>>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.

>> Okay. We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid right. So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manuvir Das who's the head of enterprise computing at NVIDIA. And before I hand it off to Rob, I just want to say for those of you who follow me at the Cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise and it's being driven by the emergence of data intensive applications and workloads. No longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like NVIDIA. So let's learn more about this collaboration and what it means to your data business. Rob, take it away. >> Thanks Mick and Dave. That was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy in accelerating the path to value and hybrid environments. And I want to drill down on this aspect. Today, every business is facing accelerating change. Everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor now. Every engagement with coworkers, customers and partners is virtual. From website metrics to customer service records and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? At Cloudera, we believe this onslaught of data offers an opportunity to make better business decisions faster and we want to make that easier for everyone, whether it's fraud detection, demand forecasting, preventative maintenance, or customer churn. Whether the goal is to save money or produce income, every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit that cloud provides. And with security and edge to AI data intimacy, that's why the partnership between Cloudera and NVIDIA together means so much. And it starts with a shared vision, making data-driven decision-making a reality for every business. And our customers will now be able to leverage virtually unlimited quantities and varieties of data to power an order of magnitude faster decision-making. And together we turbo charged the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. We're joined today by NVIDIA's Manduvir Das, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, Manuvir, thank you for joining us. Over to you now. >> Thank you Rob, for having me. It's a pleasure to be here on behalf of NVIDIA. We're so excited about this partnership with Cloudera. You know, when, when NVIDIA started many years ago, we started as a chip company focused on graphics. But as you know, over the last decade, we've really become a full stack, accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, AI being a prime example. And when we think about Cloudera, and your company, your great company, there's three things we see Rob. The first one is that for the companies that were already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing we've seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about NVIDIA's mission going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies to date who have been the early adopters using the power acceleration by changing their technology and their stacks. But more and more we see the opportunity of meeting customers where they are with tools that they're familiar with, with partners that they trust. And of course, Cloudera being a great example of that. The second part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through. But as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. And so again, the power of your platform is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute, the machine learning compute, needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And, and Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where, literally, they took the workflow they had, they took the servers they had, they added GPUs into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >> How you doing? My name's Joe Ansaldi. I'm the branch chief of the technical branch in RAS. It's actually the research division, research and statistical division of the IRS. Basically, the mission that RAS has is we do statistical and research on all things related to taxes, compliance issues, fraud issues, you know, anything that you can think of basically, we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those algorithms, the number of parameters each of those algorithms have. So that's, that's really been our challenge now. The expectation was that with NVIDIA and Cloudera's help and with the cluster, we actually build out to test this on the actual fraud detection algorithm. Our expectation was we were definitely going to see some speed up in computational processing times. And just to give you context, the size of the data set that we were, the SME was actually working her algorithm against was around four terabytes. If I recall correctly, we had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them. It was really, really quick. The definite now term, short term, what's next is going to be the subject matter expert is actually going to take our algorithm run with that. So that's definitely the now term thing we want to do. Going down, go looking forward, maybe out a couple of months, we're also looking at procuring some A-100 cards to actually test those out. As you guys can guess, our datasets are just getting bigger and bigger and bigger, and it demands to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward and then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run a, you know, run to our heart's desire, wherever our imaginations takes our SMEs to actually develop solutions. Now have the platforms to run them on. Just kind of to close out, we really would be remiss, I've worked with a lot of companies through the year and most of them been spectacular. And you guys are definitely in that category, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't thank you guys. So thank you for the opportunity. Doing fantastic. and I'd have to also, I want to thank my guys. my staff, Raul, David worked on this, Richie worked on this, Lex and Tony just, they did a fantastic job and I want to publicly thank them for all the work they did with you guys and Chev, obviously also is fantastic. So thank you everyone. >> Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and NVIDIA? Is it primarily go to market or are you doing engineering work? What's the story there? >> It's really both. It's both go to market and engineering The engineering focus is to optimize and take advantage of NVIDIA's platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both. >> Great. Thank you. Manuvir, maybe you could talk a little bit more about why can't we just use existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do NVIDIA and Cloudera bring to the table that goes beyond the conventional systems that we've known for many years? >> Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So, the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now NVIDIA has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform so that regardless of the technique the customer is using to get insight from the data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >> So, I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going towards doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start. You think about AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems, and real-time AI inference, at least even at the edge, huge potential for business value. In a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the liking. So you're putting AI into these data intensive apps within the enterprise. The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >> Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint. And new platforms like these being developed by Cloudera and NVIDIA will dramatically lower the cost of enabling this type of workload to be done. And what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation engine, supply chain management, drug province. And increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots. That AR, VR and manufacturing so driving better quality. The power grid management, automated retail, IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >> I mean, Manufir, this is like your wheelhouse. Maybe you could add something to that. >> Yeah. I mean, I agree with Rob. I mean he listed some really good use cases, you know, The way we see this at NVIDIA, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled use cases, particular use cases like a chat bot from the ground up with the hardware and the software. Almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. Now, I think we are in the first phase of the democratization. For example, the work we do with Cloudera, which is for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. And you still come home and assemble it, but all the parts are there, the instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought a table and it showed up and somebody placed it in the right spot. Right? And they didn't really have to learn how to do AI. So these are the phases. And I think we're very excited to be going there. >> You know, Rob, the great thing about, for your customers is they don't have to build out the AI. They can, they can buy it. And just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations, and Mick I talked about this, the GIGO problem that we've all, you know, studied in college, you know, garbage in, garbage out. But, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob, over the next several years? >> So yeah, the combination of massive amounts of data that had been gathered across the enterprise in the past 10 years with an open APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency. And that's allowing us as an industry to democratize the data access while at the same time delivering the federated governance and security models. And hybrid technologies are playing a key role in making this a reality and enabling data access to be quote, hybridized, meaning access and treated in a substantially similar way, irrespective of the physical location of where that data actually resides. >> And that's great. That is really the value layer that you guys are building out on top of all this great infrastructure that the hyperscalers have have given us. You know, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you could go first and then Manuvir, you could bring us home. Where do you guys want to see the relationship go between Cloudera and NVIDIA? In other words, how should we as outside observers be, be thinking about and measuring your project, specifically in the industry's progress generally? >> Yes. I think we're very aligned on this and for Cloudera, it's all about helping companies move forward, leverage every bit of their data and all the places that it may be hosted and partnering with our customers, working closely with our technology ecosystem of partners, means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >> Yeah and I agree with Rob and for us at NVIDIA, you know, we, this partnership started with data analytics. As you know, Spark is a very powerful technology for data analytics. People who use Spark rely on Cloudera for that. And the first thing we did together was to really accelerate Spark in a seamless manner. But we're accelerating machine learning. We're accelerating artificial intelligence together. And I think for NVIDIA it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity.

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. It's just not evenly distributed. If we think hard enough and we apply things properly, uh, we can again take these technologies to, you know, underserved environments, um, in healthcare. Nobody should be technologically disadvantage. >>When is a car not just a car when it's a connected data driven ecosystem, dozens of sensors and edge devices gathering up data from just about anything road, infrastructure, other vehicles, and even pedestrians to create safer vehicles, smarter logistics, and more actionable insights. All the data from the connected car supports an entire ecosystem from manufacturers, building safer vehicles and fleet managers, tracking assets to insurers monitoring, driving behaviors to make roads safer. Now you can control the data journey from edge to AI. With Cloudera in the connected car, data is captured, consolidated and enriched with Cloudera data flow cloud Dara's data engineering, operational database and data warehouse provide the foundation to develop service center applications, sales reports, and engineering dashboards. With data science workbench data scientists can continuously train AI models and use data flow to push the models back to the edge, to enhance the car's performance as the industry's first enterprise data cloud Cloudera supports on-premise public and multi-cloud deployments delivering multifunction analytics on data anywhere with common security governance and metadata management powered by Cloudera SDX, an open platform built on open source, working with open compute architectures and open data stores all the way from edge to AI powering the connected car. >>The future has arrived. >>The Dawn of a retail Renaissance is here and shopping will never be the same again. Today's connected. Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.

(upbeat music) >> Welcome to this cube conversation. I'm Lisa Martin. I'm joined by Aamir Lakhani, the Lead Researcher and Cybersecurity Expert at FortiGuard Labs at Fortinet. Aamir, welcome back to theCube. >> Hey, it's always good to be back on. >> It is, even though we're still in this work from anywhere environment, and that's one of the things that I want to talk to you about. We're in this environment now, I've lost count, 16 months, 17 months? And we now have this distribution of folks working still from home, maybe some in the office, and a good portion that probably want to remain remote. And one of the things that, that you guys have seen in this time is this huge uptick and sophistication in phishing attacks. Talk to me about what's going on. >> You know, it's a funny thing you mention that, Lisa, every attack that I've seen in the last 16 months usually has a phishing component, and over the last, even just the last couple of weeks, we've seen some really sophisticated attacks, attacks that are against industrial control systems, against critical infrastructure, against large corporations, government entities, and almost every one of those attacks, whether it's a ransomware attack, whether it's a denial of service attack, usually has a phishing component. And the sad part is usually the initial attack vector, how attackers are getting into the network, a lot of times as the first step is through phishing. And, you know, it works, it's a method that has always worked. It works just as well today as it always did, so attackers are basically going back to the well and basically making their phishing attacks more complicated, and more sophisticated, and it's much more effective than it ever used to be. >> Tell me how they're making it more sophisticated because I know, I've seen interesting examples through Twitter, for example, of people that are very well-versed, you might even consider them cybersecurity experts, who've just almost fallen for a phishing email that looks so legitimate. How is it getting more sophisticated? >> Well, what attackers are doing is they're definitely playing on your emotions. They understand that there's a lot of things happening in the world, and sometimes we get a little emotion about it, whether it's, "Hey, how do you get the latest vaccine?" Maybe information, you know, around getting jobs, going back to work, LinkedIn, is a good example. A lot of people are looking for jobs. When the U.S. elections were happening, and there was a lot of phishing attacks around, political donations, and affiliations. They kind of kind of find these hot button items that they know people are really going to not think first about security, and really think like, "Hey, how do I respond back to this?" and really attack them that way. The other thing that we're seeing on how it's getting complicated is, it used to be like a phishing attack. You know, it used to be pretty simple, like click on a link. Now what they're doing is they're actually targeting organizations and what you do as a job. For example, I've seen a lot of phishing attacks against the HR, the human resource departments, and I feel sad for anyone in human resources because their job all day is to basically open files, and emails from strangers, and that's what attackers are doing. They're like, "Hey, I want to apply for a cybersecurity position. "And by the way, my resume is encrypted. "Please click on this link to see "my secure version of my resume". And when they do that, you know, HR person may be thinking, "Hey, this is a cybersecurity guy, like good. "He's actually sending me an encrypted link." In reality, when they click on that button, it's attacking their machine, and actually getting into their organization. The attacks are getting into the organization. So they're using more and more tricks to actually technically bypass some of the security tools you may have. >> So getting more sophisticated by preying on emotions, and also using technology, and things that an HR person, like you said, would think, "Great, this is the level of sophistication that this applicant has. How do they, how do organizations start reducing those attacks, that are falling victim to these attacks? >> Yeah, so I was thinking, at Fortinet we always mention, like at FortiGuard labs, that training and security awareness is some of the best ways you can protect against this attack. At Fortinet we have our training advancement agenda, that's out of Fortinet.com/training/taa. Basically what that does, well what we emphasize, what we preach, is that training is the key and education is the key, in helping protect against those attacks. And, you know, you can train anyone these days, at least some level of, you know, awareness. My mom used to call me up, and used to tell me like, "Hey, I got the IRS calling me, "should I answer these questions?" I was like, "No, absolutely not, like this is dangerous, "the IRS doesn't call you up and asking you "for your credit card number." I actually had my mum go for our level, one of our training, and she actually gets it. She's like, "Okay, I get why I shouldn't call the, you know, "answer the questions from the IRS now." So I say any type of training, to anyone you can give, and you can start it off like with people in high school, with people in elementary school, all the way up to professionals, I think it helps in all levels. >> So first of all, your mom sounds like my mom, and I need to get my mom to do this training, I really do. But one of the things that kind of highlights is the fact that there are five generations in the workforce. So there, and in every industry, there is a huge variety of people that understand technology, and know to be suspicious. And that's one of the things I think that's challenging for organizations, because if a lot of that responsibility falls on the person, the more sophisticated, the more personalized this phishing email is, the more likely I'm to think this is legitimate instead of questioning it. So that training that you're talking about, tell me a little bit more about that. You mentioned a variety of ages and generations, that folks as young as high school kids, and then folks in our parents' generation can also go on and learn how to navigate through basic emails, for example, to look for, to see what to look for. >> Yeah, it's not only emails. So attackers, like I said, they are getting sophisticated. We are seeing phishing attacks, not only through emails, but through applications, mobile applications. There's actually like some advanced phishing techniques now on smart speakers. When you ask your smart speaker, a certain skill like, "Hey, tell me my balance, "tell me what the weather is." There's like some phishing attacks there. So there's phishing attacks all across the board. Obviously, when we talk about phishing we're mostly talking about email attacks, but every generation kind of has their tools kind of has their, you know, techniques or apps that they're comfortable with. So, and we're trained, like a lot of my friends are trained to basically click on any app, download any app, allow, they don't really read the pop-ups that say like, "Do you want to share information?" They'll just start sharing information. People in the workforce, like sometimes that are not paying attention, they're just clicking on emails, and attackers realize this, most of the time when attacks happen, it's not when you're paying attention. It's like when we're on our Zoom calls, and we're actually like looking at our phones, looking at emails, multitasking, and that's when your attention kind of diverts a little bit, And that's when attackers are really jumping in, and really trying to take advantage of that situation. And that's, I think that's a good idea about the training is because it opens up your eyes to understand, hey, it's more about just emails, it's really about every way we can use technology, can be a vector on how we get attacked, and we have a couple of good examples on that as well. >> Let's talk about that, cause I want to see how easy it is for the bad actors to create phishing attacks. You were saying, it's not just email, it's through apps, it's through my smart speaker, which is one of the reasons I don't have one. But talk to me about how easy it is for them to actually set these up. >> Yeah, so we have, I think we have a demo we can show, an example that we can show, of what's going on. And what I'm showing here is basically how easy you can download proof of concept apps. Now, what I'm showing here is actually a defensive tool, it's for defenders, and people that want to test for security on testing, phishing, and how susceptible their organization may be to phishing. But you can see like attackers could do something very similar. This tool is called Black Eye. And what it does is allows me to create multiple different types of phishing websites. I can create a custom one, or I can use a template that's already created. Once I use this template, for example I'm using the LinkedIn template here, it's going to create a website for me. It already, this website, I can embed into a link if I was, if I was potentially a bad guy, I could hide it behind a link. I could potentially change the website to make it look more like LinkedIn. But when I go to the LinkedIn fake website, this phishing website, which is hosted, you'll see, it kind of looks like LinkedIn. It actually has that little security box, that little green box, because it generates a certificate as well. And when I go to the real LinkedIn website, yes, the real LinkedIn website does look a little different. It's using a more updated template, a more updated website, but most people aren't going to notice the difference between the real LinkedIn website, and here, where we have the fake LinkedIn website. And I'll just show you like, if I log in and I'm going to log in with a demo account, this is actually a honeypot demo account that we have, just to showcase this tool. But I'll log in here, and you'll see from our test box, as soon as we log in, and we go back to the attacker's point of view, he's captured the username, the password, but not only that he has the IP address, the ISP, the location of where the victim is coming from. So they have a lot of different types of information that they've captured. And this is just one simple way of doing the attack. Now, one thing to remember, I know I speak very fast, but at the same time, this is real time. I didn't like copy and paste anything, I just recorded this in real time, and replayed this. And this is how easy it is for an attacker to potentially start setting up a system where they can attack victims. >> That's remarkable, because I mean, I'm in LinkedIn every day, and I don't know, you talked about, we're all busy, multitasking, and things like that. I don't know that I would've, nothing that you showed caught my attention. So how would I know to, what would I know to look for as a user, as a potential victim? How do I look for something on that page to tell me "think twice about this? >> Yeah, it's getting much more difficult these days. I mean, one of the things that I do is I try and make sure I type in like the addresses, especially when I get links in emails, I try not to like, just click on the link directly. I try and look at what's behind that link, is it really going to the LinkedIn website, you know, I'll try and go ahead and type in it, type in the website in the web browser. But mostly I think the thing that we can do to all protect ourselves is like kind of slow down. One of the reasons I mentioned LinkedIn is not because LinkedIn is doing anything bad. They're actually taking a lot precautions on being secure. But you know, people, these days are very emotion, they're going back to work, they're maybe looking for new jobs, or they're trying to get back into the workforce after a pandemic. So there's a lot of people that are getting phishing attacks from attackers, and it's a really mean thing. They're taking once again, advantage of that emotion, like someone needs a job, so let me go ahead and send them a LinkedIn link, and this time they're just stealing their username and passwords. >> That's remarkable. I think another thing you can do, can you hover over the link, and if it looks suspicious, if it doesn't go to like linkedin.com, for example, in this case, that's one way, right, is to check out what that actual URL is. >> Yeah, absolutely, and that's a great way of doing that, so we definitely recommend that. Look at the, hover over the link, look over the links, type in the links directly if you can. And you can see like, you know, attackers are getting sophisticated.. We used to tell people, look for that green lock box, attackers can now generate that green lockbox, so you have to do a little more due diligence. Just keep your eyes a little sharper these days. >> Do you thing phishing is, and I know a lot of us understand what it is, but do you think it's as common ransomware was up? I think Derek told me 7X in the second half of calendar year, 2020, Is phishing becoming more of a household word like ransomware is? Or is that something that you think actually will help more organizations, and more people and more generations be just more aware of let me just take a step back, and check that this is legitimate. >> Yeah, so phishing, you have to remember is it's like the initial attack. So the demo that I just showed you, you could say the true attack was me possibly stealing the username and password, but a phishing would be the way that someone would get to get to that. Like by essentially mimicking the LinkedIn website, as I showed in the example. So ransomware is an attack, it's the main attack. Usually the attack that attackers are going for, but how they get into the system is usually through a phishing site. They'll usually try and phish your username and password to your corporate site, maybe your VPN services, or your remote desktop services. So phishing is usually in conjunction with another attack, and that's the scary part is attackers have a lot of attacks that you can choose from, but the attacks that they're normally normally conducting to get that initial access to your system is phishing. >> So besides training, which is obviously absolutely critical, how can organizations protect themselves against this threat landscape that I imagine is only going to continue to grow? >> Yeah, no, it's definitely going to continue to grow. And as I said, I really believe education is the best thing you can do. But on top of that, you know, just I would say, you know, cyber hygiene. The basic things that we always mention every time, it was like, make sure like your security products are up to date, make sure they're installed, make sure your patches are up to date, which is very difficult, but that does start helping things. Make sure you're using the latest version of your web browser. There's a lot of web browsers these days has some sort of anti-phishing type of tools in them as well, especially for websites. So they can kind of detect things. There's a once again, a lot of just even free plugins, security plugins, that are available, that kind of detect a lot of phishing sites as well. So there's a lot of things I think people can do to protect themselves from a technology standpoint. You know, with basic cyber hygiene, as well as security awareness. >> So you think this is really preventable, essentially. >> I don't think it's 100% preventable, because I think, you know, attackers are always going to take advantage of those times in our emotion when our emotions are heightened, and they're going to take advantage of just us sometimes like not paying as much attention to as we can. But I think you can definitely reduce that attack surface. The more we educate ourselves. >> Absolutely, tell me that training website again. >> Sure things, so it's basically Fortinet.com/training/taa. >> Excellent, and can you access different levels? Like if I literally point my mom to that website, can she access something that would be at her 75 year old brain level? >> Absolutely, so we have different levels out there. I would suggest that I go trying, everyone should try basically Level 1, NSC Level 1. That's our Security Institute. So that's really good awareness for everyone on all sorts of different levels. But we have training, geared towards specific individuals, and different age groups as well. >> Excellent, and it's one of those things that culturally is difficult I think for Americans, slow down, right? We don't do that, especially when people are still working from home, and probably now it's summertime, kids are out of school, things are a little bit more chaotic. That that best practice of an organization really keeping up with their cyber hygiene and us as individuals slowing down, checking something are really some of the best ways. Aamir, this is such an interesting topic. Thank you for showing us how easy it is to create phishing attacks, and what some of the things are that we as individuals, and companies can do to protect ourselves against it. >> Hey, no problem, glad to be here. >> For Aamir Lakhani, I'm Lisa Martin, you're watching this Cube conversation. (soft music)

>>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.

>> Narrator: From around the globe. It's theCUBE with digital coverage of Postgres vision 2021 brought to you by EDB. >> Welcome back to Postgres Vision 21. My name is Dave Vellante. We're thrilled to welcome Carl Olofsen to theCUBE. Carl is a research vice president at IDC focused on data management. The long-time database analyst is the technologist and market observer. Carl, good to see you again. >> Thanks Dave. Glad to be here. >> All right. Let's let's get into it. Let's talk about, let's go right to the, to the source the open source database space. You know, how, what changes have you seen over the last couple of years in that marketplace? >> Well, this is a dynamic area and it's continuing to evolve. When we first saw the initial open source products like mysQl and PostgreSQL on the early days they were very limited in terms of functionality. They were espoused largely by sort of true believers. You know, people who said everything should be open source. And we saw that mainly they were being used for what I would call rather prosaic database applications. But as time has gone by they both of these products improve. Now there's one key difference, of course, which is a mySQL is company owned open source. So the IP belongs to Oracle corporation. Whereas PostgreSQL is community open source, which means that the IP belongs to the PostgreSQL community. And that can have a big difference in terms of things like licensing and so forth, which really matters now that we're coming into the cloud space because as open-source products moving into the cloud space the revenue model is based on subscriptions. And of course they are always based on subscription to open source cause you don't charge for the license. So what you charge for its support, but in the cloud what you can do is you can set up a database service, excuse me, a database service and then you charge for that service. And if it's open source or it's not open source that actually doesn't matter to the user. If you see what that I mean because they still are paying a subscription fee for a service and they get the service. The main difference between the two types is that if you're a commercial provider of PostgreSQL like enterprise DB, you don't have control over where it goes and you don't have control over the IP and how people use it in different ways. Whereas Oracle owns mySQL so they have a lot more control and they can do things to it on their own. They don't have to consult the community. Now there's also, non-relational open source including MongoDB. And as you may be aware, MongoDB has changed their license. So that it's not possible for third party to offer Mongo DB as a complete managed database service without paying a license fee to MongoDB for that. And that's because they own the IP too. And we're going to see a lot more of this sort of thing. I have conversations with open source all the time and they are getting a little concerned that it has become possible for somebody to simply take their technology, make a lot of money off that. And no money goes back to the community. No money goes back to the IRS. It's a company it's just stays with the supplier. So I think, you know it'll be interesting to see how all this is over time. >> So you're suggesting that the Postgres model then is, is I guess I'll use the word cleaner. And so that feels like it's a it's a benefit or is it a two-edged sword kind of thing? I mean, you were saying before, you know a company controls the IP so they could do things without having to go to the community. So maybe they can do things faster. But at the other hand like you said, you get handcuffed. You think you're going to be able to get a, you know a managed service, but then all of a sudden you're not and the rules change midstream saying it, am I correct? That Postgres, the model is cleaner for the customer? >> Well, you know, I mean, a lot of my friends who are in the open source community don't even consider company owned open source to be true open source because the IP is controlled by a company, not by a community. >> Dave: Right >> So from that perspective certainly Postgres SQL is considered, I don't know if you want to use the word cleaner or more pure or something along those lines, but also because of that the nature of community open source it can be used in many different ways. And so we see Postgres popping up all over the place sometimes partially and sometimes altogether, in other words, a service, a cloud service, we'll take a piece of Postgres and stick it on top of their own technology and offer it. And the reason they do that is they know there are a lot of developers out there who already know how to code for Postgres. So they are immediately first-class users of the service that they're offering. >> So, talk a little bit more about what you're seeing. You just mentioned a lot of different use cases. That's interesting. I didn't realize that was, that was happening. The, what are you seeing in terms of adoption in let's say the last 18, 24 months specific to Postgres? >> Yeah, we're seeing a fair amount of adoption in especially in the middle market. And of course there is rapid adoption in the tech sector. Now, why would that be? Well it's because they have armies of technologists. Who know how to program this stuff. You know, when you, you know, a lot of them will use PostgreSQL without a contract without a support contract, they'll just support themselves. And they can do that because they have the technicians who are capable of doing it. Most regular businesses can't do that. They don't have the staff so they need that support contract. And so that's where a company like enterpriseDB comes. I mentioned them only because they're the leading supplier Postgres to all their other suppliers. >> I was talking to Josh Burgers, red hat and he was, you know, he had just come off a Cubacon and he was explaining kind of what's happening in that community. Big focus of course on security and the whole, you know, so-called shift left. We were having a good discussion about, you know when does it make sense to use, you know Postgres in a container environment should you use Postgres and Kubernetes and he sort of suggested that things have rapidly evolved. There's still, you know, considerations but what are you seeing in terms of the adoption of microservices architectures containers, generally Kubernetes how has that affected the use of things like postsgres? >> So those are all different things or need to be kind of custody. >> Pick your favorite. >> They're related then. So microservices, the microservice concept is that you take an application break it up into little pieces and each one becomes a microservice that's invoked through an API. And then you have this whole structure API system that you use to drive the application and they run. They typically, they run in containers usually Kubernetes govern containers but the reason you do this and this is basically a efficiency because especially in the cloud, you want only to pay for what you use. So when you're running a microservice based application. Applications have lots of little pieces when something needs to be done, microservice fires up it does the thing that needs to be done. It goes away. You only pay for that fraction of a second that the microservice is running. Whereas in a conventional application you load this big heavyweight application. It does stop. It sets some weights with things and does more stuff and sits and waits for things. And you pay for compute for that entire period. So it's much more cost effective to use a microservices application. The thing is that microservice, the concept of microservices is based on the idea that the code is stateless but database code isn't stateless cause it has its attraction to the database which is the ultimate kind of like stateful environment right? So it's a tricky business. Most database technologies that are claimed to be container-based actually run in containers the way they run in servers. In other words, they're not microservice-based they do run in containers. And the reason they're doing that is for portability so that you can deploy them anywhere and you can move them around. But you know deploying a microservice based database is, well, it's it's a big technical project. I mean, that is hard to do. >> Right and so talk about, I mean again we're talking to Josh it was clear that that Kubernetes has evolved, you know quite rapidly at the same time there were cautions. In other words, he would say I think suggested things like, you know, there were known at one point, there were known, you know flaws and known bugs that ship the code that's been been remediated or moderated in terms of that practice but still there's there's considerations just in terms of the frequency of updates. I think he gave the example of when was the last time you know, JVM got, you know, overhauled. And so what kind of considerations should customers think about when considering them, they want the Kubernetes they want the flexibility and the agility but at the same time, if they're going to put it production, they've got to be careful, right? >> Yeah, I think you need to make sure you're using you're using functions that are well-established, you know you wouldn't want to put something into production that's new. They say, oh, here's a new, here's a new operation. Let's try that. And then, you know, you get in trouble. So you want to deal conservative that way you know, Kubernetes is open-source so and the updates and the testing and all that follows a rather slow formal process, you know from the time that the submission comes in to the time that it goes out, whereas you mentioned JVMs JV, but it was owned by Oracle. And so JVMs are managed like products. Now there's a whole sort of legal thing I don't want to get into it as to whether it's legal. They claim it's not libero third parties to build JVMs without paying a licensing. I don't want to talk about that, but it's based on a very state that has a very stable base, you know whereas this area of Kubernetes and govern containers is still rapidly evolving but this is like any technology, right? I mean, when you, if you're going to commit your enterprise to functions that run on an emerging technology then you are accepting some risk. You know, that there's no question about it. >> So we talked about the cloud earlier and the whole trend toward managed services. I mean, how does that specifically apply to Postgres? You can kind of imagine like a sidecar, a little bit of Postgres mixed in with, you know, other services. So what do you see and what do you, what's your telescope say in terms of the the Postgres adoption cloud? How do you see that progressing? >> I think there's a lot of potential. There's a lot of potential there. I think we are nowhere near the option that it should be able to achieve. I say that because for one thing, even though we analyze the future at IDC, that doesn't mean we actually know the future. So I can't say what its adoption will be but I can say that there's a lot of potential there. There's a tremendous number of Postgres developers out there. So there's a huge potential for adoption. And especially in cloud adoption, the main thing that would help that is independent. And I know that enterpriseDB has one independent a managed cloud service. So I think they do. >> Yeah I think so. >> But you know, why do I say that? I say that because alternatives these days there are some small companies that maybe they'll survive and maybe they won't, but that, you know, do you want to get involved with them or the cloud platform providers, but if you use their Postgres you're locked into that cloud platform. You know, if you use Amazon, go press on RDS, right? You're not, you become quickly locked in because you're starting using all the AWS tools that surround it to build and manage your application. And then you can't move. If you see what I mean. >> Dave: Yeah . >> They have have an RDS labor Aurora, and this is actually one of the things that it's really just a thin layer of Postgres interaction code underneath Aurora is their own product. so that's an even deeper level of commitment. >> So what has to happen for, so obviously cloud, you know, big trend. So the Postgres community then adopts the code base for the cloud. Obviously EDB has, you know hundreds of developers contributing to that, but so what does that mean to be able to run in the cloud? Is that making it cloud native? Is that extensions? Is it, you know, what technically has to occur and what has occurred and how mature is it? >> Well, so smaller user organizations are able to migrate fairly quickly cloud because most of their applications are you know, commercially purchased. They're like factories applications. When they move to the cloud, they get the SAS one and often the SAS equivalent runs on Postgres. So that's just fine. Larger enterprises are a real mess. If you've ever been in a large enterprise data center you know what I'm talking about? It's just, there's just servers and storage everywhere. There's, all these applications, databases connections. They are not moving to the cloud anytime soon. But what they are doing is setting up things like private cloud environments and applying in there. And this is a place where if you're thinking about moving to something like a Postgres you know most of these enterprises use the big commercial databases. Oracle SQLserver DB two and so forth. If you're thinking of moving from that to a a PostgreSQL development say, then the smart thing to do would be first to do all your work in the private cloud where you'd have complete control over the environment. It also makes sense still to have a commercial support contract from a vendor that you trust, because I've said this again, unless you are, you know, Cisco or somebody, you know, some super tech company that's got all the technicians you need to do the work. You really don't want to take on that level of risk. If you see that, I mean. Another advantage to working with a supplier, a support supplier, especially if you have a close, intimate relationship is they will speed your security patches on a regular basis which is really important these days, because data security is as you know, a growing concern all over the place. >> So let's stay on the skillsets for a minute. Where do you see the gaps within enterprises? What kind of expertise you mentioned, you know support contracts, what are the types of things that a customer should look for in terms of the the expertise to apply to supporting Postgres databases? >> Well, obviously you want them to do the basics that any software company does, right? You want them to provide you with regular updates and binary form that you can load and, you know test and run. You want to have the you know, 24 hour hotline you know, telephone support, all that kind of thing. I think it's also important to have a solid ability on the part of the vendor that you're working with to provide you with advice and counseling as you, especially, if you're migrating from another technology, help your people convert from what they were using to what they're going to be using. So those are all aspects that I would look for in a vendor for supporting a product like PostgreSQL. >> When you think about the migration to the cloud, you know of course Amazon talks a lot about cloud migration. They have a lot of tooling associated with that. >> Carl: Right. >> But when you step back and look at it it did to a point earlier, I mean a lot of the hardcore mission, critical stuff isn't going to move it, hasn't moved, but a lot of the fat middle, you know, is, are good candidates for it. >> Carl: Right. >> How do you think about that? And how do you look at that? I mean, obviously Oracle is trying to shove everything into OCI and they're, you know, they're all in because they realized that could make a lot of money doing that. But what do you, what are the sort of parameters that we should think about when considering that kind of migration, moving a legacy database into the cloud? >> Well, it has to be done piecemeal. You're not going to be able to do it all at once. You know, if you have hundreds of applications, you're not just you don't even want to, you know, it's a good time to take you into it. And what you've got running, ask yourself are these applications really serving the business interests today and will they in the future or is this a good time to maybe consider something else? Even if you have a packaged application, there might be one that is more aligned with your future goals. So it's important to do that. Look at your data integration, try to simplify it. You know, most data integration that most companies has done piecemeal project by project. They don't reference each other. So you have this chaos of ETL jobs and transformation rules and things like that that are just, you know, even difficult to manage. Now, just forget about any kind of migration or transformation considerations, just trying to run it now is becoming increasingly difficult. You know, maybe you want to change your strategy for doing data integration. Maybe you want to consolidate you want to put more data in one database. I'm not an advocate of the idea that you can put all application data in one database by the way, we know from bitter experience that doesn't work, but we can be rational about the kinds of databases that we use and how they sit together. >> Well, I mean, you've been following this for a long time and you saw the sort of rise and fall of the big data meme. And you know, this idea that you can shove everything into a single place, have a single version of the truth. It's like, it's just never seemed to happen. >> Carl: Right. >> So, you know, Postgres has been around a long time. It's evolved. I mean, I remember when, you know, VMware's ascendancy and people are like, okay, should I, you know should I virtualize my Postgres database is your, you know similar conversations that we were having earlier about Kubernetes. You've seen the move to the cloud. We're going to have this conversation about the edge at some point in time. So what's your outlook for Postgres, the Postgres community and, you know database market overall? >> Well, I really think the future for database growth is in the cloud. That's what all the data we're looking at and the case that's what our recent surveys indicate. As I said before, the rate of change depends on the size of the enterprise. Smaller advices are moving rapidly, large enterprises much more slowly and cautiously for the very simple reason that it's a very complex proposition. And also in some cases, they're wondering if they can move certain data or will they be violating your some sort of regulatory constraint or contractual issue. So they need to deal with those things too. That's why the private cloud is the perfect place to get started and get technology all lined up storing your data center is still under your control no legal issues there, but you can start, you know converting your applications to micro-service architected applications running in containers. You can start replacing your database servers with ones that can run in a container environment and maybe in the future, maybe hope that in the future, some of those will actually also be able to run as microservices. I don't think it's impossible but it just involves programming the database server in a very different way than we've done in the past. But you do those things. You can do those things under your own control over time in your own dataset. And then you reach a point where you want to take the elements of your application environment and say, what pieces of this, can I move to the cloud without creating disruption and issues regarding things like data egress and latency from cloud to data center and that kind of thing. And prepare for that. And then you're doing the step wise and then you start converting in a stepwise manner. I think ultimately it just makes so much sense to be in the cloud that the cloud vendors have economies of scale. They can deploy large numbers of servers and storage systems to satisfy the needs of large numbers of customers and create, you know great considerable savings. Some of which of course becomes their profit which is what's due to them. And some of that comes back to the users. So that's what I expect. We're going to see. And oh gosh, I would say that starting from about three years from now the larger enterprises start making their move and then you'll really start to see changes in the numbers in terms of cloud and cloud revenue. >> Great stuff, Carl, thank you for that. So any cool research you're working on lately, how you're spending your your work time, anything you want to plug? >> Well, working a lot on just as these questions, you know cloud migration is a hot topic, another which is really sort of off the subject. And what we've been talking about is graph database which I've been doing a fair amount of research into. I think that's going to be really important in the coming years and really, you know working with my colleagues in a project called the future of intelligence which looks at all the different related elements not just database, data integration but artificial intelligence, data communications and so on and so forth and how they come together to create a more intelligent enterprise. And that's a major initiative that I see. It's one of the, we call the future of initiatives. >> Great, Carls, thanks so much for coming back to theCUBE. It's great to have you, man. I appreciate it. >> Well, I enjoyed it. Now I have to do it again sometime. >> All right you got it. All right thank you everybody for watching theCUBEs. Continuous coverage of Postgres vision 21. This is Dave Vellante keep it right there. (upbeat music)

(upbeat music) >> Welcome to this CUBE conversation. I am Lisa Martin, excited to welcome back one of our distinguished alumni, Derek Manky joins me next. Chief security Insights and Global Threat Alliances at Fortinet's FortiGuard Labs. Derek, welcome back to the program. >> Yes, it's great to be here and great to see you again, Lisa. Thanks for having me. >> Likewise, yeah, so a lot has happened. I know we've seen you during this virtual world, but so much has happened with ransomware in the last year. It's unbelievable, we had this dramatic shift to a distributed workforce, you had personal devices on in network perimeters and non-trusted devices or trusted devices on home networks and lots of change there. Talk to me about some of the things that you and FortiGuard Labs have seen with respect to the evolution of ransomware. >> Yeah, sure, so it's becoming worse, no doubt. We highlighted this in our Threat Landscape Report. If we just take a step back looking at ransomware itself, it actually started in the late 1980s. And it didn't, that was very, they relied on snail mail. It was obviously there was no market for it at the time. It was just a proof of concept, a failed experiment if you will. But it really started getting hot a decade ago, 10 years ago but the technology back then wasn't the cryptography they're using, the technique wasn't as strong as easily reversed. And so they didn't really get to a lot of revenue or business from the cyber criminal perspective. That is absolutely not the case today. Now they have very smart cryptography they're experts when say they, the cyber criminals at their game. They know there's a lot of the attack surfaces growing. There's a lot of vulnerable people out there. There's a lot of vulnerable devices. And this is what we saw in our threat landscape group. What we saw at seven times increase in ransomware activity in the second half of 2020. And that momentum is continuing in 2021. It's being fueled by what you just talked about. By the work from anywhere, work from home environment a lot of vulnerable devices unpatched. And these are the vehicles that the ransomware is the payload of course, that's the way that they're monetizing this. But the reality is that the attack surface has expanded, there's more vulnerable people and cyber criminals are absolutely capitalizing on that. >> Right, we've even seen cyber criminals capitalizing on the pandemic fears with things that were around the World Health Organization or COVID-19 or going after healthcare. Did you see an uptick in healthcare threats and activities as well in the last year? >> Yeah, definitely, so I would start to say that first of all, the... Nobody is immune when it comes to ransomware. This is such again, a hot target or a technique that the cybercriminals are using. So when we look at the verticals, absolutely healthcare is in the top five that we've seen, but the key difference is there's two houses here, right? You have what we call the broad blanketed ransomware attacks. So these aren't going after any particular vertical. They're really just trying to spray as much as they can through phishing campaigns, not through... there's a lot of web traffic out there. We see a lot of things that are used to open playing on that COVID-19 theme we got, right? Emails from HR or taxes and scams. It's all related to ransomware because these are how they're trying to get the masses to open that up, pay some data sorry, pay some cryptocurrency to get access to their data back. Oftentimes they're being held for extortions. They may have photos or video or audio captures. So it's a lot of fear they're trying to steal these people but probably the more concern is just what you talked about, healthcare, operational technology. These are large business revenue streams. These are take cases of targeted ransoms which is much different because instead of a big volumetric attack, these are premeditated. They're going after with specific targets in mind specific social engineering rules. And they know that they're hitting the corporate assets or in the case of healthcare critical systems where it hurts they know that there's high stakes and so they're demanding high returns in terms of ransoms as well. >> With respect to the broad ransomware attacks versus targeted a couple of questions to kind of dissect that. Are the targeted attacks, are they in like behind the network firewall longer and faster, longer and getting more information? Are they demanding higher ransom versus the broader attacks? What's what are some of the distinctions there besides what you mentioned? >> Yeah, absolutely so the targeted texts are more about execution, right? So if we look at the attack chain and they're doing more in terms of reconnaissance, they're spending more cycles and investment really on their end in terms of weaponization, how they can actually get into the system, how they can remain undetected, collecting and gathering information. What we're seeing with groups like Ragnar Locker as an example, they're going in and they're collecting in some cases, terabytes of information, a lot, they're going after definitely intellectual property, things like source code, also PII for customers as an example, and they're holding them. They have a whole business strategy and plan in mind on their place, right? They hold them for ransom. They're often, it's essentially a denial of service in some cases of taking a revenue stream or applications offline so a business can't function. And then what they're doing is that they're actually setting up crime services on their end. They, a lot of the the newest ransom notes that we're seeing in these targeted attacks are setting up channels to what they call a live chat support channel that the victim would log into and actually talk directly live to the cybercriminal or one of their associates to be able to negotiate the ransom. And they're trying to have in their point of view they're trying frame this as a good thing and say, we're going to show you that our technology works. We can decrypt some of the files on your system as an example just to prove that we are who we say we are but then they go on to say, instead of $10 million, we can negotiate down to 6 million, this is a good deal, you're getting 30% off or whatever it is but the fact is that they know by the time they've gotten to this they've done all their homework before that, right? They've done the targets, they've done all the things that they can to know that they have the organization in their grasp, right? >> One of the things that you mentioned just something I never thought about as ransomware as a business, the sophistication level is just growing and growing and growing and growing. And of course, even other bad actors, they have access to all the emerging technologies that the good guys do. But talk to me about this business of ransomware because that's what it seems like it really has become. >> Absolutely, it is massively sad. If you look at the cybercrime ecosystem like the way that they're actually pulling this off it's not just one individual or one cyber crime ring that, let's say five to 10 people that are trying to orchestrate this. These are big rings, we actually work closely as an example to, we're doing everything from the FortiGuard Labs with following the latest ransomware trends doing the protection and mitigation but also working to find out who these people are, what are their tactics and really attribute it and paint a picture of these organizations. And they're big, we worked on some cases where there's over 50 people just in one ransomware gang. One of the cases we worked on, they were making over $60 million US in three months, as an example. And in some cases, keep in mind one of these targeted attacks like in terms of ransom demands and the targeted cases they can be an excess of $10 million just for one ransom attack. And like I said, we're seeing a seven times increase in the amount of attack activity. And what they're doing in terms of the business is they've set up affiliate marketing. Essentially, they have affiliates in the middle that will actually distribute the ransomware. So they're basically outsourcing this to other individuals. If they hit people with their ransomware and the people pay then the affiliate in the middle will actually get a commission cut of that, very high, typically 40 to 50%. And that's really what's making this lucrative business model too. >> Wow, My jaw is dropping just the sophistication but also the different levels to which they've put a business together. And unfortunately, for every industry it sounds very lucrative, so how then Derek do organizations protect themselves against this, especially knowing that a lot of this work from home stuff is going to persist. Some people want to stay home, what not. The proliferation of devices is only going to continue. So what are organizations start and how can you guys help? >> Start with the people, so we'll talk about three things, people, technology and processes. The people, unfortunately, this is not just about ransomware but definitely applies to ransomware but any attack, humans are still often the weakest link in terms of education, right? A lot of these ransomware campaigns will be going after people using nowadays seems like tax themes purporting to be from the IRS as an example or human resources departments or governments and health authorities, vaccination scams all these things, right? But what they're trying to do is to get people to click on that link, still to open up a malicious attachment that will then infect them with the ransomware. This of course, if an employee is up to date and hones their skills so that they know basically a zero trust mentality is what I like to talk about. You wouldn't just invite a stranger into your house to open a package that you didn't order but people are doing this a lot of the times with email. So really starting with the people first is important. There's a lot of free training information and security. There is awareness training, we offer that at Fortinet. There's even advanced training we do through our NSC program as an example. But then on top of that there's things like phishing tests that you can do regularly, penetration testing as well, exercises like that are very important because that is really the first line of defense. Moving past that you want to get into the technology piece. And of course, there's a whole, this is a security fabric. There's a whole array of solutions. Like I said, everything needs to be integrated. So we have an EDR and XDR as an example sitting on the end point, cause oftentimes they still need to get that ransomware payload to run on the end point. So having a technology like EDR goes a long way to be able to detect the threat, quarantine and block it. There's also of course a multi-factor authentication when it comes to identifying who's connecting to these environments. Patch management, we talk about all the time. That's part of the technology piece. The reality is that we highlight in the threat landscape report the software vulnerabilities that these rats more gangs are going after are two to three years old. They're not breaking within the last month they're two to three years old. So it's still about the patch management cycle, having that holistic integrated security architecture and the fabric is really important. NAC network access control is zero trust, network access is really important as well. One of the biggest culprits we're seeing with these ransom attacks is using IOT devices as launchpads as an example into networks 'cause they're in these work from home environments and there's a lot of unsecured or uninspected devices sitting on those networks. Finally process, right? So it's always good to have it all in your defense plan training and education, technology for mitigation but then also thinking about the what if scenario, right? So incident response planning, what do we do if we get hit? Of course we never recommend to pay the ransom. So it's good to have a plan in place. It's good to identify what your corporate assets are and the likely targets that cyber-criminals are going to go after and make sure that you have rigid security controls and threat intelligence like FortiGuard Labs applied to that. >> Yeah, you talk about the weakest link they are people I know you and I talked about that on numerous segments. It's one of the biggest challenges but I've seen some people that are really experts in security read a phishing email and almost fall for it. Like it looked so legitimately from like their bank for example. So in that case, what are some of the things that businesses can do when it looks so legitimate that it probably is going to have a unfortunately a good conversion rate? >> Yeah, so this is what I was talking about earlier that these targeted attacks especially when it comes to spear, when it comes to the reconnaissance they got so clever, it can be can so realistic. That's the, it becomes a very effective weapon. That's why the sophistication and the risk is rising like I said but that's why you want to have this multilayered approach, right? So if that first line of defense does yield, if they do click on the link, if they do try to open the malicious attachment, first of all again through the next generation firewall Sandboxing solutions like that, this technology is capable of inspecting that, acting like is this, we even have a FortiAI as an example, artificial intelligence, machine learning that can actually scan this events and know is this actually an attack? So that element goes a long way to actually scrub it like content CDR as well, content disarm as an example this is a way to actually scrub that content. So it doesn't actually run it in the first place but if it does run again, this is where EDR comes in like I said, at the end of the day they're also trying to get information out of the network. So having things like a Platinum Protection through the next generation firewall like with FortiGuard security subscription services is really important too. So it's all about that layered approach. You don't want just one single point of failure. You really want it, this is what we call the attack chain and the kill chain. There's no magic bullet when it comes to attackers moving, they have to go through a lot of phases to reach their end game. So having that layer of defense approach and blocking it at any one of those phases. So even if that human does click on it you're still mitigating the attack and protecting the damage. Keep in mind a lot of damages in some cases kind of a million dollars plus. >> Right, is that the average ransom, 10 million US dollars. >> So the average cost of data breaches that we're seeing which are often related to ransom attacks is close to that in the US, I believe it's around just under $9 million about 8.7 million, just for one data breach. And often those data breaches now, again what's happening is that the data it's not just about encrypting the data, getting access because a lot of organizations part of the technology piece and the process that we recommend is backups as well of data. I would say, organizations are getting better at that now but it's one thing to back up your data. But if that data is breached again, cybercriminals are now moving to this model of extorting that saying, unless you pay us this money we're going to go out and make this public. We're going to put it on paste and we're going to sell it to nefarious people on the dark web as well. >> One more thing I want to ask you in terms of proliferation we talked about the distributed workforce but one of the things, and here we are using Zoom to talk to each other, instead of getting to sit together in person we saw this massive proliferation in collaboration tools to keep people connected, families businesses. I talked a bit a lot of businesses who initially will say, oh we're using Microsoft 365 and they're protecting the data while they're not or Salesforce or Slack. And that shared responsibility model is something that I've been hearing a lot more about lately that businesses needing to recognize for those cloud applications that we're using and in which there's a lot of data traversing it could include PII or IP. We're responsible for that as the customer to protect our data, the vendor's responsible for protecting the integrity of the infrastructure. Share it with us a little bit about that in terms of your thoughts on like data protection and backup for those SaaS applications. >> Yeah, great question, great question tough one. It is so, I mean ultimately everybody has to have, I believe it has to have their position in this. It's not, it is a collaborative environment. Everyone has to be a stakeholder in this even down to the end users, the employees being educated and up-to-date as an example, the IT departments and security operation centers of vendors being able to do all the threat intelligence and scrubbing. But then when you extend that to the public cloud what is the cloud security stack look at, right? How integrated is that? Are there scrubbing and protection controls sitting on the cloud environments? What data is being sent to that, should it be cited center as an example? what's the retention period? How long does the data live on there? It's the same thing as when you go out and you buy one of these IOT devices as an example from say, a big box store and you go and just plug it into your network. It's the same questions we should be asking, right? What's the security like on this device model? Who's making it, what data is it going to ask for me? The same thing when you're installing an application on your mobile phone, this is what I mean about that zero trust environment. It should be earned trust. So it's a big thing, right? To be able to ask those questions and then only do it on a sort of need to know and medium basis. The good news is that a lot of CloudStack now and environments are integrating security controls. We integrated quite well with Fortinet as an example but this is an issue of supply chain. It's really important to know what lives upstream and how they're handling the data and how they're protecting it absolutely. >> Such interesting information and it's a topic ransomware that we could continue talking about, Derek, thank you for joining me on the program today updating us on what's going on, how it's evolving and ultimately what organizations in any industry need to do with protecting people and technology and processes to really start reducing their risks. I thank you so much for joining me today. >> All right it's a pleasure, thank you. >> Likewise Derek Manky I'm Lisa Martin. You're watching this CUBE conversation. (upbeat music)

(upbeat music) >> Welcome to this CUBE conversation. I am Lisa Martin, excited to welcome back one of our distinguished alumni, Derek Manky joins me next. Chief security Insights and Global Threat Alliances at Fortinet's FortiGuard Labs. Derek, welcome back to the program. >> Yes, it's great to be here and great to see you again, Lisa. Thanks for having me. >> Likewise, yeah, so a lot has happened. I know we've seen you during this virtual world, but so much has happened with ransomware in the last year. It's unbelievable, we had about 14 months ago, this dramatic shift to a distributed workforce, you had personal devices on in network perimeters and non-trusted devices or trusted devices on home networks and lots of change there. Talk to me about some of the things that you and FortiGuard Labs have seen with respect to the evolution of ransomware. >> Yeah, sure, so it's becoming worse, no doubt. We highlighted this in our Threat Landscape Report. If we just take a step back looking at ransomware itself, it actually started in the late 1980s. And it didn't, that was very, they relied on snail mail. It was obviously there was no market for it at the time. It was just a proof of concept, a failed experiment if you will. But it really started getting hot a decade ago, 10 years ago but the technology back then wasn't the cryptography they're using, the technique wasn't as strong as easily reversed. And so they didn't really get to a lot of revenue or business from the cyber criminal perspective. That is absolutely not the case today. Now they have very smart cryptography they're experts when say they, the cyber criminals at their game. They know there's a lot of the attack surfaces growing. There's a lot of vulnerable people out there. There's a lot of vulnerable devices. And this is what we saw in our threat landscape group. What we saw at seven times increase in ransomware activity in the second half of 2020. And that momentum is continuing in 2021. It's being fueled by what you just talked about. By the work from anywhere, work from home environment a lot of vulnerable devices unpatched. And these are the vehicles that the ransomware is the payload of course, that's the way that they're monetizing this. But the reality is that the attack surface has expanded, there's more vulnerable people and cyber criminals are absolutely capitalizing on that. >> Right, we've even seen cyber criminals capitalizing on the pandemic fears with things that were around the World Health Organization or COVID-19 or going after healthcare. Did you see an uptick in healthcare threats and activities as well in the last year? >> Yeah, definitely, so I would start to say that first of all, the... Nobody is immune when it comes to ransomware. This is such again, a hot target or a technique that the cybercriminals are using. So when we look at the verticals, absolutely healthcare is in the top five that we've seen, but the key difference is there's two houses here, right? You have what we call the broad blanketed ransomware attacks. So these aren't going after any particular vertical. They're really just trying to spray as much as they can through phishing campaigns, not through... there's a lot of web traffic out there. We see a lot of things that are used to open playing on that COVID-19 theme we got, right? Emails from HR or taxes and scams. It's all related to ransomware because these are how they're trying to get the masses to open that up, pay some data sorry, pay some cryptocurrency to get access to their data back. Oftentimes they're being held for extortions. They may have photos or video or audio captures. So it's a lot of fear they're trying to steal these people but probably the more concern is just what you talked about, healthcare, operational technology. These are large business revenue streams. These are take cases of targeted ransoms which is much different because instead of a big volumetric attack, these are premeditated. They're going after with specific targets in mind specific social engineering rules. And they know that they're hitting the corporate assets or in the case of healthcare critical systems where it hurts they know that there's high stakes and so they're demanding high returns in terms of ransoms as well. >> With respect to the broad ransomware attacks versus targeted a couple of questions to kind of dissect that. Are the targeted attacks, are they in like behind the network firewall longer and faster, longer and getting more information? Are they demanding higher ransom versus the broader attacks? What's what are some of the distinctions there besides what you mentioned? >> Yeah, absolutely so the targeted texts are more about execution, right? So if we look at the attack chain and they're doing more in terms of reconnaissance, they're spending more cycles and investment really on their end in terms of weaponization, how they can actually get into the system, how they can remain undetected, collecting and gathering information. What we're seeing with groups like Ragnar Locker as an example, they're going in and they're collecting in some cases, terabytes of information, a lot, they're going after definitely intellectual property, things like source code, also PII for customers as an example, and they're holding them. They have a whole business strategy and plan in mind on their place, right? They hold them for ransom. They're often, it's essentially a denial of service in some cases of taking a revenue stream or applications offline so a business can't function. And then what they're doing is that they're actually setting up crime services on their end. They, a lot of the the newest ransom notes that we're seeing in these targeted attacks are setting up channels to what they call a live chat support channel that the victim would log into and actually talk directly live to the cybercriminal or one of their associates to be able to negotiate the ransom. And they're trying to have in their point of view they're trying frame this as a good thing and say, we're going to show you that our technology works. We can decrypt some of the files on your system as an example just to prove that we are who we say we are but then they go on to say, instead of $10 million, we can negotiate down to 6 million, this is a good deal, you're getting 30% off or whatever it is but the fact is that they know by the time they've gotten to this they've done all their homework before that, right? They've done the targets, they've done all the things that they can to know that they have the organization in their grasp, right? >> One of the things that you mentioned just something I never thought about as ransomware as a business, the sophistication level is just growing and growing and growing and growing. And of course, even other bad actors, they have access to all the emerging technologies that the good guys do. But talk to me about this business of ransomware because that's what it seems like it really has become. >> Absolutely, it is massively sad. If you look at the cybercrime ecosystem like the way that they're actually pulling this off it's not just one individual or one cyber crime ring that, let's say five to 10 people that are trying to orchestrate this. These are big rings, we actually work closely as an example to, we're doing everything from the FortiGuard Labs with following the latest around some of the trends doing the protection and mitigation but also working to find out who these people are, what are their tactics and really attribute it and paint a picture of these organizations. And they're big, we're working some cases where there's over 50 people just in one ransomware gang. One of the cases we worked on, they were making over $60 million US in three months, as an example. And in some cases, keep in mind one of these targeted attacks like in terms of ransom demands and the targeted cases they can be an excess of $10 million just for one ransom attack. And like I said, we're seeing a seven times increase in the amount of attack activity. And what they're doing in terms of the business is they've set up affiliate marketing. Essentially, they have affiliates in the middle that will actually distribute the ransomware. So they're basically outsourcing this to other individuals. If they hit people with their ransomware and the people pay then the affiliate in the middle will actually get a commission cut of that, very high, typically 40 to 50%. And that's really what's making this lucrative business model too. >> Wow, My jaw is dropping just the sophistication but also the different levels to which they've put a business together. And unfortunately, for every industry it sounds very lucrative, so how then Derek do organizations protect themselves against this, especially knowing that a lot of this work from home stuff is going to persist. Some people want to stay home, what not. The proliferation of devices is only going to continue. So what are organizations start and how can you guys help? >> Start with the people, so we'll talk about three things, people, technology and processes. The people, unfortunately, this is not just about ransomware but definitely applies to ransomware but any attack, humans are still often the weakest link in terms of education, right? A lot of these ransomware campaigns will be going after people using nowadays seems like tax themes purporting to be from the IRS as an example or human resources departments or governments and health authorities, vaccination scams all these things, right? But what they're trying to do is to get people to click on that link, still to open up a malicious attachment that will then infect them with the ransomware. This of course, if an employee is up to date and hones their skills so that they know basically a zero trust mentality is what I like to talk about. You wouldn't just invite a stranger into your house to open a package that you didn't order but people are doing this a lot of the times with email. So really starting with the people first is important. There's a lot of free training information and security. There is awareness training, we offer that at Fortinet. There's even advanced training we do through our NSC program as an example. But then on top of that there's things like phishing tests that you can do regularly, penetration testing as well, exercises like that are very important because that is really the first line of defense. Moving past that you want to get into the technology piece. And of course, there's a whole, this is a security fabric. There's a whole array of solutions. Like I said, everything needs to be integrated. So we have an EDR and XDR as an example sitting on the end point, cause oftentimes they still need to get that ransomware payload to run on the end point. So having a technology like EDR goes a long way to be able to detect the threat, quarantine and block it. There's also of course a multi-factor authentication when it comes to identifying who's connecting to these environments. Patch management, we talk about all the time. That's part of the technology piece. The reality is that we highlight in the threat landscape report the software vulnerabilities that these rats more gangs are going after are two to three years old. They're not breaking within the last month they're two to three years old. So it's still about the patch management cycle, having that holistic integrated security architecture and the fabric is really important. NAC network access control is zero trust, network access is really important as well. One of the biggest culprits we're seeing with these ransom attacks is using IOT devices as launchpads as an example into networks 'cause they're in these work from home environments and there's a lot of unsecured or uninspected devices sitting on those networks. Finally process, right? So it's always good to have it all in your defense plan training and education, technology for mitigation but then also thinking about the what if scenario, right? So incident response planning, what do we do if we get hit? Of course we never recommend to pay the ransom. So it's good to have a plan in place. It's good to identify what your corporate assets are and the likely targets that cyber-criminals are going to go after and make sure that you have rigid security controls and threat intelligence like FortiGuard Labs applied to that. >> Yeah, you talk about the weakest link they are people I know you and I talked about that on numerous segments. It's one of the biggest challenges but I've seen some people that are really experts in security read a phishing email and almost fall for it. Like it looked so legitimately from like their bank for example. So in that case, what are some of the things that businesses can do when it looks so legitimate that it probably is going to have a unfortunately a good conversion rate? >> Yeah, so this is what I was talking about earlier that these targeted attacks especially when it comes to spear, when it comes to the reconnaissance they got so clever, it can be can so realistic. That's the, it becomes a very effective weapon. That's why the sophistication and the risk is rising like I said but that's why you want to have this multilayered approach, right? So if that first line of defense does yield, if they do click on the link, if they do try to open the malicious attachment, first of all again through the next generation firewall Sandboxing solutions like that, this technology is capable of inspecting that, acting like is this, we even have a FortiAI as an example, artificial intelligence, machine learning that can actually scan this events and know is this actually an attack? So that element goes a long way to actually scrub it like content CDR as well, content disarm as an example this is a way to actually scrub that content. So it doesn't actually run it in the first place but if it does run again, this is where EDR comes in like I said, at the end of the day they're also trying to get information out of the network. So having things like a Platinum Protection through the next generation firewall like with FortiGuard security subscription services is really important too. So it's all about that layered approach. You don't want just one single point of failure. You really want it, this is what we call the attack chain and the kill chain. There's no magic bullet when it comes to attackers moving, they have to go through a lot of phases to reach their end game. So having that layer of defense approach and blocking it at any one of those phases. So even if that human does click on it you're still mitigating the attack and protecting the damage. Keep in mind a lot of damages in some cases kind of a million dollars plus. >> Right, is that the average ransom, 10 million US dollars. >> So the average cost of data breaches ever seen which are often related to ransom attacks is close to that in the US, I believe it's around just under $9 million about 8.7 million, just for one data breach. And often those data breaches now, again what's happening is that the data it's not just about encrypting the data, getting access because a lot of organizations part of the technology piece and the process that we recommend is backups as well of data. I would say, organizations are getting better at that now but it's one thing to back up your data. But if that data is breached again, cybercriminals are now moving to this model of extorting that saying, unless you pay us this money we're going to go out and make this public. We're going to put it on piece and we're going to sell it to nefarious people on the dark web as well. >> One more thing I want to ask you in terms of proliferation we talked about the distributed workforce but one of the things, and here we are using Zoom to talk to each other, instead of getting to sit together in person we saw this massive proliferation in collaboration tools to keep people connected, families businesses. I talked a bit a lot of businesses who initially will say, oh we're using Microsoft 365 and they're protecting the data while they're not or Salesforce or Slack. And that shared responsibility model is something that I've been hearing a lot more about lately that businesses needing to recognize for those cloud applications that we're using and in which there's a lot of data traversing it could include PII or IP. We're responsible for that as the customer to protect our data, the vendor's responsible for protecting the integrity of the infrastructure. Share it with us a little bit about that in terms of your thoughts on like data protection and backup for those SaaS applications. >> Yeah, great question, great question tough one. It is so, I mean ultimately everybody has to have, I believe it has to have their position in this. It's not, it is a collaborative environment. Everyone has to be a stakeholder in this even down to the end users, the employees being educated and up-to-date as an example, the IT departments and security operation centers of vendors being able to do all the threat intelligence and scrubbing. But then when you extend that to the public cloud what is the cloud security stack look at, right? How integrated is that? Are there scrubbing and protection controls sitting on the cloud environments? What data is being sent to that, should it be cited center as an example? what's the retention period? How long does the data live on there? It's the same thing as when you go out and you buy one of these IOT devices as an example from say, a big box store and you go and just plug it into your network. It's the same questions we should be asking, right? What's the security like on this device model? Who's making it, what data is it going to ask for me? The same thing when you're installing an application on your mobile phone, this is what I mean about that zero trust environment. It should be earned trust. So it's a big thing, right? To be able to ask those questions and then only do it on a sort of need to know and medium basis. The good news is that a lot of CloudStack now and environments are integrating security controls. We integrated quite well with Fortinet as an example but this is an issue of supply chain. It's really important to know what lives upstream and how they're handling the data and how they're protecting it absolutely. >> Such interesting information and it's a topic ransomware that we could continue talking about, Derek, thank you for joining me on the program today updating us on what's going on, how it's evolving and ultimately what organizations in any industry need to do with protecting people and technology and processes to really start reducing their risks. I thank you so much for joining me today. >> All right it's a pleasure, thank you. >> Likewise Derek Manky I'm Lisa Martin. You're watching this CUBE conversation. (upbeat music)

>> From around the globe, it's theCube, with digital coverage of AWS re:Invent 2020. Special coverage sponsored by, AWS Worldwide Public Sector. >> Okay, welcome back to theCube's coverage, of re:Invent 2020 virtual. It's theCube virtual, I'm John Farrow your host, we're here celebrating, the special coverage of public sector with Sandy Carter, vice president of AWS Public Sector Partners. She heads up the partner group within Public Sector, now in multiple for about a year now. Right Sandy, or so? >> Right, you got it, John. >> About a year? Congratulations, welcome back to theCube, >> Thank you. >> for reason- >> Always a pleasure to be here and what an exciting re:Invent right? >> It's been exciting, we've got wall-to-wall coverage, multiple sets, a lot of actions, virtual it's three weeks, we're not in person we have to do it remote this year. So when real life comes back, we'll bring the Cube back. But I want to take a minute to step back, take a minute to explain your role for the folks that are new to theCube virtual and what you're doing over there at Public Sector. Take a moment to introduce yourself to the new viewers. >> Well, welcome. theCube is phenomenal, and of course we love our new virtual re:Invent as well, as John said, my name is Sandy Carter and I'm vice president with our public sector partners group. So what does that mean? That means I get to work with thousands of partners globally covering exciting verticals like, space and healthcare, education, state and local government, federal government, and more. And what I get to do is, to help our partners learn more about AWS so that they can help our customers really be successful in the marketplace. >> What has been the most, exciting thing for you in the job? >> Well, you know, I love, wow, I love everything about it, but I think one of the things I love the most, is how we in Public Sector, really make technology have a meaningful impact on the world. So John, I get to work with partners like Orbis which is a non-profit they're fighting preventable blindness. They're a partner of ours. They've got something called CyberSec AI which enables us to use machine learning over 20 different machine learning algorithms to detect common eye diseases in seconds. So, you know, that purpose for me is so important. We also work with a partner called Twist Inc it's hard to say, but it just does a phenomenal job with AWS IoT and helps make water pumps, smart pumps. So they are in 7,300 remote locations around the world helping us with clean water. So for me that's probably the most exciting and meaningful part of the job that I have today. >> And it's so impactful because you guys really knew Amazon's business model has always been about enablement from startups to now up and running Public Sector; entities, agencies, education, healthcare, again, and even in spaces, this IoT in space. But you've been on the 100 partner tour over a 100 days. What did you learn, what are you hearing from partners now? What's the messages that you're hearing? >> Well, first of all, it was so exciting. I had a 100 different partner meetings in a 100 days because John, just like you, I missed going around the world and meeting in person. So I said, well, if I can't meet in person I will do a virtual tour and I talked to partners, in 68 different countries. So a couple of things I heard, one is a lot of love for our map program and that's our migration acceleration program. We now have funding available for partners as they assess migration, we can mobilize it and as they migrate it. And you may or may not know, but we have over twice the number of migration competency partners doing business in Public Sector this year, than we did last year. The second thing we heard was that, partners really love our marketing programs. We had some really nice success this year showcasing value for our customers with cyber security. And I love that because security is so important. Andy Jassy always talks about how her customers really have that as priority zeros. So we were able to work with a couple of different areas that we were very proud at and I loved that the partners were too. We did some repeatable solutions with our consulting partners. And then I think the third big takeaway that I saw was just our partners love the AWS technology. I heard a lot about AI and ML. We offered this new program called The Rapid Adoption Assistance Program. It's going global in 2021, and so we help partners brainstorm and envision what they could do with it. And then of course, 5G. 5G is ushering in, kind of a new era of new demand. And we going to to do a PartnerCast on all about 5G for partners in the first quarter. >> Okay, I'm going to put you on the spot. What are the three most talked about programs that you heard? >> Oh, wow, let's see. The three most talked about programs that I heard about, the first one was, is something I'm really excited about. It's called a Think Big for Small Business. It really focuses in on diverse partner groups and types. What it does is it provides just a little bit of extra boost to our small and medium businesses to help them get some of the benefits of our AWS partner program. So companies like MFT they're based down in South Africa it's a husband and wife team that focus on that Black Economic Empowerment rating and they use the program to get some of the go to market capability. So that's number one. Let's see, you said three. Okay, so number two would be our ProServe ready pilot. This helps to accelerate our partner activation and enablement and provides partners a way to get badged on the ProServe best practices get trained up and does opportunity matching. And I think a lot of partners were kind of buzzing about that program and wanting to know more about it. And then ,last but not least, the one that I think of probably really has impact to time to compliance it's called ATO or Authority to Operate and what we do is we help our partners, both technology partners and consulting partners get support for compliance framework. So FedRAMP, of course, we have over 129 solutions right now that are FedRAMPed but we also added John, PCI for financial HIPPA for healthcare, for public safety, IRS 1075 for international GDPR and of course for defense, aisle four, five and six, and CMMC. That program is amazing because it cuts the time to market and have cuts across and have and really steps partners through all of our best practices. I think those are the top three. >> Yeah, I've been like a broken record for the folks that don't know all my interviews I've done with Public Sector over the years. The last one is interesting and I think that's a secret sauce that you guys have done, the compliance piece, being an entrepreneur and starting companies that first three steps in a cloud of dust momentum the flywheel to get going. It's always the hardest and getting the certification if you don't have the resources, it's time consuming. I think you guys really cracked the code on that. I really want to call that out 'cause that's I think really super valuable for the folks that pay attention to and of course sales enablement through the program. So great stuff. Now, given that's all cool, (hands claps) the question I have and I hear all the time is, okay, I'm involved I got a lot of pressure pandemic has forced me to rethink I don't have a lot of IT I don't have a big budget I always complaint but not anymore. Mandate is move fast, get built out, leverage the cloud. Okay, I want to get going. What's the best ways for me to grow with Public Sector? How do I do that if I'm a customer, I really want to... I won't say take a shortcut because there's probably no shortage. How do I throttle up? Quickly, what's your take on that? >> Well, John, first I want to give one star that came to us from a Twilio. They had interviewed a ton of companies and they found that there was more digital transformation since March since when the pandemic started to now than in the last five years. So that just blew me away. And I know all of our partners are looking to see how they can really grow based on that. So if you're a consulting partner, one of the things that we say to help you grow is we've already done some integrations and if you can take advantage of those that can speed up your time to market. So I know know this one, the VMware Cloud on AWS. what a powerful integration, it provides protection of skillsets to your customer, increases your time to market because now VMware, vSphere, VSAN is all on AWS. So it's the same user interface and it really helps to reduce costs. And there's another integration that I think really helps which is Amazon connect one of our fastest growing areas because it's a ML AI, breads solution to help with call centers. It's been integrated with Salesforce but the Service Cloud and the Sales Cloud. So how powerful is that this integrated customer workflow? So I think both of those are really interesting for our consulting partners. >> That's a great point. In fact, well, that's the big part of the story here at re:Invent. These three weeks has been the integration. Salesforce as you mentioned connect has been huge and partner- >> Huge >> so just just great success again, I've seen great momentum. People are seeing their jobs being saved, they're saving lives. People are pretty excited and it's certainly a lot of work you've done in healthcare and education two big areas of activity which is really hard corporation, really, really hard. So congratulations on that and great work. Great to see you, I going to ask you one final question. What's the big message for your customers watching as they prepare for 2021 real life is coming back vaccines on the horizon. We're hearing some good news a lot of great cloud help there. What's your message to send to 2021? >> 2021, for our partners for 2021, one, there is a tremendous growth ahead and tremendous value that our partners have added. And that's both on the mission side, which both Theresa and I discussed during our sessions as well as technology. So I think first messages is, there's lots of growth ahead and a lot of ways that we can add value. Second is, all of those programs and initiatives, there's so much help out there for partners. So look for how you could really accelerate using some of those areas on your customer journey as you're going along. And then finally, I just want John, everybody to know , that we love our partners and AWS is there to help you every step of the way. And if you need anything at all obviously reach out to your PDM or your account manager or you're always welcome to reach out to me. And my final message is just, thank you, through so many different things that have happened in 2020, our partners have come through amazingly with passion with value and just with persistence, never stopping. So thank you to all of our partners out there who've really added so much value to our customers. >> And Amazon is recognizing the leadership of partners in the work you're doing. Your leadership session was awesome for the folks who missed it, check it out on demand. Thank you very much, Sandy for coming on the sharing the update. >> Thank you, John, and great to see all your partners out there. >> Okay, this is theCube virtual covering AWS re:Invent 2020 virtual three weeks, wall-to-wall coverage. A lot of videos ,check out all the videos on demand the leadership sessions, theCube videos and of course the Public Sector video on demand. Micro-site with theCube. I'm John Furrier, thanks for watching. (upbeat music)

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Hi, I'm stupid, man. And welcome to a cube conversation. I'm coming to you from our Boston area studio, and we're gonna be talking about the networking giant. So, uh, joining me is the first time on the program some of the members been on and the cover launch of Pensando so vivid Jane, his CTO and co founder of Pensando Bipin thanks so much for joining us. >>Thank you. It was very nice talking to you. >>All right, so in a big theme we've been talking about for a number of years now is multi cloud. And, you know, I go back and think about you know, that the concept of cloud and even, you know, I've been around long enough You think about the and one of the challenges you look at is well, security is always a challenge. The other things network bandwidth is not infinite. The speed of light has not been solved, though you know, help us understand is you know the first I guess give our audience a little bit of your background. As I said, anybody in the networking world knows less team, though. Tell us, you know, have you been on the journey with them for all of that? Or And you know what brought you and Sandy? >>Yes. Yes. Um, I mean, I've been in the journey with the team since 2000 and six, so it's pretty long, I would say 14 years now, and it's been tremendous. Um uh, at heart, I'm an engineer who takes, you know, Brian brilliant things and taking upon challenges. And I've got multiple startups before this been in a new era, The more startups before that. And of course, you know, they were not experience more independent startups. And, you know, all through the course, I have gained appreciation for, like, you know, starting all the way from silicon to build a distributed systems and a u io all the way up to the fully consumable, you know, system. So I I totally understand the the angle I need to look at this time in a holistic manner. Having contributed to Cisco, UCS of and Nexus products on. Before joining pensando, I was, um I was contributing with my own open source container networking project, which is quite exciting to see How do you evangelize, You know, my own my own core, and that was fun. And that's where I come from, But, uh, but I I'm I'm a software engineer. To start off it started contributing to a six, then started going into the application world with containers trying to pull a container networking with, Ah, we did a server product with Cisco UCS and on and pretty much all over the stack with respect, participation. So that's my background. Um, but it's being exciting to consider what's next for me. And I was largely trying to see >>so, so definitive, actually, if I If I could jump in there, right, you know, I think back the UCS it was, You know, some of those ways I gather virtualization had been around for quite a number of years at that point. But, you know, how do you optimize it you're in. How do you transform infrastructure toe live for those environments, though? You know, UCS, You know, remember, people get back saying, you know, Cisco getting into services like Well, they are. They are because they're changing that compute model really caught that. You know, Cisco led that way. If the urge instructor, so many things you talked about that we'll get to later in the interview open for station. When I look out today, you know infrastructure's paint a lot and cloud obviously, is a huge impact, but also the application. So help us understand kind of the the waves that were writing together And, you know, what was it that you know in Santo decided to build in order to meet what you know, the customers of a require >>Um So I think, you know, going back to the UCS common that you had We started off thinking, for example, what are what were the challenges with respect is scaling out the deployment of servers and we quickly realized that manageability is number one challenge on. And of course, you know when we speak about manageability, it comes down to the underpinnings of what you're building. Are you Are you able to see the entire infrastructure together, or are you still seeing those big pieces? And that's when I think UCS was born to say that Look, we need to bring everything together that could be consumed in a holistic manner. And for that you have to have all those components there are There are somewhat independent to be consumed as a unified thing. And which is why I think it was a unified computing system. UCS. Um and then I think, you know, and Sanders a journey that takes it to probably not just that concept, but in general, the the challenges and the disruptions that we're seeing to the next level. So, I mean, just to summarize, I would say we started off looking at all the disruptions that are happening in the industry. And there are many of those I'm happy to talk about, which means we looked at, uh and then we looked at What are the consumption models that people are largely, you know, finding it very appealing these days because the days in which you're going to write a spirit to do something is still pretty old you want to be able to consume and most this after consumable way, How can we build, you know, how can you build systems that are programmable in the field? Those kind of things? The consumption model reliability software is the friendly factor there, and highly appealing to you guys and all their last one. You know, at least we also we also wanted to be really heard in the game, competitiveness wise. So those were like the the overarching set of things there that we started to think about, like, what descriptions are we going to solve, um, and how the consumption model needs to be for or ah, for the future of infrastructure. And how can we get that key, which is which is far ahead and better than anything that exists out there? So that's where we started to look at. Let's bring something which is bigger B sphere and and something. Even if we have the possibility of feeling it. Let's go ahead and they're doing their anything. >>Yeah, and absolutely. There's been so much discussion over the last decade or so about about software's eating the world and what's going on there yet you know, your your team mates. It's a lot of times it's been the chip set. There have been some huge ripples in the industry, you know, major acquisitions by some of the big, disruptive companies out there. Apple made a silicon acquisition, you know, everybody paid and that will have. You can't talk about disruption today without talking about Amazon. And, of course, when Amazon bought Annapurna Labs, you know, those of us looking at the Enterprise and the clouds base was like, Keep an eye on this. And absolutely, it's been something over the last year or so now, where we've seen Amazon roll things out and, of course, a critical component of what Amazon's doing from outposts. So with that as the stage there, you talked about wanting to be interesting leading, you know Amazon, you know, is really sick, and it's setting the bar that everyone is measured against. And when I look at the solution pensando, the kind of best comparable analogy that we've seen is, you know, look at what Nitro chip can do. This is an alternative for all of the other 1000 for customers that might not want to get them from Amazon. Is that a fair comparison? And how would you line up what founder is doing compared to what Amazon has done there? >>Um, so you know, what you've seen in the Amazon announcement really is possible. Amazon is a great benchmark to beat eso No make mistakes. We are very happy to say that, you know, we are We are doing by comfortably so But then, you know, Amazon is more than more than just the just the chips that are that they are building. I mean, what you consume is what they're building and underneath the engines are really part up by by the nicety off all these things that they're very, um, having said that, you know, And Sandra was consisting off both the you know, it's recognized us as a team which has been in traditionally building chips. But yet I think you know, the the Iot or the the previous venture from Mpls Team was somewhat of an eye opening as to how bringing things together is much more value in op, ex and and simplifying things is a huge, huge value compared to just putting performance and those things. So why this is important? That is another aspect which is important in trying to simplify things and make it consumable like software. And Sandra itself has probably, you know, I would say, Ah, good chunk, like about 60% of people in software team and not the, you know, basic harbor t This is not to say that, you know, we, you know, we are under emphasizing one versus the other. Software is a bigger beast when you start trying to build all those programs on a programmable and doing that here and start to roll out those applications on. So that's why I think the emphasis on software is there. Having said that, you know, it's the software that runs the data path pipeline. There's also a layer of software that we're building that can help manage all you know, all the product in a more cohesive manner and unified. >>Okay, that's Ah, thank you for laying that out. You mentioned you've got some background and open for definitely an area where, for a number of years, you know, Amazon has not exactly, uh, open source. Not exactly been a strength for AWS. They have put a lot of effort. They've done some president IRS over the last couple of years. >>And >>how do you see open source fitting into the space? What is I kind of the philosophy of pensando when it comes to open source. And where do you see it playing in the You know, this network piece of the multi cloud. >>Yeah, no, I think it's It's ah, it's a squared, relevant in a way that you know of the cloud native movement on how applications with very Onda normalization of AP eyes across multiple clouds. Israel, We are all seeing the benefits offered. And I think that that trend will continue and which is all driven through open source Ah, you know, community that exist in, you know, in the heart of the word. So personally for me, I think I learned a whole lot of things in the open source community. You know, the importance off evangelizing whatever you're working on, the reason to have convinced other people about contributing into what you're working on on. Frankly, I also learned how difficult it is to make revenues in an open source based part of that strategy. So I think you know that those were the things that I got away from it when I was doing my own open source project of container networking. Um, but at the same time, pensando, uh, you know, we have to make sure that we are 100% aligned with anything that's happening in open source. Never replicated, Um, anything that might be that might be happening in open source instead tried to make people use those things in the best possible way and in the most efficient way and the easiest possible way to use those. So our strategy largely is that, you know, embrace open source which exists are there from an infrastructure point of view, we are collaborating and communicating with less of the users are Hello. I think we're going to standardize most of things we're looking in before community. So our stands largely is that, you know, if we are building a programmable platform than the community is what is gonna driver and we are very much working towards a step by step, of course, trying to get through, you know, a stable state where we could we could not just empower people who are who are taking up the open source efforts which are going on. But at the same time, we can also contribute our program are programs into the open source community and defining the right abstractions into into the community. Um, because we came out of stealth pretty recently, you'll start seeing that and helping those activity as Well, >>excellent. Well, you know the launch of Pensando you had a phenomenal lineup. Not only you know, John Chambers obviously has the relationship with your theme, but you know, oh, am partners of Hewlett Packard, Enterprise and IBM, as well as the Marquis of Goldman Sachs. Things look a little bit different in the first half of 2020 and then they didn't end of 29 teams. So, you know, curious, You know that the global pandemic, the rippling financial implications, you know, what does that mean? The pensando. How has that impacted conversations that you're having with your >>Well, one thing I know at a broader level, let me cover, um, where things are heading. And in that sense, you know, I see that network and the infrastructure in general cloud infrastructure networking it's going to become. And we have realized it's this during during during recent early 20 twenties that that is going to be very important to have the have a new underpinning infrastructure that is not just working efficiently, securely, but is, you know, highly cost effective and very high performance, you know, ranging from people who are trying to connect from home to people who are trying to use videoconferencing and people who are going to be more and more use cloud based services even to order simple of the data being, you know, going to source for so network will become essential, you know, essential element for four things as we go forward. And we do see that being embraced by our customers and and things where we were trying to communicate that, you know, look, you will need performance and cost benefits are becoming more and more real Now. It's like, oh, things that we were having things in the pipeline for us. We need to work on that now. And the reason is because the things that we anticipated the demand increase, which is gonna happen over the fear of years, is happening literally in a few months. And so that is what we see. We are definitely, you know, very well poised to take advantage of their of their demand for sure. But also the fact that you know it needs to be done super efficiently. And so I think we are. You know, we are right. Well, I would say, you know, situated to be able to take advantage of start. >>Yeah, absolutely. You know, one thing you can't control as a company is you know what the global situation is when you come out of stealth and, you know, move through some of those early phases, you know, you've been part of You said a number of startups you've been part of been in give us a little bit of the inside baseball of, you know, being part of Rondo. You know, any stories on a little some of the ups and downs on the multi year journey to get where you are today? >>Definitely. I think. You know, um, minutes aren't good. They are largely an execution play. Relatively independent startup is is going to be about you know, how we cracked the overall market market fit and, ah, on execution, Of course, on deal with maybe in a competition in a different way, of course, like maybe big companies are our great partners. At the same time, you have to navigate that. So the overall the overall landscape in Spain and forces forces not is it's quite different. We can be much more border than we are independent company In trying to disrupt almost anything because we don't have any point of view to define per se. We do exactly, You know, what could be the most disruptive way, too, to potentially benefit the users on day? That's a big, you know, big change. I would say, um, we are being but paranoid as well at the same time, impractical to look at. You know how how we could navigate this situation in a very practical may. And the journey off, often independent startup is, you know, personally, for me, this is this is my fourth in different and start and best off. Off off, all independent. Once, I would stay largely because the kind of tradition that we're getting being an independent company is so huge. I'm just concerned about those things. But what We're really trying to trying to ensure that, you know, we can't get our stuff, but I want you and we started. >>Excellent. Guess what? One of the other things about being a startup is You know what you know adjustments You need to make along the way. So I'm curious. As you know, you've gone to some of your early customers. Any feedback or adjustments in some of the use cases or, you know, things that you've learned along the way that you can share. >>Um, fundamentally, at a base level, we haven't shifted from what we started off. We look at disruptions on on how consumption models are going to be changing, how speeds and feeds are gonna become important because, you know, because most law is going to be almost operating, how we how we deliver things into and containers are going to be a primary, you know, vehicle to deliver and build applications. So we recognize those disruptions, and we haven't changed, But normally from those disruptions that we wanted people after her. Uh, but at the same time, I think, you know, as we went and socialize our ideas and on architecture and designs with customers, we realized that that they are giving us lots more feedback on work all we could do and ah, and starting to become like we could take on different segments of market and not just one. So why stick ourselves to the data center power? Why not work on something on edge, blur wine or wine are real solutions for five G where latency and and performance is super crucial. Why don't take up on, you know, branch that use cases. So there are many things that are opening up. Um, and largely the you know, the shift. Or I would say the the inclination of what we should change versus not is happening with respect to where our customers are driving us. And and it is very important to make sure that you know the users of our lives Articulating all of the shift happens as opposed to, uh, you know, as opposed to anything else. We listen to them like super, super carefully, uh, and at the same time trying to make sure that we not only meet their means for you there their demands. So, um, definitely, you know, from the from the overall landscape of things, we are starting to get a lot more than what we are capture, which is good news For the same time, we're trying to also, uh, take on one part. I'm you know, >>all right, Vivienne, I can't let you off the hook as the cto without talking a little bit about that. You know, I think earlier in my career there was the old discussion and said you know, we should have started it, you know, a year or two ago. But, you know, we didn't. So we should start it today with changing pace of technology. You know, I've always said, you know, if I could I'd rather wait a year because I could take the next generation. I can take advantage of all these other things, but I can't wait, because then I'd never ship any things that I need to start now, Give us a little bit, you know, Look out in the future. How is your architecture designed to be able to take advantage of all the wonders coming with five G and everything there, Um, and anything that we should be looking at, You know, through the next kind of 12 18 months on the roadmap that you could share >>your Ah, yes. So, um, I would first of all say that we didn't build a part of, actually, what we build was a platform on which we can build multiple products. And we started we started off going there because we thought that, you know, the the platform that we're building is capable of capable of doing a lot more things than than one use case that we start off with. And so, to that point, I would say that yes. I mean, he started focusing on one product initially on the possibilities off. Trying to take it to multiple segments is is normally very much there. But we are already, you know, having those conversations to see what is the core set of use cases that we could we could get into for different segments. Besides the data center, you know, public Private Data center, you're looking at edge. We're looking by the looking at, Yeah, you mentioned this is as well as the, you know, storage and conversion infrastructure. So I would say that the food of all those things that we're starting to engage is going to start showing up in next 18 months. I could actually I think we are very well boys to take advantage of what we have. The hardware that we're shipping is going to be 100% compatible with four programs, but I don't those. So that is that is lot more possibilities are interesting. More use cases as people. The software's architecture that we have built is very extensible as well. Eh so we believe that. You know, uh, we believe that we can normally satisfy those use cases, but we're starting to you get into those things now, which will start to show up in and actually useful products of unusable for us with customer testimonials and then maybe 12 to 18 months from now. All >>right, well, thank you so much. It's great to catch up with. You really appreciate you coming on. >>Thank you to Because they're talking to you. And, you know, I appreciate your time. >>All right, I'm stew minimum. And be sure to check out the cube dot net for all the coverage. Go see the launch that we did. So in the second half of 2019. Thank you for watching you. Yeah, Yeah, yeah, yeah.

>> Narrator: From Miami Beach, Florida, it's theCUBE, covering Acronis Global Cyber Summit 2019. Brought to you by Acronis. >> Okay, welcome back everyone. It's theCUBE's two day coverage of Acronis' Global Cyber Summit 2019, here in Miami Beach, at the Fontainebleau Hotel. I'm John Furrier, host of theCUBE. We're with Katya Fisher, Partner Chief and Chief Privacy Officer at Greenspoon Marder. Legal advice is right here on theCUBE, ask her anything. We're going to do a session here. Thanks for coming on, appreciate it. >> Thank you very much, I'm going to have to do the little disclaimer that all lawyers do, which is, nothing here is to be construed as advice. It's just opinions and information only. >> I didn't mean to set you up like that. All kidding aside, you closed for the panel here for Acronis' conference. Obviously, cyber protection's their gig. Data protection, cyber protection. Makes sense, I think that category is evolving from a niche, typical enterprise niche, to a much more holistic view as data becomes you know, critical in the security piece of it. What was on the, what were you guys talking about in the panel? >> Well, so, the first issue that you have to understand is that cyber protection is something that has now become critical for pretty much every individual on the planet, as well as governments. So something that we talked about on the panel today was how governments are actually dealing with incoming cyber threats. Because now, they have to take a look at it from the perspective of, first of all, how they themselves are going to become technologically savvy enough to protect themselves, and to protect their data, but also, in terms of regulation and how to protect citizens. So, that was what the panel discussion was about today. >> On the regulatory front, we've been covering on SiliconANGLE, our journalism site, the innovation balance, is regulatory action helpful or hurtful to innovation? Where is the balance? What is the education needed? What's your thoughts on this, where are we? I mean early stages, where's the progress? What needs to get done? What's your view on the current situation? >> So, I'm an attorney, so my views are perhaps a bit more conservative than some of the technologists you might speak with and some of my clients as well. I think that regulation is, as a general matter, it can be a good thing. And it can be quite necessary. The issues that we see right now, with regard to regulation, I think one of the hottest issues today is with respect to data laws and data privacy laws. And that's obviously something that I think everyone is familiar with. I mean take a look at, in the United States alone. We've seen the city of Baltimore dealing with breaches. We've seen other parts of the government, from the Federal level all the way down to municipalities, dealing with breaches in cyber attacks. We've seen data breaches from banks, Capital One, right? I believe Dunkin' Donuts suffered a breach. Equifax, and then at the same time we've also seen individuals up in arms over companies like 23andMe and Facebook, and how data is used and processed. So data seems to be a very very hot button issue today across the board. So something that we're really thinking about now is, first of all, with respect to the regulatory climate, how to deal with it, not only in the United States, but on a global level, because, when we talk about technology and the internet right, we're in an era of globalization. We're in an era where a lot of these things go across boarders and therefore we have to be mindful of the regulatory regimes in other places. So, I'll give you an example. You might be familiar with the GDPR. So the GDPR is in the European Union. It's been in effect now for the last year and a half, but it affects all my U.S. clients. We still have to take a look at the GDPR because at the end of the day my clients, my firm, might be dealing with foreign companies, foreign individuals, companies that have some sort of nexus in the European Union, et cetera. So because of that, even though the GDPR is a set of regulations specific to the European Union, it becomes extremely important in the context of the United States and globally. At the same time, the GDPR has certain issues that then end up conflicting often times with some of the regulations that we have here in the United States. So, for example, the right to be forgotten is perhaps the most famous clause or part of the GDPR and the right to be forgotten is this concept in the GDPR that an individual can have information erased about him or her in order to protect his or her privacy. The problem is that from a technical's perspective, first of all, it's an issue because it becomes very very difficult to figure out where data is stored, if you're using third-party processors, et cetera. But from a regulatory perspective, the conflict comes in when you take a look at certain U.S. laws. So take a look for example at banking regulations in the United States. Banks have to hold some types of data for seven years and other types of data they can never delete. Right? Lawyers. I am licensed by the New York State Bar Association. Lawyers have their own rules and regulations with regard to how they store data and how they store information. HIPAA, medical records. So, you see these conflicts and there are ways to deal with them appropriately, but it becomes some food for thought. >> So it's complicated. >> It's really complicated >> There's a lot of conflicts. >> Yeah. >> First of all, I talked to a storage guy. He's like data? I don't even know which drive that's on. Storage is not elevated up to the level of state-of-the-art, from a tracking standpoint. So, it's just on the business logic is complicated. I can't imagine that. So, I guess my question to you is that, are you finding that the jurisdictional issue, is it the biggest problem, in terms of crossport and the business side or is the technical underpinnings, that with GDPR's the problem or both? What's your-- >> I mean it's both, right? They're a lot of issues. You're right, it's very complicated. I mean, in the United States we don't have some sort of overarching federal law. There's no cyber protection law in the United States. There's no overarching data protection law. So, even in the U.S. alone, because of federalism, we have HIPAA and we have COPPA which protects children and we have other types of acts, but then we also have state regulations. So, in California you have the California Privacy Act. In New York you have certain regulations with regard to cyber security and you have to deal with this patchwork. So, that becomes something that adds a new layer of complexity and a new layer of issues, as we take a look, even within the U.S. alone, as to how to deal with all of this. And then we start looking at the GDPR and all of this. From a technical perspective. I'm not a technologist, but. >> Katya, let me ask you a question on the (mumbles) and business front. (mumbles) I think one of the things. I'm saying it might or may not be an issue, but I want to get your legal weigh-in on this. >> Katya: Sure. >> It used to be when you started a company, you go to Delaware, very friendly, domicile in Delaware, do some formation there, whether you're a C corp or whatever, that's where we tend to go, raise some money, get some preferred stock, you're in business. >> Is there a shift in where companies with domicile, their entity, or restructure their companies around this complexity? Because, there's two schools of thought. This brute force act, everything coming at you, or you restructure your corporate formation to handle some of the nuances, whether it's I have a Cayman or a Bermuda... whatever's going on in the regulatory regime, whether it's innovative or not. Are people thinking like that? Or, what's your take on it? What's some of the data you're seeing from the field around, restructuring around the problem? >> So, with respect to restructuring, specifically around data laws and data protection laws, I'm not seeing too much of that, simple because of the fact that regulations like the GDPR are just so all-encompassing. With respect to companies setting up in Delaware as opposed to other jurisdictions, those are usually based on two issues, right, two core ones, if I can condense it. One has to do with the court system and how favorable a court system is to the corporation, and the second is taxes. So, a lot of times when you see companies that are doing all of this restructuring, where they're setting up in offshore zones, or et cetera, it's usually because of some sort of a tax benefit. It might be because of the fact that, I don't know, for example, intellectual property. If you have a company that's been licensing IP to the United States, there's a 30% withholding tax when royalties are paid back overseas. So a lot of times when you're looking at an international structuring, you're trying to figure out a jurisdiction that might have a tax treaty with the United States, that will create some sort of an opportunity to get rid of that 30% withholding. So, that's where things usually come into play with regard to taxes and IP. I haven't seen yet, on the side of looking for courts that are more favorable to companies, with respect to data privacy and data protection. I just haven't seen that happen yet because I think that it's too soon. >> How do companies defend themselves against claims that come out of these new relations? I mean GDPR, I've called it the shitstorm when it came out. I never was a big fan of it. It just didn't. I mean, I get the concept, but I kind of understood the technical issues, but let's just say that you're a small growing business and you don't have the army of lawyers or if someone makes a claim on you, I have to defend it. How are companies defending themselves? Do they just shut down? Do they hire you guys? I mean, obviously lawyers need to be involved. But, at some point there's a line of where having a U.S. company and someone consumes my media in Germany and it says, hey I'm a German citizen. You American company, delete my records. How does that work? Do I have to be responsible for that? I mean, what's? >> So, it's really case-by-case basis. First of all, obviously, with regard to what I was talking about earlier, with respect to the fact that there are certain regulations in the U.S. that conflict with GDPR and the right to be forgotten. If you can actually assert a defense and sort of a good reason for why you have to maintain that information, that's step one. Step two is, if it's some complaint that you received, is to delete the person's information. There's an easier way to do it. >> Yeah, just do what they want. >> Just comply with what they want. If somebody wants to be off of a mailing list, take them off the mailing list. The third is, putting in best practices. So, I'm sure a lot of things that people see online, it's always great to go ahead and obtain legal counsel, even if you're consulting with a lawyer just for an hour or two, just to really understand your particular situation. But, take a look at privacy policies online. Take a look at the fact that cookies now have a pop-up whenever you go to a website. I'm sure you've noticed this, right? >> John: Yeah. So, there are little things like this. Think about the fact that there are, what is known as clickwrap agreements. So, usually you have to consent. You have to check a box or uncheck a box with respect to reading privacy policies, being approved for having your email address and contact information somewhere. So, use some common sense. >> So, basically don't ignore the prompt. >> Don't ignore the problem. >> Don't ignore it. Don't stick your head in the sand. It'll bite you. >> Correct. And the thing is, to be honest, for most people, for most small companies, it's not that difficult to comply. When we start talking about mid-size and large businesses, the next level, the next step, obviously beyond hiring attorneys and the like, is try to comply with standards and certifications. For example, there's what is known as ISO standards. Your company can go through the ISO 27001 certification process. I think it costs around approximately $20,000. But, it's an opportunity to go ahead, go through that process, understand how compliant you are, and because you have the certification, you're then able to go to your customers and say, hey, we've been through this, we're certified. >> Yeah. Well, I want to get, Katya, your thoughts, as we wrap up on this segment, around Crypto and Blockchain. Obviously, we're bullish on Blockchain. We think this is a supply chain. (mumbles) Blockchain can be a good force, although some think there's some work needs to be done on the whole energy side of it, which is, we would agree. But, still. I'm not going to make that be a wet blanket of excitement. But cryptocurrency has been fraudulent. It's been. The SCC's been cracking down in the U.S., in the news. Lieber's falling apart, although, I called that separately, but, (laughing) it had nothing to do with that Lieber. It was more of Facebook, but. Telegram. We were talking about that, others. People are getting handcuffed on this stuff. They're really kind of clamping down. But, overseas in Asia, it's still an unregulated, seems to be (mumbles) kind of market. Your advice to clients was to shy away, be careful? >> My advice to clients is as follows. First of all, Blockchain and cryptocurrency are not the same thing. Right? Cryptocurrency is a use case coming out of Blockchain technology. I think that in the United States, the best way to think about it is to understand that the term cryptocurrency, from a regulatory perspective, is actually a misnomer. It's not a currency. It's property. Right? It's an asset. It's digital assets. So, if you think about it the same way that we think of shares in a company, it's actually much easier to become compliant, because, then you can understand that it's going to be subject to U.S. securities laws, just like other securities. It's going to be taxed, just like securities are taxed, which means that it's going to be subject to long and short-term capitol gain, and it's also going to be subject to the other regulatory restrictions that are adherent to securities, both on the federal and state level. >> It's interesting that you mentioned security. The word security. If you look back at the ICO craze, internet coin offerings, crypto offerings, whatever you call it, The people who got whacked the most were the ones that went out as a utility tokens. Not to get nerdy on this, but utility and security are two types of tokens. The ones that went out and raised money as the utility token had no product, raised money using the utility that doesn't exist. That's essentially a security. And, so, no wonder why they're getting slapped. >> They're securities. Look, Bitcoin, different story, because Bitcoin is the closest to being I guess, what we could consider to be truly decentralized, right? And the regulatory climate around Bitcoin is a little bit different from what I'm talking about, with respects to securities laws. Although, from a tax perspective, it's the same. It's taxed as property. It's not taxed the way that foreign currency is taxed. But ultimately, yeah. You had a lot of cowboys who went out, and made a lot of money, and were just breaking the law, and now everyone is shocked when they see what's going on with this cease-and-desist order from the SCC against Telegram, and these other issues. But, none of it is particularly surprising because at the end of the day we have regulations in place, we have a regulatory regime, and most people just chose to ignore it. >> It's interesting how fast the SCC modernized their thinking around this. They really. From a speed standpoint, all government agencies tend to be glacier speed kind of movement. They were pretty fast. I mean, they kind of huddled on this for a couple months and came out with direction. They've been proactive. I got to say. I was usually skeptical of most government organization. I don't think they well inform. In this case, I think the SCC did a good job. >> So, I think that the issue is as follows. You know, Crypto is a very very very small portion of what the SCC deals with, so, they actually paid an inordinate amount of attention to this, and, I think that they did it for a couple of reasons. One is because, you asked me in the beginning of this interview about regulations versus innovation. And, I don't think anyone wants to stifle innovation in America. It's a very interesting technology. It's very interesting ideas, right? No one wants that to go away and no one wants people to stop experimenting and stop dreaming bigger. At the same time, the other issue that we've seen now, especially, not only with the SCC, but with the IRS now getting involved, is the fact that even though this is something very very small, they are very concerned about where the technology could go in the future. The IRS is extremely concerned about erosion of the tax space. So, because of that, it makes a lot of sense for them to pay attention to this very very early on, nip this in the bud, and help guide it back into the right direction. >> I think that's a good balance. Great point. Innovation doesn't want to be stifled at all, absolutely. What's new and exciting for you? Share some personal or business updates in your world. What's going on? What's getting you excited these days, in the field? >> What's getting me excited these days? Well, I have to tell you that one thing that actually has gotten me excited these days is the fact that the Blockchain and cryptocurrency industries have grown up, substantially. And, now we're able to take a look at those industries in tandem with the tech industry at large, because they seem to sort of be going off in a different direction, and now we're taking a look at it, and now you can really see sort of where the areas that things are going to get exciting. I look at my clients and I see the things that they're doing and I'm always excited for them, and I'm always interested to see what new things that they'll innovate, because, again, I'm not a technologist. So, for me, that's a lot of fun. And, in addition to that, I think that other areas are extremely exciting as well. I'm a big fan of Acronis. I'm a big fan of cyber protection issues, data protection, data regulation. I think something that's really interesting in the world of data regulation, that actually has come out of the Blockchain community, in a way, is the notion of data as a personal right, as personal property. So, one of the big things is the idea that now that we've seen these massive data breaches with Facebook and 23andME, and the way that big government, big companies, are using individuals' datas, the idea that if data were to be personal property, it would be used very very differently. And technologists who are using Blockchain technology say that Blockchain technology might actually be able to make that happen. Because if you could have a decentralized Facebook, let's say, people could own their own data and then use that data as they want to and be compensated for it. So, that's really interesting, right-- Yeah, but, if you're just going to use the product, they might as well own their data, right? >> Katya: Exactly. >> Katya, thanks for coming on theCUBE. Thanks for the insight. Great, compelling narrative. Thanks for sharing. >> Sure, thank you very much. >> Appreciate it. I'm John Furrier here on theCUBE, Miami Beach, at the Fontainebleau hotel for Acronis' Global Cyber Summit 2019. We'll be back with more coverage after this short break.

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Hello and welcome back to the Cubes. Live coverage here and savor still were alive for IBM. Think twenty nineteen. The Cubes Exclusive contract. Jon for a stimulant in our next two guests of the Cloud gurus and IBM and VM Where A. J. Patel senior vice president general manager Cloud Providers Software Business Unit. Good to see you again. Baron. Scram A general manager. IBM Cloud Guys. Thanks for Spend the time. Get to the cloud gurus. Get it? They're having What's going on? Having privilege. Osti Cloud's been around. We've seen the public Cloud Momentum hybrid Certainly been around for a while. Multi clouds of big conversation. People are having role of data that is super important. Aye, aye, anywhere you guys, an IBM have announced because I've been on this. I'm on >> a journey or a >> library for awhile. On premise. It was on VM, where all the good stuff's happening. This the customers customers want this talk about the relationship you guys have with IBM. >> You know, the broad of'em were IBM relationship over nine, ten years old. I had the privilege of being part of the cloud the last couple years. The momentum is amazing. Over seventeen hundred plus customers and the Enterprise customers, not your you know, one node trial customer. These are really mission critical enterprise customers using this at that scale, and the number one thing we hear from customers is make it easy for me to leverage Plowed right, operate in the world when I'm using my own prim and my public cloud assets make it seamless, and this is really what we've talked about a lot, right? How do we provide that ubiquitous digital platform for them to operate in this hybrid world? And we're privileged to have IBM Of the great partner in this journey >> are some of the IBM cloud, Ginny Rometty said on CNBC this morning. We saw the interview with my friend John Ford over there. Aye, aye. Anywhere means going run on any cloud. Watson with containers. That's cloud DNA. Sitting the cloud with good Burnett ease and containers is changing the game. Now you can run a lot of things everywhere. This's what customers want. End to end from on. Premise to wherever. How has that changed the IBM cloud posture? Its products? You share a little bit of that. >> You absolutely so look I mean, people have their data in different places, and as you know, it's a really expensive to move stuff around. You gotta make sure it's safe, etcetera, So we want to take our applications and run them against the data wherever they are right? And when you think about today's landscape in the cloud industry, I think it's a perfect storm, a good, perfect storm and that containers and Kubernetes, you know, everyone's rallying around at the ecosystem that consumers, the providers. And it just makes us easy for us to take that capability and really make it available on multicloud. And that's what we're doing. >> to talk about your joint customers. Because the BM where has a lot of operators running, running virtually change? For a long time, you guys have been big supporters of that and open source that really grew that whole generation that was seeing with cloud talk about your customers, your mo mentum, Howyou, guys air, just ballpark. How many customers you guys have together? And what if some of the things that they're doing >> all right? So I know this is a really interesting story. I was actually away from IBM for just over two years. But one of the last things I did when I was an IBM the first time around was actually start this Veum where partnership and seated the team that did it. So coming back, it's really interesting to see the uptake it's had, You know, we've got, like, seven hundred customers together over seventeen hundred customers. Together, we've moved tens of thousands of'em workloads, and as I just said, we've done it in a mission. Critical fashion across multiple zones across multiple regions. On now, you know, we want to take it to the next level. We want to make sure that these people that have moved their basic infrastructure and the mission critical infrastructure across the public cloud can extend those applications by leveraging the cloud near application that we have on our cloud. Plus, we want to make it possible for them to move their workloads to other parts of the IBM ecosystem in terms of our capabilities. >> Any one of the things we found was the notion of modernizer infrastructure, first lift and then transform. He's starting to materialize, and we used to talk about this has really the way the best way to use, cowed or use hybrid cloud was start by just uplifting your infrastructure and whether it's west back, you ask for some customers. I respect a great example. I think that we're talking about it in the Parisian. I joined presentation tomorrow or you look at, you know, Kaiser, who's going to be on stage tomorrow? We're seeing industries across the board are saying, You know, I have a lot of complexity sitting on aging hardware, older versions of infrastructure software. How do I modernize A platform first lifted, shifted to leverage a cloud. And then I could transform my application using more and more portable service that'S covering decides to provide a kind of infrastructure portability. But what about my data, Right. What about if I could run my application with the data? So I think we're starting to see the securing of the use of cloud based on workloads and averaging that's that's >> Yeah, a J. What wonder if we could dig a little love level deeper on that? Because, you know, I think backto, you know, fifteen years or so ago, it was bm where allowed me to not have to worry about my infrastructure. My, you know OS in my you know, server that I was running on might be going end of life. Well, let me shove it in a V M. And then I couldn't stand the life, and then I can manage how that happens. Course. The critique I would have is maybe it's time to update that that application anyway, so I like the message that you're saying about Okay, let me get a to a process where I'm a little bit freer of where, and then I can do the hard work of updating that data. Updating that application, you know, help us understand. >> It's no longer about just unlocking the compute right, which was worth trying the server. It's What about my network we talked about earlier? Do I need a suffered If our network well, the reality is, everything is going programmable. If you want a program of infrastructure, it's compute network storage all software defined. So the building block for us is a suffer to find data center running on the infrastructure that IBM pride sixty plus data centers bare metal at Scholastic and then leering that with IBM cloud private, whether it's hosted or on premise, fear gives you that full stack that nirvana, the people talk about supportable stack going, talk about >> right and adding to what he said, right? You said, You know, it's not about just moving your old stuff to the to the cloud. Absolutely. So as I said in one of the earlier conversations that we have, we had is we have a whole wealth of new services, whether it's Blockchain R. I o. T or the that used. You spoke about leveraging those capabilities to further extend your app and give it a new lease of life to provide new insights is what it's all about. >> What? Well, that that that's great, because it's one thing to just say, Okay, I get it there. Can I get better utilization? Is that change my pricing? But it's the services, and that's kind of the promise of the cloud is, you know, if I built something in my environment, that's great and I can update and I can get updates. But if I put it in your environment, you can help manage some of those things as well as I should have access to all of these services. IBM's got a broad ecosystem can you give us? You know what are some of the low hanging fruit is to people when they get there, that they're unlocking data that they're using things like a I What? What What are some of the most prevalent services that people are adding when they go to the IBM clouds? >> So when you look at people who first moved their work list of the cloud, typically they tend to dip their toe in the water. They take what's running on Prem. They used the IRS capabilities in the cloud and start to move it there. But the real innovation really starts to happen further up the stock, so to speak. The platform is a service, things like a II OT blocked and all the things that I mentioned, eso es very natural. Next movement is to start to modernize those applications and add to it. Capability is that it could never have before because, you know it was built in a monolith and it was on prim, and it was kind of stuck there. So now the composition that the cloud gives you with all of these rich services where innovation happens first, that is the real benefit to our customers. >> Every she said, you took a little hiatus from IBM and went out outside IBM. Where did you go and what did you learn? What was that? Goldman Jack. JP Morgan, Where were you? >> So it was a large bank. You know, I'm not not allowed to say the name of the bank. >> One of those two. It >> was a large bank on, and it wasn't the U S. So that narrows down the field. Some >> What is it like to go outside? They'll come inside. U C Davis for cutting edge bank. Now you got IBM Cloud. You feel good about where things are. >> Yeah. You know, if you look at what a lot of these banks are trying to do, they start to attack the cloud journey saying we're going to take everything that ran in the bank for years and years and years. And we're going to, you know, make them micro services and put them all on public cloud. And that's when you really hit the eighty twenty percent problem because you've got a large monolith that don't lend themselves to be re factored and moved out. Tio, eh, Public cloud. So you know again, Enter communities and containers, etcetera. These allow you a way to modernize your applications where you can either deploy those containerized You know, piers you go type models on prim or on public. And if you have a rich enough set of services both on Prem in on the public loud, you can pretty much decide how much of it runs on Trevor's is becoming much more clouds >> moment choice. So really, it's finding deployment. So basically, what you're saying is that we get this right. I want to get your reaction. This You don't have to kill the old to bring in the new containers and Cooper netease and now service measures around the corner. You can bring in new work clothes, take advantage of the cutting edge technology and manage your life cycle of the work loads on the old side or it just can play along. I >> think what we're finding is, you know, we moved from hybrid being a destination to an operating model, and it's no longer about doing this at scale like my multi clark. Any given applications tied to a cloud or destination? It's a late binding decision, but as an aggregate. I may be amusing multiple close, right. So that more model we're moving to is really about a loving developer. Super your workload centric and services centric to see Where do I want to run in Africa? >> Okay, what one of the challenges with multi cloud is their skill sets. I need to worry about it. It can be complex. I want to touch on three points and love to get both your viewpoints, networking, security and management. How do we help tackle that? Make that simple >> right off customers? >> Yeah, sure. So you know, I think when you think about clouds, public clouds especially it's beyond your data center and the mindset out there as if it's beyond my data center. It can be safe. But when you start to build those constructs in the modern era, you really do take care of a lot of things that perhaps you're on Prem pieces that not take into consideration when they were built like many decades ago. Right? So with the IBM public Cloud, for example, you know, security's at the heart of it. We have a leadership position. There was one of the things that we've announced is people keep protect for not only Veum, where workload visa and we sphere etcetera, but also for other applications making use off our public cloud services. Then, when you talk about our Z, you know we have a hardware as security model, which is fifty one forty, level two or dash to level four, which nobody else in the industry has. So when you put your key in there on ly, the customer can take it out, not him. Azaz clouds of his providers can touch it. It will basically disintegrate, you know, sort of speak >> H ey. Talk about VM wears customer base inside the IBM ecosystem. What's new? What should they pay attention to? As you guys continue the momentum. >> So I think if you look at the last two years, it's been around what we call these larger enterprise. Dedicated clouds. Exciting thing in the horizon is we're adding a multi tenant IRS on top of this BM, we're dedicated. So being able to provide that Brett off access thing with dedicated multi tenant public out I, as fully programmable, allows us to go downmarket. So expect the customer kind of go up being able to consume it on a pay as you go basis leveraging kind of multi tenant with dedicated, but it's highly secure or for depth test. So are the use cases kind of joke. We're going to see a much larger sort of use cases that I'm most excited about >> is the bottom line. Bottom line me. I'm the customer. Bottom line me. What's in it for me? What I got >> for the customers with a safest choice, right? It's the mission critical secure cloud. You can now run the same application on Prem in a dedicated environment in public, Claude on IBM or in a multi tenant >> world. And on the Klaxon match on the cloud sign. I could take advantage of all the things you have and take advantage of that. Watson A. I think that Rob Thomas has been talking about Oh yeah, >> absolutely. And again. You know the way that we built I c P forty, which is IBM plowed private for data. You know, it's all containerized. It's orchestrated by Coop, so you can not only build it. You can either run it on crime. You can run it on our public loud or you can run it on other people's public clouds as well >> nourished for customers and for people. They're looking at IBM Cloud and re evaluating you guys now again saying Or for the first time, what should they look at? Cloud private? What key thing would you point someone to look at, IBM? They were going to inspect your cloud offering >> so again, and it's back to my story in the bank. Right? It's, uh you can't do everything in the public cloud, right? There are just certain things that need to remain on creme On. We'll be so for the foreseeable future. So when you take a look at our hybrid story, the fact that it is has a consistent based on which it is built on. It is a industry standard open source base. You know, you build your application to suit the needs of an application, right? Is it low lately? See, Put it on. Crim. You need some cloud Native services. Put it on the public cloud. Do you need to be near your data that lives on somebody else's cloud? Go put it on their cloud. Right. So it really is not a one. Size fits all its whatever your business >> customer where he is, right? That's often >> the way flexibility, choice, flexibility. Enjoy the store for all things cloud. >> Yeah, last thing I want to ask is where to developers fit in tow this joint Solucion >> es O. So I think the biggest thing is that's trying to change for us is making these services available in a portable manner. When do I couldn't lock into the public cloud service with particular data and unlocking that from the infrastructures will be a key trend. So for us, it's about staying true to Coburn eddies and upstream with the distribution. So it's portable for wanting more and more services and making it easy for them to access a catalogue of services on a bagel manner but then making operation a viable. So then you're deployed. You can support the day two operations that are needed. So it's a full life cycle with developers not having to worry about the heavy burden of running an operating. What >> exactly? You know, it's all about the developers. As you well know in the cloud world, the developer is the operator. So as long as you can give him or her, the right set of tools to do C. I C. Dev ops on DH get things out there in a consistent fashion, whether it is on a tram or a public cloud. I think it's a win for all. >> That's exactly the trend We're seeing operations moving to more developers and more big time operational scale questions where your programming, the infrastructure. Absolutely. Developers. You don't want to deal with it >> and making it work. Listen tricks. So you know when to deploy. What workload? Having full control. That's part of the deployment >> exam. Alright, final question. I know we got a break. We're in tight on time. Final point share perspective of what's what's important here happening. And IBM. Think twenty nineteen people who didn't make it here in San Francisco are watching. You have to top cloud executives on VM wear and IBM here as biased towards cloud, of course. But you know, if you're watching, what's the most important story happening this week? What's what's going on with IBM? Think Why is this conference this week important? >> I think for us, it's basically saying We're here to meet you where you are, regardless, where you on your customer journey. It's all about choice. It's no longer only about public Cloud, and you now have a lot of capably of your finger trips to take your legacy workloads or your neck, new workplace or any app anywhere we can help you on that journey. That would be the case with >> you, and I wouldn't go that right, said it slightly differently. You know, a lot of the public service of public cloud service providers kind of bring you over to their public loud, and then you're kind of stuck over there and customers don't like that. I mean, you look at the statistics for everybody has at least two or more public clouds. They're worried about the connective ity, the interoperability, the security costs, the cost, the skills to manage all of it. And I think we have the perfect solution of solutions that really start Teo. Speak to that problem. >> So the world's getting more complex as more functionalities here, Software's gonna distract it away. Developers need clean environment to work in programmable infrastructure. >> And you know where an IBM Safe Choice, choice, choice. >> We have to go on top to cloud executives here. Inside the cue from IBM of'em were bringing all the coverage. Was the Cube here in the lobby of Mosconi North on Howard Street in San Francisco for IBM? Think twenty. Stay with us for more coverage after this short break. Thank you. Thank you.

- [Announcer] Live, from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media, and its ecosystem partners. - Hello everyone, welcome back to CUBE NYC. This is a CUBE special presentation of something that we've done now for the past couple of years. IBM has sponsored an influencer panel on some of the hottest topics in the industry, and of course, there's no hotter topic right now than AI. So, we've got nine of the top influencers in the AI space, and we're in Hell's Kitchen, and it's going to get hot in here. (laughing) And these guys, we're going to cover the gamut. So, first of all, folks, thanks so much for joining us today, really, as John said earlier, we love the collaboration with you all, and we'll definitely see you on social after the fact. I'm Dave Vellante, with my cohost for this session, Peter Burris, and again, thank you to IBM for sponsoring this and organizing this. IBM has a big event down here, in conjunction with Strata, called Change the Game, Winning with AI. We run theCUBE NYC, we've been here all week. So, here's the format. I'm going to kick it off, and then we'll see where it goes. So, I'm going to introduce each of the panelists, and then ask you guys to answer a question, I'm sorry, first, tell us a little bit about yourself, briefly, and then answer one of the following questions. Two big themes that have come up this week. One has been, because this is our ninth year covering what used to be Hadoop World, which kind of morphed into big data. Question is, AI, big data, same wine, new bottle? Or is it really substantive, and driving business value? So, that's one question to ponder. The other one is, you've heard the term, the phrase, data is the new oil. Is data really the new oil? Wonder what you think about that? Okay, so, Chris Penn, let's start with you. Chris is cofounder of Trust Insight, long time CUBE alum, and friend. Thanks for coming on. Tell us a little bit about yourself, and then pick one of those questions. - Sure, we're a data science consulting firm. We're an IBM business partner. When it comes to "data is the new oil," I love that expression because it's completely accurate. Crude oil is useless, you have to extract it out of the ground, refine it, and then bring it to distribution. Data is the same way, where you have to have developers and data architects get the data out. You need data scientists and tools, like Watson Studio, to refine it, and then you need to put it into production, and that's where marketing technologists, technologists, business analytics folks, and tools like Watson Machine Learning help bring the data and make it useful. - Okay, great, thank you. Tony Flath is a tech and media consultant, focus on cloud and cyber security, welcome. - Thank you. - Tell us a little bit about yourself and your thoughts on one of those questions. - Sure thing, well, thanks so much for having us on this show, really appreciate it. My background is in cloud, cyber security, and certainly in emerging tech with artificial intelligence. Certainly touched it from a cyber security play, how you can use machine learning, machine control, for better controlling security across the gamut. But I'll touch on your question about wine, is it a new bottle, new wine? Where does this come from, from artificial intelligence? And I really see it as a whole new wine that is coming along. When you look at emerging technology, and you look at all the deep learning that's happening, it's going just beyond being able to machine learn and know what's happening, it's making some meaning to that data. And things are being done with that data, from robotics, from automation, from all kinds of different things, where we're at a point in society where data, our technology is getting beyond us. Prior to this, it's always been command and control. You control data from a keyboard. Well, this is passing us. So, my passion and perspective on this is, the humanization of it, of IT. How do you ensure that people are in that process, right? - Excellent, and we're going to come back and talk about that. - Thanks so much. - Carla Gentry, @DataNerd? Great to see you live, as opposed to just in the ether on Twitter. Data scientist, and owner of Analytical Solution. Welcome, your thoughts? - Thank you for having us. Mine is, is data the new oil? And I'd like to rephrase that is, data equals human lives. So, with all the other artificial intelligence and everything that's going on, and all the algorithms and models that's being created, we have to think about things being biased, being fair, and understand that this data has impacts on people's lives. - Great. Steve Ardire, my paisan. - Paisan. - AI startup adviser, welcome, thanks for coming to theCUBE. - Thanks Dave. So, uh, my first career was geology, and I view AI as the new oil, but data is the new oil, but AI is the refinery. I've used that many times before. In fact, really, I've moved from just AI to augmented intelligence. So, augmented intelligence is really the way forward. This was a presentation I gave at IBM Think last spring, has almost 100,000 impressions right now, and the fundamental reason why is machines can attend to vastly more information than humans, but you still need humans in the loop, and we can talk about what they're bringing in terms of common sense reasoning, because big data does the who, what, when, and where, but not the why, and why is really the Holy Grail for causal analysis and reasoning. - Excellent, Bob Hayes, Business Over Broadway, welcome, great to see you again. - Thanks for having me. So, my background is in psychology, industrial psychology, and I'm interested in things like customer experience, data science, machine learning, so forth. And I'll answer the question around big data versus AI. And I think there's other terms we could talk about, big data, data science, machine learning, AI. And to me, it's kind of all the same. It's always been about analytics, and getting value from your data, big, small, what have you. And there's subtle differences among those terms. Machine learning is just about making a prediction, and knowing if things are classified correctly. Data science is more about understanding why things work, and understanding maybe the ethics behind it, what variables are predicting that outcome. But still, it's all the same thing, it's all about using data in a way that we can get value from that, as a society, in residences. - Excellent, thank you. Theo Lau, founder of Unconventional Ventures. What's your story? - Yeah, so, my background is driving technology innovation. So, together with my partner, what our work does is we work with organizations to try to help them leverage technology to drive systematic financial wellness. We connect founders, startup founders, with funders, we help them get money in the ecosystem. We also work with them to look at, how do we leverage emerging technology to do something good for the society. So, very much on point to what Bob was saying about. So when I look at AI, it is not new, right, it's been around for quite a while. But what's different is the amount of technological power that we have allow us to do so much more than what we were able to do before. And so, what my mantra is, great ideas can come from anywhere in the society, but it's our job to be able to leverage technology to shine a spotlight on people who can use this to do something different, to help seniors in our country to do better in their financial planning. - Okay, so, in your mind, it's not just a same wine, new bottle, it's more substantive than that. - [Theo] It's more substantive, it's a much better bottle. - Karen Lopez, senior project manager for Architect InfoAdvisors, welcome. - Thank you. So, I'm DataChick on twitter, and so that kind of tells my focus is that I'm here, I also call myself a data evangelist, and that means I'm there at organizations helping stand up for the data, because to me, that's the proxy for standing up for the people, and the places and the events that that data describes. That means I have a focus on security, data privacy and protection as well. And I'm going to kind of combine your two questions about whether data is the new wine bottle, I think is the combination. Oh, see, now I'm talking about alcohol. (laughing) But anyway, you know, all analogies are imperfect, so whether we say it's the new wine, or, you know, same wine, or whether it's oil, is that the analogy's good for both of them, but unlike oil, the amount of data's just growing like crazy, and the oil, we know at some point, I kind of doubt that we're going to hit peak data where we have not enough data, like we're going to do with oil. But that says to me that, how did we get here with big data, with machine learning and AI? And from my point of view, as someone who's been focused on data for 35 years, we have hit this perfect storm of open source technologies, cloud architectures and cloud services, data innovation, that if we didn't have those, we wouldn't be talking about large machine learning and deep learning-type things. So, because we have all these things coming together at the same time, we're now at explosions of data, which means we also have to protect them, and protect the people from doing harm with data, we need to do data for good things, and all of that. - Great, definite differences, we're not running out of data, data's like the terrible tribbles. (laughing) - Yes, but it's very cuddly, data is. - Yeah, cuddly data. Mark Lynd, founder of Relevant Track? - That's right. - I like the name. What's your story? - Well, thank you, and it actually plays into what my interest is. It's mainly around AI in enterprise operations and cyber security. You know, these teams that are in enterprise operations both, it can be sales, marketing, all the way through the organization, as well as cyber security, they're often under-sourced. And they need, what Steve pointed out, they need augmented intelligence, they need to take AI, the big data, all the information they have, and make use of that in a way where they're able to, even though they're under-sourced, make some use and some value for the organization, you know, make better use of the resources they have to grow and support the strategic goals of the organization. And oftentimes, when you get to budgeting, it doesn't really align, you know, you're short people, you're short time, but the data continues to grow, as Karen pointed out. So, when you take those together, using AI to augment, provided augmented intelligence, to help them get through that data, make real tangible decisions based on information versus just raw data, especially around cyber security, which is a big hit right now, is really a great place to be, and there's a lot of stuff going on, and a lot of exciting stuff in that area. - Great, thank you. Kevin L. Jackson, author and founder of GovCloud. GovCloud, that's big. - Yeah, GovCloud Network. Thank you very much for having me on the show. Up and working on cloud computing, initially in the federal government, with the intelligence community, as they adopted cloud computing for a lot of the nation's major missions. And what has happened is now I'm working a lot with commercial organizations and with the security of that data. And I'm going to sort of, on your questions, piggyback on Karen. There was a time when you would get a couple of bottles of wine, and they would come in, and you would savor that wine, and sip it, and it would take a few days to get through it, and you would enjoy it. The problem now is that you don't get a couple of bottles of wine into your house, you get two or three tankers of data. So, it's not that it's a new wine, you're just getting a lot of it. And the infrastructures that you need, before you could have a couple of computers, and a couple of people, now you need cloud, you need automated infrastructures, you need huge capabilities, and artificial intelligence and AI, it's what we can use as the tool on top of these huge infrastructures to drink that, you know. - Fire hose of wine. - Fire hose of wine. (laughs) - Everybody's having a good time. - Everybody's having a great time. (laughs) - Yeah, things are booming right now. Excellent, well, thank you all for those intros. Peter, I want to ask you a question. So, I heard there's some similarities and some definite differences with regard to data being the new oil. You have a perspective on this, and I wonder if you could inject it into the conversation. - Sure, so, the perspective that we take in a lot of conversations, a lot of folks here in theCUBE, what we've learned, and I'll kind of answer both questions a little bit. First off, on the question of data as the new oil, we definitely think that data is the new asset that business is going to be built on, in fact, our perspective is that there really is a difference between business and digital business, and that difference is data as an asset. And if you want to understand data transformation, you understand the degree to which businesses reinstitutionalizing work, reorganizing its people, reestablishing its mission around what you can do with data as an asset. The difference between data and oil is that oil still follows the economics of scarcity. Data is one of those things, you can copy it, you can share it, you can easily corrupt it, you can mess it up, you can do all kinds of awful things with it if you're not careful. And it's that core fundamental proposition that as an asset, when we think about cyber security, we think, in many respects, that is the approach to how we can go about privatizing data so that we can predict who's actually going to be able to appropriate returns on it. So, it's a good analogy, but as you said, it's not entirely perfect, but it's not perfect in a really fundamental way. It's not following the laws of scarcity, and that has an enormous effect. - In other words, I could put oil in my car, or I could put oil in my house, but I can't put the same oil in both. - Can't put it in both places. And now, the issue of the wine, I think it's, we think that it is, in fact, it is a new wine, and very simple abstraction, or generalization we come up with is the issue of agency. That analytics has historically not taken on agency, it hasn't acted on behalf of the brand. AI is going to act on behalf of the brand. Now, you're going to need both of them, you can't separate them. - A lot of implications there in terms of bias. - Absolutely. - In terms of privacy. You have a thought, here, Chris? - Well, the scarcity is our compute power, and our ability for us to process it. I mean, it's the same as oil, there's a ton of oil under the ground, right, we can't get to it as efficiently, or without severe environmental consequences to use it. Yeah, when you use it, it's transformed, but our scarcity is compute power, and our ability to use it intelligently. - Or even when you find it. I have data, I can apply it to six different applications, I have oil, I can apply it to one, and that's going to matter in how we think about work. - But one thing I'd like to add, sort of, you're talking about data as an asset. The issue we're having right now is we're trying to learn how to manage that asset. Artificial intelligence is a way of managing that asset, and that's important if you're going to use and leverage big data. - Yeah, but see, everybody's talking about the quantity, the quantity, it's not always the quantity. You know, we can have just oodles and oodles of data, but if it's not clean data, if it's not alphanumeric data, which is what's needed for machine learning. So, having lots of data is great, but you have to think about the signal versus the noise. So, sometimes you get so much data, you're looking at over-fitting, sometimes you get so much data, you're looking at biases within the data. So, it's not the amount of data, it's the, now that we have all of this data, making sure that we look at relevant data, to make sure we look at clean data. - One more thought, and we have a lot to cover, I want to get inside your big brain. - I was just thinking about it from a cyber security perspective, one of my customers, they were looking at the data that just comes from the perimeter, your firewalls, routers, all of that, and then not even looking internally, just the perimeter alone, and the amount of data being pulled off of those. And then trying to correlate that data so it makes some type of business sense, or they can determine if there's incidents that may happen, and take a predictive action, or threats that might be there because they haven't taken a certain action prior, it's overwhelming to them. So, having AI now, to be able to go through the logs to look at, and there's so many different types of data that come to those logs, but being able to pull that information, as well as looking at end points, and all that, and people's houses, which are an extension of the network oftentimes, it's an amazing amount of data, and they're only looking at a small portion today because they know, there's not enough resources, there's not enough trained people to do all that work. So, AI is doing a wonderful way of doing that. And some of the tools now are starting to mature and be sophisticated enough where they provide that augmented intelligence that Steve talked about earlier. - So, it's complicated. There's infrastructure, there's security, there's a lot of software, there's skills, and on and on. At IBM Think this year, Ginni Rometty talked about, there were a couple of themes, one was augmented intelligence, that was something that was clear. She also talked a lot about privacy, and you own your data, etc. One of the things that struck me was her discussion about incumbent disruptors. So, if you look at the top five companies, roughly, Facebook with fake news has dropped down a little bit, but top five companies in terms of market cap in the US. They're data companies, all right. Apple just hit a trillion, Amazon, Google, etc. How do those incumbents close the gap? Is that concept of incumbent disruptors actually something that is being put into practice? I mean, you guys work with a lot of practitioners. How are they going to close that gap with the data haves, meaning data at their core of their business, versus the data have-nots, it's not that they don't have a lot of data, but it's in silos, it's hard to get to? - Yeah, I got one more thing, so, you know, these companies, and whoever's going to be big next is, you have a digital persona, whether you want it or not. So, if you live in a farm out in the middle of Oklahoma, you still have a digital persona, people are collecting data on you, they're putting profiles of you, and the big companies know about you, and people that first interact with you, they're going to know that you have this digital persona. Personal AI, when AI from these companies could be used simply and easily, from a personal deal, to fill in those gaps, and to have a digital persona that supports your family, your growth, both personal and professional growth, and those type of things, there's a lot of applications for AI on a personal, enterprise, even small business, that have not been done yet, but the data is being collected now. So, you talk about the oil, the oil is being built right now, lots, and lots, and lots of it. It's the applications to use that, and turn that into something personally, professionally, educationally, powerful, that's what's missing. But it's coming. - Thank you, so, I'll add to that, and in answer to your question you raised. So, one example we always used in banking is, if you look at the big banks, right, and then you look at from a consumer perspective, and there's a lot of talk about Amazon being a bank. But the thing is, Amazon doesn't need to be a bank, they provide banking services, from a consumer perspective they don't really care if you're a bank or you're not a bank, but what's different between Amazon and some of the banks is that Amazon, like you say, has a lot of data, and they know how to make use of the data to offer something as relevant that consumers want. Whereas banks, they have a lot of data, but they're all silos, right. So, it's not just a matter of whether or not you have the data, it's also, can you actually access it and make something useful out of it so that you can create something that consumers want? Because otherwise, you're just a pipe. - Totally agree, like, when you look at it from a perspective of, there's a lot of terms out there, digital transformation is thrown out so much, right, and go to cloud, and you migrate to cloud, and you're going to take everything over, but really, when you look at it, and you both touched on it, it's the economics. You have to look at the data from an economics perspective, and how do you make some kind of way to take this data meaningful to your customers, that's going to work effectively for them, that they're going to drive? So, when you look at the big, big cloud providers, I think the push in things that's going to happen in the next few years is there's just going to be a bigger migration to public cloud. So then, between those, they have to differentiate themselves. Obvious is artificial intelligence, in a way that makes it easy to aggregate data from across platforms, to aggregate data from multi-cloud, effectively. To use that data in a meaningful way that's going to drive, not only better decisions for your business, and better outcomes, but drives our opportunities for customers, drives opportunities for employees and how they work. We're at a really interesting point in technology where we get to tell technology what to do. It's going beyond us, it's no longer what we're telling it to do, it's going to go beyond us. So, how we effectively manage that is going to be where we see that data flow, and those big five or big four, really take that to the next level. - Now, one of the things that Ginni Rometty said was, I forget the exact step, but it was like, 80% of the data, is not searchable. Kind of implying that it's sitting somewhere behind a firewall, presumably on somebody's premises. So, it was kind of interesting. You're talking about, certainly, a lot of momentum for public cloud, but at the same time, a lot of data is going to stay where it is. - Yeah, we're assuming that a lot of this data is just sitting there, available and ready, and we look at the desperate, or disparate kind of database situation, where you have 29 databases, and two of them have unique quantifiers that tie together, and the rest of them don't. So, there's nothing that you can do with that data. So, artificial intelligence is just that, it's artificial intelligence, so, they know, that's machine learning, that's natural language, that's classification, there's a lot of different parts of that that are moving, but we also have to have IT, good data infrastructure, master data management, compliance, there's so many moving parts to this, that it's not just about the data anymore. - I want to ask Steve to chime in here, go ahead. - Yeah, so, we also have to change the mentality that it's not just enterprise data. There's data on the web, the biggest thing is Internet of Things, the amount of sensor data will make the current data look like chump change. So, data is moving faster, okay. And this is where the sophistication of machine learning needs to kick in, going from just mostly supervised-learning today, to unsupervised learning. And in order to really get into, as I said, big data, and credible AI does the who, what, where, when, and how, but not the why. And this is really the Holy Grail to crack, and it's actually under a new moniker, it's called explainable AI, because it moves beyond just correlation into root cause analysis. Once we have that, then you have the means to be able to tap into augmented intelligence, where humans are working with the machines. - Karen, please. - Yeah, so, one of the things, like what Carla was saying, and what a lot of us had said, I like to think of the advent of ML technologies and AI are going to help me as a data architect to love my data better, right? So, that includes protecting it, but also, when you say that 80% of the data is unsearchable, it's not just an access problem, it's that no one knows what it was, what the sovereignty was, what the metadata was, what the quality was, or why there's huge anomalies in it. So, my favorite story about this is, in the 1980s, about, I forget the exact number, but like, 8 million children disappeared out of the US in April, at April 15th. And that was when the IRS enacted a rule that, in order to have a dependent, a deduction for a dependent on your tax returns, they had to have a valid social security number, and people who had accidentally miscounted their children and over-claimed them, (laughter) over the years them, stopped doing that. Well, some days it does feel like you have eight children running around. (laughter) - Agreed. - When, when that rule came about, literally, and they're not all children, because they're dependents, but literally millions of children disappeared off the face of the earth in April, but if you were doing analytics, or AI and ML, and you don't know that this anomaly happened, I can imagine in a hundred years, someone is saying some catastrophic event happened in April, 1983. (laughter) And what caused that, was it healthcare? Was it a meteor? Was it the clown attacking them? - That's where I was going. - Right. So, those are really important things that I want to use AI and ML to help me, not only document and capture that stuff, but to provide that information to the people, the data scientists and the analysts that are using the data. - Great story, thank you. Bob, you got a thought? You got the mic, go, jump in here. - Well, yeah, I do have a thought, actually. I was talking about, what Karen was talking about. I think it's really important that, not only that we understand AI, and machine learning, and data science, but that the regular folks and companies understand that, at the basic level. Because those are the people who will ask the questions, or who know what questions to ask of the data. And if they don't have the tools, and the knowledge of how to get access to that data, or even how to pose a question, then that data is going to be less valuable, I think, to companies. And the more that everybody knows about data, even people in congress. Remember when Zuckerberg talked about? (laughter) - That was scary. - How do you make money? It's like, we all know this. But, we need to educate the masses on just basic data analytics. - We could have an hour-long panel on that. - Yeah, absolutely. - Peter, you and I were talking about, we had a couple of questions, sort of, how far can we take artificial intelligence? How far should we? You know, so that brings in to the conversation of ethics, and bias, why don't you pick it up? - Yeah, so, one of the crucial things that we all are implying is that, at some point in time, AI is going to become a feature of the operations of our homes, our businesses. And as these technologies get more powerful, and they diffuse, and know about how to use them, diffuses more broadly, and you put more options into the hands of more people, the question slowly starts to turn from can we do it, to should we do it? And, one of the issues that I introduce is that I think the difference between big data and AI, specifically, is this notion of agency. The AI will act on behalf of, perhaps you, or it will act on behalf of your business. And that conversation is not being had, today. It's being had in arguments between Elon Musk and Mark Zuckerberg, which pretty quickly get pretty boring. (laughing) At the end of the day, the real question is, should this machine, whether in concert with others, or not, be acting on behalf of me, on behalf of my business, or, and when I say on behalf of me, I'm also talking about privacy. Because Facebook is acting on behalf of me, it's not just what's going on in my home. So, the question of, can it be done? A lot of things can be done, and an increasing number of things will be able to be done. We got to start having a conversation about should it be done? - So, humans exhibit tribal behavior, they exhibit bias. Their machine's going to pick that up, go ahead, please. - Yeah, one thing that sort of tag onto agency of artificial intelligence. Every industry, every business is now about identifying information and data sources, and their appropriate sinks, and learning how to draw value out of connecting the sources with the sinks. Artificial intelligence enables you to identify those sources and sinks, and when it gets agency, it will be able to make decisions on your behalf about what data is good, what data means, and who it should be. - What actions are good. - Well, what actions are good. - And what data was used to make those actions. - Absolutely. - And was that the right data, and is there bias of data? And all the way down, all the turtles down. - So, all this, the data pedigree will be driven by the agency of artificial intelligence, and this is a big issue. - It's really fundamental to understand and educate people on, there are four fundamental types of bias, so there's, in machine learning, there's intentional bias, "Hey, we're going to make "the algorithm generate a certain outcome "regardless of what the data says." There's the source of the data itself, historical data that's trained on the models built on flawed data, the model will behave in a flawed way. There's target source, which is, for example, we know that if you pull data from a certain social network, that network itself has an inherent bias. No matter how representative you try to make the data, it's still going to have flaws in it. Or, if you pull healthcare data about, for example, African-Americans from the US healthcare system, because of societal biases, that data will always be flawed. And then there's tool bias, there's limitations to what the tools can do, and so we will intentionally exclude some kinds of data, or not use it because we don't know how to, our tools are not able to, and if we don't teach people what those biases are, they won't know to look for them, and I know. - Yeah, it's like, one of the things that we were talking about before, I mean, artificial intelligence is not going to just create itself, it's lines of code, it's input, and it spits out output. So, if it learns from these learning sets, we don't want AI to become another buzzword. We don't want everybody to be an "AR guru" that has no idea what AI is. It takes months, and months, and months for these machines to learn. These learning sets are so very important, because that input is how this machine, think of it as your child, and that's basically the way artificial intelligence is learning, like your child. You're feeding it these learning sets, and then eventually it will make its own decisions. So, we know from some of us having children that you teach them the best that you can, but then later on, when they're doing their own thing, they're really, it's like a little myna bird, they've heard everything that you've said. (laughing) Not only the things that you said to them directly, but the things that you said indirectly. - Well, there are some very good AI researchers that might disagree with that metaphor, exactly. (laughing) But, having said that, what I think is very interesting about this conversation is that this notion of bias, one of the things that fascinates me about where AI goes, are we going to find a situation where tribalism more deeply infects business? Because we know that human beings do not seek out the best information, they seek out information that reinforces their beliefs. And that happens in business today. My line of business versus your line of business, engineering versus sales, that happens today, but it happens at a planning level, and when we start talking about AI, we have to put the appropriate dampers, understand the biases, so that we don't end up with deep tribalism inside of business. Because AI could have the deleterious effect that it actually starts ripping apart organizations. - Well, input is data, and then the output is, could be a lot of things. - Could be a lot of things. - And that's where I said data equals human lives. So that we look at the case in New York where the penal system was using this artificial intelligence to make choices on people that were released from prison, and they saw that that was a miserable failure, because that people that release actually re-offended, some committed murder and other things. So, I mean, it's, it's more than what anybody really thinks. It's not just, oh, well, we'll just train the machines, and a couple of weeks later they're good, we never have to touch them again. These things have to be continuously tweaked. So, just because you built an algorithm or a model doesn't mean you're done. You got to go back later, and continue to tweak these models. - Mark, you got the mic. - Yeah, no, I think one thing we've talked a lot about the data that's collected, but what about the data that's not collected? Incomplete profiles, incomplete datasets, that's a form of bias, and sometimes that's the worst. Because they'll fill that in, right, and then you can get some bias, but there's also a real issue for that around cyber security. Logs are not always complete, things are not always done, and when things are doing that, people make assumptions based on what they've collected, not what they didn't collect. So, when they're looking at this, and they're using the AI on it, that's only on the data collected, not on that that wasn't collected. So, if something is down for a little while, and no data's collected off that, the assumption is, well, it was down, or it was impacted, or there was a breach, or whatever, it could be any of those. So, you got to, there's still this human need, there's still the need for humans to look at the data and realize that there is the bias in there, there is, we're just looking at what data was collected, and you're going to have to make your own thoughts around that, and assumptions on how to actually use that data before you go make those decisions that can impact lots of people, at a human level, enterprise's profitability, things like that. And too often, people think of AI, when it comes out of there, that's the word. Well, it's not the word. - Can I ask a question about this? - Please. - Does that mean that we shouldn't act? - It does not. - Okay. - So, where's the fine line? - Yeah, I think. - Going back to this notion of can we do it, or should we do it? Should we act? - Yeah, I think you should do it, but you should use it for what it is. It's augmenting, it's helping you, assisting you to make a valued or good decision. And hopefully it's a better decision than you would've made without it. - I think it's great, I think also, your answer's right too, that you have to iterate faster, and faster, and faster, and discover sources of information, or sources of data that you're not currently using, and, that's why this thing starts getting really important. - I think you touch on a really good point about, should you or shouldn't you? You look at Google, and you look at the data that they've been using, and some of that out there, from a digital twin perspective, is not being approved, or not authorized, and even once they've made changes, it's still floating around out there. Where do you know where it is? So, there's this dilemma of, how do you have a digital twin that you want to have, and is going to work for you, and is going to do things for you to make your life easier, to do these things, mundane tasks, whatever? But how do you also control it to do things you don't want it to do? - Ad-based business models are inherently evil. (laughing) - Well, there's incentives to appropriate our data, and so, are things like blockchain potentially going to give users the ability to control their data? We'll see. - No, I, I'm sorry, but that's actually a really important point. The idea of consensus algorithms, whether it's blockchain or not, blockchain includes games, and something along those lines, whether it's Byzantine fault tolerance, or whether it's Paxos, consensus-based algorithms are going to be really, really important. Parts of this conversation, because the data's going to be more distributed, and you're going to have more elements participating in it. And so, something that allows, especially in the machine-to-machine world, which is a lot of what we're talking about right here, you may not have blockchain, because there's no need for a sense of incentive, which is what blockchain can help provide. - And there's no middleman. - And, well, all right, but there's really, the thing that makes blockchain so powerful is it liberates new classes of applications. But for a lot of the stuff that we're talking about, you can use a very powerful consensus algorithm without having a game side, and do some really amazing things at scale. - So, looking at blockchain, that's a great thing to bring up, right. I think what's inherently wrong with the way we do things today, and the whole overall design of technology, whether it be on-prem, or off-prem, is both the lock and key is behind the same wall. Whether that wall is in a cloud, or behind a firewall. So, really, when there is an audit, or when there is a forensics, it always comes down to a sysadmin, or something else, and the system administrator will have the finger pointed at them, because it all resides, you can edit it, you can augment it, or you can do things with it that you can't really determine. Now, take, as an example, blockchain, where you've got really the source of truth. Now you can take and have the lock in one place, and the key in another place. So that's certainly going to be interesting to see how that unfolds. - So, one of the things, it's good that, we've hit a lot of buzzwords, right now, right? (laughing) AI, and ML, block. - Bingo. - We got the blockchain bingo, yeah, yeah. So, one of the things is, you also brought up, I mean, ethics and everything, and one of the things that I've noticed over the last year or so is that, as I attend briefings or demos, everyone is now claiming that their product is AI or ML-enabled, or blockchain-enabled. And when you try to get answers to the questions, what you really find out is that some things are being pushed as, because they have if-then statements somewhere in their code, and therefore that's artificial intelligence or machine learning. - [Peter] At least it's not "go-to." (laughing) - Yeah, you're that experienced as well. (laughing) So, I mean, this is part of the thing you try to do as a practitioner, as an analyst, as an influencer, is trying to, you know, the hype of it all. And recently, I attended one where they said they use blockchain, and I couldn't figure it out, and it turns out they use GUIDs to identify things, and that's not blockchain, it's an identifier. (laughing) So, one of the ethics things that I think we, as an enterprise community, have to deal with, is the over-promising of AI, and ML, and deep learning, and recognition. It's not, I don't really consider it visual recognition services if they just look for red pixels. I mean, that's not quite the same thing. Yet, this is also making things much harder for your average CIO, or worse, CFO, to understand whether they're getting any value from these technologies. - Old bottle. - Old bottle, right. - And I wonder if the data companies, like that you talked about, or the top five, I'm more concerned about their nearly, or actual $1 trillion valuations having an impact on their ability of other companies to disrupt or enter into the field more so than their data technologies. Again, we're coming to another perfect storm of the companies that have data as their asset, even though it's still not on their financial statements, which is another indicator whether it's really an asset, is that, do we need to think about the terms of AI, about whose hands it's in, and who's, like, once one large trillion-dollar company decides that you are not a profitable company, how many other companies are going to buy that data and make that decision about you? - Well, and for the first time in business history, I think, this is true, we're seeing, because of digital, because it's data, you're seeing tech companies traverse industries, get into, whether it's content, or music, or publishing, or groceries, and that's powerful, and that's awful scary. - If you're a manger, one of the things your ownership is asking you to do is to reduce asset specificities, so that their capital could be applied to more productive uses. Data reduces asset specificities. It brings into question the whole notion of vertical industry. You're absolutely right. But you know, one quick question I got for you, playing off of this is, again, it goes back to this notion of can we do it, and should we do it? I find it interesting, if you look at those top five, all data companies, but all of them are very different business models, or they can classify the two different business models. Apple is transactional, Microsoft is transactional, Google is ad-based, Facebook is ad-based, before the fake news stuff. Amazon's kind of playing it both sides. - Yeah, they're kind of all on a collision course though, aren't they? - But, well, that's what's going to be interesting. I think, at some point in time, the "can we do it, should we do it" question is, brands are going to be identified by whether or not they have gone through that process of thinking about, should we do it, and say no. Apple is clearly, for example, incorporating that into their brand. - Well, Silicon Valley, broadly defined, if I include Seattle, and maybe Armlock, not so much IBM. But they've got a dual disruption agenda, they've always disrupted horizontal tech. Now they're disrupting vertical industries. - I was actually just going to pick up on what she was talking about, we were talking about buzzword, right. So, one we haven't heard yet is voice. Voice is another big buzzword right now, when you couple that with IoT and AI, here you go, bingo, do I got three points? (laughing) Voice recognition, voice technology, so all of the smart speakers, if you think about that in the world, there are 7,000 languages being spoken, but yet if you look at Google Home, you look at Siri, you look at any of the devices, I would challenge you, it would have a lot of problem understanding my accent, and even when my British accent creeps out, or it would have trouble understanding seniors, because the way they talk, it's very different than a typical 25-year-old person living in Silicon Valley, right. So, how do we solve that, especially going forward? We're seeing voice technology is going to be so more prominent in our homes, we're going to have it in the cars, we have it in the kitchen, it does everything, it listens to everything that we are talking about, not talking about, and records it. And to your point, is it going to start making decisions on our behalf, but then my question is, how much does it actually understand us? - So, I just want one short story. Siri can't translate a word that I ask it to translate into French, because my phone's set to Canadian English, and that's not supported. So I live in a bilingual French English country, and it can't translate. - But what this is really bringing up is if you look at society, and culture, what's legal, what's ethical, changes across the years. What was right 200 years ago is not right now, and what was right 50 years ago is not right now. - It changes across countries. - It changes across countries, it changes across regions. So, what does this mean when our AI has agency? How do we make ethical AI if we don't even know how to manage the change of what's right and what's wrong in human society? - One of the most important questions we have to worry about, right? - Absolutely. - But it also says one more thing, just before we go on. It also says that the issue of economies of scale, in the cloud. - Yes. - Are going to be strongly impacted, not just by how big you can build your data centers, but some of those regulatory issues that are going to influence strongly what constitutes good experience, good law, good acting on my behalf, agency. - And one thing that's underappreciated in the marketplace right now is the impact of data sovereignty, if you get back to data, countries are now recognizing the importance of managing that data, and they're implementing data sovereignty rules. Everyone talks about California issuing a new law that's aligned with GDPR, and you know what that meant. There are 30 other states in the United States alone that are modifying their laws to address this issue. - Steve. - So, um, so, we got a number of years, no matter what Ray Kurzweil says, until we get to artificial general intelligence. - The singularity's not so near? (laughing) - You know that he's changed the date over the last 10 years. - I did know it. - Quite a bit. And I don't even prognosticate where it's going to be. But really, where we're at right now, I keep coming back to, is that's why augmented intelligence is really going to be the new rage, humans working with machines. One of the hot topics, and the reason I chose to speak about it is, is the future of work. I don't care if you're a millennial, mid-career, or a baby boomer, people are paranoid. As machines get smarter, if your job is routine cognitive, yes, you have a higher propensity to be automated. So, this really shifts a number of things. A, you have to be a lifelong learner, you've got to learn new skillsets. And the dynamics are changing fast. Now, this is also a great equalizer for emerging startups, and even in SMBs. As the AI improves, they can become more nimble. So back to your point regarding colossal trillion dollar, wait a second, there's going to be quite a sea change going on right now, and regarding demographics, in 2020, millennials take over as the majority of the workforce, by 2025 it's 75%. - Great news. (laughing) - As a baby boomer, I try my damnedest to stay relevant. - Yeah, surround yourself with millennials is the takeaway there. - Or retire. (laughs) - Not yet. - One thing I think, this goes back to what Karen was saying, if you want a basic standard to put around the stuff, look at the old ISO 38500 framework. Business strategy, technology strategy. You have risk, compliance, change management, operations, and most importantly, the balance sheet in the financials. AI and what Tony was saying, digital transformation, if it's of meaning, it belongs on a balance sheet, and should factor into how you value your company. All the cyber security, and all of the compliance, and all of the regulation, is all stuff, this framework exists, so look it up, and every time you start some kind of new machine learning project, or data sense project, say, have we checked the box on each of these standards that's within this machine? And if you haven't, maybe slow down and do your homework. - To see a day when data is going to be valued on the balance sheet. - It is. - It's already valued as part of the current, but it's good will. - Certainly market value, as we were just talking about. - Well, we're talking about all of the companies that have opted in, right. There's tens of thousands of small businesses just in this region alone that are opt-out. They're small family businesses, or businesses that really aren't even technology-aware. But data's being collected about them, it's being on Yelp, they're being rated, they're being reviewed, the success to their business is out of their hands. And I think what's really going to be interesting is, you look at the big data, you look at AI, you look at things like that, blockchain may even be a potential for some of that, because of mutability, but it's when all of those businesses, when the technology becomes a cost, it's cost-prohibitive now, for a lot of them, or they just don't want to do it, and they're proudly opt-out. In fact, we talked about that last night at dinner. But when they opt-in, the company that can do that, and can reach out to them in a way that is economically feasible, and bring them back in, where they control their data, where they control their information, and they do it in such a way where it helps them build their business, and it may be a generational business that's been passed on. Those kind of things are going to make a big impact, not only on the cloud, but the data being stored in the cloud, the AI, the applications that you talked about earlier, we talked about that. And that's where this bias, and some of these other things are going to have a tremendous impact if they're not dealt with now, at least ethically. - Well, I feel like we just got started, we're out of time. Time for a couple more comments, and then officially we have to wrap up. - Yeah, I had one thing to say, I mean, really, Henry Ford, and the creation of the automobile, back in the early 1900s, changed everything, because now we're no longer stuck in the country, we can get away from our parents, we can date without grandma and grandpa setting on the porch with us. (laughing) We can take long trips, so now we're looked at, we've sprawled out, we're not all living in the country anymore, and it changed America. So, AI has that same capabilities, it will automate mundane routine tasks that nobody wanted to do anyway. So, a lot of that will change things, but it's not going to be any different than the way things changed in the early 1900s. - It's like you were saying, constant reinvention. - I think that's a great point, let me make one observation on that. Every period of significant industrial change was preceded by the formation, a period of formation of new assets that nobody knew what to do with. Whether it was, what do we do, you know, industrial manufacturing, it was row houses with long shafts tied to an engine that was coal-fired, and drove a bunch of looms. Same thing, railroads, large factories for Henry Ford, before he figured out how to do an information-based notion of mass production. This is the period of asset formation for the next generation of social structures. - Those ship-makers are going to be all over these cars, I mean, you're going to have augmented reality right there, on your windshield. - Karen, bring it home. Give us the drop-the-mic moment. (laughing) - No pressure. - Your AV guys are not happy with that. So, I think the, it all comes down to, it's a people problem, a challenge, let's say that. The whole AI ML thing, people, it's a legal compliance thing. Enterprises are going to struggle with trying to meet five billion different types of compliance rules around data and its uses, about enforcement, because ROI is going to make risk of incarceration as well as return on investment, and we'll have to manage both of those. I think businesses are struggling with a lot of this complexity, and you just opened a whole bunch of questions that we didn't really have solid, "Oh, you can fix it by doing this." So, it's important that we think of this new world of data focus, data-driven, everything like that, is that the entire IT and business community needs to realize that focusing on data means we have to change how we do things and how we think about it, but we also have some of the same old challenges there. - Well, I have a feeling we're going to be talking about this for quite some time. What a great way to wrap up CUBE NYC here, our third day of activities down here at 37 Pillars, or Mercantile 37. Thank you all so much for joining us today. - Thank you. - Really, wonderful insights, really appreciate it, now, all this content is going to be available on theCUBE.net. We are exposing our video cloud, and our video search engine, so you'll be able to search our entire corpus of data. I can't wait to start searching and clipping up this session. Again, thank you so much, and thank you for watching. We'll see you next time.

from Las Vegas it's the queue covering VMworld 2018 brought to you by VMware and its ecosystem partners welcome back to the Mandalay Bay in Las Vegas everybody you're watching the cube the leader and live tech coverage my name is Dave Volante I'm here with my co-host Peter Burroughs Peter great to be working with you we haven't done much this week but I'm really excited to put a great week despite that it's been a great week this day three of our wall-to-wall coverage last year at vmworld one of the biggest hottest trends was data protection same thing this year a lot of buzz a lot of hype a lot of parties Rio Barrett is here so the vice president of product marketing for the data protection division of Dell EMC welcome great to see you again great to be here brian linden is here he is the IT Directorate Melanson Heath out of Austin as well Brian thanks very much for coming on thanks for having me so Rio I mean we talked and I have talked about this yeah what's going on in data protection I mean VMworld it's not it's become the hottest topic absolutely seeing you guys some of the VC funded startups or trying to duke it out throwing big parties all right you guys got all the customers everybody wants them you're fighting like crazy cloud has now come in what's your take what's going on that's really exciting I mean data protection I started out my career in data protection you know but move forward and back in data protection is hotter than ever it's it's great and I think it has to do with the trends that are happening out in the market the big mega trends that are happening we talked about distribution you know data moving out of the data center where the four walls are no longer defining how you secure something so security recoverability are becoming really critical as you talk about edge and data moving to the edge on to cloud computing and multi cloud computing I think it's going to be one of those frontiers that the enterprise still wants to have a reign over how do I recover my data no matter where it's sitting and how do I get it back and how do I secure it so it's very exciting so Brian talked about Melanson heath set it up the company you know tax accounting Boston based in New England etc your and really want to understand the drivers in IT but start with the company please yes lesson Heath is a top-10 accounting regional accounting firm in New England we have offices in Massachusetts New Hampshire and Maine we service other clients in Vermont etc a large portion of our focus is on auditing we do a lot of misrata it's school districts town cities we also do traditional tax accounting there's been advisory the full gamut of accounting professional services you run IT yes okay what are the big drivers in your business and how are they forcing you to sort of rethink the way in which you generally approach IT but specifically approach data protection over the years we've you know we've gone from the traditional everything on premise to moving things to the cloud whether it's a SAS provider or or whatever so we really need to be able to secure our data no matter where it is whether it's in the cloud game it'll have a backup locally between our various offices etc and uptime is paramount we have deadlines that don't don't shift the IRS does not care if we have a storm or we have something wrong with our building we have our professionals have hard deadlines so I one of my tasks is to make sure that no matter what happens we have a timely backup plan and I need to be able to focus on the business and not be focusing on worrying about the backup and data protection so obviously the other part that equation is the recovery plan so really you know we this is our ninth year of the cube and at the time you know when we first started it was a lot of talk about re-architecting backup to handle the the the V blender if you will and the lack of resources now all the conversation Brian just mentioned is cloud so how are you guys - that from a product standpoint oh my god yeah this has been a big topic of conversation I think one of the areas where we really differentiated you know one of the areas that Brian is in the middle of his mid-market and we see a big propensity for an appetite for cloud from an agility standpoint from time to respond standpoint and one of the biggest trends and we heard about it at yesterday's keynote as well is cloud as a disaster recovery site especially for customers that might not have a secondary site so we recently introduced a product called the DP 4400 Brian's actually the first customer to purchase the product so in July we announced it one of the key differentiation of that product is the ease of which customers can now access cloud you know whether it's for a long term retention or cloud disaster recovery without needing any additional hardware literally it's at the fingertips you manage it exactly the way you would you can manage it directly from your VMware operational tools and have access to cloud as a secondary site whether it's for dr or long term retention so that's one of the ways for mid market customers we're really bringing that cloud and bringing it at their fingertips from a recoverability standpoint and then we've done some exciting announcements Beth was here with yang-ming talking about some of the innovations that we've been delivering in cloud whether you're a service provider whether you're a big enterprise across our portfolio so I think we have that's by far one of our key differentiations and better together stories with VMware so I'm really fascinated Brian about some of the things are doing let me let me throw a thesis at you and Andrea you've probably heard this we tend to think that there's a difference between business and digital business and that difference is the degree to which a digital business uses data as an asset in many respects if you start thinking in those terms then data protection for the new world is not just the technical data is protecting your digital business now if you think about an accounting we normally associate accounting with manual processes manual activities but there's a lot more data being generated by your clients by your by the people that are providing the services how is this relationship between data the value of your business and the value of your service is driving you to adopt these new classes of solutions for millions and Heath we are almost completely paperless so all of our data all of our work product goes through technology so we need to you know it's it's imperative that we be protected if servers go down if the site goes down our professionals don't do work and time is money so you know it first is the old thinking of having paper storage or just having local backups if there's a significant enough then we can leverage the cloud and be able to disperse our staff to places where they can sit down with a computer and do work additionally like you said we're collecting a lot more data you know our various software processes are using more machine learning to get more out of that data so having that protected as it expands is critical so increasingly the services that you're providing to your clients are themselves becoming more digital as well that's correct yes so as you think about where this ends up would you characterize yourself as especially interested in the DP 4400 and the set of services that around that as facilitating that process are you going to be able to tell a better story to your business about how they can adopt new practices offer new services etc that are more digital in nature because of this I think so I think having the DP 4400 with its cloud connections will help our our partners our principals become more comfortable with the cloud and and not not fear it they've tended to be you know a little more insular and want to see and feel and you know know that the data is there so you know being able to recover to the cloud or just use the cloud natively is going to be a game-changer for for our firm and our business just add one thing that we've talked about with Brian one of the capabilities with the DP 4400 is the instant access and restore capabilities and we're seeing more of a trend especially in secondary storage platforms much like the ones we're using with DP 4400 we're basically all your data is there right so you're doing your data for recovery your data for disaster recovery for replication is in a place and we're seeing a trend towards wanting to have flash nvme cache to be able to actually do instant access and restore not only for recoverability purposes for app tests and dev type applications and data sharing so that trend has already left the station and even in our mid tier products like DP 4400 well you know targeted specifically for commercial buyers and midsize organizations we're bringing that enterprise class capabilities and making it available to them to be able to leverage not only cloud but also on-premise and your cloud is you all cloud you some cloud you hire hybrid we do have a lot of on-premise we are migrating things over the years to the cloud and that's certainly going to be the trend and is that in effect or in part what's driving you to rethink how you approach data protection or how did that affect your data protection decisions I think having the capacity to touch all types of systems and services is is critical we need to be thinking not what we're doing now but we're gonna do any year five years from now and you know just looking back to the past five years it's a completely different IT environment so ok so I want to translate a little marketing into what it means for the customers but we agree oh when you guys announced with DP 4400 it was simply powerful was kind of attack okay so what is what are you looking for from the standpoint of simplicity and a same question on on on on power simplicity that you know the DP 4400 is a 2-u unit goes right in the rack it's not use of various interconnected components that you have to you know figure out how to connect it's one interface it's extremely simple and quick to deploy you know I have a very lean IT shop we don't have a lot of time a lot of people to be devoting hours and days and weeks to getting a deed protection environment set up our previous solutions we're much more complicated different interfaces always changing interfaces and they didn't really work well I need you know I need to be able to just set it and forget it it's it's an insurance policy is what it is you know when something goes wrong I need to know what's going to happen - from the moment that the disaster is to recognize - when our staff will be able to get back up and working okay and I the DP for 4,400 just makes that extremely simple okay so it's simple not just simpler know that right it's simple example and what about the powerful piece what is it what does that mean the power of having everything in one unit it's one interface you know giving me and my staff the power to do what we need to do without having to have a degree in data protection it's very simple to learn very simple to use it just works and a couple of the things Brian and I talked about earlier was really no one wants to impact production to do data protection write it like you said it's an insurance policy so the performance of the platform is really significant I think performance performance without compromising efficiency because at the end of the day cost is a big consideration especially for midsize organizations when they're buying a solution so I think it's really hey it's simple to use simple to deploy but it's powerful because you can get your stuff done in the you know a lot of times for data protection which is almost zero these days with the efficiency I got also saying really quickly that I would also presume that because every single document is so valuable and so essential power also relates to being able to sustain the organization of that day absolutely absolutely more you know going further into power as we was indicating is the is the performance of the backups the deduplication rate sending things over the over the network to our disaster recovery site very quickly very efficiently we can pull back you know do backups during business hours don't have to throttle it to just the overnight hours which those hours are you know off hours are getting fewer further between because in tax season in particular we have people working seven days a week all day so to send that data it's work needs to go in comp in a compact form doesn't prevent our staff from doing work whenever they want to want and need to be able to do it organizations increasingly focusing on the data data has more value means it's got to be protected in new ways bring in cloud requires new architectures games on is a big market you know thirty billion dollar plus ten when you add it all up rating it on a lot of people want it you're the leader congratulations guys all right thanks very much for coming on the cube Thanks all right keep it right there buddy the cube will be back from VMworld 2018 right after this short break [Music]