, what are your thoughts? >> I think one of the struggles is just I'm not sure that organizations really have a good appreciation of what they mean when they talk about digital transformation. I think there's in most of the industries it is an initiative that's getting a lot of press within the organizations and folks want to go through digital transformation but in some cases that means having a more interactive experience with consumers and it's maybe through sensors or different ways to capture data but if they haven't solved the data problem it just becomes another source of data that we're going to mismanage and so I do think there's a risk that we're going to see the same outcome from digital that we have when folks have tried other approaches to integrate information, and if you don't solve the basic blocking and tackling having data that has higher velocity and more granularity, if you're not able to solve that because you haven't tackled the bigger problem, I'm not sure it's going to have the impact that folks really expect. >> You mentioned that at GSK you collected 15 petabytes of data of which only one petabyte was structured. So you had to make sense of all that unstructured data. What did you learn about that process? About how to unlock value from unstructured data as a result of that? >> Yeah, and I think this is something. I think it's extremely important in the unstructured data to apply advanced analytics against the data to go through a process of making sense of that information and a lot of folks talk about or have talked about historically around text mining of trying to extract an entity out of unstructured data and using that for the value. There's a few steps before you even get to that point, and first of all it's classifying the information to understand which documents do you care about and which documents do you not care about and I always use the story that in this vast amount of documents there's going to be, somebody has probably uploaded the cafeteria menu from 10 years ago. That has no scientific value, whereas a protocol document for a clinical trial has significant value, you don't want to look through manually a billion documents to separate those, so you have to apply the technology even in that first step of classification, and then there's a number of steps that ultimately lead you to understanding the relationship of the knowledge that's in the documents. >> Side question on that, so you had discussed okay, if it's a menu, get rid of it but there's certain restrictions where you got to keep data for decades. It struck me, what about work in process? Especially in the pharmaceutical industry. I mean, post Federal Rules of Civil Procedure was everybody looking for a smoking gun. So, how are organizations dealing with what to keep and what to get rid of? >> Yeah, and I think certainly the thinking has been to remove the excess and it's to your point, how do you draw the line as to what is excess, right, so you don't want to just keep every document because then if an organization is involved in any type of litigation and there's disclosure requirements, you don't want to have to have thousands of documents. At the same time, there are requirements and so it's like a lot of things. It's figuring out how do you abide by the requirements, but that is not an easy thing to do, and it really is another driver, certainly document retention has been a big thing over a number of years but I think people have not applied advanced analytics to the level that they can to really help support that. >> Another Einstein bro-mahd, you know. Keep everything you must but no more. So, you put forth a proposal where you basically had this sort of three approaches, well, combined three approaches. The crawlers to go, the spiders to go out and do the discovery and I presume that's where the classification is done? >> That's really the identification of all of the source information >> Okay, so find out what you got, okay. >> so that's kind of the start. Find out what you have. >> Step two is the data repository. Putting that in, I thought it was when I heard you I said okay it must be a logical data repository, but you said you basically told the CIO we're copying all the data and putting it into essentially one place. >> A physical location, yes. >> Okay, and then so I got another question about that and then use bots in the pipeline to move the data and then you sort of drew the diagram of the back end to all the databases. Unstructured, structured, and then all the fun stuff up front, visualization. >> Which people love to focus on the fun stuff, right? Especially, you can't tell how many articles are on you got to apply deep learning and machine learning and that's where the answers are, we have to have the data and that's the piece that people are missing. >> So, my question there is you had this tactical mindset, it seems like you picked a good workload, the clinical trials and you had at least conceptually a good chance of success. Is that a fair statement? >> Well, the clinical trials was one aspect. Again, we tackled the entire data landscape. So it was all of the data across all of R&D. It wasn't limited to just, that's that top down and bottom up, so the bottom up is tackle everything in the landscape. The top down is what's important to the organization for decision making. >> So, that's actually the entire R&D application portfolio. >> Both internal and external. >> So my follow up question there is so that largely was kind of an inside the four walls of GSK, workload or not necessarily. My question was what about, you hear about these emerging Edge applications, and that's got to be a nightmare for what you described. In other words, putting all the data into one physical place, so it must be like a snake swallowing a basketball. Thoughts on that? >> I think some of it really does depend on you're always going to have these, IOT is another example where it's a large amount of streaming information, and so I'm not proposing that all data in every format in every location needs to be centralized and homogenized, I think you have to add some intelligence on top of that but certainly from an edge perspective or an IOT perspective or sensors. The data that you want to then make decisions around, so you're probably going to have a filter level that will impact those things coming in, then you filter it down to where you're going to really want to make decisions on that and then that comes together with the other-- >> So it's a prioritization exercise, and that presumably can be automated. >> Right, but I think we always have these cases where we can say well what about this case, and you know I guess what I'm saying is I've not seen organizations tackle their own data landscape challenges and really do it in an aggressive way to get value out of the data that's within their four walls. It's always like I mentioned in the keynote. It's always let's do a very small proof of concept, let's take a very narrow chunk. And what ultimately ends up happening is that becomes the only solution they build and then they go to another area and they build another solution and that's why we end up with 15 or 25-- (all talk over each other) >> The conventional wisdom is you start small. >> And fail. >> And you go on from there, you fail and that's now how you get big things done. >> Well that's not how you support analytic algorithms like machine learning and deep learning. You can't feed those just fragmented data of one aspect of your business and expect it to learn intelligent things to then make recommendations, you've got to have a much broader perspective. >> I want to ask you about one statistic you shared. You found 26 thousand relational database schemas for capturing experimental data and you standardized those into one. How? >> Yeah, I mean we took advantage of the Tamr technology that Michael Stonebraker created here at MIT a number of years ago which is really, again, it's applying advanced analytics to the data and using the content of the data and the characteristics of the data to go from dispersed schemas into a unified schema. So if you look across 26 thousand schemas using machine learning, you then can understand what's the consolidated view that gives you one perspective across all of those different schemas, 'cause ultimately when you give people flexibility they love to take advantage of it but it doesn't mean that they're actually doing things in an extremely different way, 'cause ultimately they're capturing the same kind of data. They're just calling things different names and they might be using different formats but in that particular case we use Tamr very heavily, and that again is back to my example of using advanced analytics on the data to make it available to do the fun stuff. The visualization and the advanced analytics. >> So Mark, the last question is you well know that the CDO role emerged in these highly regulated industries and I guess in the case of pharma quasi-regulated industries but now it seems to be permeating all industries. We have Goka-lan from McDonald's and virtually every industry is at least thinking about this role or has some kind of de facto CDO, so if you were slotted in to a CDO role, let's make it generic. I know it depends on the industry but where do you start as a CDO for an organization large company that doesn't have a CDO. Even a mid-sized organization, where do you start? >> Yeah, I mean my approach is that a true CDO is maximizing the strategic value of data within the organization. It isn't a regulatory requirement. I know a lot of the banks started there 'cause they needed someone to be responsible for data quality and data privacy but for me the most critical thing is understanding the strategic objectives of the organization and how will data be used differently in the future to drive decisions and actions and the effectiveness of the business. In some cases, there was a lot of discussion around monetizing the value of data. People immediately took that to can we sell our data and make money as a different revenue stream, I'm not a proponent of that. It's internally monetizing your data. How do you triple the size of the business by using data as a strategic advantage and how do you change the executives so what is good enough today is not good enough tomorrow because they are really focused on using data as their decision making tool, and that to me is the difference that a CDO needs to make is really using data to drive those strategic decision points. >> And that nuance you mentioned I think is really important. Inderpal Bhandari, who is the Chief Data Officer of IBM often says how can you monetize the data and you're right, I don't think he means selling data, it's how does data contribute, if I could rephrase what you said, contribute to the value of the organization, that can be cutting costs, that can be driving new revenue streams, that could be saving lives if you're a hospital, improving productivity. >> Yeah, and I think what I've shared typically shared with executives when I've been in the CDO role is that they need to change their behavior, right? If a CDO comes in to an organization and a year later, the executives are still making decisions on the same data PowerPoints with spinning logos and they said ooh, we've got to have 'em. If they're still making decisions that way then the CDO has not been successful. The executives have to change what their level of expectation is in order to make a decision. >> Change agents, top down, bottom up, last question. >> Going back to GSK, now that they've completed this massive data consolidation project how are things different for that business? >> Yeah, I mean you look how Barron joined as the President of R&D about a year and a half ago and his primary focus is using data and analytics and machine learning to drive the decision making in the discovery of a new medicine and the environment that has been created is a key component to that strategic initiative and so they are actually completely changing the way they're selecting new targets for new medicines based on data and analytics. >> Mark, thanks so much for coming on theCUBE. >> Thanks for having me. >> Great keynote this morning, you're welcome. All right, keep it right there everybody. We'll be back with our next guest. This is theCUBE, Dave Vellante with Paul Gillin. Be right back from MIT. (upbeat music)

>> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019, brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World here in Las Vegas. I'm your host, Rebecca Knight along with my co-host John Furrier, we are joined by Jitesh Ghai, he is the Senior Vice President and General Manager Data Quality, Security and Governance at Informatica. Thank you so much for coming or returning to the show Jitesh. >> My pleasure, happy to be here. >> So, this is a real moment for data governance, we have the anniversary of GDPR and the California Privacy Act it's a topic at Dabos, there is growing concern among the public and lawmakers over security and privacy, give us the lay of the land from your perspective. >> Right, you know it is a moment for data governance, what's exciting in the space is governance was born out of risk and compliance and managing for risk and compliance, but really what it was mandating was healthy data management practices, how do we give the regulators comfort that our data is of high quality, that we know the lineage of where data is coming from that we know how the business relies on the data what is critical data? And while it was born to give the regulators comfort, what organizations very quickly realized is well when you democratize data, you need to give everybody that comfort, you need to give your data scientists, your data analysts, that same level of contextual understanding of their data right, where did it come from? What's the quality of it? How does the business use it, rely on it? And so that has been a tremendous opportunity for us, we've supported organizations, financial services from a BCBS 239 CCAR, counterparty credit risk, but what's happened is from a data democratization, data scale perspective, self-service analytics perspective, is what moved from terabytes to petabytes. We've moved from data warehouses, to data lakes and you can't democratize data unless there's a governed framework. I don't know, it sounds kind of like wait, democratizing data is supposed to be free data everywhere, but without some governed framework, it's a bit of a mess, and so what we're enabling organizations is the effective consumption and understanding of where their data is, discovering it, so that the right people can consume the data that they care about, the right data scientists can build the right models, the right analysts can build the right reports and the executives get the right confidence on what reports they're getting, what KPI's they're getting. >> One of the things that we talked last year, you had a couple customers on, you had told a great story, you guys had had the benefit as a long-standing company, 25 years in the private for large-customer base, but the markets changed, you mentioned governance I mean we're in the one year-anniversary of GDPR. >> Right. >> And I think everyone's kind of like OK what happened last year? More privacy laws are coming and one of the themes this year is clarity with data, but also in the industry you know access to data, making data addressable, because AI needs data sets, cloud has proven that, SAS business models, using data winning formula, that's clear if you're born in the cloud. Enterprises now want that same kind of SAS-like execution on the applications side, whether it's SAS or using AI for instance, >> Right. >> So when you have more regulation, inherent nature is to oh like more complexity, how are customers dealing with the complexity of this, because they want to free it up, but at the same time they want to make sure that they can respect the laws for individuals, but also governments aren't that smart either so you know, the balance there, what's the strategy? >> And therein lies the challenges with privacy specifically, it's not just about quality counterparty credit risk in like five or seven systems in a data warehouse, it's all the data in your enterprise, it's the data in production, there's the data in your DevOps environment, it's all your data literally, structured all the way to unstructured data like Word, PDFs, Powerpoints. And you need a governing framework around it, you need to enable organizations to be able to discover where is there sensitive information, how is there sensitive information proliferating through the organization? Is it protected? Is it not protected? And what's particularly, you know, we're all consumers, I'm pretty confident some or all of our data has been breached at some point, enabling organizations, what these privacy regulations are doing is they are giving us, as individuals, rights to go to the organizations we transact with and ask them, what are you doing with out data? Forget my data or at least tell me how you're processing it and get my consent for the data. >> Yeah, I mean policy and business models are certainly driving that and with regulation, I see that, but the question is that when you move the impact to the enterprise, you got storage drives. You store it on drives as a storage administrator you've got software abstractions with data, like you guys do. So, it's complicated, so the question is, for you, is what are customers doing now? What's the answer to all this? >> The answer really comes down to you need to scale to the scope of the problem, it's a thousand x-increase, you're going from terabytes to petabytes right? And so, you need an AI, an ML, an intelligent solution that can discover all of this information, but it can map it to John Furrier, this is where John Furrier's information is, it's in the human capital management system, the CRM system, organizations know, may start knowing whether sensitive data is, but hey don't know who it belongs to, so when you go to invoke your right to be forgotten or portability, today, what we're enabling organizations with is hey, we'll help you discover the sensitive information, but we'll also tell you who it belongs to, so that when John shows up or Rebecca, you show up, you just have to punch in their name and we'll tell you all the systems, that it's in. That is something that requires teams of database administrators, lawyers, system administrators that needs to be automated, to truly realize the potential of these privacy regulations, while enabling organizations to continue to innovate and disrupt with data. >> What's your take on whether or not consumers truly understand the scope of these privacy regulations, I mean talking about GDPR and you get the pop-ups that say do you consent and you just say yes, I just need to get to this site and so you blithely, just press yes, yes, yes so you are technically giving your consent, but do you, I mean what's your take, do consumers truly understand what they're doing here? >> You know, I think historically, we've all said yes, yes, yes, over the last, I would say two years with growing regulations and significant breaches, there is a change in customer expectations, you know, there's a stat out there in the event of a data breach, two-thirds of consumers of a particular organization blame the organization for the breach, not the hackers, right, so it's a mindshift in all of us, where you're the custodian of my data I'm counting on you, whatever organization I'm transacting with ,to ensure and preserve my privacy, ensure my data's protected. So, that's a big shift that's happened, so whether you're doing it for regulatory reasons, CCPA North America, there's several other state-wide regulations coming out or GDPR, the consumer expectation, forget regulations, it's brand preservation, it's customer trust, it's customer experience, that organizations are really having to solve for from a privacy standpoint. >> Tell what the news around yesterday around the shift of the trust pieces, because that's a huge deal. Because trust is shifting, expectations are shifting, so when you have shifting expectations, with users and buyers, customers, the experience has to shift. So, take us through what's the new things? >> Well, the new things are, you know, you look at we're enabling organizations to be data-driven, we're enabling organizations to transform, build new products, new services, be more efficient and for that, you need to enable them to get access to data. The counter, the tension on the other end is how do we get them broad-based access while ensuring privacy, right, and that's the balance. How do we enable them to be customer-centric and optimal in engaging with their customers while preserving the privacy of their customers and that really comes down to having a detailed understanding of what your critical data is, where is it in the organization and how an organization is using that data. Enabling an organization to know that they're processing data with the appropriate consent. >> What's interesting to me, when I was with press yesterday, is also the addition of how the cloud players are coming onboard, because you know, one constituent that's not mentioned in that statement is that you guys are kind of keeping an eye on, that are impacted by this, is developers, because you know developers like infrastructures coded with DevOps. Don't want to be provisioning networks and storage, they just write to the API's. Data is kind of going through that similar experience where, if I'm a developer doing an IOT app, I'm just going to use the cloud. I put the data there, I don't need to have a mismatch of mechanisms to deal with some governance compliance rules. >> Correct and that's why it needs to be built-in by design. And you know there's this connotation that- >> Explain that, what does built-in by design mean. >> Well you need to have privacy built-into how you as a business operate, how you as a DevOps team or development team, build products, if that's built-in to how you operate, you enable the innovation without falling into the pitfalls of oh you know what we broke some privacy regulations there we breached our customers trust there, we used data or engaged with them in manner that they weren't comfortable with. >> So, don't retro-fit after the fact? Think holistically on the front-end of the transformation in architecture. >> It's an enabler, in that if you do it right to begin with, you can continue to innovate and engage effectively, versus bolting it on as an afterthought and retro-fitting. >> It really seems like it is this evolution in thinking from this risk and compliance, overdoing this to check all the boxes, versus here are our constraints, but our constraints are actually liberating, is what you're saying. >> Right, but you can't democratize data, without giving the consumers of that data an understanding of the quality of that data, the trustworthiness of that data, the relevance of the data to the business, you give them that and now you're enabling your analytics, your data scientists, your analytics organizations to innovate with that data with confidence and if you do it within a framework of privacy, you're ensuring that you're preserving customer trust while you're automating and building intelligent and engaging customer experiences. >> What I love about the data business right now, is it's exciting because it's real specific examples of impact, security, you know, national security, to hackers, to just general security, privacy of the laws, But, I've seen the development angles interesting too, so when you got these two things moving, customers can ignore this, it's not like back-up and recovery where same kind of ethos is there, you don't want to think about it after the fact, you want to build it in, you know, there's certainly reasons why you do that, in case there's a disaster, but data is highly impactful all the time. This is a challenge, you guys can pull this off. >> Well you know, it's a, with privacy, it's no longer about a few systems, it's all your data and so the scope is the challenge and the scale applies for privacy, the scale applies for making data available enterprise wide and that's where you need and you know we spoke about AI needs data, well data also needs AI. And that's where we're leveraging AI and ML. Building out intelligence, to help organizations solve that problem and not do it manually. >> You know, I've said it on theCUBE, you've probably heard it many times, I say it all the time, scale is the new competitive advantage. Value is the new lock-in. No proprietary software anymore, but technology is needed. I want to ask you, you've been talking about this with some of your customers last year around data is that you need more scale, because AI needs more access to data, because the more visibility into data, the smarter, machine learning and AI applications can become. So Scale is real. What is the, what are you, you guys have some scalosity in your customers, you got the end-to-end, got the catalog and everything is kind of looking good, but you have competition How would you compare to the competition, when people say hey Jitesh, a start-up just popped out or XYZ company's got the solution, why should I go with them or you? What's the difference, what's the competitive angle? >> You know, the way we're thinking the problem is founded on governance is an enabler it's not about locking things down for risk and compliance, because you know, the regulators want to know that this particular warehouse is highly tightly controlled, it's about getting the data out there, it's about enabling end-users to have a contextual understanding when you're doing that for all of your data, within around, that's a thousand X-increase in the data, it's a thousand X-increase in your constituents, you're not supporting, the risk and compliance portions of the organization, you're supporting marketing, you're supporting sales, you're supporting business operations, supply chain, customer-onboarding and so with the problem of scale, practices of the past, which were typically manual laborious, but hey at the risk of non-compliance, we just had to deal with them, don't practically in any way scale, to the requirements of the future which is a thousand X-increase in consumers and that's where intelligence and AI and ML come in. >> The question I have for you is, where should customers store their data? Is there an answer to that on premises or in the cloud? What are they doing? >> The answer is yes, (Knight laughs) the customer should store their data, what we see, the world is going to be hybrid, mainframes are still here, on-premise will still be here many years from now. >> So you're taking the middle of the road here, so >> There's Switzerland. >> You're saying whatever they want on-premise or cloud, is there a preference you see with customers? >> Well, you know it depends on the applications , depends on regulations, historically regulations especially in financial services, have mandated a more on-premise stance, but those regulations, are also evolving and so we see, the global investment banks all of a sudden, we're having all sorts of conversations about enabling them to move select portions of their data estate to the cloud, enabling them to be more agile, so the answer is yes and it will be for a very long time to come. >> Final question, one of the most pressing problems in the technology industry is the skills gap. I want to hear your thoughts on it, how as a Senior Executive at Informatica, how worried are you about finding qualified candidates for your open-roles? >> You know, it is a challenge, good news is, we're a global organization, my teams are globally-distributed. I have teams in Europe, North America and Asia and the good part about that is if you can't find it in the valley, you can certainly find the talent elsewhere, and so while, it is a challenge, we're able to find talented engineers, software developers, data scientists, to help us innovate and build the intelligence capabilities to solve the productivity challenges, the scale challenges of data consumption. >> Jitesh, talk about the skills required for people coming out of school, take your Informatica hat off, put your expertise hat on, data guru hat, knowing that data is going to continue to grow, continue to have more impact across the board, from coding to society affix, whatever, what are some of the key skills in training, classes or courses or areas of expertise that people an dial-up or dig into that might be beneficial to them that may or may not be on the radar curriculum or, say is, part of school curriculum, >> you know we engage with universities in North America, in Europe, in Asia, we have a large development center in India and we're constantly, engaging with them. We're on various boards at various universities, advisory standpoint, big data standpoint and what we're seeing is as we engage with these organizations, we're able to feed back on where the market is going, what the requirements are, the nature of data science, the enabling technologies such as platforms like Spark, languages like Python and so we're working with these schools to share our perspectives, they in turn, are incorporating this into their curriculums and how they train future data scientists. >> When you see a young gun out there that's kicking butt and taking names and data, what are some of the backgrounds? Is it math, is it philosophy, is there a certain kind of pattern that you've seen as the makeup of just the killer data person? >> You know, it's interesting, you mention philosophy, I'm a big, I've hired many philosophy majors that have been some of the best architects, having said that, from a data science perspective, it's all about stats, it's all about math and while that's an important skillset to have, we're also focused on making their lives easier, they're spending 70% of their time, doing data engineering versus data science and so while they are being educated from a stats, from a data science foundation, when they come into the industry, they end up spend 70% of their time doing data engineering, that's where we're helping them as well. >> So study your Socrates and study your stats. >> I like that. (Knight and Furrier laugh) >> Jitesh, thank you so much for coming on theCUBE. >> My pleasure, happy to be here, thank you. >> I'm Rebecca Knight for John Furrier, you are watching theCUBE.

>> Narrator: Live from London, England, it's theCUBE covering AWS summit London 2019, brought to you by Amazon Web Services. >> Welcome back to XL London everybody, My name is Dave Velante and you're watching theCUBE, the leader in live tech coverage. This is our one-day coverage of AWS summit, London. theCUBE will go up to the events we extract the signal of the noise and I have recruited a co-host Theresa Carlson who's a friend and vice president worldwide public sector at AWS and we have a really special segment for you today. Anna Sergeant is here. She's a computing teacher and Charlotte who's a student at Townley. Wait till you see what we have in store for you. Theresa, let's get it started. So first of all, welcome (mumbles). >> Well, and I'm so thrilled to be your co-host, I just wanna tell you that right now. >> That's a first for you, right? >> Yes, it is. >> I finally got one up on for you. >> Yeah, exactly, I get to be on theCUBE (mumbles). >> So here's the deal, so you have this GetIT program. Tell us what that's all about and then we'll get into it. >> Well you know, we talked about over the last few years just in general about skills. Skills development, how critical it is and important for every age and GetIT is really a continuation of what we're trying to do to create job skills around cloud computing at every age, especially in elementary and primary school years. So GetIT today, what you're going to see from both Charlotte and Anna is we did a competition, there was over 160 applicants and it got netted down to ten schools that came here today and then we had two finalists and then we deemed a winner and they're going to get support and help but also, all these schools are gonna get support and help but it's really about the experience of them learning how to utilize cloud computing in a real-world application that actually matters to them which you can also fight to kind of social responsibility which most of these young people really relate to because they want to do something that matters to them. Just tech for tech is not exciting but tech for good is very exciting and I think that's what you're gonna hear about here today. >> We love to talk about tech for good and Anna, you're at the heart of this so how did you get into this, how did you get this all started, tell us your story. >> Well, my head teacher is quite an innovative person and she's been in conversations with Amazon and Future Foundations and they came to the school with the idea last year and invited the school to be part of the pilot program and so the Amazon ambassadors delivered their presentation to the school in September and as a team in the computing department, we got together and said, well we think this is a great opportunity for girls in tech. So we actually rolled it out as an actual scheme of lessons so the whole year eight, so that's 224 year eight students got together. They all were divided into groups of their own choice and we gave them the outline or the brief and said you know, think of an app for good that would fulfill a social need in your community. So think about the community and prepare a pitch and we then set timelines and deadlines and helped them through the research and obviously spoke to Amazon, came to the London offices as well and spent some time with your colleagues in the London office and you know, and then basically helped the girls pitch their final idea. >> So Charlotte, you got this prompt essentially and then you took it from there. Tell us a little bit about yourself and then how this all came about and what you guys did with that prompt. >> And today is your birthday so happy birthday. >> Congratulations. >> Thank you. So basically I'm 13 at the moment but we've been doing this project in year eight as Anna said and basically, we were given the idea to make an app and everyone was really excited initially, but we weren't too sure about what we wanted to make it on and we were lucky enough to have the choice to choose whatever topic we wanted to make it on and kind of decide what cause we wanted to help and the solution to help it with and then we were given loads of help with the Amazon ambassadors and they really were like really kinda generous with all their help. They came to visit us and they watched our presentations and it really gained our confidence because we presented to the class and in front of the teachers and Amazon ambassadors and it's been really lovely because we've been able to gain skills that we didn't have before in computing and it's gained our confidence, it's boosted it and we've just become much like more interested in STEM and computing. >> Charlotte, let me ask you, what was your application about and what inspired you for the application? >> So my app was called Positive Of Me and we based it off of a mental health and kind of having a more positive outlook on life and we decided to do that topic because we thought that it was really important to students to have a stress-free time in school rather than always feeling stressed and under the weather because they have a lot of work or they're under-organized and stuff like that so we believe that it was quite important to help people like that so our features included like a planner, a mood tracker and just other things to kind of keep you organized and happy throughout your school life. >> So fascinated by the adoption of this approach and were you always interested in STEM or was it something that, this catalyzed your interests in your colleagues. >> I was always interested in STEM and in Townley, they like promote it a lot and they're very interested in like, because it's an all-girls school. We promote females and like we try to make sure that girls are interested in all subjects no matter what and it's been quite nice but I believe that it kind of made me more interested in STEM with my classmates because we've had a fun experience. It's not just been doing computing, it's been having a fun experience. We've been designing our own thing that we're passionate about so it's been really lovely in that sense. >> Dave: So, please go ahead. >> Well, I was gonna ask you, how did you bring it together as a group. What were kind of the core components that you worked on to bring the app together and then have the final that you got here today with. >> So we kind of thought of the idea first about mental health, that was kind of our starting point and then we developed it to what features we can include in the app. We made a mind map saying whatever features we wanted, what topics we wanted to cover and then we thought about the target audiences and they really helped us think about this in the boot camp that they hosted. It was really helpful because Amazon ambassadors came to each kind of app and they helped us with what we could include and how to build on that idea. So that helped us include the target audiences, the ages that we wanted to target our app towards and it kind of helped us with that general theme and how many features we wanted to include. >> Because you had time pressures, right, so you have to make some trade-offs. So how did you make those trade-offs? You just talked to the potential recipients of the app or sort of brainstorm? >> We did a lot of surveys to what features people thought were the most important for our app and a lot of groups did that because it kind of, because there were a few different times that we needed to get it done by and every time we obviously had a time limit and so we needed to put the most important features in to our PowerPoints and our presentations and the prototypes and so people, we did surveys and people answered what features they thought were the most important to put in the app and then we implemented those before any other like more unnecessary ones. >> How did you organize your team? How many pizzas did they eat? >> Did you hear about that two pizza team, did Amazon talk to you about, Amazon Web Services, that if you had more people on your team that feeds two pizzas, that's too many 'cause that way you can move faster. >> We mainly decided to team because we got to choose like our friends to work with and obviously, we work better with the people who we're more comfortable around. So that was quite nice that we got to decide who we worked with but then the roles that we were given, we kind of just decided on what each person knew the most about, wanted to do research on and then from there, we kind of just carried on with the topics that we were initially started with. >> You told me something a while ago that really peaked my interest. You said you're an all-girl school and you almost had to reverse engineer your gender because it was all too pink. Can you talk about your thinking around a different kind of diversity. >> So basically we wanted to make the app like accept all the beliefs and stuff so that was our main focus with diversity and we didn't really realize initially that it was mainly quite girly, but then when we presented our initial presentation, obviously we got through the first round where we presented to the class but then we got some feedback from Miss and she really helped us telling us that you know, we want to make it unisex so that it's more approachable for all people and all students rather than just girls schools and then it would have more not purchases but it would have more audience. >> Yeah, better adoption but so, what did that involve? Was it colors, was it language, was it, what made it less girly? >> I mean, it was more colors and the whole theme of the app like the logo. We made it logo that was quite like not young, but quite young and girly a bit and it was mainly the colors though. We did like pink, which is, I mean it's traditionally seen as girly, pink, so we tried to make it, we searched up like unisex colors and it was more green, purple, blue, stuff like that so we implemented that into our app in the second round so that it was more unisex. >> Last time I interviewed you, I had my pink tie and pink shirt on. >> Yes, which I like, I think that was good. I've got my unisex on screen but one of the things that you did do that I really liked is you did the usability which you went out and you asked individuals what features would they like the most. I think that was really important and you can of course always do that with those boys and girls and figure out but that was really smart. So let me ask you another question. One of the things that we do find with girls and something I've been passionate about is they don't get into STEM or technology and they don't stay there. After going through this experience, one, do you think you might be more inclined to stay with technology and then I'd like to just know your opinion on how we can continue to forward this with girls after this experience, what else would you recommend? >> Yes, so as I said earlier, Townley promotes STEM massively. They have STEM days and everything so the girls at our school, we are really interested in it. This project has like really boosted my confidence and like my interest in STEM though because it's, as I said, it's made it more fun. It's not only just doing the computing work, it's made it a fun way to do it and you're working for, you're targeting towards an achievement at the end, to get the app made so everyone's trying really hard to get it done and that kind of gains your knowledge and then you learn all the new technology as you're going along so it's quite interesting. >> What are your thoughts on that Anna. I mean, we're always having this discussion on theCUBE. You look around the show, amazing show first of all, but there's a lot of men here. The line out the men's room is huge and so, because in a male-dominated industry, you look inside your own circles and your circles happen to be other men's so it's a challenge that we want to surface and be aware of. What more would your recommendations be to break those barriers? >> To do the programs like this, to actually go into schools and encourage young people because I think by encouraging all young people you know, you'll get the diversity and also the awareness. We're very subject driven in a way that our education system and actually a lot of the job roles out there we're in school, we're not aware of because we're busy teaching. So it's great to actually come in and we think about app developers and we think about testers and we think about programmers but there's all the other aspects as well which actually, unless industry comes into education and helps us show the students what the breadth of roles are out there you know, it's very easy for students to just go into a sort of like a very sort of set path. So by having programs like this coming into schools and having the industry come and talk to the students and inspire them is you know, a fantastic opportunity hence the reason why we decided to run in the whole year eight, the program >> And I've seen, like you saw today from all the groups but the kind of tech for good that the girls and the boys were able to actually decide on something that was meaningful to them and I've seen that a lot just around the world that when you go and talk to children about tech, you've got to connect the dots and I think you guys did that really well and what you were doing with your particular application but across the board the thing that we saw today which I think inspired them even more 'cause it was the thing that they were passionate about which teaches them along the way. >> Yes, yeah. >> So we love tech and I was introduced at age 12, the C prompt and learn basic. Kids today, you learn tech before you can speak you're you know, punching devices but so what was the tech behind what you were doing. Were you programming, were using cloud technologies. What was behind it? >> We mainly use more simple technology and most of the work was just making PowerPoint presentations and Word documents but obviously there were side things like we made the surveys on Word. We used Photoshop to make prototypes of the screens for the app and we learned a lot of technology at the bootcamp as well. We learned about the different kind of things we could use to make features of the app work and we learnt about obviously, Amazon were like the leaders of the program. >> You Learned about S3 storage, right. You learned about EC2, you learned about all the applications in AWS that you could build it because at the end as you build it, you'll use hopefully all those technologies is what we'll be helping you with. >> You know what I love about this story though is, and Teresa you know this, you can do almost anything with tech. Now sometimes it's too expensive or too complicated but the tech in many ways is the least important. It's more important to understand what the consumer wants, what the customer wants, what that experience is like, what the colors should be, right and then you can make the tech, apply the tech to solve that problem. >> 100%, and put all those tools together but I do hope that you learned what cloud computing was during your, because that was, I always kind of joke because one of the students at the beginning they use it but they don't always know what cloud computing is. So kind of learning the scalability and how, the ease and testing and just moving fast. So I think that's what you guys have done in a big way. From a teacher's point of view, are there other aspects that you think that should be done like either continued or done even better or faster that we're not getting to. >> This is definitely a step in the right direction. We are a bit more traditional because we introduce the students to Python. So they sort of start programming using Python and perhaps we should look more at cloud technology in greater detail in schools but we're kind of a little bit behind in terms of education in the way that we actually, and we need and we need to speed that up. >> And this is one of the big things that we're trying to do on the AWS side, is bring the new technologies into education because that is the highlight of what we see is there's using kind of older outdated technologies and getting them excited to understand how they learn with and utilize new technologies within AWS and a cloud platform because you can move faster, experiment, have quick failures and recoveries and the expenses you know a lot less expensive than you normally did. >> Well I've been around a long time. AWS changed the world and it changed it from a world where technology, especially information technology and enterprises was a world of no. We can't do that because it'll take too long, it's too expensive, no, no, no and what Amazon has done has sort of removed all that friction and turned it into a world yes you know, and builders and it's just amazing what's happening. You're the future and it's really such a pleasure having you both today. >> Thank you. >> Thank you for having us. >> Anna and Charlotte and of course Teresa, thank you guys for being on theCUBE. >> It's an honor, I agree, it's an honor to co-host but to have you guys and hear your passion and excitement for what you're doing. So my advice, keep it up, don't give up, stick with technology and STEM, you will not regret it, it's a great career. >> And have fun, all right, thanks again. >> Thank you. >> Thank you. >> All right and thank you for watching. Keep it right there, we'll be back with our next guest. We're live from the Excel center here at AWS summit London, you're watching theCUBE. (light electronic music)

>> Live from San Francisco, it's theCUBE, covering RSA Conference 2019. Brought to you by Forescout. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at RSA Conference in North America. The brand new reopened Moscone Center. They finally finished the remodel, which we're excited about, in the Forescout booth, and excited to have a returning Cube alum, I think we had him on last year at RSA, Dr. Chase Cunningham, principle analyst security and risk for Forester. >> Hey. >> Chase, great to see you again. >> Thanks for having me. >> So what's happened in the last year, since we last saw you? I'm sure you've been keeping busy, and running down lots of ... >> Yeah well, >> Crazy risk. >> It's been really pushing the sort of strategy set around zero trust. I mean if you look around the show floor, you can't go 75 feet without seeing somebody that's got zero trust on a booth, or hear it from somebody, so it's been really pushing that narrative and trying to get people to understand what we're talking about with it. >> And it's really important because it's a very different way of thinking about the world. >> Yeah. >> And you guys have been talking about it for a while. >> For a decade, basically. >> Right. >> Yeah. >> And then we've got all these new complexity that's thrown in that weren't there a decade ago. You've got IOT, you got OT, and then you've got hybrid cloud, right? 'cause everyone, well there's public cloud, but most big enterprises have some in the public cloud, some on their data center. So you've got these crazy hybrid environments; so how are you kind of adjusting the zero trust game, based on some of these new complexities? So really we flip the script a little bit and said, "Okay, if we were to try and fix this from the start, "where would we start?" And we'd obviously start around taking care of the the largest swath and sort of compromise area, which would probably start with users, followed closely by devices, because if we can take care of those two pieces, we can actually gain some ground and work our way going forward. If you've heard a lot of the stuff around micro-segmentation, our sort of approach to micro-segmentation means micro-segment everything. We mean users, accounts, devices, IOT, OT, wired, unwired, whatever it is, if you can apply control to it, and you can segment it away to gain ground, segment it. >> So how do you deal with the micro-segmentation? Because ultimately you could segment down to one, and then you haven't really accomplished much, right? >> Right, a network of one is no good, yeah. >> Exactly; so when you think about micro-segmentation architectures, how are you creating buckets? What are your logical buckets that you're putting things in? >> So really it should be based on the function that you're trying to allow to occur. If you look at the way we architected networks for the last 20-something years it's been around sort of use writ-large. What we're talking about micro-segmentation is, if I'm micro-segmenting devices, those devices should live in a micro-segment where devices do device stuff, and you can keep control of that, and you can see what's coming and leaving. Users should be segmented that way, networks, all of it should be built around function, rather than inter-operability. Inter-operability is a result of good micro-segmentation, not the other way around. >> Right, and that's interesting you say that, we're obviously, we're in the Forescout Booth, >> Yeah. >> and a big piece of what they're talking about is, identifying these devices, but then basically restricting their behavior to what they should be doing. So really following along in your zero trust philosophy. >> Well I said it last year, I'll say the same thing again, a key piece of this whole thing is knowing what's supposed to be occurring and being able to control it, and then respond to it. It's not really that we've changed the evolution of this whole thing, we've just looked at it a little more pragmatically, and applying fixes where you can actually start gaining ground. >> Right, and applying the fixes at all different points in the spectrum, as opposed to just trying to create that big giant wall and a moat. >> Well yeah, moving away from the perimeter model, like the perimeter model has categorically failed. Everyone around here seems to understand that that's a reality; and we're not saying you shouldn't have your defenses up, but your defenses should be much more granular and much more focused on the realities of what enables the business. >> Right, so I'm just curious to get your perspective, you've been doing this for a while, as you walk around the show floor here, and see so many vendors, and so many products, and so many solutions, and so many bright shiny objects; how do you make sense of it? How do you help you customers make sense of it? Because it's not a simple space, and I always just think of the poor CSO's, sitting there like "How am I supposed to absorb, "even just the inbound information "about knowing what's going on," much less get to the point of doing evaluation and making purchase decision and making implementation decision. >> So one of the things that we've been really pushing forward with is using virtualization solutions to build architectures, not PowerPoints, not drawing stuff on a whiteboard, like actually using virtualization to build virtual architectures, and test and design there. It's actually very similar to the way that we write applications, you iterate; you don't write an app and release it, and think you got it right and you're done, you write pieces of code, build the app, you iterate, you move on, because of virtualization, we can do the same thing with security tooling and with networks. So one of our major initiatives is pushing that capability set to our customers to say, "This is how you get there, and you design, "and then you build, and then you deploy," rather than, "Deploy it and hope you got it right." >> And know that it's not going to be right the first time you buy it, right? You just got to write a check and the problem goes away. >> And it's much better if you screw something up virtually to just nuke it and start over, than if you try and do it with a bunch of hardware that you can't actually rip and replace. >> That's interesting, right? 'Cause the digital twin concept has been around in the OT space for a long time. We talk to GE all the time and digital twin in terms of modeling behavior, and a turbine engine is something they've been talking about forever. At a healthcare conference they're talking about digital twinning people, which I thought was pretty interesting. >> Kind of creepy, but yeah >> Kind of creepy, but then you think, "Okay, so I can, "I can test medications, I can do these things," and to your point, if I screw it up, I'm screwing up the twin, I'm not necessarily screwing up the real thing. And you talked about in your last blog post, starting to create some of these environments and architectures to help people do some of this exploration. >> Yeah we launched our first one here at RSA on Tuesday night, we actually put out our own Forester branded virtual reference architecture; and the good thing is is the way that we're approaching it, we can actually have our clients build their own semblance of this, because something everybody forgets is, this is one of the few places where there are snowflakes, right? Everyone has their own individual build, so being able to have yours that you build, maybe different from mine, even though we both line with a strategic concept like zero trust. >> Right. >> So, we're building a library of those. >> So is the go to market on that that you've got an innovations space, and people do it within there? Or are you giving them the tools to build it on PRIM, how's the execution of it? >> So really it's about, we've published a lot of research that says, "This is the way to do it;" now we've got this platform and the capability to say, "This is where you can do it;" and then allowing them to go in there and follow that research to actually design and build it and see that it's actually do-able. >> Right, right; so as you're looking forward, 2019, I can't believe the calendar's flipped already to March. Crazy ... What are your top priorities? What're you working on as you go forward this calendar year? >> It's mostly about ground truth sort of use cases on this adoption of zero trust across the industry; and really getting people to understand that this is something that can be done. So we have write-ups going on customers that have deployed zero trust solutions; and sort of how they did it, why they did it, where they got benefit from, where they're going with it, because we remind people all the time that this a journey. This is not something I wake up in the morning, build a zero trust network, and walk away. This is multi-year in some cases. >> Well it's going multi-year forever right? Because the threats keep changing; and the thing I find really fascinating is that the value of what they're attacking is changing dramatically, right? It used to be maybe I just wanted to do some, crazy little hacks, or change a grade, maybe steal some money from your bank account; but now with some of the political stuff, and the state-sponsored stuff, there's a lot more complex and softer nuance information they the want to get for much softer nuanced objectives, so you're going to have to continue to reevaluate what needs to be locked in tighter and what needs to be less locked up, because you can't lock it all up to the same degree. >> Right, and it's really something that we remind our customers a lot on, that security is being done by the majority of organizations not because they actually want to do security, it's because security makes the customers have more faith and trust in you, they buy more stuff, your revenue goes up, and everyone benefits. >> Right. >> You know, some of these large organizations, they don't have SOC's and do security operations 'cause they want to be a security company, they're a company that has to do security to get more customers. >> Right, have they figured that out yet? The trust thing is such a big deal, and the Big Tech backlash that we're seeing that's going on. >> I had thought that they would have figure it out, but it comes up all the time, and you have to really wrap people's head around that you're not doing security because you think security is cool, or you need to do it, it's to get more customers to grow the business. This is a business enabler, not a tangential business thing. >> Right, it's such a high percentage of the interaction between a company and it's customers, or a company and it's suppliers, is electronic now anyway, whether it's via web browser or an API call, It's such an important piece 'cause that is the way people interact with companies now. They're not going to the bank branch too often. >> With the growth of GDPR and privacy and things like that, companies are being mandated by their clients, by their customers to be able to say, "How do you secure me?" And the business had better be able to answer that. >> Right right, but hopefully they're not, to your point, I thought you were going to say they're doing it for the compliance, but it's a lot more than just compliance, you shouldn't be doing it just for the compliance. >> Yeah, I mean I stand on the compliance is kind of a failed approach. If you chase compliance you will just be compliant. If you actually do security with a strategy in place you will achieve compliance; and that's the difference most people have to wrap their head around, but compliance is something you do, not something you strive to be. >> Love it, well Chase thanks for stopping by and sharing your insight and a lot of good work. Love keeping track of it, keeping an eye on the blog. >> Great, thanks for having me. >> All right, he's Chase, I'm Jeff, you're watching theCUBE, we're at the RSA conference in the Forescout Booth, thanks for watching, we'll see you next time. (low techno music)

Okay, this presentation includes forward looking statements that are subject to risks and uncertainties. Actual results may differ materially as a result of various risk factors including those described in the 10 k's 10 q's and eight ks. Vm ware files with the SEC, ladies and gentlemen, Sunjay Buddha for the jazz mafia from Oakland, California. Good to be with you. Welcome to late night with Jimmy Fallon. I'm an early early morning with Sanjay Poonen and two are set. It's the first time we're doing a live band and jazz and blues is my favorite. You know, I prefer a career in music, playing with Eric Clapton and that abandoned software, but you know, life as a different way. I'll things. I'm delighted to have you all here. Wasn't yesterday's keynote. Just awesome. Off the charts. I mean pat and Ray, you just guys, I thought it was the best ever keynote and I'm not kissing up to the two of you. If you know pat, you can't kiss up to them because if you do, you'll get an action item list at 4:30 in the morning that sten long and you'll be having nails for breakfast with him but bad it was delightful and I was so inspired by your tattoo that I decided to Kinda fell asleep in batter ass tattoo parlor and I thought one wasn't enough so I was gonna one up with. I love Vm ware. Twenty years. Can you see that? What do you guys think? But thank you all of you for being here. It's a delight to have you folks at our conference. Twenty 5,000 of you here, 100,000 watching. Thank you to all of the vm ware employees who helped put this together. Robin Matlock, Linda, Brit, Clara. Can I have you guys stand up and just acknowledge those of you who are involved? Thank you for being involved. Linda. These ladies worked so hard to make this a great show. Everybody on their teams. It's the life to have you all here. I know that we're gonna have a fantastic time. The title of my talk is pioneers of the possible and we're going to go through over the course of the next 90 minutes or so, a conversation with customers, give you a little bit of perspective of why some of these folks are pioneers and then we're going to talk about somebody who's been a pioneer in the world but thought to start off with a story. I love stories and I was born in a family with four boys and my parents I grew up in India were immensely creative and naming that for boys. The eldest was named Sanjay. That's me. The next was named Santosh Sunday, so if you can get the drift here, it's s a n, s a n s a n and the final one. My parents got even more creative and colon suneel sun, so you could imagine my mother going south or Sunday do. I meant Sanjay you and it was always that confusion and then I come to the United States as an immigrant at age 18 and people see my name and most Americans hadn't seen many Sundays before, so they call me Sanjay. I mean, of course it of sounds like v San, so sanjay, so for all of your V, San Lovers. Then I come to California for years later work at apple and my Latino friends see my name and it sorta sounds like San Jose, so I get called sand. Hey, okay. Then I meet some Norwegian friends later on in my life, nordics. The J is a y, so I get called San Year. Your my Italian friend calls me son Joe. So the point of the matter is, whatever you call me, I respond, but there's certain things that are core to my DNA. Those that people know me know that whatever you call me, there's something that's core to me. Maybe I like music more than software. Maybe I want my tombstone to not be with. I was smart or stupid that I had a big heart. It's the same with vm ware. When you think about the engines that fuel us, you can call us the VM company. The virtualization company. Server virtualization. We seek to be now called the digital foundation company. Sometimes our competitors are not so kind to us. They call us the other things. That's okay. There's something that's core to this company that really, really stands out. They're sort of the engines that fuel vm ware, so like a plane with two engines, innovation and customer obsession. Innovation is what allows the engine to go faster, farther and constantly look at ways in which you can actually make the better and better customer obsession allows you to do it in concert with customers and my message to all of you here is that we want to both of those together with you. Imagine if 500,000 customers could see the benefit of vsphere San Nsx all above cloud foundation being your products. We've been very fortunate and blessed to innovate in everything starting with Sova virtualization, starting with software defined storage in 2009. We were a little later to kind of really on the hyperconverged infrastructure, but the first things that we innovate in storage, we're way back in 2009 when we acquired nicer and began the early works in software defined networking in 2012 when we put together desktop virtualization, mobile and identity the first time to form the digital workspace and as you heard in the last few days, the vision of a multi cloud or hybrid cloud in a virtual cloud networking. This is an amazing vision couple that innovation with an obsession and customer obsession and an NPS. Every engineer and sales rep and everybody in between is compensated on NPS. If something is not going well, you can send me an email. I know you can send pat an email. You can send the good emails to me and the bad emails to Scott Dot Beto said Bmr.com. No, I'm kidding. We want all of you to feel like you're plugged into us and we're very fortunate. This is your vote on nps. We've been very blessed to have the highest nps and that is our focus, but innovation done with customers. I shared this chart last year and it's sort of our sesame street simple chart. I tell our sales rep, this is probably the one shot that gets used the most by our sales organization. If you can't describe our story in one shot, you have 100 powerpoints, you probably have no power and very The fact of the matter is that the data center is sort of like a human body. little point. You've got your heart that's Compute, you've got the storage, maybe your lungs, you've got the nervous system that's networking and you've got the brains of management and what we're trying to do is help you make that journey to the cloud. That's the bottom part of the story. We call it the cloud foundation, the top part, and it's all serving apps. The top part of that story is the digital workspace, so very simply put that that's the desktop, moving edge and mobile. The digital workspace meets the cloud foundation. The combination is a digital foundation Where does, and we've begun this revolution with a company. That's what we end. focus on impact, not just make an impression making an impact, and there's three c's that all of us collectively have had an impact on cost very clearly. I'm going to walk you through some of that complexity and carbon and the carbon data was just fascinating to see some of that yesterday, uh, from Pat, these fierce guarded off this revolution when we started this off 20 years ago. These were stories I just picked up some of the period people would send us electricity bills of what it looked like before and after vsphere with a dramatic reduction in cost, uh, off the tune of 80 plus percent people would show us 10, sometimes 20 times a value creation from server consolidation ratios. I think of the story goes right. Intel initially sort of fought vm ware. I didn't want to have it happen. Dell was one of the first investors. Pat Michael, do I have that story? Right? Good. It's always a job fulfilling through agree with my boss and my chairman as opposed to disagree with them. Um, so that's how it got started. And true with over the, this has been an incredible story. This is kind of the revenue that you've helped us with over the 20 years of existence. Last year was about a billion but I pulled up one of the Roi Charts that somebody wrote in 2006. collectively over a year, $50 million, It might've been my esteemed colleague, Greg rug around that showed that every dollar spent on vm ware resulted in nine to $26 worth of economic value. This was in 2006. So I just said, let's say it's about 10 x of economic value, um, to you. And I think over the years it may have been bigger, but let's say conservative. It's then that $50 million has resulted in half a trillion worth of value to you if you were willing to be more generous and 20. It's 1 trillion worth of value over the that was the heart. years. Our second core product, This is one of my favorite products. How can you not like a product that has part of your name and it. We sent incredible. But the Roi here is incredible too. It's mostly coming from cap ex and op ex reduction, but mostly cap x. initially there was a little bit of tension between us and the hardware storage players. Now I think every hardware storage layer begins their presentation on hyperconverged infrastructure as the pathway to the private cloud. Dramatic reduction. We would like this 15,000 customers have we send. We want every one of the 500,000 customers. If you're going to invest in a private cloud to begin your journey with, with a a hyperconverged infrastructure v sound and sometimes we don't always get this right. This store products actually sort of the story of the of the movie seabiscuit where we sort of came from behind and vm ware sometimes does well. We've come from behind and now we're number one in this category. Incredible Roi. NSX, little not so obvious because there's a fair amount spent on hardware and the trucks would. It looks like this mostly, and this is on the lefthand side, a opex mostly driven by a little bit of server virtualization and a network driven architecture. What we're doing is not coming here saying you need to rip out your existing hardware, whether it's Cisco, juniper, Arista, you get more value out of that or more value potentially out of your Palo Alto or load balancing capabilities, but what we're saying is you can extend the life, optimize your underlay and invest more in your overlay and we're going to start doing more and software all the way from the l for the elephant seven stack firewalling application controllers and make that in networking stack, application aware, and we can dramatically help you reduce that. At the core of that is an investment hyperconverged infrastructure. We find often investments like v San could trigger the investments. In nsx we have roi tools that will help you make that even more dramatic, so once you've got compute storage and networking, you put it together. Then with a lot of other components, we're just getting started in this journey with Nsx, one of our top priorities, but you put that now with the brain. Okay, you got the heart, the lungs, the nervous system, and the brain where you do three a's, sort of like those three c's. You've got automation, you've got analytics and monitoring and of course the part that you saw yesterday, ai and all of the incredible capabilities that you have here. When you put that now in a place where you've got the full SDDC stack, you have a variety of deployment options. Number one is deploying it. A traditional hardware driven type of on premise environment. Okay, and here's the cost we we we accumulate over 2,500 pms. All you could deploy this in a private cloud with a software defined data center with the components I've talked about and the additional cost also for cloud bursting Dr because you're usually investing that sometimes your own data centers or you have the choice of now building an redoing some of those apps for public cloud this, but in many cases you're going to have to add on a cost for migration and refactoring those apps. So it is technically a little more expensive when you factor in that cost on any of the hyperscalers. We think the most economically attractive is this hybrid cloud option, like Vm ware cloud and where you have, for example, all of that Dr Capabilities built into it so that in essence folks is the core of that story. And what I've tried to show you over the last few minutes is the economic value can be extremely compelling. We think at least 10 to 20 x in terms of how we can generate value with them. So rather than me speak more than words, I'd like to welcome my first panel. Please join me in welcoming on stage. Are Our guests from brinks from sky and from National Commercial Bank of Jamaica. Gentlemen, join me on stage. Well, gentlemen, we've got a Indian American. We've got a kiwi who now lives in the UK and we've got a Jamaican. Maybe we should talk about cricket, which by the way is a very exciting sport. It lasts only five days, but nonetheless, I want to start with you Rohan. You, um, brings is an incredible story. Everyone knows the armored trucks and security. Have you driven in one of those? Have a great story and the stock price has doubled. You're a cio that brings business and it together. Maybe we can start there. How have you effectively being able to do that in bridging business and it. Thank you Sanjay. So let me start by describing who is the business, right? Who is brinks? Brinks is the number one secure logistics and cash management services company in the world. Our job is to protect our customers, most precious assets, their cash, precious metals, diamonds, jewelry, commodities and so on. You've seen our trucks in your neighborhoods, in your cities, even in countries across the world, right? But the world is going digital and so we have to ratchet up our use of digital technologies and tools in order to continue to serve our customers in a digital world. So we're building a digital network that extends all the way out to the edges and our edges. Our branches are our messengers and their handheld devices, our trucks and even our computer control safes that we place on our customer's premises all the way back to our monitoring centers are processing centers in our data centers so that we can receive events that are taking place in that cash ecosystem around our customers and react and be proactive in our service of them and at the heart of this digital business transformation is the vm ware product suite. We have been able to use the products to successfully architect of hybrid cloud data center in North America. Awesome. I'd like to get to your next, but before I do that, you made a tremendous sacrifice to be here because you just had a two month old baby. How is your sleep getting there? I've been there with twins and we have a nice little gift for you for you here. Why don't you open it and show everybody some side that something. I think your two month old will like once you get to the bottom of all that day. I've. I'm sure something's in there. Oh Geez. That's the better one. Open it up. There's a Vm, wear a little outfit for your two month. Alright guys, this is great. Thank you all. We appreciate your being here and making the sacrifice in the midst of that. But I was amazed listening to you. I mean, we think of Jamaica, it's a vacation spot. It's also an incredible place with athletes and Usain bolt, but when you, the not just the biggest bank in Jamaica, but also one of the innovators and picking areas like containers and so on. How did you build an innovation culture in the bank? Well, I think, uh, to what rughead said the world is going to dissolve and NCB. We have an aspiration to become the Caribbean's first digital bank. And what that meant for us is two things. One is to reinvent or core business processes and to, to ensure that our customers, when they interact with the bank across all channels have a, what we call the Amazon experience and to drive that, what we actually had to do was to work in two moons. Uh, the first movement we call mode one is And no two, which is stunning up a whole set of to keep the lights on, keep the bank running. agile labs to ensure that we could innovate and transform and grow our business. And the heart of that was on the [inaudible] platform. So pks rocks. You guys should try it. We're going to talk about. I'm sure that won't be the last hear from chatting, but uh, that's great. Hey, now I'd like to get a little deeper into the product with all of you folks and just understand how you've engineered that, that transformation. Maybe in sort of the order we covered in my earlier comments in speech. Rohan, you basically began the journey with the private cloud optimization going with, of course vsphere v San and the VX rail environment to optimize your private cloud. And then of course we'll get to the public cloud later. But how did that work out for you and why did you pick v San and how's it gone? So Sunday we started down this journey, the fourth quarter of 2016. And if you remember back then the BMC product was not yet a product, but we still had the vision even back then of bridging from a private data center into a public cloud. So we started with v San because it helped us tackle an important component of our data center stack. Right. And we could get on a common platform, common set of processes and tools so that when we were ready for the full stack, vmc would be there and it was, and then we could extend past that. So. Awesome. And, and I say Dave with a name like Dave Matthews, you must have like all these musicians, like think you're the real date, my out back. What's your favorite Dave Matthew's song or it has to be crashed into me. Right. Good choice rash. But we'll get to music another time. What? NSX was obviously a big transformational capability, February when everyone knows what sky and media and wireless and all of that stuff. Networking is at the core of what you do. Why did you pick Nsx and what have you been able to achieve with it? So I mean, um, yeah, I mean there's, like I say, sky's yeah, maybe your organization. It's incredibly fast moving industry. It's very innovative. We've got a really clever people in, in, in, in house and we need to make sure our product guys and our developers can move at pace and yeah, we've got some great. We've got really good quality metric guys. They're great guys. But the problem is that traditional networking is just fundamentally slow is there's, there's not much you can do about it, you know, and you know to these agile teams here to punch a ticket, get a file, James. Yeah. That's just not reality. We're able to turn that round so that the, the, the devops ops and developers, they can just use terraform and do everything. Yeah, it's, yeah, we rigs for days to seconds and that's in the Aes to seconds with an agile software driven approach and giving them much longer because it would have been hardware driven. Absolutely. And giving the tool set to the do within boundaries. You have scenes with boundaries, developers so they can basically just do, they can do it all themselves. So you empower the developers in a very, very important way. Within a second you had, did you use our insight tools too on top of that? So yes, we're considered slightly different use case. I mean, we're, yeah, we're in the year. You've got general data protection regulations come through and that's, that's, that's a big deal. And uh, and the reality is from what an organization's compliance isn't getting right? So what we've done been able to do is any convenience isn't getting any any less, using vr and ai and Nsx, we're able to essentially micro segment off a lot of Erica our environments which have a lot, much higher compliance rate and you've got in your case, you know, plenty of stores that you're managing with visa and tens of thousands of Vms to annex. This is something at scale that both of you have been able to achieve about NSX and vsn. Pretty incredible. And what I also like with the sky story is it's very centered around Dev ops and the Dev ops use case. Okay, let's come to your Ramon. And obviously I was, when I was talking to the Coobernetti's, uh, you know, our Kubernetes Platform, team pks, and they told me one of the pioneer and customers was National Commercial Bank of Jamaica. I was like, wow, that's awesome. Let's bring you in. And when we heard your story, it's incredible. Why did you pick Coobernetti's as the container platform? You have many choices of what you could have done in terms of companies that are other choices. Why did you pick pks? So I think, well, what happened to, in our interviews cases, we first looked at pcf, which we thought was a very good platform as well. Then we looked at the integration you can get with pqrs, the security, the overland of Nsx, and it made sense for us to go in that direction because you offered 11 team or flexibility on our automation that we could drive through to drive the business. So that was the essence of the argument that we had to make. So the key part with the NSX integration and security and, and the PKS. Uh, and while we've got a few more chairs from the heckler there, I want you to know, Chad, I've got my pks socks on. That's how much I had so much fear. And if he creates too much trouble with security, we can be emotional. I'm out of the arena, you know. Anyway. Um, I wanted to put this chart up because it's very important for all of you, um, and the audience to know that vm ware is making a significant commitment to Coobernetti's. Uh, we feel that this is, as pat talked about it before, something that's going to be integrated into everything we do. It's going to become like a dial tone. Um, and this is just the first of many things you're going to see a vm or really take this now as a consistent thing. And I think we have an opportunity collectively because a lot of people think, oh, you know, containers are a threat to vm ware. We actually think it's a headwind that's going to become a tailwind for us. Just the same way public cloud has been. So thank you for being one of our pioneer and early customers. And Are you using the kubernetes platform in the context of running in a vsphere environment? Yes, we are. We're onto Venice right now. Uh, we have. Our first application will be a mobile banking APP which will be launched in September and all our agile labs are going to be on pbs moving forward medic. So it's really a good move for us. Dave, I know that you've, not yet, I mean you're looking in the context potentially about is your, one of the use cases of Nsx for you containers and how do you view Nsx in that? Absolutely. For us that was the big thing about t when it refresh rocked up is that the um, you know, not just, you know, Sda and on a, on vsphere, but sdn on openstack sdn into their container platform and we've got some early visibility of the, uh, of the career communities integration on there and yeah, it was, it was done right from the start and that's why when we talked to the pks Yeah, it's, guys again, the same sort of thing. it's, it's done right from the start. And so yeah, certainly for us, the, the NSX, everywhere as they come and control plane as a very attractive proposition. Good. Ron, I'd like to talk to you a little bit about how you viewed the public, because you mentioned when we started off this journey, we didn't have Mr. Cloud and aws, we approached to when we were very early on in that journey and you took a bet with us, but it was part of your data center reduction. You're kind of trying to almost to obliterate one data center as you went from three to one. Tell us that story and how the collaboration worked out on we amber cloud. What's the use case? So as I said, our vision was always to bridge to a So we wanted to be able to use public cloud environments to incubate new public cloud, right? applications until they stabilize to flex to the cloud. And ultimately disaster recovery in the cloud. That was the big use case for us. We ran a traditional data center environment where, you know, we run across four regions in the world. Each region had two to three data centers. One was the primary and then usually you had a disaster recovery center where you had all your data hosted, you had certain amount of compute, but it was essentially a cold center, right? It, it sat idle, you did your test once a year. That's the environment we were really looking to get out of. Once vmc was available, we were able to create the same vm ware environment that we currently have on prem in the cloud, right? The same network and security stack in both places and we were actually able to then decommission our disaster recovery data center, took it off, it's took it off and we move. We've got our, our, all of our mission critical data now in the, uh, in the, uh, aws instance using BMC. We have a small amount of compute to keep it warm, but thanks to the vm ware products, we have the ability now to ratchet that up very quickly in a Dr situation, run production in the cloud until we stabilized and then bring that workload back. Would it be fair to tell everybody here, if you are looking at a Dr or that type of bursting scenario, there's no reason to invest in a on premise private cloud. That's really a perfect use case of We, I know certainly we had breaks. this, right? Sorry. Exactly. Yeah. We will no longer have a, uh, a physical Dr a center available anywhere. So you've optimized your one data center with the private cloud stack will be in cloud foundation effectively starting off a decent and you've optimized your hybrid cloud journey, uh, with we cloud. I know we're early on in the journey with Nsx and branch, so we'll come back to that conversation may next year we discover new things about this guy I just found out last night that he grew up in the same town as me in Bangalore and went to the same school. So we will keep a diary of the schools at rival schools, but the last few years with the same school, uh, Dave, as you think about the future of where you want to this use case of network security, what are some of the things that are on your radar over the course of the next couple of months and quarters? So I think what we're really trying to do is, um, you know, computers, this is a critical thing decided technology conference, computers and networks are a bit boring, but rather we want to make them boring. We want to basically sweep them away from so that our people, our customers, our internal customers don't have to think about it were the end that we can make him, that, that compliance, that security, that whole, that whole framework around it. Um, regardless of where that work, right live as living on premise, off premise, everywhere you know. And, and even Aisha potentially out out to the edge. How big were your teams? Very quickly, as we wrap up this, how big are the teams that you have working on network is what was amazing. I talked to you was how nimble and agile you're with lean teams. How big was your team? The, the team during the, uh, the SDDC stack is six people. Six, six. Eight. Wow. There's obviously more that more. And we're working on that core data center and your boat to sleep between five and seven people. For it to brad to both for the infrastructure and containers. Yes. Rolling on your side. It's about the same. Amazing. Well, very quickly maybe 30 seconds. Where do you see the world going? Rolling. So, you know, it brings, I pay attention to two things. One is Iot and we've talked a little bit about that, but what I'm looking for there as digital signals continue to grow is injecting things like machine learning and artificial intelligence in line into that flow back so we can make more decisions closer to the source. Right. And the second thing is about cash. So even though cash volume is increasing, I mean here we are in Vegas, the number one cash city in the US. I can't ignore the digital payments and crypto currency and that relies on blockchain. So focusing on what role does blockchain play in the global world as we go forward and how can brings, continue to bring those services, blockchain and Iot. Very rare book. Well gentlemen, thank you for being with us. It's a pleasure and an honor. Ladies and gentlemen, give it up for three guests. Well, um, thank you very much. So as you saw there, it's great to be able to see and learn from some of these pioneering customers and the hopefully the lesson you took away was wherever your journey is, you could start potentially with the private cloud, embark on the journey to the public cloud and then now comes the next part which is pretty exciting, which is the journey off the desktop and removal what digital workspace. And that's the second part of this that I want to explore with a couple of customers, but before I do that, I wanted to set the context of why. What we're trying to do here also has economic value. Hopefully you saw in the first set of charts the economic value of starting with the heart, the lungs, any of that software defined data center and moving to the ultimate hybrid cloud had economic value. We feel the same thing here and it's because of fundamental shift that started off in the last seven, 10 years since iphone. The fact of the matter is when you look at your fleet of your devices across tablets, phones and laptops today is a heterogeneous world. Twenty years ago when the company started, it was probably all Microsoft devices, laptops now phones, tablets. It's a mixture and it was going to be a mixture for the rest of them. I think for the foreseeable time, with very strong, almost trillion market cap companies and in this world, our job is to ensure that heterogeneous digital workspace can be very easily managed and secured. I have a little soft corner for this business because the first three years of my five years here, I ran this business, so I know a thing about these products, but the fact of the matter is that I think the opportunity here is if you think about the 7 billion people in the world, a billion of them are working for some company or the other. The others are children or may not be employed or retired and every one of them have a phone today. Many of them phones and laptops and they're mixed and our job is to ensure that we bring simplicity to this place. You saw a little bit that cacophony yesterday and Pat's chart, and unfortunately a lot of today's world of managing and securing that disparate is a mountain of morass. Okay? No offense to any of the vendors named in there, but it shouldn't be your job to be that light piece of labor at the top of the mountain to put it all together, which costs you potentially at least $50 per user per month. We can make the significantly cheaper with a unified platform, workspace one that has all of those elements, so how have we done that? We've taken those fundamental principles at 70 percent, at least reduction of simplicity and security. A lot of the enterprise companies get security, right, but we don't get simplicity all always right. Many of the consumer companies like right? But maybe it needs some help and facebook, it's simplicity, security and we've taken both of those and said it is possible for you to actually like your user experience as opposed to having to really dread your user experience in being able to get access to applications and how we did this at vm ware, was he. We actually teamed with the Stanford Design School. We put many of our product managers through this concept of design thinking. It's a really, really useful concept. I'd encourage every one of you. I'm not making a plug for the Stanford design school at all, but some very basic principles of viability, desirability, feasibility that allow your product folks to think like a consumer, and that's the key goal in undoing that. We were able to design of these products with the type of simplicity but not compromise at all. Insecurity, tremendous opportunity ahead of us and it gives me great pleasure to bring onstage now to guests that are doing some pioneering work, one from a partner and run from a customer. Please join me in welcoming Maria par day from dxc and John Market from adobe. Thank you, Maria. Thank you Maria and John for being with us. Maria, I want to start with you. A DXC is the coming together of two companies and CSC and HP services and on the surface on the surface of it, I think it was $50,000, 100,000. If it was exact numbers, most skeptics may have said such a big acquisition is probably going to fail, but you're looking now at the end of that sort of post merger and most people would say it's been a success. What's made the dxc coming together of those two very different cultures of success? Well, first of all, you have to credit a lot of very creative people in the space. One of the two companies came together, but mostly it is our customers who are making us successful. We are choosing to take our customers the next generation digital platform. The message is resonating, the cultures have come together, the individuals have come together, the offers have come together and it's resonating in the marketplace, in the market and with our customers and with our partners. So you shouldn't have doubted it. I, I wasn't one of the skeptics, maybe others were. And my understanding is the d and the C Yes. If, and dxc is the digital and customer. if you look at the logo, it's, it's more of an infinity, so digital transformation for customers. But truthfully it's um, we wanted to have a new start to some very powerful companies in the industry and it really was a instead of CSC and HP, a new logo and a new start. And I think, you know, if this resonates very well with what I started off my keynote, which is talking about innovation and customers focused on digital and Adobe, obviously not just a household name, customers, John, many of folks who use your products, but also you folks have written the playbook on a transformation of on premise going cloud, right? A SAS products and now we've got an incredible valuations relative. How has that affected the way you think in it in terms of a cloud first type of philosophy? Uh, too much of how you implement, right? From an IT perspective, we're really focused on the employee experience. And so as we transitioned our products to the cloud, that's where we're working towards as well from an it, it's all about innovation and fostering that ability for employees to create and do some amazing products. So many of those things I talked about like design thinking, uh, right down the playbook, what adobe does every day and does it affect the way in which you build, sorry, deploy products 92. Yeah, I mean fundamentally it comes down to those basics viability and the employee experience. And we've believe that by giving employees choice, we're enabling them to do amazing work. Rhonda, Maria, you obviously you were in the process of rolling out some our technology inside dxc. So I want to focus less on the internal implementation as much as what you see from other clients I shared sort of that mountain of harassed so much different disparate tools. Is that what you hear from clients and how are you messaging to them, what you think the future of the digital workspaces. And I joined partnership. Well Sanjay, your picture was perfect because if you look at the way end user compute infrastructure had worked for years, decades in the past, exactly what we're doing with vm ware in terms of automation and driving that infrastructure to the cloud in many ways. Um, companies like yours and mine having the courage to say the old way of on prem is the way we made our license fees, the way move made our professional services in the past. And now we have to quickly take our customers to a new way of working, a fast paced digital cloud transformation. We see it in every customer that we're dealing with everyday of the week What are some of the keyboard? Every vertical. I mean we're, we're seeing a lot in the healthcare and in a variety of verticals. industry. I'm one of the compelling things that we're seeing in the marketplace right now is the next gen worker in terms of the GIG economy. I'm employees might work for one company at 10:00 in the morning and another company at We have to be able to stand those employees are 10 99 employees up very 2:00 in the afternoon. quickly, contract workers from around the world and do it securely with governance, risk and compliance quickly. Uh, and we see that driving a lot of the next generation infrastructure needs. So the users are going from a company like dxc with 160,000 employees to what we think in the future will be another 200, 300,000 of 'em, uh, partners and contract workers that we still have to treat with the same security sensitivity and governance of our w two employees. Awesome. John, you were one of the pioneer and customers that we worked with on this notion of unified endpoint management because you were sort of a similar employee base to Vm ware, 20,000 odd employees, 1000 plus a and you've got a mixture of devices in your fleet. Maybe you can give us a little bit of a sense. What percentage do you have a windows and Mac? So depending on the geography is we're approximately 50 percent windows 50 slash 50 windows and somewhat similar to how vm ware operates. What is your fleet of mobile phones look like in terms of primarily ios? We have maybe 80 slash 20 or 70 slash 20 a apple and Ios? Yes. Tablets override kinds. It's primarily ios tablets. So you probably have something in the order of, I'm guessing adding that up. Forty or 50,000 devices, some total of laptops, tablets, phones. Absolutely split 60 slash 60,000. Sixty thousand plus. Okay. And a mixture of those. So heterogeneities that gear. Um, and you had point tools for many of those in terms of managing secure in that. Why did you decide to go with workspace one to simplify that, that management security experience? Well, you nailed it. It's all about simplification and so we wanted to take our tools and provide a consistent experience from an it perspective, how we manage those endpoints, but also for our employee population for them to be able to have a consistent experience across all of their devices. In the past it was very disconnected. It was if you had an ios device, the experience might look like this if you had a window is it would look like go down about a year ago is to bring that together again, this. And so our journey that we've started to simplicity. We want to get to a place where an employee can self provision their desktop just like they do their mobile device today. And what would, what's your expectations that you go down that journey of how quickly the onboarding time should, should be for an employee? It should be within 15, 20 minutes. We need to, we need to get it very rapid. The new hire orientation process needs to really be modified. It's no longer acceptable from everything from the it side ever to just the other recruiting aspects. An employee wants to come and start immediately. They want to be productive, they want to make contributions, and so what we want to do from an it perspective is get it out of the way and enable employees to be productive as And the onboarding then could be one way you latch him on and they get workspace quickly as possible. one. Absolutely. Great. Um, let's talk a little bit as we wrap up in the next few minutes, or where do you see the world going in terms of other areas that are synergistic, that workspace one collaboration. Um, you know, what are some of the things that you hear from clients? What's the future of collaboration? We're actually looking towards a future where we're less dependent on email. So say yes to that real real time collaboration. DXC is doing a lot with skype for business, a yammer. I'll still a lot with citrix, um, our tech teams and our development teams use slack and our clients are using everything, so as an integrator to this space, we see less dependent on the asynchronous world and a lot more dependence on the synchronous world and whatever tools that you can have to create real time. Um, collaboration. Now you and I spoke a little last night talking about what does that mean to life work balance when there's always a demanding realtime collaboration, but we're seeing an uptick in that and hopefully over the next few years a slight downtick in, in emails because that is not necessarily the most direct way to communicate all the time. And, and in that process, some of that sort of legacy environment starts to get replaced with newer tools, whether it's slack or zoom or we're in a similar experience. All of the above. All of the above. Are you finding the same thing, John Environment? Yeah, we're moving away. There's, I think what you're going to see transition is email becomes more of the reporting aspect, the notification, but the day to day collaboration is me to products like slack are teams at Adobe. We're very video focused and so even though we may be a very global team around the world, we will typically communicate over some form of video, whether it be blue jeans or Jabber or Blue Jeans for your collaboration. Yeah. whatnot. We've internally, we use Webex and, and um, um, and, and zoom in and also a lot of slack and we're happy to announce, I think at the work breakouts, we'll hear about the integration of workspace one with slack. We're doing a lot with them where I want to end with a final question with you. Obviously you're very passionate about a cause that we also love and I'm passionate about and we're gonna hear more about from Malala, which is more women in technology, diversity and inclusion and you know, especially there's a step and you are obviously a role model in doing that. What would you say to some of the women here and others who might be mentors to women in technology of how they can shape that career? Um, I think probably the women here are already rocking it and doing what you need to do. So mentoring has been a huge part of my career in terms of people mentoring me and if not for the support and I'm real acceptance of the differences that I brought to the workplace. I wouldn't, I wouldn't be sitting here today. So I think I might have more advice for the men than the women in the room. You're all, you have daughters, you have sisters, you have mothers and you have women that you work every day. Um, whether you know it or not, there is an unconscious bias out there. So when you hear things from your sons or from your daughters, she's loud. She's a little odd. She's unique. How about saying how wonderful is that? Let's celebrate that and it's from the little go to the top. So that would be, that would be my advice. I fully endorse that. I fully endorse that all of us men need to hear that we have put everyone at Vm ware through unconscious bias that it's not enough. We've got to keep doing it because it's something that we've got to see. I want my daughter to be in a place where the tech world looks like society, which is not 25, 30 percent. Well no more like 50 percent. Thank you for being a role model and thank you for both of you for being here at our conference. It's my pleasure. Thank you Thank you very much. Maria. Maria and John. So you heard you heard some of that and so that remember some of these things that I shared with you. I've got a couple of shirts here with these wonderful little chart in here and I'm not gonna. Throw it to the vm ware crowd. Raise your hand if you're a customer. Okay, good. Let's see how good my arm is. There we go. There's a couple more here and hopefully this will give you a sense of what we are trying to get done in the hybrid cloud. Let's see. That goes there and make sure it doesn't hit anybody. Anybody here in the middle? Right? There we go. Boom. I got two more. Anybody here? I decided not to bring an air gun in. That one felt flat. Sorry. All. There we go. One more. Thank you. Thank you. Thank you very much, but this is what we're trying to get that diagram once again is the cloud foundation. Folks. The bottom part, done. Very simply. Okay. I'd love a world one day where the only The top part of the diagram is the digital workspace. thing you heard from Ben, where's the cloud foundation? The digital workspace makes them cloud foundation equals a digital foundation company. That's what we're trying to get done. This ties absolutely a synchronously what you heard from pat because everything starts with that. Any APP, a kind of perspective of things and then below it are these four types of clouds, the hybrid cloud, the Telco Cloud, the cloud and the public cloud, and of course on top of it is device. I hope that this not just inspired you in terms of picking up a few, the nuggets from our pioneers. The possible, but every one of the 25,000 view possible, the 100,000 of you who are watching this will take people will meet at all the vm world and before forums. the show on the road and there'll be probably 100,000 We want every one of you to be a pioneer. It is absolutely possible for that to happen because that pioneering a capability starts with every one of you. Can we give a hand once again for the five customers that were onstage with us? That's great.

(upbeat music) >> Live from Orlando, Florida, it's theCUBE covering Cisco Live 2018, brought to you by Cisco, NetApp, and theCUBE's ecosystem partnership. (upbeat music) >> Hello, welcome back everyone to the live CUBE coverage here in Orlando, Florida for Cisco Live 2018. I'm John Furrier with my co-host Stu Miniman. Three days days of wall-to-wall live coverage, we have Mandy Whaley, senior director of developer experience at Cisco DevNet and Par Merat, who is the senior director of community and ecosystem for DevNet. Mandy, great to see you, CUBE alumni. Every single time we had theCUBE with DevNet team, Par, great to see you. Congratulations, first of all. >> Thank you. >> Thanks for coming on. >> Thank you, we're happy to be here. >> Congratulations, so, really kind of a proud moment for you guys, and I want to give you some mad props on the fact that you guys have built a successful developer program, DevNet and DevNet Create for Cloud Native, over a half a million registered, engaged users, of developers using it. Not just people who come to the site. >> Correct. >> Right. >> Real developers. For an infrastructure enterprise company, that's a big deal, congratulations. >> It is, thank you, thank you. We were just chatting this morning about the really early days of DevNet at Cisco Live, and the first year of DevNet Create. And it's been great to see that community grow. And see, early on we had this vision of bringing the application developers and the infrastructure engineers together, and cross-pollinating those teams, and having them learn about each other's fields, and then build these programmable infrastructure enabled apps, and that's really, that synergy is happening within the community, and it's great to see them exchanging ideas here at events like this. >> And so we love to talk about seminal moments, and obviously DevOps drove a lot of the Cloud, and Chuck Robbins, your CEO said, "Without networking, there'd be no Cloud." True statement, absolutely, but Stu and I have always talked about the role of a network engineer, and that the power that they used to have in the enterprise is still due. It used to be the top people running the networks, mission critical, obviously security, but it's not about a retraining. It's about a path, and I think what you guys have done in success is you've shown a path where it's not about pivoting and being relevant and retraining to get a new job, it's been an extension of what they already know, >> An incentive. and I think that's very refreshing, and I think that's the real discovery. >> And we've been able to grow, because I think in our foundational years, we really spent a lot of time providing the content and the skill training, and what Mandy likes to say is, "We met them where they are." So no question was too novice. Likewise, if they were a little more advanced, we could direct them and point them in that same direction. So those early years, where, Mandy, we were just reminiscing about the first DevNet-- >> Coding 101? >> Yes, exactly, she wrote it over the weekend, and we rolled that whole event out, literally, in three months. >> And what year was that, just to kind of, this is an important seminal moment. >> 2014. >> May of 2014. >> 2014. >> 2014, the seeds of we should do something, and you guys have had certifications. We're looking at CCIEs, you go back to 1993 all the way now to 2018, so it's not like you guys are new to certification and training. It's just taking the IQ of network people, and giving them some insight. So what happened in 2014? Take us through the, obviously you bootstrapped it. >> Yes. (laughs) >> What happened, what happened next? >> We did. >> Everyone's like, whoa, >> So-- >> we can't, we're not, we're staying below the stack here. >> Well, we knew there was a lot of buzz around SDN and programmability, and we both actually, I should even back up further. We were both on the DevNet team when the DevNet program was Powerpoints, so we weren't even there yet. >> Right, when we were just planning what it even could be, like the ideas of having a developer program, and like Par was saying, we knew SDN was coming. We knew Network Controllers were coming. We didn't know what they were gonna be called, we didn't know what those APIs looked like, but we said, "The network engineers are gonna need "to know how to make REST API calls. "They're gonna need to know how to operate in Python." And so we started this program building around that vision before the portfolio is where it is today. Like today, now, we have APIs across the whole portfolio, Data Center, service provider, enterprise, and then up and down from the devices, all the way to controllers, up to the analytics level. So the portfolio's really filled out, and we've been able to bring that community along with it, which has been great. >> I want to dig into the north/south, east/west and that whole, kind of the Cloud paradigm, but I got to ask you, on a personal question, although relevant to the DevNet success. Was there a moment where, actually the seminal moments of 2014, was there a moment where you were like, "Wow, this is working." and like the, you know, (laughs) pinch me moment, or was it more of, "We got to get more resources, this is not just, "this thing's flying." >> Well it's always that. That's always the challenge. >> When was the point where >> We are, >> you said, "This is actually >> We are very-- >> "the best path, it's working, double down." When was that happening? >> I mean, I think after we started teaching those very early coding coding classes, I got this, like, flood of email from people who had attended them that said, "I took this task, I automated it, "it saved my team months of work," and getting that flow of information back from the community was early signs to me, from the technical level of, there's value, this is gonna take off, and then I think we just saw that kind of grow and grow. >> Mushroom, just kept it going. >> The other thing that I heard from a network engineer, which really resonated with me, was, you were saying, the network guy or gal likes to be there and solve the problem, and they're sort of at this deep level of control. And what I heard them say about the programmability skills was that that was another tool that they added to their sort of toolbox that let them be that person in the moment, solving that problem. And they could just solve it in a new way, so hearing the network engineers say that they have adopted programmability in that fashion, that let me know that that was gonna work, I think. >> All right, so let's get into some of the meat and potatoes, because you guys have some really good announcements. We saw you have the code ecosystem that you announced at DevNet Create, which is your emerging Cloud Native worlds coming together. That's available now. >> Yes, it's fully released. >> So take a minute to, so give us the update. >> Yes, so DevNet Code Exchange is developer.cisco.com/codeexchange so you can go there, it's live, and the idea behind this was we wanted to make it easy for the community to contribute, and also to discover code written by the community. So it's on GitHub. You can go and search on GitHub, but you get back a ton of hits if you go search Cisco on GitHub, which is great, but what we wanted to have was a curated list that you can filter by product, by language. I sometimes joke that it's like Zappos for sample code cause you can go on and say, "I want black boots, "you know the two inch heel." You can say, "I want, I want code for DNA Center, "or ACI, and I want it in Python," and then see all of the repositories submitted by the community. And then the community can also share their codes. "Hey, I've been working on this project. "I'm gonna add it to Code Exchange, so that other people "can build off of it and find it." So it's really about this community contribution, which is a strategic initiative for DevNet for this year. >> Mandy, how does that tie into other networking initiatives happening in the industry? I think of OpenDaylight, a lot of stuff happening, Docker comes this week, Kubernetes, and networking's a critical piece of all of these environments. >> Yeah, so some of the projects that you'll find in Code Exchange are things that relate. So we have some really good open-source community projects around YANG models and the tooling to help you deal with YANG models. So those might be in Code Exchange, but those are also part of the OpenDaylight community, and being worked in that. So because it is all open-source, because it is freely shared, and it's really just a way to improve discoverability, we can share easily back and forth between those communities. >> The Code Exchange is designed to really help people peer-to-peer work together and reuse code, but in the classic >> Reuse code within >> open-source ethos. >> the community. Exactly. >> Okay, so Par, you have something going on with Ecosystem Exchange. >> We do. >> Okay, so it sounds like Code Exchange, ecosystem partners, matchmaking service. What is it, take a minute to explain. >> It's kinda the next level up, and what I think we have to understand is, when we've got Code Exchange and Ecosystem Exchange under the umbrella of exchange, because within our 500, half a million community of developers, where they work, what we've found is predominately at SIs, at our VARs, at our ISVs. So these are the builders, so Code Exchange will even help that persona because they can come and see what's already been built. "Is there something that can jumpstart my development?" And if there's not, then they can work with each other, right? So if I am looking for a partner, a VAR in Australia to help me roll out my application, my navigation application, which needs to know and get data from the network, I can partner through this exchange because I can go in, see everyone, and be able to make that connection digitally versus organically. And this really started, you asked earlier what was one of the pinnacle moments? Well at these DevNet Zones, what we found is that an ISV would partner and start talking to an SI or to a VAR, and they'd start doing business planning, because what this is all about is driving those business outcomes for our customer base. And we're finding more and more they're trying to work together. >> So you're enabling people to get, do some work together, but not try and be a marketplace where you're actually charging a transaction. It's really kind of a matchmaking-- >> This is all about discovery right now. >> Community-driven discovery around business. Yeah, it's interesting, a heard a story in the hallway about DevNet, cause I love to get the examples of, we love what we're doing by the way, but want to get the examples, overheard a guy saying, "We were basically "cratering a business, jumped into the DevNet program, "and turned it around," because there was deals happening. So the organic nature of the community allowed for him to get his hands dirty and leverage it, but actually build business value. >> That's exactly right. >> That's a huge, >> That's exactly right. >> at the end of the day, people love to play with code, but they're building something for business purposes or open-source projects. >> And that's what this is about. It's really transitioning from the, "I'm gonna build," to now there's business value associated with it, and that's spectacular. >> I think so much of my career you talk, the poor network administrators, like "Help, help, "I'm gonna lock myself for a month, "and I'm gonna do all this scripting," and then three months later their business comes and asks for something that, "I need to go it again," because it's not repeatable. It's what we say is that the challenge has been that undifferentiated heavy lifting that too many companies do. >> Exactly. >> Well, that's exactly it, and the interesting thing, especially around intent-based networking is that's opening up a whole new opportunity of innovation and services. And one of the things that isn't very much different with our Ecosystem Exchange is it's the whole portfolio, so we have SIs in there as well as ISVs. And most marketplaces or catalogs really look at it in a silo version. >> I have one example of kinda the two coming together that's really interesting. So, Meraki, which is the wireless network, has really great indoor location-based services you can get from the WiFi. And then there's been ISVs who have built indoor wave finding on top of it, they're really great applications. But those software companies don't necessarily know how to go install a Meraki network or sell a Meraki network to something like this. And so it's been a great way to see how some of those wave finding companies can get together with the people who actually go sell and install and admin Meraki networks, and, but come together, cause they would have a hard time finding each other otherwise. >> And the example is actually rolled out here at Cisco Live. We've, Cisco Live partnered with an ISV to embed a Cloud-based service in their app, which is navigation. So you can go into the Cisco Live app, tap on the session that you want to see. A map will come up that will navigate you from where you are here to get there, and this is, I think this is the second largest conference center in the United States, so having that map >> So you need it. >> is really important. >> I've gotten lost twice. >> We've all got the steps to prove that that is, but, yeah, and that actually brings, one of the questions I had was, is it typically some new thing, to do wireless rollouts and SD-WAN on discovery, or is it core networking, or is it kind of across the board as to when people get involved? >> It's definitely both. It's definitely both. I mean, from the Code Exchange piece, I've talked to a lot of customers this week who are saying, "We've got our core networking teams. "We want to move towards more automation. "We're trying to figure out how to get started." And so we give them all the resources to get started, like our video series and then now Code Exchange. And then I heard from some people here, they actually coded up some things and submitted it to Code Exchange while they were here because they had an idea for just a simple, quick automation piece that they needed. And they were like, "I bet somebody else "needs it too," so it was definitely in that. >> I noticed you guys also have your Cisco team I was talking to, some of the folks here have patents are being filed. So internally at Cisco, it's kind of a wind of change happening, where, >> It is exciting times. >> IoT cameras, I just saw a solution behind us here where you plug a Rasberry Pi hardware prototype to an AP, makes the camera a video. Now it looks like facial recognition, saves the metadata, never stores video, so this is kind of the new model. >> Pretty remarkable. So final question I want to ask you is, as you guys continue to build community, you're looking for feedback, the role of integrating is critical. You mentioned this Cisco example about going to market together. It used to be, "Hey, I'm an integrator of our solution, "business planning," okay, and then you gotta go to the Cisco rep, and then there's, they're dislocated. More and more it's coming together. >> It is. >> How are you guys bridging that, those two worlds? How are you tying it together? What's the plan? >> So we're, what we're finding is a lot of those partners are also sort of morphing. So they're not just one thing anymore, and so what we're doing is we're working with them, enabling them on our platforms, providing solid APIs that they can leverage, transitioning or expanding the code, the skillsets of their workers, and then we're partnering them up with our business partners and with our ISVs, and doing a lot of that matchmaking. And with Ecosystem Exchange, again, they'll now be able to take that to a digital format, so we're seeing the whole wave of the market taking them. >> So you guys see it coming. You're on that wave. >> Yes. >> All right, real quick, I know we're short on time, but I would, Mandy, if you could just talk about what Susie Wee, you're leader talked about on stage on the keynote, she mentioned DNA Center. Can you just take a quick second, describe what that is, why it's important, and impact to the community. >> Yes, so we're really excited about DNA Center platform. DNA Center is the controller, kind of at the heart of all of our new enterprise networking software. So it sits on top of the devices, and it exposes a whole library of APIs. It'll let you do Assurance, policy, get device information. It would allow you to build a kind of self-service ops models, so you could give more power to your power users to get access to network resources, on-board new devices, things like that. >> So it sets the services. >> So it's APIs, and then you can build the services on top. And part of that is also the Assurance, which Dave Geckeler showed in his keynote, which we're really excited about. So, in DevNet we've been working to build all the resources around those APIs, and we have many code samples in Code Exchange. We actually have a community contribution sprint going on right now, and that's called Code Intent with DevNet, and it's all around DNA Center. It's asking developers to take a business intent and turn it into code, and close the loop with Assurance, and submit that back to DevNet. >> That's great. It's a real business process >> We're real excited about >> improvement with code, >> that, yeah, so you're enabling that, and slinging APIs around, having fun, are you having fun? >> Definitely having fun. >> Par? >> We always have fun >> Absolutely >> on this team. >> We always have fun, yeah. >> It's a great team. >> I can say working with you guys up close has been fun to work, and congratulations. You guys have worked really hard and built a very successful, growing ecosystem of developers and partners, congratulations. >> Thank you. You guys have helped. >> Thank you. >> Thanks for supporting >> We appreciate it. theCUBE, really appreciate, this is crew of the DevNet team talking about, back in the early days, 2014, when it started, now it's booming. One of the successful developer programs in the enterprise here. Cisco's really showing the path. It's all about the community and the ecosystems, theCUBE, of course, doing our share. Broadcasting here live in Orlando at Cisco Live 2018. Stay with us for more live coverage after this short break. (upbeat music)

>> Announcer: Live from the Moscone Center, it's theCUBE covering AWS Summit San Francisco, 2018, brought to you by Amazon Web Services. (techy music playing) >> Welcome back, I'm Stu Miniman joined by my cohost Jeff Frick, and this is theCUBE's live coverage of AWS Summit San Francisco. We are thrilled to welcome back to the program Sandy Carter, who's a vice president with Amazon Web Services. Been with the company about a year. We've had you on the program many times, but first time since you've been at AWS, so... >> That's right, I'm celebrating my year yesterday with Amazon Web Services. >> Stu: And no cake, all right. >> I had a cake yesterday, actually, cake and champagne, by the way. (laughing) >> Sandy, we always love to hear, you know, you talk to so many customers, you know, bring us back for a little bit. What brought you to AWS, what's exciting to your customers when you're talking to them today? >> Well, you know, I really love innovation, I love being innovative, and you know, bar none Amazon is the most innovative company out there today, but really what brought me to Amazon was their focus on the customer, really "obsession" on the customer. When they say obsession they really mean obsession. They work backwards from the customer. We really have this big, big thrust. In fact, one of my favorite stories is when I first came to Amazon we'd be in these meetings and people would say, "Well, what does Low Flying Hawk think about this," or "What does Low Flying Hawk think about that," and I was like, "Who is Low Flying Hawk?" Well, he's a person who would give comments on a forum and just a person who wasn't even spending millions of dollars with Amazon but just had a lot of big clout. We actually just opened a building named Low Flying Hawk, believe it or not. >> Jeff: Have you identified this person? >> They do know who he is, yes. (laughing) But it's really, it just symbolizes the focus that Amazon has on the customer and why that's so important. >> And Sandy, at re:Invent you actually, you spoke to the analyst, I was listening to the session. It's not just kind of, people think AWS they think public cloud. You work for Amazon, it's everything kind of across what you think of Amazon.com, AWS, everything from drones and using Kindles and everything like that. Can you give us a little bit of kind of that pan view of how Amazon looks at innovation? >> Yeah, so it's really interesting. Amazon is very methodical in the way that we innovate, and what we do is we really try to understand the customer. We work backwards from the customer, so we do a press release first, we do frequently asked questions next, and then we do a narrative-- >> You're saying you do an internal press release, yes, yes. >> Yeah, internal press release. Internal frequently asked questions, and then we review a six-page document, no PowerPoints whatsoever, which enables us to debate and learn from each other and just iterate on the idea that makes it better and better and better so that when we come out with it it's a really powerful idea and powerful concept, something that the customers really want. >> So, we'll ask you what you're doing now, but one more kind of transition question, what was your biggest surprise? You know, there's a lot of kind of mystery from people on the outside looking in in terms of culture, and we know it's car driving and innovative growing like crazy company, not only in business but in terms of people. What was your biggest surprise once you kind of got on the inside door? >> My biggest surprise was just how incredibly encouraging and supportive the team is at AWS. My boss is Matt Garman, he's been supportive since day one, you know, Andy, they just cheer you on. They want you to do well and I've really never been at a company that everybody's really pulling for you to be successful, not political infighting but really pulling for you to be successful. So, that's really was the biggest surprise to me, and then that customer obsession. Like, it's not customer focus, it really is customer obsession. >> Right, I think it's so well illustrated by the, again not AWS, but Amazon with the store, right, with no cash register, no people. >> Sandy: Amazon Go. >> To think about that-- >> Sandy: Yeah. >> From the customer point of view is nobody likes to stand in line at the grocery store, so it's such a clean illustration of a customer centric way to attack the problem. >> And I love that because what we did is we opened up the beta first for employees, so we would go in and play with it and test it out, and then we opened it up in Seattle and we would give customer tours. Now it's open to the public in Seattle, so it just again shows you that iterative process that Amazon uses and it's super cool, have you guys been? >> Jeff: Have not been. >> Ugh, in fact, my daughter went in. She put on a mask, she was going to fool the system but it wasn't fooled. All the ML and all the AI worked brilliantly. >> I love how everyone loves to get so creative and try to, you know, get through the system, right, try to break the system. >> I know, but my daughter, that's what I would figure for sure. (laughing) >> So, what are you working on now? You've been there a year, what are you working on? >> So, we are innovating around the enterprise workload, so we know that a lot of startups and cloud native companies have moved to the cloud, but we're still seeing a lot of enterprises that are trying to figure out what their strategy is, and so, Stu and Jeff, what I've been working on is how do we help enterprises in the best way possible. How can we innovate to get them migrated over as fast as possible? So for instance, we have Windows that runs on AWS. It's actually been running there longer than with any other vendor and we have amazing performance, amazing reliability. We just released an ML, machine learning OMI for Windows so that you can use and leverage all that great Windows support and applications that you have, and then you guys saw earlier I was talking to VMware. We know that a lot of customers want to do hybrid cloud on their journey to going all-in with the cloud, and so we formed this great partnership with VMware, produced an offering called VMware Cloud on AWS and we're seeing great traction there. Like Scribd's network just talked about how they're using it for disaster recovery. Other customers are using it to migrate. One CIO migrated 143 workloads in a weekend using that solution. So, it just helps them to get to that hybrid state before they go all-in on the cloud. >> So, are they, I was going to say, are they building a mirror instance of what their on-prem VMware stack is in the Amazon version? Is that how they're kind of negotiating that transition or how does that work? >> So, with VMware they don't have to refactor, so they can just go straight over. With Microsoft workloads what we're seeing a lot of times is maybe they'll bring a sequel app over and they'll just do a lift and shift, and then once they feel comfortable with the cloud they'll go to Aurora, which as you've found was the fastest growing service that AWS has ever had, and so we see a lot of that, you know, movement. Bring it over, lift and shift, learning and you know, if you think about it, if you're a large enterprise one of your big challenges is how do I get my people trained, how do I get them up to speed, and so we've done... Like, we've got a full dot net stack that runs on AWS, so their people don't even have to learn a new language. They can develop in Visual Studio and use PowerShell but work on AWS and bring that over. >> You know, Sandy, bring us inside your customers because the challenge for most enterprises is they have so many applications. >> Sandy: Yeah. >> And you mentioned lift and shift. >> Sandy: Yeah. >> You know, I know some consultant's out there like, "Lift and shift is horrible, don't do it." It's like, well, there's some things you'll build new in the cloud, there's some things you'll do a little bit, and there's some stuff today lift and shift makes sense and then down the road I might, you know, move and I've seen, you know, it was like the seven Rs that Amazon has as to do you re-platform, refactor-- >> That's right. >> You know, all that and everything, so I mean, there's many paths to get there. What are some of the patterns you're hearing from customers? How do they, how is it easier for them to kind of move forward and not get stuck? >> Well, we're seeing a lot of data center evacuations, so those tend to be really fast movement and that's typically-- >> Jeff: Data center evacuation-- >> Yeah, that's what-- >> I haven't heard that one. >> Yeah, that's what, evacuation, they've got to get out of their data center buyer for a certain date for whatever reason, right? They had a flood or a corporate mandate or something going on, and so we are seeing those and those are, Stu, like lift and shift quickly. We are seeing a lot of customers who will create new applications using containers and serverless that we talked about today a lot, and that's really around the innovative, new stuff that they're doing, right. So, Just Eat, for instance, is a large... They do online food service out of the UK. I love their solution because what they're doing is they're using Alexa to now order food, so you can say, "Alexa, I want a pizza delivered "in 20 minutes, what's the best pizza place "that I can get in 20 minutes?" Or "I want sushi tonight," and Alexa will come back and say, "Well, it's going to take "an hour and a half, you had sushi two days ago. "Maybe you want to do Thai food tonight." (laughing) And so it's really incredible, and then they even innovated and they're using Amazon Fire for group ordering. So, if there's a big football game or something going on they'll use Amazon Fire to do that group ordering. All that is coming in through Alexa, but the back end is still Windows on AWS. So, I love the fact that they're creating these new apps but they're using some of that lift and shift to get the data and the training and all that moving and grooving, too. >> Yeah, what do you, from the training standpoint, how, you know, ready are customers to retrain their people, you know, where are there shortages of skillsets, and how's Amazon, you know, helping in that whole movement? >> Well, training is essential because you've got so many great people at enterprises who have these great skills, so what we see a lot of people doing is leveraging things like dot net on AWS. So, they actually... They have something they know, dot net, but yet they're learning about the cloud, and so we're helping them do that training as they're going along but they still have something very familiar. Folks like Capital One did a huge training effort. They trained 1,000 people in a year on cloud. They did deep dives with a Tiger Team on cloud to get them really into the architecture and really understanding what was going on, so they could leverage all those great skills that they had in IT. So, we're seeing everything from, "I got to use some of the current tools that I have," to "Let me completely move to something new." >> And how have you, you've been in the Bay Area also for about a year, right, if I recall? >> Actually, I just moved, I moved to Seattle. >> Jeff: Oh, you did make the move, I was going to say-- >> I did. (laughing) >> "So, are they going to make you move up north?" >> I did because I was-- >> You timed it in the spring, not in November? >> I did, there you go. (laughing) When it's nice and sunny, but it's great. >> Exactly. >> It's great to live in Seattle. Amazon has such a culture that is in person, you know, so many people work there. It's really exhilarating to go into the office and brainstorm and whiteboard with people right there, and then our EBCs are there, so our executive briefing center is there, so customers come in all the time because they want to go see Amazon Go, and so it's really an exciting, energizing place to be. >> Yeah, I love the line that Warner used this morning is that AWS customers are builders and they have a bias for action. So, how do you help customers kind of translate some of the, you know, the culture that Amazon's living and kind of acting like a startup for such a large company into kind of the enterprise mindset? >> That's a great question, so we just proposed this digital innovation workshop. We are doing this now with customers. So, we're teaching them how to work backwards from the customer, how to really understand what a customer need is and how to make sure they're not biased when they're getting that customer need coming in. How to do, build an empathy map and how to write that press release, that internal press release and think differently. So, we're actually teaching customers to do it. It's one of our hottest areas today. When customers do that they commit to doing a proof of concept with us on AWS on one of the new, innovative ideas. So, we've seen a lot of great and exciting innovation coming out of that. >> All right, well, Sandy Carter, so glad we could catch up with you again. Thanks for bringing discussion of innovation, what's happening in the enterprise customers to our audience. For Jeff Frick, I'm Stu Miniman, we'll be back will lots more coverage here, you're watching theCUBE. (techy music playing)

>> Hi, I'm Peter Burris, and welcome to another Wikibon Action Item. (funky electronic music) This was a very interesting week in the tech industry, specifically because IBM's Think Conference aggregated in a large number of people. Now, The CUBE was there. Dave Vellante, John Furrier, and myself all participated in somewhere in the vicinity of 60 or 70 interviews with thought leaders in the industry, including a number of very senior IBM executives. The reason why this becomes so important is because IBM made a proposal to the industry about how some of the digital disruption that the market faces is likely to unfold. The normal approach or the normal mindset that people have used is that startups, digital native companies were going to change the way that everything was going to operate, and the dinosaurs were going to go by the wayside. IBM's interesting proposal is that the dinosaurs actually are going to learn to dance, utilizing or playing on a book title from a number of years ago. And the specific argument was laid out by Ginni Rometty in her keynote, when she said that there are number of factors that are especially important here. Factor number one is that increasingly, businesses are going to recognize that the role that their data plays in competition is on the ascending. It's getting more important. Now, this is something that Wikibon's been arguing for quite some time. In fact, we have said that the whole key to digital disruption and digital business is to acknowledge the difference between business and digital business, is the role that data and data assets play in your business. So we have strong agreement there. But on top of that, Ginni Rometty made the observation that 80% of the data that could be accessed and put the work in business has not yet been made available to the new activities, the new processes that are essential to changing the way customers are engaged, businesses operate, and overall change and disruption occurs. So her suggestion is that that 80%, that vast amount of data that could be applied that's not being tapped, is embedded deep within the incumbents. And so the core argument from IBM is that the incumbent companies, not the digital natives, not the startups, but the incumbent companies are poised to make a significant, to have a significant role in disrupting how markets operate, because of the value of their data that hasn't currently been put to work and made available to new types of work. That was the thesis that we heard this week, and that's what we're going to talk about today. Are the incumbent really going to strike back? So Dave Vellante, let me start with you. You were at Think, you heard the same type of argument. What did you walk away with? >> So when I first heard the term incumbent disruptors, I was very skeptical, and I still am. But I like the concept and I like it a lot. So let me explain why I like it and why I think there's some real challenges. If I'm a large incumbent global 2,000, I'm not going to just roll over because the world is changing and software is eating my world. Rather what I'm going to do is I'm going to use my considerable assets to compete, and so that includes my customers, my employees, my ecosystem, the partnerships that I have there, et cetera. The reason why I'm skeptical is because incumbents aren't organized around their data assets. Their data assets are stovepipe, they're all over the place. And the skills to leverage that data value, monetize that data, understand the contribution that data makes toward monetization, those skills are limited. They're bespoke and they're very narrow. They're within lines of business or divisions. So there's a huge AI gap between the true digital business and an incumbent business. Now, I don't think all is lost. I think a lot of strategies can work, from M&A to transformation projects, joint ventures, spin-offs. Yeah, IBM gave some examples. They put up Verizon and American Airlines. I don't see them yet as the incumbent disruptors. But then there was another example of IBM Maersk doing some very interesting and disrupting things, Royal Bank of Canada doing some pretty interesting things. >> But in a joint venture forum, Dave, to your point, they specifically set up a joint venture that would be organized around this data, didn't they? >> Yes, and that's really the point I'm trying to make. All is not lost. There are certain things that you can do, many things that you can do as an incumbent. And it's really game on for the next wave of innovation. >> So we agree as a general principle that data is really important, David Floyer. And that's been our thesis for quite some time. But Ginni put something out there, Ginni Rometty put something out there. My good friend, Ginni Rometty, put something out there that 80% of the data that could be applied to disruption, better customer engagement, better operations, new markets, is not being utilized. What do we think about that? Is that number real? >> If you look at the data inside any organization, there's a lot of structured data. And that has better ability to move through an organization. Equally, there's a huge amount of unstructured data that goes in emails. It goes in voicemails, it goes in shared documents. It goes in diagrams, PowerPoints, et cetera, that also is data which is very much locked up in the way that Dave Vellante was talking about, locked up in a particular process or in a particular area. So is there a large amount of data that could be used inside an organization? Is it private, is it theirs? Yes, there is. The question is, how do you tap that data? How do you organize around that data to release it? >> So this is kind of a chicken and egg kind of a problem. Neil Raden, I'm going to turn to you. When we think about this chicken and egg problem, the question is do we organize in anticipation of creating these assets? Do we establish new processes in anticipation of creating these data assets? Or do we create the data assets first and then re-institutionalize the work? And the reason why it's a chicken and egg kind of problem is because it takes an enormous amount of leadership will to affect the way a business works before the asset's in place. But it's unclear that we're going to get the asset that we want unless we affect the reorganization, institutionalization. Neil, is it going to be a chicken? Is it going to be the egg? Or is this one of the biggest problems that these guys are going to have? >> Well, I'm a little skeptical about this 80% number. I need some convincing before I comment on that. But I would rather see, when David mentioned the PowerPoint slides or email or that sort of thing, I would rather see that information curated by the application itself, rather than dragged out in broad data and reinterpreted in something. I think that's very dangerous. I think we saw that in data warehousing. (mumbling) But when you look at building data lakes, you throw all this stuff into a data lake. And then after the fact, somebody has to say, "Well, what does this data mean?" So I find it kind of a problem. >> So Jim Kobielus, a couple weeks ago Microsoft actually introduced a technology or a toolkit that could in fact be applied to move this kind of advance processing for dragging value out of a PowerPoint or a Word document or something else, close and proximate to the application. Is that, I mean, what Neil just suggested I think is a very, very good point. Are we going to see these kinds of new technologies directly embedded within applications to help users narrowly, but businesses more broadly, lift that information out of these applications so it can be freed up for other uses? >> I think yeah, on some level, Peter, this is a topic called dark data. It's been discussed in data management circles for a long time. The vast majority, I think 75 to 80% is the number that I see in the research, is locked up in terms of it's not searchable, it's not easily discoverable. It's not mashupable, I'm making up a word. But the term mashup hasn't been used in years, but I think it's a good one. What it's all about is if we want to make the most out of our incumbent's data, then we need to give the business, the business people, the tools to find the data where it is, to mash it up into new forms and analytics and so forth, in order to monetize it and sell it, make money off of it. So there are a wide range of data discovery and other tools that support a fairly self-service combination and composition of composite data object. I don't know that, however, that the culture of monetizing existing dataset and pulling dark data into productized forms, I don't think that's taken root in any organization anywhere. I think that's just something that consultants talk about as something that gee, should be done, but I don't think it's happening in the real world. >> And I think you're probably correct about that, but I still think Neil raised a great point. And I would expect, and I think we all believe, that increasingly this is not going to come as a result of massive changes in adoption of new data science, like practices everywhere, but an embedding of these technologies. Machine learning algorithms, approaches to finding patterns within application data, in the applications themselves, which is exactly what Neil was saying. So I think that what we're going to see, and I wanted some validation from you guys about this, is increasingly tools being used by application providers to reveal data that's in applications, and not open source, independent tool chains that then ex-post-facto get applied to all kinds of different data sources in an attempt for the organization to pull the stuff out. David Floyer, what do you think? >> I agree with you. I think there's a great opportunity for the IT industry in this area to put together solutions which can go and fit in. On the basis of existing applications, there's a huge amount of potential, for example, of ERP systems to link in with IOT systems, for example, and provide a data across an organization. Rather than designing your own IOT system, I think people are going to buy-in pre-made ones. They're going to put the devices in, the data's going to come in, and the AI work will be done as part of that, as part of implementing that. And right across the board, there is tremendous opportunity to improve the applications that currently exist, or put in new versions of applications to address this question of data sharing across an organization. >> Yeah, I think that's going to be a big piece of what happens. And it also says, Neil Raden, something about whether or not enormous machine learning deities in the sky, some of which might start with the letter W, are going to be the best and only way to unlock this data. Is this going to be something that, we're suggesting now that it's something that's going to be increasingly-distributed closer to applications, less invasive and disruptive to people, more invasive and disruptive to the applications and the systems that are in place. And what do you think, Neil? Is that a better way of thinking about this? >> Yeah, let me give you an example. Data science the way it's been practiced is a mess. You have one person who's trying to find the data, trying to understand the data, complete your selection, designing experiments, doing runs, and so forth, coming up with formulas and then putting them in the cluster with funny names so they can try to remember which one was which. And now what you have are a number of software companies who've come up with brilliant ways of managing that process, of really helping the data science to create a work process in curating the data and so forth. So if you want to know something about this particular model, you don't have to go to the person and say, "Why did you do that model? "What exactly were you thinking?" That information would be available right there in the workbench. And I think that's a good model for, frankly, everything. >> So let's-- >> Development pipeline toolkits. That's a hot theme. >> Yeah, it's a very hot theme. But Jim, I don't think you think but I'm going to test it. I don't think we're going to see AI pipeline toolkits be immediately or be accessed by your average end user who's putting together a contract, so that that toolkit or so that data is automatically munched and ingested or ingested and munched by some AI pipeline. This is going to happen in an application. So the person's going to continue to do their work, and then the tooling will or will not grab that information and then combine it with other things through the application itself into the pipeline. We got that right? >> Yeah, but I think this is all being, everything you described is being embedded in applications that are making calls to backend cloud services that have themselves been built by data scientists and exposed through rest APIs. Steve, Peter, everything you're describing is coming to applications fairly rapidly. >> I think that's a good point, but I want to test it. I want to test that. So Ralph Finos, you've been paying a lot of attention during reporting season to what some of the big guys are saying on some of their calls and in some of their public statements. One company in particular, Oracle, has been finessing a transformation, shall we say? What are they saying about how this is going as we think about their customer base, the transformation of their customer base, and the degree to which applications are or are not playing a role in those transformations? >> Yeah, I think in their last earnings call a couple days ago that the point that they were making around the decline and the-- >> Again, this is Oracle. So in Oracle's last earnings call, yeah. >> Yeah, I'm sorry, yeah. And the decline and the revenue growth rate in the public cloud, the SAS end of their business, was a function really of a slowdown of the original acquisitions they made to kind of show up as being a transformative cloud vendor, and that are basically beginning to run out of gas. And I think if you're looking at marketing applications and sales-related applications and content-type of applications, those are kind of hitting a natural high of growth. And I think what they were saying is that from a migration perspective on ERP, that that's going to take a while to get done. They were saying something like 10 or 15% of their customer base had just begun doing some sort of migration. And that's a data around ERP and those kinds of applications. So it's a long slog ahead of them, but I'd rather be in their shoes, I think, for the long run than trying to kind of jazz up in the near-term some kind of pseudo-SAS cloud growth based on acquisition and low-lying fruit. >> Yeah, because they have a public cloud, right? I mean, at least they're in the game. >> Yeah, and they have to show they're in the game. >> Yeah, and specifically they're talking about their applications as clouds themselves. So they're not just saying here's a set of resources that you can build, too. They're saying here's a set of SAS-based applications that you can build around. >> Dave: Right. Go ahead, Ralph, sorry. >> Yeah, yeah. And I think the notion there is the migration to their ERP and their systems of record applications that they're saying, this is going to take a long time for people to do that migration because of complexity in process. >> So the last point, or Dave Vellante, did you have a point you want to make before I jump into a new thought here? >> I just compare and contrast IBM and Oracle. They have public clouds, they have SAS. Many others don't. I think this is a major different point of differentiation. >> Alright, so we've talked about whether or not this notion of data as a source of value's important, and we agree it is. We still don't know whether or not 80% is the right number, but it is some large number that's currently not being utilized and applied to work differently than the data currently is. And that likely creates some significant opportunities for transformation. Do we ultimately think that the incumbents, again, I mention the chicken and the egg problem. Do we ultimately think that the incumbents are... Is this going to be a test of whether or not the incumbents are going to be around in 10 years? The degree to which they enact the types of transformation we thought about. Dave Vellante, you said you were skeptical. You heard the story. We've had the conversation. Will incumbents who do this in fact be in a better position? >> Well, incumbents that do take action absolutely will be in a better position. But I think that's the real question. I personally believe that every industry is going to get disrupted by digital, and I think a lot of companies are not prepared for this and are going to be in deep trouble. >> Alright, so one more thought, because we're talking about industries overall. There's so many elements we haven't gotten to, but there's one absolute thing I want to talk about. Specifically the difference between B2C and B2B companies. Clearly the B2C industries have been disrupted, many of them pretty significantly, over the last few years. Not too long ago, I have multiple not-necessarily-good memories of running the aisles of Toys R Us sometime after 10 o'clock at night, right around December 24th. I can't do that anymore, and it's not because my kids are grown. Or I won't be able to do that soon anymore. So B2C industries seem to have been moved faster, because the digital natives are able to take advantage of the fact that a lot of these B2C industries did not have direct and strong relationships with those customers. I would posit that a lot of the B2B industries are really where the action's going to take. And the kind of way I would think about it, and David Floyer, I'll turn to you first. The way I would think about it is that in the B2C world, it's new markets and new ways of doing things, which is where the disruption's going to take place. So more of a substitution as opposed to a churn. But in the B2B markets, it's disrupting greater efficiencies, greater automation, greater engagement with existing customers, as well as finding new businesses and opportunities. What do you think about that? >> I think the B2B market is much more stable. Relationships, business relationships, very, very important. They take a long time to change. >> Peter: But much of that isn't digital. >> A lot of that is not digital. I agree with that. However, I think that the underlying change that's happening is one of automation. B2B are struggling to put into place automation with robots, automation everywhere. What you see, for example, in Amazon is a dedication to automation, to making things more efficient. And I think that's, to me, the biggest challenges, owning up to the fact that they have to change their automation, get themselves far more efficient. And if they don't succeed in doing that, then their ability to survive or their likelihood of being taken over with a reverse takeover becomes higher and higher and higher. So how do you go about that level, huge increase in automation that is needed to survive, I think is the biggest question for B2B players. >> And when we think about automation, David Floyer, we're not talking about the manufacturing arms or only talking about the manufacturing arms. We're talking about a lot of new software automation. Dave Vellante, Jim Kobielus, RPA is kind of a new thing. Dave, we saw some interesting things at Think. Bring us up to speed quickly on what the community at Think was talking about with RPA. >> Well, I tell you. There were a lot of people in financial services, which is IBM's stronghold. And they're using software robots to automate a lot of the backend stuff that humans were doing. That's a major, major use case. I would say 25 to 30% of the financial services organizations that I talked to had active RPA projects ongoing at the moment. I don't know. Jim, what are your thoughts? >> Yeah, I think backend automation is where B2B disruption is happening. As the organizations are able to automate more of their backend, digitize more of their backend functions and accelerate them and improve the throughput of transactions, are those that will clean up. I think for the B2C space, it's the frontend automation of the digitalization of the engagement channels. But RPA is essentially a key that's unlocking backend automation for everybody, because it allows more of the frontend business analysts and those who are not traditionally BPM, or business process re-engineering professionals, to begin to take standard administrative processes and begin to automate them from, as it were, the outside-in in a greater way. So I think RPA is a secret key for that. I think we'll see some of the more disruptive organizations, businesses, take RPA and use it to essentially just reverse-engineer, as it were, existing processes, but in an automated fashion, and drive that improvement but in the backend by AI. >> I just love the term software robots. I just think that that's, I think that so strongly evokes what's going to happen here. >> If I could add, I think there's a huge need to simplify that space. The other thing I witnessed at IBM Think is it's still pretty complicated. It's still a heavy lift. There's a lot of big services component to this, which is probably why IBM loves it. But there's a massive market, I think, to simplify the adoption or RPA. >> I completely agree. We have to open the aperture as well. Again, the goal is not to train people new things, new data science, new automation stuff, but to provide tools and increasingly embed those tools into stuff that people are already using, so that the disruption and the changes happen more as a consequence of continuing to do what the people do. Alright, so let's hit the action item we're on, guys. It's been a great conversation. Again, we haven't talked about GDPR. We haven't talked about a wide array of different factors that are going to be an issue. I think this is something we're going to talk about. But on the narrow issue of can the disruptors strike back? Neil Raden, let's start with you. Neil Raden, action item. >> I've been saying since 1975 that I should be hanging around with a better class of people, but I do spend a lot of time in the insurance industry. And I have been getting a consensus that in the next five to 10 years, there will no longer be underwriters for claims adjustments. That business is ready for massive, massive change. >> And those are disruptors, largely. Jim Kobielus, action item. >> Action item. In terms of business disruption, is just not to imagine that because you were the incumbent in the past era in some solution category that's declining, that that automatically guarantees you, that makes your data fit for seizing opportunities in the future. As we've learned from Blockbuster Video, the fact that they had all this customer data didn't give them any defenses against Netflix coming along and cleaning their coffin, putting them out of business. So the next generation of disruptor will not have any legacy data to work from, and they'll be able to work miracles because they made a strategic bet on some frontend digital channel that made all the difference. >> Ralph Finos, action item. >> Yeah, I think there's a notion here of siege mentality. And I think the incumbents are in the castle walls, and the disruptors are outside the castle walls. And sometimes the disruptors, you know, scale the walls. Sometimes they don't. But I think being inside the walls is a long-run tougher thing to be at. >> Dave Vellante, action item. >> I want to pick up on something Neil said. I think it's alluring for some of these industries, like insurance and financial services and healthcare, even parts of government, that really haven't been disrupted in a huge way yet to say, "Well, I'll wait and I'll see what happens." I think that's a huge mistake. I think you have to start immediately thinking about strategies, particularly around your data, as we talked about earlier. Maybe it's M&A, maybe it's joint ventures, maybe it's spinning out new companies. But the time is past where you should be acting. >> David Floyer, action item. >> I think that it's easier to focus on something that you can actually do. So my action item is that the focus of most B2B companies should be looking at all of their processes and incrementally automating them, taking out the people cost, taking out the cost, other costs, automating those processes as much as possible. That, in my opinion, is the most likely path to being in the position that you can continue to be competitive. Without that focus, it's likely that you're going to be disrupted. >> Alright. So the one thing I'll say about that, David, is when I think you say people cost I think you mean the administrative cost associated with people. >> And people doing things, automating jobs. >> Alright, so we have been talking here in today's Wikibon Action Item about the question, will the incumbents be able to strike back? The argument we heard at IBM Think this past week, and this is the third week of March, was that data is an asset that can be applied to significantly disrupt industries, and that incumbents have a lot of data that hasn't been bought into play in the disruptive flow. And IBM's argument is that we're going to see a lot of incumbents start putting their data into play, more of their data assets into play. And that's going to have a significant impact ultimately on industry structure, customer engagement, the nature of the products and services that are available over the course of the next decade. We agree. We generally agree. We might nitpick about whether it's 80%, whether it's 60%. But in general, the observation is an enormous amount of data that exists within a large company, that's related to how they conduct business, is siloed and locked away and is used once and is not made available, is dark and is not made available for derivative uses. That could, in fact, lead to significant consequential improvements in how a business's transaction costs are ultimately distributed. Automation's going to be a big deal. David Floyer's mentioned this in the past. I'm also of the opinion that there's going to be a lot of new opportunities for revenue enhancement and products. I think that's going to be as big, but it's very clear that to start it makes an enormous amount of sense to take a look at where your existing transaction costs are, where existing information asymmetries exist, and see what you can do to unlock that data, make it available to other processes, and start to do a better job of automating local and specific to those activities. And we generally ask our clients to take a look at what is your value proposition? What are the outcomes that are necessary for that value proposition? What activities are most important to creating those outcomes? And then find those that, by doing a better job of unlocking new data, you can better automate those activities. In general, our belief is that there's a significant difference between B2C and B2B businesses. Why? Because a lot of B2C businesses never really had that direct connection, therefore never really had as much of the market and customer data about what was going on. A lot of point-of-sale perhaps, but not a lot of other types of data. And then the disruptors stepped in and created direct relationships, gathered that data and were able to rapidly innovate products and services that served consumers differently. Where a lot of that new opportunity exists is in the B2B world. And here's where the real incumbents are going to start flexing their muscles over the course of the next decade, as they find those opportunities to engage differently, to automate existing practices and activities, change their cost model, and introduce new approaches to operating that are cloud-based, blockchain-based, data-based, based on data, and find new ways to utilize their people. If there's one big caution we have about this, it's this. Ultimately, the tooling is not broadly mature. The people necessary to build a lot of these tools are increasingly moving into the traditional disruptors, the legacy disruptors if we will. AWS, Netflix, Microsoft, companies more along those lines. That talent is very dear still in the industry, and it's going to require an enormous effort to bring those new types of technologies that can in fact liberate some of this data. We looked at things like RPA, robot process automation. We look at the big application providers to increasingly imbue their products and services with some of these new technologies. And ultimately, paradoxically perhaps, we look for the incumbent disruptors to find ways to disrupt without disrupting their own employees and customers. So embedding more of these new technologies in an ethical way directly into the systems and applications that serve people, so that the people face minimal changes to learning new tricks, because the systems themselves have gotten much more automated and much more... Are able to learn and evolve and adjust much more rapidly in a way that still corresponds to the way people do work. So our action item. Any company in the B2B space that is waiting for data to emerge as an asset in their business, so that they can then do all the institutional, re-institutionalizing of work and reorganizing of work and new types of investment, is not going to be in business in 10 years. Or it's going to have a very tough time with it. The big challenge for the board and the CIO, and it's not successfully been done in the past, at least not too often, is to start the process today without necessarily having access to the data, of starting to think about how the work's going to change, think about the way their organization's going to have to be set up. This is not business process re-engineering. This is organizing around future value of data, the options that data can create, and employ that approach to start doing local automation, serve customers, and change the way partnerships work, and ultimately plan out for an extended period of time how their digital business is going to evolve. Once again, I want to thank David Floyer here in the studio with me. Neil Raden, Dave Vellante, Ralph Finos, Jim Kobielus remote. Thanks very much guys. For all of our clients, once again this has been a Wikibon Action Item. We'll talk to you again. Thanks for watching. (funky electronic music)

>> Announcer: Live from the Washington D.C. It's the CUBE covering AWS Public Sector Summit 2017. Brought to you be Amazon Web Services and it's partner Ecosystem. >> Welcome back here on the CUBE as we continue our coverage of the AWS Public Sector Summit 2017. Along with John Furrier, I'm John Walls we're in the Walter E. Washington Convention Center. For the sixth show, of almost 10,000 attending. somewhere in that ball park. It's come along way in a very short period of time. AWS has a lot to feel good about. >> It's a good reinvent for Public Sector. It's huge. >> And not just to think about government. We think about education as well. We had a couple of segments about that. We are going to talk about government with our next guest. If we get a name wrong on this segment shame on us, John Stephenson with John Walls and John Furrier. John's a senior manage at Public Policy at AWS. John nice to have you with us we appreciate that. >> Thank you for having me. >> Thank you for your time. So your focus primarily state and local governments. What exactly as the conduit do you want to bring to their table from of AWS? >> Well I'm Senior Manager, of Public Policy for Amazon Web Services in the Eastern United States. I handle state and local government relations in the Eastern U.S. from Texas to Main and then South Florida. I help our business and also our partners in government to understand how public policy can enable cloud and modern technologies. It's a very exciting place to be because there's a lot going on in state and local government when it comes to IT modernization and cloud right now. >> I think about government too. There's that big umbrella we can put on (mumbles). It's public service. But federal government has a place and state and local. I think much more responsive, much more grass roots. So those applications are much more immediate. I would think. Does that come into play with you? That you need to be a little more nimble. Or you're helping your clients to be a litter more nimble or more agile? >> Absolutely, if you look at what state and local governments are doing. Essential services from delivering health care to taking out the trash, providing public safety, providing education it's handled at the state and local government. If you look at the number of times you touch government. It is state and local. Think about renewing a driver license. Think about paying a parking ticket. Think about getting a zoning permit for remodeling of your house. You're dealing with state and local government. The demands on state and local government are also higher. They're holding more data on citizens than the Federal government. They are undergoing massive population changes. It's either positive or negative. State and local governments which have budget constraints. Need to be more nimble, more innovative. They are natural early adopters and first movers of technology. If you look at some of the more exciting things about technology that are happening in the government space. I think it's happening at state and local government in the U.S. >> Smart cities by the way is the hottest trend. Intel one of the key sponsors of this show. We had two folks on here. AI is going to be a real nice gateway for some of these innovations on their side. They have 5G opportunities. They have transformation. Lot of technology going on under the covers, under the hood if you will. One of them is smart cities and that is something that is just mind blowing. Just from a technology stand point but even more mind blowing from a policy perspective. Who sets the rules? What side does the car run on? What digital services are the citizens going to get? Who pays for them? What does the government do? What does the private sector do? These are issues that need to be grappled with. Your thoughts on how you guys look at that? And how are your constituents engaging with that and thinking about it? >> I'm glad you mentioned smart cities because there's a lot of activity going on in that space. If you look at the internet of things technologies alone. One of the enablers of smart cities. As many as 53% of state and local government according to NASCIO are looking at these technologies or deploying them. It's great to see that because that will enable a lot of potential from smarter government services, better government services, improving service delivery and improving constituent fulfillment. Which resonates with us, as part of Amazon. We're all about our customer fulfillment and delighting our customer. >> Lower prices and ship things faster that's Bezos' ethos. That's Amazon's culture. >> Exactly. >> And you could deliver services any digital service. >> Everything we do starts with the customer and we work backwards. In the conversations I've had with policy makers in the state and local governments. They see smart cities as a way to do that. Everything from improving transportation in places like Columbus, Ohio. To improving connectivity and engagement with the internet in places Kansas City, Missouri. And new ways of delivering services in places like New York and Los Angeles. It's very exciting stuff. Policy makers are coming to us and others in the industry. What are the policies? What are the best practices that can enable these technologies? We've been working with them. Providing information on what we're seeing around the world. How open data can be made (mumbles). How security and compliance can be built into applications. And we're happy to provide that because we know from working in the cloud ourselves. The potential that's there for state and local government. >> You want to foster innovation but at the same time you don't want to create this restrictive environment. Or have legacy be the baggage that holds things back. In fact you look at some of the best smart cities implementation. It's Singapore. It's Dubai. It's areas all over the world. In some cases it didn't have real strong infrastructure. So now come back to your role. As you look at the U.S. which has great infrastructure. Except for broadband connectivity, we'd be faster. They have some pre-existing conditions. They're under pressure. The cloud is a prefect vehicle for them. Because they can come in with their existing stuff. Get apps and services online quicker. How are you dealing with the challenge of? OK, calm down we're not going to take over the world. No, skynet's not coming. You know terminator reference. That's a concern, privacy. Lot of in policy issues, to be dealt with. How do you handle those? >> I think with any policy issue. I've been in public policy for a while now. It really starts with education. Understanding in really simple layman's terms. What the cloud is. And what it is not. It is a very transformative technology. It is not an end all one size fits all technology. What we've done is help educate policy makers by understanding the potential of cloud. What it can do in terms of cost savings, improved security, and being more agile. And to tell that story, we don't use PowerPoints at Amazon. We're not coming in and giving PowerPoint presentations. >> Good ole flesh pounding, hand shakes, and hit the streets. >> We'll more importantly it's sharing the customer's stories We're talking with them about what's happening at the New York City Department of Transportation. We're talking with them about what's happening at the city of Los Angeles with their emergency operation center. About how cities are using cloud technologies to deliver far superior products and services faster. >> So what is New York doing and what is L.A. doing specifically? >> New York city they have their iRide application to help citizens get from one point to the other much more quickly and safely as part of their Vision Zero campaign. Anyone who's been in New York, and I've been in New York quite a few times. Knows that traffic and be a real pain getting from part of Manhattan to the other. So what iRide does, is it helps people navigate Manhattan and the other boroughs much more quickly and efficiently using all the modes of transportation available to them. The city of New York was able to deploy that much more quickly, to many more people. They're able to update it, keep it secure thanks to cloud technology offered by AWS. The city of Los Angeles. They face cyber attacks everyday. Then there are the huge cost of maintaining that security. But with cloud they're able to build out event management systems and integrate those with their Homeland Security technologies and practices. And to be able to do it for a fraction of the cost using traditional systems, traditional IT, and traditional practices. It's very exciting. Suddenly local government can move at the speed and agility of a startup. Which has made Amazon very innovative. Last year we launched over a thousand new services and features. Local governments are seeing that. They want to be more like us and others in the industry. That are using cloud to deliver new products and services. And be better at their job. >> And the education, I say it probably patience in the educational role. You think about just the civil liberties of the citizens. That's really job one. Because I think most people get spooked. Whoa all this surveillance. The thing about it, just watching Patriots Day with my family. You know the Boston bombing, Boston strong with Mark Wahlberg. These things actually happen all the time. And we take for granted the some of the things we have in the surveillance community for the kinds of data that's out there. The same time that's the balance. Can you bring me value with my liberties. It's the same compliance scheme. Same governance game. This is the public sector. >> Well, that's where I think cloud has a great story to tell With cloud you get the benefits of economies of scale. Of Amazon with security and also with privacy. We have multiple compliance frameworks. Everything from HIPPA, FERPA, CJIS, Criminal Justice Information Systems. We are zealous guardians of security and our customer's privacy. We don't look at data. We don't share data about with out our customer's permission. We have very strong safeguards. That's why if you look a the customer base of Amazon from banks to government agencies, health care companies. Even companies like Netflix and you would think they're a competitor of ours. They're running their IT in AWS. They trust us even though with Amazon video and Amazon prime. You would think they're a competitor. But they've put that level of trust in us and our systems and our practices that they can put their data there. And we're hearing it from customer after customer. That they feel more safer and more secure with their data in the cloud offered by AWS. And we've shared that with government officials. And they take great comfort in those statements. >> You hit on something earlier. When you said that state governments and local governments have more data at their disposal than the federal government has about their consumers. Because of that, how much higher do find their concerns to be, in terms of cyber security, in terms of hack proof secured networks and systems as opposed to what might happen at the federal level. Cause we think federal. We think big. About what happened with the U.S. government's payment systems last year OPM. State and local they've got a lot more data they're protecting >> I've had a great opportunity in my current job to talk with a lot of IT officials and policy makers in the state. And, often times a meeting will start. And they'll say I've read about this. I've heard about this. And we're often able to say that's not an issue with the cloud offered the AWS. Or that's something we've already addressed through our security and compliance frame works. For example, I was in one meeting and a state policy maker asked me, well what do you do about HIPPA compliance. We have HIPPA compliance in AWS. And then he tried to ask questions, well what about this, what about that. And each time our team was able to tell the state policy maker. We meet that. We exceed that. We actually help write the standard for that compliance frame work. What we've been able to show that policy maker and others. The cloud just offers a far superior security posture than what they can do on their own. It's taken some time because the cloud is new. And as we like to say, it's still day one in this field. But we are very confident as word gets out. More and more people will be trusting particularly in state government their data to the cloud. Because of the superiority it offers on so many different levels. >> Well certainly the words getting out. This event here is just as big as it's ever been (mumbles). Use to be a little summit, now it's grown. There's a lot of interest. >> It's very exciting for me. I've been to reinvent now twice. And this is just so delightful to see so many people from government from the U.S. from internationally here to learn about the cloud share their stories. It's really inspirational to see what's possible. >> That's a testament to Teresa Carlson. Who was just years ago knocking on doors. That was before cloud was cloud. Now it's just come a long way. Congratulations to the whole team. >> Thank you. It's really to delightful to see. And I can't wait to see what's in store for next year and after that. >> We still got a little bit here to go John Don't kick us out. John Stephenson, Public Policy at AWS. Thanks for being with us we appreciate that. >> Thank you. >> Thank you. With John Furrier, I'm John Walls and we'll be back with more here on the CUBE from Washington D.C. right after this. (upbeat music)

>> Narrator: Live from San Francisco it's The Cube covering Informatica World 2017, brought to you by Informatica. >> Okay, welcome back everyone we're here live in San Francisco for the Cube's exclusive coverage of Informatica World 2017. I'm John Furrier of SiliconAngle Media. My cohost, Peter Burris, head of research at SiliconAngle Media as well as the general manager of Wikibon.com, Wikibon research, check it out. Some great research there on IoT, big data, and certainly cloud computing. Our next guest is Graeme Thompson, Executive Vice President and Chief Information Officer for Informatica, great to see you, welcome back to the Cube. >> Nice to see you, John. >> Conference here, lot of customers, you've got an executive summit, dinner last night, you're kind of like the sounding board, they go to you for the checkpoint, hey, does this story jive, what's going on internally, 'cause you're living through a transformation as well at Informatica. Your customers are going through a transformation as well. We're at this tipping point. What's your take so far of the conference, and is that still the case? Anything you'd like to share on that would be great. >> Yeah, I mean we're proud to have some of the world's best companies using our products to do meaningful and important things. And the scale that some of these companies are doing it at is just staggering. I met with someone last night at dinner and, at Allegis, the talent management organization, and they process and keep up to date 55 million resumes every day. And they extract the metadata from those resumes to match the right candidate to the right job. And you know, that's interesting for them as a company but the societal impact of that is significant. Imagine, I mean we're all starved for talent, and you're matching the right talent with the right opportunity more often than not, using the intelligence of the data, it's pretty interesting. And then of course, I know you had Andrew McIntyre from the Cubs on yesterday, I mean how can you not love that story of how an organization as great and renown as the Cubs is using data to transform it's business operation. It's really amazing. >> We had Bruce Chizen on who's Executive Chairman of the Board of Informatica, was on the board at Oracle, but Peter asked him an interesting question that I'll ask you. What's your definition of strategic data management? >> That's a good one, so the way I define it is, if the basis of your competition is on digital assets compared to physical assets. So we're no longer dealing with plant or machinery or even capital, it's digital assets. If that is the basis of your competition, then the data that you rely on is the very foundation of that. And then it becomes strategic just like money is strategic. And the access to talent is strategic. The ability to leverage the data within your company, about your company, is strategic, and you have to be able to do it on-prem, you have to be able to do it on the cloud, you have to be able to do it in the real world where most of us live, which is in both worlds. And that to me, that's what makes it strategic. >> But let me build on that Graeme, 'cause in many respects the whole concept of digital transformation is, oh let me step back. One of the premises of business is to try to reduce what's known in financial or economic worlds as an asset specificity. So traditionally we've looked at assets and said, this asset's going to be applied to that use, and this asset's going to be applied to that use, and if it's the use isn't needed or it's not being applied, you lose the value of the asset. One of the basic premises of digital business and business generally is how to we reduce asset specificity, and data let's us do that by turning an aircraft engine into a service, we have transformed the role that that asset plays in our customer's business. So you're absolutely right, it's the ratio of physical to digital assets, but all businesses have to find ways to reduce their asset specificity by adding digital on top of it so they can appropriate that asset to a lot of new purposes. Do you agree with that? >> Absolutely, so take, so I know you talked to Sally about the data leak. So take a user case like customer support. Who in a software company knows more about the customer, what product their running, what version of product their running, what they're using it for because of the connectors they have. Nobody in the company knows more about that than the customer support organization. But that asset, the most profitable use of that information, may be in marketing, because then we can help our customers adopt something more quickly, we can help them get value from it more quickly. And it helps us because it helps us focus our R&D effort where the customers are really using the product instead of having to guess. So I think you're spot on, if you can remove the constraint on the asset to be for who paid for it, for one particular purpose and make it available to the entire enterprise and outside the enterprise, then you really start to see the value. >> The thing that you mentioned about digital assets Peter, and the Wikibon team talk about this all the time in their research, digital assets, is the data. Whether it's content or whatever. Certainly we're in the content business, but... >> Peter: Well digital assets are data. >> Are data, exactly, and whether it's content or whatever aspect it is. So I've got to ask you... >> Software, software is a digital asset. >> Data is at the center of it all. So I've got to ask you, there's been a lot of artificial intelligence watching going on in the industry. I call it augmented intelligence because it's really not yet artificial by the strictest, purest definition, but machine learning is very relevant. We talked about IoT when you were last in our studio. How is it impacting your business and customer's business? Because that's the real proof in the pudding, if you will. And customers are trying to sift through the BS that they're hearing from other folks. I'm not saying that you guys are saying BS, but what's the acid test? How do you differentiate between smokescreen and real deal? >> I think it comes down to, like any other technology investment, is what is the business outcome that it generated? So if you're trying to... So humans make mistakes, if you're trying to eliminate human error from a process, a machine can execute that process more repeatably and more accurately than a human. It's not about reducing cost, that's only semi-interesting. It's about enabling outcomes that weren't possible before. So you think about healthcare industry. Everyone talks about self-driving cars and how safer it'll be if the cars aren't dependent on a human, but one thing I read recently is we kill more people in the US by prescribing the wrong drug or the wrong dosage than we do on the roads. So humans work hard, but they make mistakes. If we can have the machine do that job because a human can tell it how to do the job and it can learn over time, then you can eliminate that error. And we're able to do things that we can only imagine. >> Machines rarely get tired, they rarely lose attention, blah blah blah blah blah, and it's all those things, and that's where the augmentation is. And there will be the other forms of artificial intelligence, the algorithms have been around for a long time. The hardware now can support it, and the data is being generated to apply it. >> The data's available and the cost of compute is approaching zero. So we're able to do things that the government could only do before. >> Graeme, I want to get your thoughts on data integration. Certainly we saw yesterday the news with Google Spanner. You guys were one of three companies that was early on, before they announced their general release of Spanner Worldwide, the attributed database, horizontally scaled database. Big deal, but you guys were also on the front end of that as it says in their blog post, and you guys are really strong at data integration. What are some of the challenges that the customers face with integration? What are the key things? Because that seems to be, whether you go multi-cloud or hybrid-cloud today, which is a gateway to multi-cloud, which is happening pretty fast, data integration is pretty important. >> Yes, so as a CIO this is something that is a very hot topic for me, and it's not a new hot topic, it was a hot topic 15 years ago when we went nuts and deployed all these client server applications because they were cheap and easy. And then you had to think about, oh these different disconnected applications don't serve an end-to-end process anymore, now we have to stitch them all together. That was hard, but it was all on-prem and you had access to it all. >> Peter: It was all programed. >> Right, whereas now, like you said you've got Salesforce, you've got Workday, you've got Great People, you've got your on-prem stuff, you've got applications that you're hosting on someone's PAS cloud and the IAS cloud and the SAS cloud, but to execute an end-to-end business process to generate an outcome you have to tie it all together. So instead of thinking about... >> John: And it's not on-prem so you can't touch it, and it's not on, you don't have it. >> Right so you can't hand code that, you could, but I would argue that that would be an unintelligent way to do it, which is where Microservices API has come in. So you can leverage the R&D efforts that the great software vendors like Salesforce create for us. And then you use Microservices to plug into that instead of having an army of people hand-coding interfaces, which is what we used to do 15 years ago. >> That's the human error point. I mean, it could be spaghetti code, all kinds of errors could happen. >> But also the maintenance of that is just virtually impossible given the speed and the fact that human beings are now thinking about new ways of doing things. You just can't keep up with that. >> I mean the coding thing's a big deal. We used to call it, back in the day, spaghetti code cause it's like all this integrated purpose-built coding for one purpose to glue it together. >> Right and then you change one data element and you have to rewrite or retest the whole thing. >> John: A guy leaves or a girl leaves, it's a nightmare, right? With APIs and Microservices you're decoupling that. That's kind of what I think you're getting at, right? >> Exactly, and that's what the whole iPass space is about. You can decouple the user experience from the data and just have, what does a user have to do, and then Microservices and APIs will take care of the work behind the scenes between the applications and that really lets... There's this concept of a citizen integrator. So 15 years ago, it was kind of a modern thought to have business people write reports. I think it won't be long before we'll be able to give the business teams the ability to do integration between applications without depending on me. >> I was talking with a young developer the other day and I'm like, yeah you know your coding is like me doing PowerPoints. They're like, what do you mean, it's so easy. No, it's not that easy. >> Well we've been building macros, good or bad, inside for example things like Excel for a long time and one of the primary drivers, in fact of a lot of the BI stuff, was citizen coders building macros and said I need the data to make my little macro run. Now I don't want to say that that is... That's not what we're talking about, we're talking about something that's considerably more robust where we can be very very creative in thinking about how we might use the data. And then being able to discover it and find it and very quickly and with a low-code orientation being able to make the actual application happen that has consequential impact in the marketplace. So Graeme, you're in a company that's trying to help customers move through some of these transitions. You're in a crucial role because we know where the data is, we know how to integrate it. >> Graeme: You did? >> Well we're discovering where the data is, we have tools that's going to help us, we're learning how to integrate it. But one of the big challenges is to get the business to adopt new orientations to the role that data's going to play. That to me is one of the key roles of the CIO, having worked with a lot of CIOs over the years. For a very very simple example, agile development does not line up with annual budget finance. How are you with Informatica helping to acculturate executive teams to think through new processes, new approaches to doing these things so that the business is better able to use the data so that consequential action happens as these concepts of these great insights that you're generating? >> So the whole change in management effort is a huge and complex thing to overcome. But I have a personal passion about making sure that you always remind people why they're doing it. Too often as product people or technologists, we get into the how and the what and we forget the why. And as soon as it gets difficult people abandon because it starts to get too hard, it starts to get painful, and if they've lost sight of the big why they're not going to role their sleeves up and gut it out and get through the process. So that's the first thing you have to do is remind them that the prize at the end is worth the pain. And it will be painful because no longer are you optimizing just your function. You have to think about what happens upstream from you, what happens downstream from you, and try and optimize things at the enterprise level. And that's not how most people were brought up. It's not how their measured, it's not how their compensated, but that's what's really required if you're going to make that transformation I think end-to-end. >> But it's also, even our language, we talk about innovation in this industry as though it was synonymous with just creating something new. Certainly our research very strongly shows that there's a difference between inventing something which is an engineering act and innovating around something which is a social act. Exactly what you just said. How do we get people to adopt things and change behaviors and fully utilize something and embed it within their practices so that we get derivative innovation and all of the other stuff that we're looking for? >> Yeah there's no easy recipe. People are different so people require a different story in order to have them buy in. Some people are loss-framed people, where you got to explain here's what's going to be bad if you don't do this. Other people are gain-framed people where you can say if we can accomplish this, we'll be able to do these great things. And it would be great if everyone was the same and one story worked for everyone, but it doesn't. So it's almost a feet on the street. Go talk to people and just keep reminding everyone why you're doing this and why it's going to be worth it. >> Peter: A little bit of behavioral economics there. >> John: Graeme I want to ask you one final question. You mention client server and how it was easy on-prem in the old days, get your arms around things, which is the IT practice, you know? That's the way it was done. In the cloud, a little bit more complex. But to take that a little step further, I want to get your thoughts on something. You lived through the world of server sprawl. More servers, more glue, you get your arms around it but then it got bloated, IT got bloated. And that's one of the catalysts for going to the cloud is efficiencies, bottom-line costs. But now, top line revenue now is a mandate. So now we have SAS sprawl. So with APIs, a little bit more security concern, but your thoughts on the now we have a SASification happening or API economy. So you have a lot more APIs, there's Microservices coming on the scene, it's emerging very quickly, still emergent. Embryonic some will say, not so, but I think it's embryonic still. Okay server sprawl, client server, VM sprawl, now you got SAS sprawl. Your thoughts on this dynamic and how a CIO tackles that? >> Yes, so it's the modern equivalent of your legacy technical debt. So it's a modern mess instead of an old mess, but it's the same problem. You know, you have to stitch these applications together and it's made worse by the ease of consuming these SAS applications. So one business function can go off and buy an application that's just for them, and the adjacent business function goes off and buys another application that's just for them. And before you know where you are, you're single sign-on page has three pages because you've got so many applications that you're using to run your business. So I think we have to be more thoughtful and not make the same mistake that we made after 2000 when we went nuts on all these client server applications and make sure that we're thinking about the end-to-end business outcome. >> John: So the unification layer is what, Identity, is it the data? I mean how do you think about that just conceptually? >> Well I think you still need a sensible portfolio of applications. I don't advocate that you just go buy every great application that's out there. If your business doesn't compete based on the capability that that application provides, you've got no business innovating. Just be as good as the next guy. But if you compete based on something, go pick the very best application you can but deploy it thoughtfully. Make sure it's integrated, make sure it serves the end-to-end... >> Well I'm also fascinated by the role that Clair might play here at going and looking at the metadata associated with some of these SAS applications to help us identify patterns and utilization. I think Clair and the thing that was announced here actually could have an impact in thinking about some of these things. >> The Clairvoyant app is a great one, Clair, I mean... She, he, it's vendor neutral, that's a whole different story, only kidding. Final thought Graeme on this show? Just color perspective, what's your thought so far just on the show vibe for the folks who aren't here, what's it like? >> So when you and I met a couple weeks ago we talked about the fact that I'd just joined the company just after last year's show. So I have nothing to compare it to, but the energy level is phenomenal. The feedback from the customer's I've talked to just reinforces that we have really really important customers and we're really important to them. You know, the customers are the ones driving this digital transformation and we're proud to be helping them. And every conversation I've had with customers has really reinforced that and it's great, I can't wait to get back to the office. >> And as we say the KPI, the metric of the transformation of the world is not quadrants or category winners, it's customer wins. >> Graeme: Absolutely. >> And I think that's a great point. Graeme Thompson, Executive Vice President and Chief Information Officer of Informatica sharing his insight. He is an integral part of their transformation as well as his customers. Informatica World coverage with the Cube continues. I'm John Furrier with Peter Burris with Wikimon.com. We'll be back with more, stay with us after this short break. (electronic music)