>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition at fraud waste and abuse per the government accountability office is broad as an attempt to obtain something about a value through unwelcomed misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal, uh, benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically for the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external perpetrators, again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, uh, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, those are broad stroke areas. What are the actual use cases that, um, agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use great, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment integrity. So fraud has its, um, uh, underpinnings in quite a few different government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that Chev is going to talk about later it's how do I look at more, that real, that streaming information? >>How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in essence, looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in, um, some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, uh, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific constituent, are there areas where we're seeing, uh, um, other aspects of a fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at, uh, simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. >>Um, and again, that really lends itself to a new opportunities. And on that, I'm going to turn it over to Shev to talk about, uh, the reference architecture for, uh, doing these baskets. >>Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection using, uh, Cloudera underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our data sets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so then comes clutter's platform and this reference architecture that needs to before you, so, uh, let's start on the left-hand side of this reference architecture with the collect phase. >>So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different porosities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with clutter data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL string builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or exploratory data analysis and visualization, uh, can all be enabled through clever visual patient technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutter's technology, right? And so, uh, the IRS is one of, uh, clutters customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually, uh, recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around, uh, how quad areas working in the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining Chevy and I today, we greatly appreciate your time and look forward to future >>Conversation..

>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud. Isn't an attempt to obtain something about value through unwelcome misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external, uh, perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically about 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from permit out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's a broad stroke areas. What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're gonna use focused specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of, um, unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has it it's, um, uh, underpinnings inquiry, like you different on government agencies and difficult, different analytical methods, and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models. We're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that shad is going to talk about later is how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the, the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like a constituent, are there areas where we're seeing, uh, >>Um, other >>Aspects of, of fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, uh, agent-based modeling techniques, where we're looking at simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to chef to talk about, uh, the reference architecture for, uh, doing these buckets. >>Thanks, Cindy. Um, so I'm gonna walk you through an example, reference architecture for fraud detection using, uh, Cloudera's underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. We need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, thinking, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on a patch NIFA in mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geolocation that's in that transaction data can be enriched with previously known locations of that very same individual. And all of that enriched data can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stricted to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So coffee is going to pretty much provide you with, uh, extremely fast resilient and fault tolerance storage. And it's also gonna give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone, uh, allowed that, you know, 17. So you can store that data in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL stream builder, which enables us to write, uh, streaming SQL jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks. And these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutters technology, right? And so, uh, the IRS is one of, uh, clutter's customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their spark based analytics and their machine learning, uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection, uh, looking at neural network analysis, time series information. So next steps we'd love to have additional conversation with you. You can also find on some additional information around, I have caught areas working in the, the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us Sheva and I today. We greatly appreciate your time and look forward to future progress. >>Good day, everyone. Thank you for joining me. I'm Sydney. Mike joined by Rick Taylor of Cloudera. Uh, we're here to talk about predictive maintenance for the public sector and how to increase assets, service, reliability on today's agenda. We'll talk specifically around how to optimize your equipment maintenance, how to reduce costs, asset failure with data and analytics. We'll go into a little more depth on, um, what type of data, the analytical methods that we're typically seeing used, um, the associated, uh, Brooke, we'll go over a case study as well as a reference architecture. So by basic definition, uh, predictive maintenance is about determining when an asset should be maintained and what specific maintenance activities need to be performed either based upon an assets of actual condition or state. It's also about predicting and preventing failures and performing maintenance on your time on your schedule to avoid costly unplanned downtime. >>McKinsey has looked at analyzing predictive maintenance costs across multiple industries and has identified that there's the opportunity to reduce overall predictive maintenance costs by roughly 50% with different types of analytical methods. So let's look at those three types of models. First, we've got our traditional type of method for maintenance, and that's really about our corrective maintenance, and that's when we're performing maintenance on an asset, um, after the equipment fails. But the challenges with that is we end up with unplanned. We end up with disruptions in our schedules, um, as well as reduced quality, um, around the performance of the asset. And then we started looking at preventive maintenance and preventative maintenance is really when we're performing maintenance on a set schedule. Um, the challenges with that is we're typically doing it regardless of the actual condition of the asset, um, which has resulted in unnecessary downtime and expense. Um, and specifically we're really now focused on pre uh, condition-based maintenance, which is looking at leveraging predictive maintenance techniques based upon actual conditions and real time events and processes. Um, within that we've seen organizations, um, and again, source from McKenzie have a 50% reduction in downtime, as well as an overall 40% reduction in maintenance costs. Again, this is really looking at things across multiple industries, but let's look at it in the context of the public sector and based upon some activity by the department of energy, um, several years ago, >>Um, they've really >>Looked at what does predictive maintenance mean to the public sector? What is the benefit, uh, looking at increasing return on investment of assets, reducing, uh, you know, reduction in downtime, um, as well as overall maintenance costs. So corrective or reactive based maintenance is really about performing once there's been a failure. Um, and then the movement towards, uh, preventative, which is based upon a set schedule or looking at predictive where we're monitoring real-time conditions. Um, and most importantly is now actually leveraging IOT and data and analytics to further reduce those overall downtimes. And there's a research report by the, uh, department of energy that goes into more specifics, um, on the opportunity within the public sector. So, Rick, let's talk a little bit about what are some of the challenges, uh, regarding data, uh, regarding predictive maintenance. >>Some of the challenges include having data silos, historically our government organizations and organizations in the commercial space as well, have multiple data silos. They've spun up over time. There are multiple business units and note, there's no single view of assets. And oftentimes there's redundant information stored in, in these silos of information. Uh, couple that with huge increases in data volume data growing exponentially, along with new types of data that we can ingest there's social media, there's semi and unstructured data sources and the real time data that we can now collect from the internet of things. And so the challenge is to collect all these assets together and begin to extract intelligence from them and insights and, and that in turn then fuels, uh, machine learning and, um, and, and what we call artificial intelligence, which enables predictive maintenance. Next slide. So >>Let's look specifically at, you know, the, the types of use cases and I'm going to Rick and I are going to focus on those use cases, where do we see predictive maintenance coming into the procurement facility, supply chain, operations and logistics. Um, we've got various level of maturity. So, you know, we're talking about predictive maintenance. We're also talking about, uh, using, uh, information, whether it be on a, um, a connected asset or a vehicle doing monitoring, uh, to also leveraging predictive maintenance on how do we bring about, uh, looking at data from connected warehouses facilities and buildings all bring on an opportunity to both increase the quality and effectiveness of the missions within the agencies to also looking at re uh, looking at cost efficiency, as well as looking at risk and safety and the types of data, um, you know, that Rick mentioned around, you know, the new types of information, some of those data elements that we typically have seen is looking at failure history. >>So when has that an asset or a machine or a component within a machine failed in the past? Uh, we've also looking at bringing together a maintenance history, looking at a specific machine. Are we getting error codes off of a machine or assets, uh, looking at when we've replaced certain components to looking at, um, how are we actually leveraging the assets? What were the operating conditions, uh, um, pulling off data from a sensor on that asset? Um, also looking at the, um, the features of an asset, whether it's, you know, engine size it's make and model, um, where's the asset located on to also looking at who's operated the asset, uh, you know, whether it be their certifications, what's their experience, um, how are they leveraging the assets and then also bringing in together, um, some of the, the pattern analysis that we've seen. So what are the operating limits? Um, are we getting service reliability? Are we getting a product recall information from the actual manufacturer? So, Rick, I know the data landscape has really changed. Let's, let's go over looking at some of those components. Sure. >>So this slide depicts sort of the, some of the inputs that inform a predictive maintenance program. So, as we've talked a little bit about the silos of information, the ERP system of record, perhaps the spares and the service history. So we want, what we want to do is combine that information with sensor data, whether it's a facility and equipment sensors, um, uh, or temperature and humidity, for example, all this stuff is then combined together, uh, and then use to develop machine learning models that better inform, uh, predictive maintenance, because we'll do need to keep, uh, to take into account the environmental factors that may cause additional wear and tear on the asset that we're monitoring. So here's some examples of private sector, uh, maintenance use cases that also have broad applicability across the government. For example, one of the busiest airports in Europe is running cloud era on Azure to capture secure and correlate sensor data collected from equipment within the airport, the people moving equipment more specifically, the escalators, the elevators, and the baggage carousels. >>The objective here is to prevent breakdowns and improve airport efficiency and passenger safety. Another example is a container shipping port. In this case, we use IOT data and machine learning, help customers recognize how their cargo handling equipment is performing in different weather conditions to understand how usage relates to failure rates and to detect anomalies and transport systems. These all improve for another example is Navistar Navistar, leading manufacturer of commercial trucks, buses, and military vehicles. Typically vehicle maintenance, as Cindy mentioned, is based on miles traveled or based on a schedule or a time since the last service. But these are only two of the thousands of data points that can signal the need for maintenance. And as it turns out, unscheduled maintenance and vehicle breakdowns account for a large share of the total cost for vehicle owner. So to help fleet owners move from a reactive approach to a more predictive model, Navistar built an IOT enabled remote diagnostics platform called on command. >>The platform brings in over 70 sensor data feeds for more than 375,000 connected vehicles. These include engine performance, trucks, speed, acceleration, cooling temperature, and break where this data is then correlated with other Navistar and third-party data sources, including weather geo location, vehicle usage, traffic warranty, and parts inventory information. So the platform then uses machine learning and advanced analytics to automatically detect problems early and predict maintenance requirements. So how does the fleet operator use this information? They can monitor truck health and performance from smartphones or tablets and prioritize needed repairs. Also, they can identify that the nearest service location that has the relevant parts, the train technicians and the available service space. So sort of wrapping up the, the benefits Navistar's helped fleet owners reduce maintenance by more than 30%. The same platform is also used to help school buses run safely. And on time, for example, one school district with 110 buses that travel over a million miles annually reduce the number of PTOs needed year over year, thanks to predictive insights delivered by this platform. >>So I'd like to take a moment and walk through the data. Life cycle is depicted in this diagram. So data ingest from the edge may include feeds from the factory floor or things like connected vehicles, whether they're trucks, aircraft, heavy equipment, cargo vessels, et cetera. Next, the data lands on a secure and governed data platform. Whereas combined with data from existing systems of record to provide additional insights, and this platform supports multiple analytic functions working together on the same data while maintaining strict security governance and control measures once processed the data is used to train machine learning models, which are then deployed into production, monitored, and retrained as needed to maintain accuracy. The process data is also typically placed in a data warehouse and use to support business intelligence, analytics, and dashboards. And in fact, this data lifecycle is representative of one of our government customers doing condition-based maintenance across a variety of aircraft. >>And the benefits they've discovered include less unscheduled maintenance and a reduction in mean man hours to repair increased maintenance efficiencies, improved aircraft availability, and the ability to avoid cascading component failures, which typically cost more in repair cost and downtime. Also, they're able to better forecast the requirements for replacement parts and consumables and last, and certainly very importantly, this leads to enhanced safety. This chart overlays the secure open source Cloudera platform used in support of the data life cycle. We've been discussing Cloudera data flow, the data ingest data movement and real time streaming data query capabilities. So data flow gives us the capability to bring data in from the asset of interest from the internet of things. While the data platform provides a secure governed data lake and visibility across the full machine learning life cycle eliminates silos and streamlines workflows across teams. The platform includes an integrated suite of secure analytic applications. And two that we're specifically calling out here are Cloudera machine learning, which supports the collaborative data science and machine learning environment, which facilitates machine learning and AI and the cloud era data warehouse, which supports the analytics and business intelligence, including those dashboards for leadership Cindy, over to you, Rick, >>Thank you. And I hope that, uh, Rick and I provided you some insights on how predictive maintenance condition-based maintenance is being used and can be used within your respective agency, bringing together, um, data sources that maybe you're having challenges with today. Uh, bringing that, uh, more real-time information in from a streaming perspective, blending that industrial IOT, as well as historical information together to help actually, uh, optimize maintenance and reduce costs within the, uh, each of your agencies, uh, to learn a little bit more about Cloudera, um, and our, what we're doing from a predictive maintenance please, uh, business@cloudera.com solutions slash public sector. And we look forward to scheduling a meeting with you, and on that, we appreciate your time today and thank you very much.

>>Hi, this is Cindy Mikey, vice president of industry solutions at Cloudera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and Shev we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud is an attempt to obtain something about a value through unwelcomed. Misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external are perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's broad stroke areas? What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has its, um, uh, underpinnings in quite a few different on government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to lock at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case, that shadow is going to talk about later it's how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So quite a different variety of data and the, the breadth, um, and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a sense, looking at a more extensive on data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be, um, investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like, uh, constituent, are there areas where we're seeing, uh, um, other aspects of, of fraud potentially being, uh, occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at simulation, Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, the public sector. >>Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to Chevy to talk about, uh, the reference architecture for doing these buckets. >>Sure. Yeah. Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection, using Cloudera as underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or anomalous behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create from normal behavior profiles and these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jace on or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to know downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin is going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer API APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transform data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on from machine learning, right? So the next step in the architecture is to leverage, uh, clutter SQL stream builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real-time. Uh, I'll, you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage Q2, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed, and we've our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, even deep learning techniques with neural networks. Uh, and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the X one, uh, scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. Uh, and this entire pipeline is powered by clutters technology. Uh, Cindy, next slide please. >>Right. And so, uh, the IRS is one of, uh, clutter as customers. That's leveraging our platform today and implementing a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, uh, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around how called areas working in federal government, by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us today. Uh, we greatly appreciate your time and look forward to future conversations. Thank you.

>>from around the globe. It's the Cube with digital coverage of VM World 2020 brought to you by VM Ware and its ecosystem partners. Hello and welcome back to the cubes. Virtual coverage of VM World 2020 Virtual I'm John for your host of the Cube, our 11th year covering V emeralds. Not in person. It's virtual. I'm with my coast, Dave. A lot, of course. Ah, guest has been on every year since the cubes existed. Sanjay Putin, who is now the chief operating officer for VM Ware Sanjay, Great to see you. It's our 11th years. Virtual. We're not in person. Usually high five are going around. But hey, virtual fist pump, >>virtual pissed bump to you, John and Dave, always a pleasure to talk to you. I give you more than a virtual pistol. Here's a virtual hug. >>Well, so >>great. Back at great. >>Great to have you on. First of all, a lot more people attending the emerald this year because it's virtual again, it doesn't have the face to face. It is a community and technical events, so people do value that face to face. Um, but it is virtually a ton of content, great guests. You guys have a great program here, Very customer centric. Kind of. The theme is, you know, unpredictable future eyes is really what it's all about. We've talked about covert you've been on before. What's going on in your perspective? What's the theme of your main talks? >>Ah, yeah. Thank you, John. It's always a pleasure to talk to you folks. We we felt as we thought, about how we could make this content dynamic. We always want to make it fresh. You know, a virtual show of this kind and program of this kind. We all are becoming experts at many Ted talks or ESPN. Whatever your favorite program is 60 minutes on becoming digital producers of content. So it has to be crisp, and everybody I think was doing this has found ways by which you reduce the content. You know, Pat and I would have normally given 90 minute keynotes on day one and then 90 minutes again on day two. So 180 minutes worth of content were reduced that now into something that is that entire 180 minutes in something that is but 60 minutes. You you get a chance to use as you've seen from the keynote an incredible, incredible, you know, packed array of both announcements from Pat myself. So we really thought about how we could organize this in a way where the content was clear, crisp and compelling. Thekla's piece of it needed also be concise, but then supplemented with hundreds of sessions that were as often as possible, made it a goal that if you're gonna do a break out session that has to be incorporate or lead with the customer, so you'll see not just that we have some incredible sea level speakers from customers that have featured in in our pattern, Mikey notes like John Donahoe, CEO of Nike or Lorry beer C I, a global sea of JPMorgan Chase partner Baba, who is CEO of Zuma Jensen Wang, who is CEO of video. Incredible people. Then we also had some luminaries. We're gonna be talking in our vision track people like in the annuity. I mean, one of the most powerful women the world many years ranked by Fortune magazine, chairman, CEO Pepsi or Bryan Stevenson, the person who start in just mercy. If you watch that movie, he's a really key fighter for social justice and criminal. You know, reform and jails and the incarceration systems. And Malala made an appearance. Do I asked her personally, I got to know her and her dad's and she spoke two years ago. I asked her toe making appearance with us. So it's a really, really exciting until we get to do some creative stuff in terms of digital content this year. >>So on the product side and the momentum side, you have great decisions you guys have made in the past. We covered that with Pat Gelsinger, but the business performance has been very strong with VM. Where, uh, props to you guys, Where does this all tie together for in your mind? Because you have the transformation going on in a highly accelerated rate. You know, cov were not in person, but Cove in 19 has proven, uh, customers that they have to move faster. It's a highly accelerated world, a lot. Lots changing. Multi cloud has been on the radar. You got security. All the things you guys are doing, you got the AI announcements that have been pumping. Thean video thing was pretty solid. That project Monterey. What does the customer walk away from this year and and with VM where? What is the main theme? What what's their call to action? What's what do they need to be doing? >>I think there's sort of three things we would encourage customers to really think about. Number one is, as they think about everything in infrastructure, serves APS as they think about their APS. We want them to really push the frontier of how they modernize their athletic applications. And we think that whole initiative off how you modernized applications driven by containers. You know, 20 years ago when I was a developer coming out of college C, C plus, plus Java and then emerge, these companies have worked on J two ee frameworks. Web Logic, Be Aware logic and IBM Web Street. It made the development off. Whatever is e commerce applications of portals? Whatever was in the late nineties, early two thousands much, much easier. That entire world has gotten even easier and much more Micro service based now with containers. We've been talking about kubernetes for a while, but now we've become the leading enterprise, contain a platform making some incredible investments, but we want to not just broaden this platform. We simplified. It is You've heard everything in the end. What works in threes, right? It's sort of like almost t shirt sizing small, medium, large. So we now have tens Ooh, in the standard. The advanced the enterprise editions with lots of packaging behind that. That makes it a very broad and deep platform. We also have a basic version of it. So in some sense it's sort of like an extra small. In addition to the small medium large so tends to and everything around at modernization, I think would be message number one number two alongside modernization. You're also thinking about migration of your workloads and the breadth and depth of, um, er Cloud Foundation now of being able to really solve, not just use cases, you are traditionally done, but also new ai use cases. Was the reason Jensen and us kind of partner that, and I mean what a great company and video has become. You know, the king maker of these ai driven applications? Why not run those AI applications on the best infrastructure on the planet? Remember, that's a coming together of both of our platforms to help customers. You know automotive banking fraud detection is a number of AI use cases that now get our best and we want it. And the same thing then applies to Project Monterey, which takes the B c f e m A Cloud Foundation proposition to smart Knicks on Dell, HP Lenovo are embracing the in video Intel's and Pen Sandoz in that smart make architectural, however, that so that entire world of multi cloud being operative Phobia Macleod Foundation on Prem and all of its extended use cases like AI or Smart Knicks or Edge, but then also into the AWS Azure, Google Multi Cloud world. We obviously had a preferred relationship with Amazon that's going incredibly well, but you also saw some announcements last week from, uh, Microsoft Azure about azure BMR solutions at their conference ignite. So we feel very good about the migration opportunity alongside of modernization on the third priority, gentlemen would be security. It's obviously a topic that I most recently taken uninterested in my day job is CEO of the company running the front office customer facing revenue functions by night job by Joe Coffin has been driving. The security strategy for the company has been incredibly enlightening to talk, to see SOS and drive this intrinsic security or zero trust from the network to end point and workload and cloud security. And we made some exciting announcements there around bringing together MAWR capabilities with NSX and Z scaler and a problem black and workload security. And of course, Lassiter wouldn't cover all of this. But I would say if I was a attendee of the conference those the three things I want them to take away what BMR is doing in the future of APS what you're doing, the future of a multi cloud world and how we're making security relevant for distributed workforce. >>I know David >>so much to talk about here, Sanjay. So, uh, talk about modern APS? That's one of the five franchise platforms VM Ware has a history of going from, you know, Challenger toe dominant player. You saw that with end user computing, and there's many, many other examples, so you are clearly one of the top, you know. Let's call it five or six platforms out there. We know what those are, uh, and but critical to that modern APS. Focus is developers, and I think it's fair to say that that's not your wheelhouse today, but you're making moves there. You agree that that is, that is a critical part of modern APS, and you update us on what you're doing for that community to really take a leadership position there. >>Yeah, no, I think it's a very good point, David. We way seek to constantly say humble and hungry. There's never any assumption from us that VM Ware is completely earned anyplace off rightful leadership until we get thousands, tens of thousands. You know, we have a half a million customers running on our virtualization sets of products that have made us successful for 20 years 70 million virtual machines. But we have toe earn that right and containers, and I think there will be probably 10 times as many containers is their virtual machines. So if it took us 20 years to not just become the leader in in virtual machines but have 70 million virtual machines, I don't think it will be 20 years before there's a billion containers and we seek to be the leader in that platform. Now, why, Why VM Where and why do you think we can win in their long term. What are we doing with developers Number one? We do think there is a container capability independent of virtual machine. And that's what you know, this entire world of what hefty on pivotal brought to us on. You know, many of the hundreds of customers that are using what was formerly pivotal and FDR now what's called Tan Xue have I mean the the case. Studies of what those customers are doing are absolutely incredible. When I listen to them, you take Dick's sporting goods. I mean, they are building curbside, pick up a lot of the world. Now the pandemic is doing e commerce and curbside pick up people are going to the store, That's all based on Tan Xue. We've had companies within this sort of world of pandemic working on contact, tracing app. Some of the diagnostic tools built without they were the lab services and on the 10 zoo platform banks. Large banks are increasingly standardizing on a lot of their consumer facing or wealth management type of applications, anything that they're building rapidly on this container platform. So it's incredible the use cases I'm hearing public sector. The U. S. Air Force was talking about how they've done this. Many of them are not public about how they're modernizing dams, and I tend to learn the best from these vertical use case studies. I mean, I spend a significant part of my life is you know, it s a P and increasingly I want to help the company become a lot more vertical. Use case in banking, public sector, telco manufacturing, CPG retail top four or five where we're seeing a lot of recurrence of these. The Tan Xue portfolio actually brings us closest to almost that s a P type of dialogue because we're having an apse dialogue in the in the speak of an industry as opposed to bits and bytes Notice I haven't talked at all about kubernetes or containers. I'm talking about the business problem being solved in a retailer or a bank or public sector or whatever have you now from a developer audience, which was the second part of your question? Dave, you know, we talked about this, I think a year or two ago. We have five million developers today that we've been able to, you know, as bringing these acquisitions earn some audience with about two or three million from from the spring community and two or three million from the economic community. So think of those five million people who don't know us because of two acquisitions we don't. Obviously spring was inside Vienna where went out of pivotal and then came back. So we really have spent a lot of time with that community. A few weeks ago, we had spring one. You guys are aware of that? That conference record number of attendees okay, Registered, I think of all 40 or 50,000, which is, you know, much bigger than the physical event. And then a substantial number of them attended live physical. So we saw a great momentum out of spring one, and we're really going to take care of that, That that community base of developers as they care about Java Manami also doing really, really well. But then I think the rial audience it now has to come from us becoming part of the conversation. That coupon at AWS re invent at ignite not just the world, I mean via world is not gonna be the only place where infrastructure and developers come to. We're gonna have to be at other events which are very prominent and then have a developer marketplace. So it's gonna be a multiyear effort. We're okay with that. To grow that group of about five million developers that we today Kate or two on then I think there will be three or four other companies that also play very prominently to developers AWS, Microsoft and Google. And if we're one among those three or four companies and remembers including that list, we feel very good about our ability to be in a place where this is a shared community, takes a village to approach and an appeal to those developers. I think there will be one of those four companies that's doing this for many years to >>come. Santa, I got to get your take on. I love your reference to the Web days and how the development environment change and how the simplicity came along very relevant to how we're seeing this digital transformation. But I want to get your thoughts on how you guys were doing pre and now during and Post Cove it. You already had a complicated thing coming on. You had multi cloud. You guys were expanding your into end you had acquisitions, you mentioned a few of them. And then cove it hit. Okay, so now you have Everything is changing you got. He's got more complex city. You have more solutions, and then the customer psychology is change. You got to spectrums of customers, people trying to save their business because it's changed, their customer behavior has changed. And you have other customers that are doubling down because they have a tailwind from Cove it, whether it's a modern app, you know, coming like Zoom and others are doing well because of the environment. So you got your customers air in this in this in this, in this storm, you know, they're trying to save down, modernized or or or go faster. How are you guys changing? Because it's impacted how you sell. People are selling differently, how you implement and how you support customers, because you already had kind of the whole multi cloud going on with the modern APS. I get that, but Cove, it has changed things. How are you guys adopting and changing to meet the customer needs who are just trying to save their business on re factor or double down and continue >>John. Great question. I think I also talked about some of this in one of your previous digital events that you and I talked about. I mean, you go back to the last week of February 1st week of March, actually back up, even in January, my last trip on a plane. Ah, major trip outside this country was the World Economic Forum in Davos. And, you know, there were thousands of us packed into the small digits in Switzerland. I was sitting having dinner with Andy Jassy in a restaurant one night that day. Little did we know. A month later, everything would change on DWhite. We began to do in late February. Early March was first. Take care of employees. You always wanna have the pulse, check employees and be in touch with them. Because the health and safety of employees is much more important than the profits of, um, where you know. So we took care of that. Make sure that folks were taking care of older parents were in good place. We fortunately not lost anyone to death. Covert. We had some covert cases, but they've recovered on. This is an incredible pandemic that connects all of us in the human fabric. It has no separation off skin color or ethnicity or gender, a little bit of difference in people who are older, who might be more affected or prone to it. But we just have to, and it's taught me to be a significantly more empathetic. I began to do certain things that I didn't do before, but I felt was the right thing to do. For example, I've begun to do 25 30 minute calls with every one of my key countries. You know, as I know you, I run customer operations, all of the go to market field teams reporting to me on. I felt it was important for me to be showing up, not just in the big company meetings. We do that and big town halls where you know, some fractions. 30,000 people of VM ware attend, but, you know, go on, do a town hall for everybody in a virtual zoom session in Japan. But in their time zone. So 10 o'clock my time in the night, uh, then do one in China and Australia kind of almost travel around the world virtually, and it's not long calls 25 30 minutes, where 1st 10 or 15 minutes I'm sharing with them what I'm seeing across other countries, the world encouraging them to focus on a few priorities, which I'll talk about in a second and then listening to them for 10 15 minutes and be, uh and then the call on time or maybe even a little earlier, because every one of us is going to resume button going from call to call the call. We're tired of T. There's also mental, you know, fatigue that we've gotta worry about. Mental well, being long term. So that's one that I personally began to change. I began to also get energy because in the past, you know, I would travel to Europe or Asia. You know, 40 50%. My life has travel. It takes a day out of your life on either end, your jet lag. And then even when you get to a Tokyo or Beijing or to Bangalore or the London, getting between sites of these customers is like a 45 minute, sometimes in our commute. Now I'm able to do many of these 25 30 minute call, so I set myself a goal to talk to 1000 chief security officers. I know a lot of CEOs and CFOs from my times at S A P and VM ware, but I didn't know many security officers who often either work for a CEO or report directly to the legal counsel on accountable to the audit committee of the board. And I got a list of these 1,002,000 people we called email them. Man, I gotta tell you, people willing to talk to me just coming, you know, into this I'm about 500 into that. And it was role modeling to my teams that the top of the company is willing to spend as much time as possible. And I have probably gotten a lot more productive in customer conversations now than ever before. And then the final piece of your question, which is what do we tell the customer in terms about portfolio? So these were just more the practices that I was able to adapt during this time that have given me energy on dial, kind of get scared of two things from the portfolio perspective. I think we began to don't notice two things. One is Theo entire move of migration and modernization around the cloud. I describe that as you know, for example, moving to Amazon is a migration opportunity to azure modernization. Is that whole Tan Xue Eminem? Migration of modernization is highly relevant right now. In fact, taking more speed data center spending might be on hold on freeze as people kind of holding till depend, emmick or the GDP recovers. But migration of modernization is accelerating, so we wanna accelerate that part of our portfolio. One of the products we have a cloud on Amazon or Cloud Health or Tan Xue and maybe the other offerings for the other public dog. The second part about portfolio that we're seeing acceleration around is distributed workforce security work from home work from anywhere. And that's that combination off workspace, one for both endpoint management, virtual desktops, common black envelope loud and the announcements we've now made with Z scaler for, uh, distributed work for security or what the analysts called secure access. So message. That's beautiful because everyone working from home, even if they come back to the office, needs a very different model of security and were now becoming a leader in that area. of security. So these two parts of the portfolio you take the five franchise pillars and put them into these two buckets. We began to see momentum. And the final thing, I would say, Guys, just on a soft note. You know, I've had to just think about ways in which I balance work and family. It's just really easy. You know what, 67 months into this pandemic to burn out? Ah, now I've encouraged my team. We've got to think about this as a marathon, not a sprint. Do the personal things that you wanna do that will make your life better through this pandemic. That in practice is that you keep after it. I'll give you one example. I began biking with my kids and during the summer months were able to bike later. Even now in the fall, we're able to do that often, and I hope that's a practice I'm able to do much more often, even after the pandemic. So develop some activities with your family or with the people that you love the most that are seeing you a lot more and hopefully enjoying that time with them that you will keep even after this pandemic ends. >>So, Sanjay, I love that you're spending all this time with CSOs. I mean, I have a Well, maybe not not 1000 but dozens. And they're such smart people. They're really, you know, in the thick of things you mentioned, you know, your partnership with the scale ahead. Scott Stricklin on who is the C. C so of Wyndham? He was talking about the security club. But since the pandemic, there's really three waves. There's the cloud security, the identity, access management and endpoint security. And one of the things that CSOs will tell you is the lack of talent is their biggest challenge. And they're drowning in all these products. And so how should we think about your approach to security and potentially simplifying their lives? >>Yeah. You know, Dave, we talked about this, I think last year, maybe the year before, and what we were trying to do in security was really simplified because the security industry is like 5000 vendors, and it's like, you know, going to a doctor and she tells you to stay healthy. You gotta have 5000 tablets. You just cannot eat that many tablets you take you days, weeks, maybe a month to eat that many tablets. So ah, grand simplification has to happen where that health becomes part of your diet. You eat your proteins and vegetables, you drink your water, do your exercise. And the analogy and security is we cannot deploy dozens of agents and hundreds of alerts and many, many consoles. Uh, infrastructure players like us that have control points. We have 70 million virtual machines. We have 75 million virtual switches. We have, you know, tens of million's off workspace, one of carbon black endpoints that we manage and secure its incumbent enough to take security and making a lot more part of the infrastructure. Reduce the need for dozens and dozens of point tools. And with that comes a grand simplification of both the labor involved in learning all these tools. Andi, eventually also the cost of ownership off those particular tool. So that's one other thing we're seeking to do is increasingly be apart off that education off security professionals were both investing in ah, lot of off, you know, kind of threat protection research on many of our folks you know who are in a threat. Behavioral analytics, you know, kind of thread research. And people have come out of deep hacking experience with the government and others give back to the community and teaching classes. Um, in universities, there are a couple of non profits that are really investing in security, transfer education off CSOs and their teams were contributing to that from the standpoint off the ways in which we can give back both in time talent and also a treasure. So I think is we think about this. You're going to see us making this a long term play. We have a billion dollar security business today. There's not many companies that have, you know, a billion dollar plus of security is probably just two or three, and some of them have hit a wall in terms of their progress sport. We want to be one of the leaders in cybersecurity, and we think we need to do this both in building great product satisfying customers. But then also investing in the learning, the training enable remember, one of the things of B M worlds bright is thes hands on labs and all the training enable that happened at this event. So we will use both our platform. We in world in a variety of about the virtual environments to ensure that we get the best education of security to professional. >>So >>that's gonna be exciting, Because if you look at some of the evaluations of some of the pure plays I mean, you're a cloud security business growing a triple digits and, you know, you see some of these guys with, you know, $30 billion valuations, But I wanted to ask you about the market, E v m. Where used to be so simple Right now, you guys have expanded your tam dramatically. How are you thinking about, you know, the market opportunity? You've got your five franchise platforms. I know you're very disciplined about identifying markets, and then, you know, saying, Okay, now we're gonna go compete. But how do you look at the market and the market data? Give us the update there. >>Yeah, I think. Dave, listen, you know, I like davinci statement. You know, simplicity is the greatest form of sophistication, and I think you've touched on something that which is cos we get bigger. You know, I've had the great privilege of working for two great companies. s a P and B M where the bulk of my last 15 plus years And if something I've learned, you know, it's very easy. Both companies was to throw these TLS three letter acronyms, okay? And I use an acronym and describing the three letter acronyms like er or s ex. I mean, they're all acronyms and a new employee who comes to this company. You know, Carol Property, for example. We just hired her from Google. Is our CMO her first comments like, My goodness, there is a lot of off acronyms here. I've gotta you need a glossary? I had the same reaction when I joined B. M or seven years ago and had the same reaction when I joined the S A. P 15 years ago. Now, of course, two or three years into it, you learn everything and it becomes part of your speed. We have toe constantly. It's like an accordion like you expanded by making it mawr of luminous and deep. But as you do that it gets complex, you then have to simplify it. And that's the job of all of us leaders and I this year, just exemplifying that I don't have it perfect. One of the gifts I do have this communication being able to simplify things. I recorded a five minute video off our five franchise pill. It's just so that the casual person didn't know VM where it could understand on. Then, when I'm on your shore and when on with Jim Cramer and CNBC, I try to simplify, simplify, simplify, simplify because the more you can talk and analogies and pictures, the more the casual user. I mean, of course, and some other audiences. I'm talking to investors. Get it on. Then, Of course, as you go deeper, it should be like progressive layers or feeling of an onion. You can get deeper. It's not like the entire discussion with Sanjay Putin on my team is like, you know, empty suit. It's a superficial discussion. We could go deeper, but you don't have to begin the discussion in the bowels off that, and that's really what we don't do. And then the other part of your question was, how do we think about new markets? You know, we always start with Listen, you sort of core in contact our borough come sort of Jeffrey Moore, Andi in the Jeffrey more context. You think about things that you do really well and then ask yourself outside of that what the Jason sees that are closest to you, that your customers are asking you to advance into on that, either organically to partnerships or through acquisitions. I think John and I talked about in the previous dialogue about the framework of build partner and by, and we always think about it in that order. Where do we advance and any of the moves we've made six years ago, seven years ago and I joined the I felt VM are needed to make a move into mobile to really cement opposition in end user computing. And it took me some time to convince my peers and then the board that we should by Air One, which at that time was the biggest acquisition we've ever done. Okay. Similarly, I'm sure prior to me about Joe Tucci, Pat Nelson. We're thinking about nice here, and I'm moving to networking. Those were too big, inorganic moves. +78 years of Raghu was very involved in that. The decisions we moved to the make the move in the public cloud myself. Rgu pack very involved in the decision. Their toe partner with Amazon, the change and divest be cloud air and then invested in organic effort around what's become the Claudia. That's an organic effort that was an acquisition fast forward to last year. It took me a while to really Are you internally convinced people and then make the move off the second biggest acquisition we made in carbon black and endpoint security cement the security story that we're talking about? Rgu did a similar piece of good work around ad monetization to justify that pivotal needed to come back in. So but you could see all these pieces being adjacent to the core, right? And then you ask yourself, Is that context meaning we could leave it to a partner like you don't see us get into the hardware game we're partnering with. Obviously, the players like Dell and HP, Lenovo and the smart Knick players like Intel in video. In Pensando, you see that as part of the Project Monterey announcement. But the adjacent seas, for example, last year into app modernization up the stack and into security, which I'd say Maura's adjacent horizontal to us. We're now made a lot more logical. And as we then convince ourselves that we could do it, convince our board, make the move, We then have to go and tell our customers. Right? And this entire effort of talking to CSOs What am I doing is doing the same thing that I did to my board last year, simplified to 15 minutes and get thousands of them to understand it. Received feedback, improve it, invest further. And actually, some of the moves were now making this year around our partnership in distributed Workforce Security and Cloud Security and Z scaler. What we're announcing an XDR and Security Analytics. All of the big announcements of security of this conference came from what we heard last year between the last 12 months of my last year. Well, you know, keynote around security, and now, and I predict next year it'll be even further. That's how you advance the puck every year. >>Sanjay, I want to get your thoughts. So now we have a couple minutes left. But we did pull the audience and the community to get some questions for you, since it's virtually wanted to get some representation there. So I got three questions for you. First question, what comes after Cloud and number two is VM Ware security company. And three. What company had you wish you had acquired? >>Oh, my goodness. Okay, the third one eyes gonna be the turkey is one, I think. Listen, because I'm gonna give you my personal opinion, and some of it was probably predates me, so I could probably safely So do that. And maybe put the blame on Joe Tucci or somebody else is no longer here. But let me kind of give you the first two. What comes after cloud? I think clouds gonna be with us for a long time. First off this multi cloud world, you just look at the moment, um, that AWS and azure and the other clouds all have. It's incredible on I think this that multi cloud from phenomenon. But if there's an adapt ation of it, it's gonna be three forms of cloud. People are really only focus today in private public cloud. You have to remember the edge and Telco Cloud and this pendulum off the right balance of workloads between the data center called it a private cloud. The public cloud on one end and the telco edge on the other end. I think we're in a really good position for workloads to really swing between all three of those locations. Three other part that I think comes as a sequel to Cloud is cloud native. All of the capabilities a serverless functions but also containers that you know. Obviously the one could think of that a sister topics to cloud but the entire world of containers. The other seat, uh, then cloud a cloud native will also be topics, but these were all fairly connected. That's how I'd answer the first question. A security company? Absolutely. We you know, we aspire to be one of the leading companies in cyber security. I don't think they will be only one. We have to show this by the wealth on breath of our customers. The revenue momentum we have Gartner ranking us or the analysts ranking us in top rights of magic quadrants being viewed as an innovator simplifying the stack. But listen, we weren't even on the radar. We weren't speaking of the security conferences years ago. Now we are. We have a billion dollar security business, 20,000 plus customers, really strong presences and network endpoint and workload and Cloud Security. The three Coppola's a lot more coming in Security analytics, Cloud Security distributed workforce Security. So we're here to stay. And if anything, BMR persist through this, we're planning for multi your five or 10 year timeframe. And in that course I mean, the competition is smaller. Companies that don't have the breadth and depth of the n words are Andy muscle and are going market. We just have to keep building great products and serving customer on the third man. There's so many. But I mean, I think Listen, when I was looking back, I always wondered this is before I joined so I could say the summit speculatively on. Don't you know, make this This is BMR. Sorry. This is Sanjay one's opinion. Not VM. I gotta make very, very clear. Well, listen, I would have if I was at BMO in 2012 or 2013. I would love to about service now then service. It was a great company. I don't even know maybe the company's talk, but then talk about a very successful company at that time now. Maybe their priorities were different. I wasn't at the company at the time, but I can speculate if that had happened, that would have been an interesting Now I think that was during the time of Paul Maritz here and and so on. So for them, maybe there were other priorities the company need to get done. But at that time, of course, today s so it's not as big of a even slightly bigger market cap than us. So that's not happening. But that's a great example of a good company that I think would have at that time fit very well with VM Ware. And then there's probably we don't look back and regret we move forward. I mean, I think about the acquisitions we have made the big ones. Okay, Nice era air watch pop in black. Pivotal. The big moves we've made in terms of partnership. Amazon. What? We're announcing this This, you know, this week within video and Z scaler. So you never look back and regret. You always look for >>follow up on that To follow up on that from a developer, entrepreneurial or partner Perspective. Can you share where the white spaces for people to innovate around vm Where where where can people partner and play. Whether I'm an entrepreneur in a garage or venture back, funded or say a partner pivoting and or resetting with Govind, where's the white spaces with them? >>I think that, you know, there's gonna be a number off places where the Tan Xue platform develops, as it kind of makes it relevant to developers. I mean, there's, I think the first way we think about this is to make ourselves relevant toe all of that ecosystem around the C I. C. D type apply platform. They're really good partners of ours. They're like, get lab, You know, all of the ways in which open source communities, you know will play alongside that Hash E Corp. Jay frog there number of these companies that are partnering with us and we're excited about all of their relevancy to tend to, and it's our job to go and make that marketplace better and better. You're going to hear more about that coming up from us on. Then there's the set of data companies, you know, con fluent. You know, of course, you've seen a big I p o of a snowflake. All of those data companies, we'll need a very natural synergy. If you think about the old days of middleware, middleware is always sort of separate from the database. I think that's starting to kind of coalesce. And Data and analytics placed on top of the modern day middleware, which is containers I think it's gonna be now does VM or play physically is a data company. We don't know today we're gonna partner very heavily. But picking the right set of partners been fluent is a good example of one on. There's many of the next generation database companies that you're going to see us partner with that will become part of that marketplace influence. And I think, as you see us certainly produce out the VM Ware marketplace for developers. I think this is gonna be a game changing opportunity for us to really take those five million developers and work with the leading companies. You know, I use the example of get Lab is an example get help there. Others that appeal to developers tie them into our developer framework. The one thing you learn about developers, you can't have a mindset. With that, you all come to just us. It's a very mingled village off multiple ecosystems and Venn diagrams that are coalescing. If you try to take over the world, the developer community just basically shuns you. You have to have a very vibrant way in which you are mingling, which is why I described. It's like, Listen, we want our developers to come to our conferences and reinvent and ignite and get the best experience of all those provide tools that coincide with everybody. You have to take a holistic view of this on if you do that over many years, just like the security topic. This is a multi year pursuit for us to be relevant. Developers. We feel good about the future being bright. >>David got five minutes e. >>I thought you were gonna say Zoom, Sanjay, that was That was my wildcard. >>Well, listen, you know, I think it was more recently and very fast catapult Thio success, and I don't know that that's clearly in the complete, you know, sweet spot of the anywhere. I mean, you know, unified collaboration would have probably put us in much more competition with teams and, well, back someone you always have to think about what's in the in the bailiwick of what's closest to us, but zooms a great partner. Uh, I mean, obviously you love to acquire anybody that's hot, but Eric's doing really well. I mean, Erica, I'm sure he had many people try to come to buy him. I'm just so proud of him as a friend of all that he was named to Time magazine Top 100. But what he's done is phenomenon. I think he could build a company that's just his important, his Facebook. So, you know, I encourage him. Don't sell, keep building the company and you'll build a company that's going to be, you know, the enterprise version of Facebook. And I think that's a tremendous opportunity to do this better than anybody else is doing. And you know, I'm as an immigrant. He's, you know, China. Born now American, I'm Indian born, American, assim immigrants. We both have a similar story. I learned a lot from him. I learned a lot from him, from on speed on speed and how to move fast, he tells me he learns a thing to do for me on scale. We teach each other. It's a beautiful friendship. >>We'll make sure you put in a good word for the Kiwi. One more zoom integration >>for a final word or the zoom that is the future Facebook of the enterprise. Whatever, Sanjay, Thank >>you for connecting with us. Virtually. It is a digital foundation. It is an unpredictable world. Um, it's gonna change. It could be software to find the operating models or changing you guys. We're changing how you serve customers with new chief up commercial customer officer you have in place, which is a new hire. Congratulations. And you guys were flexing with the market and you got a tailwind. So congratulations, >>John and Dave. Always a pleasure. We couldn't do this without the partnership. Also with you. Congratulations of Successful Cube. And in its new digital format, Thank you for being with us With VM world here on. Do you know all that you're doing to get the story out? The guests that you have on the show, they look forward, including the nonviable people like, Hey, can I get on the Cuban like, Absolutely. Because they look at your platform is away. I'm telling this story. Thanks for all you're doing. I wish you health and safety. >>I'm gonna bring more community. And Dave is, you know, and Sanjay, and it's easier without the travel. Get more interviews, tell more stories and tell the most important stories. And thank you for telling your story and VM World story here of the emerald 2020. Sanjay Poon in the chief operating officer here on the Cube I'm John for a day Volonte. Thanks for watching Cube Virtual. Thanks for watching.

(gentle music) >> From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi, I'm Stu Miniman. And this is a special CUBE conversation. We've been talking a lot, of course for many years about the ascendancy of cloud. And today in 2020, multicloud is a big piece of the discussion. And we're really happy to help unveil coming out of stealth Alkira, which is helping the networking challenges when it comes to multicloud and I have the two co-founders, they are brothers. I have Amir, who is the CEO and Atif, who is the CTO, the Khan brothers, thank you so much for joining us, and congratulation on the launch of the company. >> Thank you Stu for having us on the Show. It's a pleasure to see you again. >> All right, so Amir, we've had you on the program. Your previous company that you've done was of course Viptela, the two of you have worked together at, I believe, five companies, a successful companies. Acquired the most recent one into Cisco. So, Amir, obviously, strong networking team, your brother, the CTO is going to talk to us about the engineering but give us just the story of Alkira, what you've been building and now ready to unveil to the world. >> Certainly, Stu, so when around 2018 timeframe, we started looking into the next big problem to solve in the industry, which was not only a substantial from the market size perspective, but also from the customers perspective was solving a major pain point. So when we started looking into the cloud customers and started talking to our customers, they were struggling from the cloud networking perspective, even in a single cloud, and it was a new environment for them and they had to understand all the nitty gritty details of each one of these clouds and when you go to multicloud environment, it becomes exponentially complicated to address not only connectivity, but how to deploy services like firewall and other services, including load balancers and IP address management, et cetera, and remote access. So we started digging deeper into this problem and started working with the customers and took a clean sheet of paper and came up with a very comprehensive approach to offering a solution which is as-a Service. This time, we are not shipping any hardware software it is just like any other SaaS application, you just come to our portal just drag and drop, literally draw out your network and click on provision. And come back after 40 minutes or so your whole global cloud infrastructure is up and running. >> All right, Atif your brother laid out a pretty broad vision there, any of us from the networking world, we know there's a lot of complexity there. And therefore it takes a lot of work, when I want to do things simply, as-a Service is a huge growth area bring us inside the engineering challenges that you and the team have been working on to build this solution. >> Certainly, Stu, so we've been working both Amir and myself in the networking industry for more than 25 years now. And the way we have worked and what we have believed in is that we need to solve customer problems. We never believe in doing a science project. So here also we started working with customers as we have always done in the past. We understood the customers pain points, the challenges they were facing, especially in this case and in cloud networking space, multicloud networking space, based on the user requirements, users, or the customers use cases, we started building our service. And here what we have built as a complete network as-a Service. It's a multicloud network as-a service, which not only provides connectivity to multiple clouds, but also addresses the needs for bringing in networking services, as well as security services, making sure that you have a full policy based infrastructure on top of it, you have deep visibility into the clouds as well as into on-premise end to end visibility, end to end monitoring, troubleshooting. And all of it is delivered to you as-a service. So that's what we have been doing here at Alkira. >> Excellent! So when we've looked at multicloud, of course, every cloud, they have some similar things, they have some different things. They all tend to do things a little bit differently. One of the secret sauces that have been talked about for the last few years is the SD-WAN space, like you had built with the tele to help really enable those environments. So Atif we've got a diagram here, which I think will help explain a little bit as to where out here and how it plugs into these different environments, walk us through a little bit what we're seeing here, and what you're actually doing at Alkira. >> So here we are building a global unified, multicloud network. It's consumed as a service. Think of it as consuming it just like you would consume any other SaaS, like our SaaS application. So you come to Alkira's portal, you register. And then there you go, and you start building your global multicloud unified network with integrated services. So here what you see is a Alkira's cloud services exchange which comprises of the cloud exchange points. You can bring these up these cloud exchange points up anywhere on the globe. You can decide like what networking services security services you need in these cloud exchange points, you can connect to multiple clouds. From there, you can bring your existing on-prem connectivity into the CXPs. All these CXPs have a full mesh of overlay, high speed, low latency connectivity among each other. So there is a full network which comes up between these CXPs. And the whole infrastructure scales with customers as our customers scale. So it's a horizontally scalable, very highly redundant and resilient infrastructure, which we had built. >> All right, so, Amir now that we understand the basics of the technology, you've got some strong investors including Sequoia, Kleiner Perkins, give us what is being announced that you're coming out of stealth, where are you with the product? How many employees you have? And where are you with the discussion of customer adoption. >> So Stu we are obviously, bringing this to the market, and we will be announcing it on April 15. It's available for the customers to consume our solution as a service on that day. So they are welcome to reach out to us and we'll be happy to help them. And as a matter of fact, just come to our website and register for the service. And yeah, I mean you rightly said that we have a superstar team of not only the venture capital companies, but also the board members representing those companies, the Bill Coffin and Mamun Ahmed, who the leading VCs are on the board of our company, including myself and Atif. >> All right, Amir I'd love to actually bring up the second slide that we have here. Walk us through you said the service, how do people get started? How do they understand, walk us through what they do. >> So the biggest challenge when we started looking into these problems, Stu was that it was very complicated. You had to piecemeal bring up instances in the cloud and stitch them together. And when you try to integrate the services, that was a different challenge for the customers. So we want to make sure that it was so simple and clean, that the customer didn't even have to think about any underlying construct on any of the clouds, they should not have to worry about learning each individual part from the networking perspective. So here's your portal, you just come, step one is come to our portal register. Step two is you start drawing your network based on your intent, what on-prem connectivity you want to bring into this service, what type of services you need, like a lot of firewalls and then what pilots you need to connect and everything happens seamlessly, from on-prem, prem through services into the cloud, across multiple clouds. It's a seamless service that we have created and with full analytics capabilities and full governance built in. >> All right, so Atif bring us into what this means for customers, how do they manage it? Is this the networking team? Is it the cloud architects? What API's are there? How does this fit into kind of what customers are doing today? And solve some of those challenges that we laid out earlier in the discussion. >> Yes, from the customer's perspective, as I said, it's completely delivered as a service. Customers come to our portal, they draw out the network, they select the services, they click on provision and the whole network comes up within minutes. So the main thing here is that from a customer's point of view, if they are connecting to different clouds, they don't need to understand any of the underlying specifics or underlying constructs of any of the cloud in order to bring up connectivity. So what we are doing here is we are abstracting the cloud chair. So we are building a virtual cloud network. So if you think of, if you compare with what we did in the previous life, we virtualized the WAN. So here what we are doing is we are virtualizing the cloud network, so underlying doesn't matter which cloud you sit on which cloud you need to connect to, which networking services, whether a cloud native services or whether you want to consume Alkira services, or we also support like customer bringing in third party services as well. So it's all offered from our platform all offered is service to the customer. Again, no expertise required in any of the underlying networking constructs of any of these clouds. >> Give us what we should be looking at from a technology roadmap from Alkira, through the rest of 2020. >> Good question, Stu. So as I mentioned earlier, our roadmap is dictated by customer requirements, so we prioritize what customers need from us. So we have come out with a scalable platform, we have come out with a marketplace for networking services in there. In the near term, we'll be expanding our marketplace with more services. We will be addressing more use cases and when I talk about use cases, I can give you some examples. Like there's, you not just only need connectivity into cloud, you might have different requirements from throughput perspective or bandwidth perspective or different services that you need to contend your cloud when you may have certain applications such as Internet facing application where you need like traffic coming in from the internet, inbound to those applications, you might need services like a load balancer, like an external load balancer in our services exchange. You might also need like a firewall, you might need traffic engineering, or sorry, service chaining capabilities where you chain service through multiple traffic through multiple of these services like a firewall and a load balancer. So we built a platform which gives you all those capabilities going forward, we will be adding more services more use cases to it. We have a long ways ahead of us and we will be putting a lot of effort in delivering a roadmap as we go. >> All right, so Amir your technical team definitely has their hands full and robust roadmap to work on. Give us the high level, what we should be looking for Alkira, for people that are out there, multicloud and networking tends to get talked a lot. There's many big companies and small ones. What will separate Alkira from the rest of the market today? And what should we be looking to see the company's progression through 2020? >> Yeah, thanks for asking that. Yeah, certainly. I mean, from the solution perspective, Atif said that it's so fundamentally important to have a very strong basis. And that's what we have done. We are bringing out a certain number of services and now we will continue to grow on that we'll create a big marketplace. We will continue to improve on which clouds we connect to and how and we will building our own services in certain cases as well. Now, building a technology is just one piece of it, we have to go out to market with a company that the customers can trust every single department in that company, whether it's sales or how they do business with us all the business back end pieces, after we sorted out and that's what we've been working with. And then go to market partners, that is very, very important, support is very important. So let me spend a little bit of time on go to market strategy. We have been working with the service providers so that we can extend our reach not only to the large customers, but also to mid-size customers across the globe. So you will see us in the future announcing major service provider, partnerships, as well as we've been working with large SIS, WAAS and system integration partners. And also we have taking a slightly different approach this time because it's a service. So we are going with telecom master agents, which have been working with the service providers, the cloud providers, the cable providers, as a channel, and they have a huge reach into the customer base. So we have a very comprehensive strategy not only from the go to market perspective and the technology perspective, but also how we are going to support our customers and continue to build our relationship to build a lasting company. >> Yeah, Amir super important point there. Absolutely, we've seen the maturation and change in the service providers, as today they are working with many of the public cloud providers and they're, as you said, the close touch point and a trusted partner for customers. All right, so before I let you go, you two are brothers, everybody in today's day and age is spending even more time with family but your situation you've worked together for a long time. What keeps bringing the two of you together, working together and talk about that bond? >> So I mean we're a very close knit family, we have four brothers and one sister, and obviously Atif and I have been the closest because we have been working together for the longest, we've at least work in five different companies together, our families traveled together, we have three daughters each, we live about five minutes, walk from each other. And we just have this bond where we not only have the family close, but also very close knit friends circle, which we both hang out with, and we obviously have common interests in the sports as well. We play squash and tennis and workout. So Atif if you want to take a stab at that also. >> Yeah, so we've always been very close. In fact, we've been together for the last like, ever since I can remember like even college days, we were roommates for some time also, we have our circle of friends, is the same old source. So, again, we are very close. And we worked well together so we complement each other's skills. And it's worked out in the past. Hopefully it will work out again. And I look forward to working with them for many, many more years to come. >> Amir and Atif thank you so much for sharing the coming out of stealth. After all, Alkira we definitely look forward to watching your progress and seeing how you're helping customers in this multicloud world. Thank you for joining us. >> Stu thank you so much. >> Thank you for having us. >> All right, I'm Stu Miniman. And thank you so much for watching this special CUBE conversation on theCUBE. (gentle music)