(upbeat music) >> Morning guys and girls, welcome back to theCUBE's live coverage of Women in Data Science WIDS 2023 live at Stanford University. Lisa Martin here with my co-host for this segment, Tracy Zhang. We're really excited to be talking with a great female rockstar. You're going to learn a lot from her next, Jacqueline Kuo, solutions engineer at Dataiku. Welcome, Jacqueline. Great to have you. >> Thank you so much. >> Thank for being here. >> I'm so excited to be here. >> So one of the things I have to start out with, 'cause my mom Kathy Dahlia is watching, she's a New Yorker. You are a born and raised New Yorker and I learned from my mom and others. If you're born in New York no matter how long you've moved away, you are a New Yorker. There's you guys have like a secret club. (group laughs) >> I am definitely very proud of being born and raised in New York. My family immigrated to New York, New Jersey from Taiwan. So very proud Taiwanese American as well. But I absolutely love New York and I can't imagine living anywhere else. >> Yeah, yeah. >> I love it. >> So you studied, I was doing some research on you you studied mechanical engineering at MIT. >> Yes. >> That's huge. And you discovered your passion for all things data-related. You worked at IBM as an analytics consultant. Talk to us a little bit about your career path. Were you always interested in engineering STEM-related subjects from the time you were a child? >> I feel like my interests were ranging in many different things and I ended up landing in engineering, 'cause I felt like I wanted to gain a toolkit like a toolset to make some sort of change with or use my career to make some sort of change in this world. And I landed on engineering and mechanical engineering specifically, because I felt like I got to, in my undergrad do a lot of hands-on projects, learn every part of the engineering and design process to build products which is super-transferable and transferable skills sort of is like the trend in my career so far. Where after undergrad I wanted to move back to New York and mechanical engineering jobs are kind of few and fall far in between in the city. And I ended up landing at IBM doing analytics consulting, because I wanted to understand how to use data. I knew that data was really powerful and I knew that working with it could allow me to tell better stories to influence people across different industries. And that's also how I kind of landed at Dataiku to my current role, because it really does allow me to work across different industries and work on different problems that are just interesting. >> Yeah, I like the way that, how you mentioned building a toolkit when doing your studies at school. Do you think a lot of skills are still very relevant to your job at Dataiku right now? >> I think that at the core of it is just problem solving and asking questions and continuing to be curious or trying to challenge what is is currently given to you. And I think in an engineering degree you get a lot of that. >> Yeah, I'm sure. >> But I think that we've actually seen that a lot in the panels today already, that you get that through all different types of work and research and that kind of thoughtfulness comes across in all different industries too. >> Talk a little bit about some of the challenges, that data science is solving, because every company these days, whether it's an enterprise in manufacturing or a small business in retail, everybody has to be data-driven, because the end user, the end customer, whoever that is whether it's a person, an individual, a company, a B2B, expects to have a personalized custom experience and that comes from data. But you have to be able to understand that data treated properly, responsibly. Talk about some of the interesting projects that you're doing at Dataiku or maybe some that you've done in the past that are really kind of transformative across things climate change or police violence, some of the things that data science really is impacting these days. >> Yeah, absolutely. I think that what I love about coming to these conferences is that you hear about those really impactful social impact projects that I think everybody who's in data science wants to be working on. And I think at Dataiku what's great is that we do have this program called Ikig.AI where we work with nonprofits and we support them in their data and analytics projects. And so, a project I worked on was with the Clean Water, oh my goodness, the Ocean Cleanup project, Ocean Cleanup organization, which was amazing, because it was sort of outside of my day-to-day and it allowed me to work with them and help them understand better where plastic is being aggregated across the world and where it appears, whether that's on beaches or in lakes and rivers. So using data to help them better understand that. I feel like from a day-to-day though, we, in terms of our customers, they're really looking at very basic problems with data. And I say basic, not to diminish it, but really just to kind of say that it's high impact, but basic problems around how do they forecast sales better? That's a really kind of, sort of basic problem, but it's actually super-complex and really impactful for people, for companies when it comes to forecasting how much headcount they need to have in the next year or how much inventory to have if they're retail. And all of those are going to, especially for smaller companies, make a huge impact on whether they make profit or not. And so, what's great about working at Dataiku is you get to work on these high-impact projects and oftentimes I think from my perspective, I work as a solutions engineer on the commercial team. So it's just, we work generally with smaller customers and sometimes talking to them, me talking to them is like their first introduction to what data science is and what they can do with that data. And sort of using our platform to show them what the possibilities are and help them build a strategy around how they can implement data in their day-to-day. >> What's the difference? You were a data scientist by title and function, now you're a solutions engineer. Talk about the ascendancy into that and also some of the things that you and Tracy will talk about as those transferable, those transportable skills that probably maybe you learned in engineering, you brought data science now you're bringing to solutions engineering. >> Yeah, absolutely. So data science, I love working with data. I love getting in the weeds of things and I love, oftentimes that means debugging things or looking line by line at your code and trying to make it better. I found that on in the data science role, while those things I really loved, sometimes it also meant that I didn't, couldn't see or didn't have visibility into the broader picture of well like, well why are we doing this project? And who is it impacting? And because oftentimes your day-to-day is very much in the weeds. And so, I moved into sales or solutions engineering at Dataiku to get that perspective, because what a sales engineer does is support the sale from a technical perspective. And so, you really truly understand well, what is the customer looking for and what is going to influence them to make a purchase? And how do you tell the story of the impact of data? Because oftentimes they need to quantify well, if I purchase a software like Dataiku then I'm able to build this project and make this X impact on the business. And that is really powerful. That's where the storytelling comes in and that I feel like a lot of what we've been hearing today about connecting data with people who can actually do something with that data. That's really the bridge that we as sales engineers are trying to connect in that sales process. >> It's all about connectivity, isn't it? >> Yeah, definitely. We were talking about this earlier that it's about making impact and it's about people who we are analyzing data is like influencing. And I saw that one of the keywords or one of the biggest thing at Dataiku is everyday AI, so I wanted to just ask, could you please talk more about how does that weave into the problem solving and then day-to-day making an impact process? >> Yes, so I started working on Dataiku around three years ago and I fell in love with the product itself. The product that we have is we allow for people with different backgrounds. If you're coming from a data analyst background, data science, data engineering, maybe you are more of like a business subject matter expert, to all work in one unified central platform, one user interface. And why that's powerful is that when you're working with data, it's not just that data scientist working on their own and their own computer coding. We've heard today that it's all about connecting the data scientists with those business people, with maybe the data engineers and IT people who are actually going to put that model into production or other folks. And so, they all use different languages. Data scientists might use Python and R, your business people are using PowerPoint and Excel, everyone's using different tools. How do we bring them all in one place so that you can have conversations faster? So the business people can understand exactly what you're building with the data and can get their hands on that data and that model prediction faster. So that's what Dataiku does. That's the product that we have. And I completely forgot your question, 'cause I got so invested in talking about this. Oh, everyday AI. Yeah, so the goal of of Dataiku is really to allow for those maybe less technical people with less traditional data science backgrounds. Maybe they're data experts and they understand the data really well and they've been working in SQL for all their career. Maybe they're just subject matter experts and want to get more into working with data. We allow those people to do that through our no and low-code tools within our platform. Platform is very visual as well. And so, I've seen a lot of people learn data science, learn machine learning by working in the tool itself. And that's sort of, that's where everyday AI comes in, 'cause we truly believe that there are a lot of, there's a lot of unutilized expertise out there that we can bring in. And if we did give them access to data, imagine what we could do in the kind of work that they can do and become empowered basically with that. >> Yeah, we're just scratching the surface. I find data science so fascinating, especially when you talk about some of the real world applications, police violence, health inequities, climate change. Here we are in California and I don't know if you know, we're experiencing an atmospheric river again tomorrow. Californians and the rain- >> Storm is coming. >> We are not good... And I'm a native Californian, but we all know about climate change. People probably don't associate all of the data that is helping us understand it, make decisions based on what's coming what's happened in the past. I just find that so fascinating. But I really think we're truly at the beginning of really understanding the impact that being data-driven can actually mean whether you are investigating climate change or police violence or health inequities or your a grocery store that needs to become data-driven, because your consumer is expecting a personalized relevant experience. I want you to offer me up things that I know I was doing online grocery shopping, yesterday, I just got back from Europe and I was so thankful that my grocer is data-driven, because they made the process so easy for me. And but we have that expectation as consumers that it's going to be that easy, it's going to be that personalized. And what a lot of folks don't understand is the data the democratization of data, the AI that's helping make that a possibility that makes our lives easier. >> Yeah, I love that point around data is everywhere and the more we have, the actually the more access we actually are providing. 'cause now compute is cheaper, data is literally everywhere, you can get access to it very easily. And so, I feel like more people are just getting themselves involved and that's, I mean this whole conference around just bringing more women into this industry and more people with different backgrounds from minority groups so that we get their thoughts, their opinions into the work is so important and it's becoming a lot easier with all of the technology and tools just being open source being easier to access, being cheaper. And that I feel really hopeful about in this field. >> That's good. Hope is good, isn't it? >> Yes, that's all we need. But yeah, I'm glad to see that we're working towards that direction. I'm excited to see what lies in the future. >> We've been talking about numbers of women, percentages of women in technical roles for years and we've seen it hover around 25%. I was looking at some, I need to AnitaB.org stats from 2022 was just looking at this yesterday and the numbers are going up. I think the number was 26, 27.6% of women in technical roles. So we're seeing a growth there especially over pre-pandemic levels. Definitely the biggest challenge that still seems to be one of the biggest that remains is attrition. I would love to get your advice on what would you tell your younger self or the previous prior generation in terms of having the confidence and the courage to pursue engineering, pursue data science, pursue a technical role, and also stay in that role so you can be one of those females on stage that we saw today? >> Yeah, that's the goal right there one day. I think it's really about finding other people to lift and mentor and support you. And I talked to a bunch of people today who just found this conference through Googling it, and the fact that organizations like this exist really do help, because those are the people who are going to understand the struggles you're going through as a woman in this industry, which can get tough, but it gets easier when you have a community to share that with and to support you. And I do want to definitely give a plug to the WIDS@Dataiku team. >> Talk to us about that. >> Yeah, I was so fortunate to be a WIDS ambassador last year and again this year with Dataiku and I was here last year as well with Dataiku, but we have grown the WIDS effort so much over the last few years. So the first year we had two events in New York and also in London. Our Dataiku's global. So this year we additionally have one in the west coast out here in SF and another one in Singapore which is incredible to involve that team. But what I love is that everyone is really passionate about just getting more women involved in this industry. But then also what I find fortunate too at Dataiku is that we have a strong female, just a lot of women. >> Good. >> Yeah. >> A lot of women working as data scientists, solutions engineer and sales and all across the company who even if they aren't doing data work in a day-to-day, they are super-involved and excited to get more women in the technical field. And so. that's like our Empower group internally that hosts events and I feel like it's a really nice safe space for all of us to speak about challenges that we encounter and feel like we're not alone in that we have a support system to make it better. So I think from a nutrition standpoint every organization should have a female ERG to just support one another. >> Absolutely. There's so much value in a network in the community. I was talking to somebody who I'm blanking on this may have been in Barcelona last week, talking about a stat that showed that a really high percentage, 78% of people couldn't identify a female role model in technology. Of course, Sheryl Sandberg's been one of our role models and I thought a lot of people know Sheryl who's leaving or has left. And then a whole, YouTube influencers that have no idea that the CEO of YouTube for years has been a woman, who has- >> And she came last year to speak at WIDS. >> Did she? >> Yeah. >> Oh, I missed that. It must have been, we were probably filming. But we need more, we need to be, and it sounds like Dataiku was doing a great job of this. Tracy, we've talked about this earlier today. We need to see what we can be. And it sounds like Dataiku was pioneering that with that ERG program that you talked about. And I completely agree with you. That should be a standard program everywhere and women should feel empowered to raise their hand ask a question, or really embrace, "I'm interested in engineering, I'm interested in data science." Then maybe there's not a lot of women in classes. That's okay. Be the pioneer, be that next Sheryl Sandberg or the CTO of ChatGPT, Mira Murati, who's a female. We need more people that we can see and lean into that and embrace it. I think you're going to be one of them. >> I think so too. Just so that young girls like me like other who's so in school, can see, can look up to you and be like, "She's my role model and I want to be like her. And I know that there's someone to listen to me and to support me if I have any questions in this field." So yeah. >> Yeah, I mean that's how I feel about literally everyone that I'm surrounded by here. I find that you find role models and people to look up to in every conversation whenever I'm speaking with another woman in tech, because there's a journey that has had happen for you to get to that place. So it's incredible, this community. >> It is incredible. WIDS is a movement we're so proud of at theCUBE to have been a part of it since the very beginning, since 2015, I've been covering it since 2017. It's always one of my favorite events. It's so inspiring and it just goes to show the power that data can have, the influence, but also just that we're at the beginning of uncovering so much. Jacqueline's been such a pleasure having you on theCUBE. Thank you. >> Thank you. >> For sharing your story, sharing with us what Dataiku was doing and keep going. More power to you girl. We're going to see you up on that stage one of these years. >> Thank you so much. Thank you guys. >> Our pleasure. >> Our pleasure. >> For our guests and Tracy Zhang, this is Lisa Martin, you're watching theCUBE live at WIDS '23. #EmbraceEquity is this year's International Women's Day theme. Stick around, our next guest joins us in just a minute. (upbeat music)

(bright upbeat music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCUBE. We've got a great conversation featuring Arthur AI. I'm your host. I'm excited to have Adam Wenchel who's the Co-Founder and CEO. Thanks for joining us today, appreciate it. >> Yeah, thanks for having me on, John, looking forward to the conversation. >> I got to say, it's been an exciting world in AI or artificial intelligence. Just an explosion of interest kind of in the mainstream with the language models, which people don't really get, but they're seeing the benefits of some of the hype around OpenAI. Which kind of wakes everyone up to, "Oh, I get it now." And then of course the pessimism comes in, all the skeptics are out there. But this breakthrough in generative AI field is just awesome, it's really a shift, it's a wave. We've been calling it probably the biggest inflection point, then the others combined of what this can do from a surge standpoint, applications. I mean, all aspects of what we used to know is the computing industry, software industry, hardware, is completely going to get turbo. So we're totally obviously bullish on this thing. So, this is really interesting. So my first question is, I got to ask you, what's you guys taking? 'Cause you've been doing this, you're in it, and now all of a sudden you're at the beach where the big waves are. What's the explosion of interest is there? What are you seeing right now? >> Yeah, I mean, it's amazing, so for starters, I've been in AI for over 20 years and just seeing this amount of excitement and the growth, and like you said, the inflection point we've hit in the last six months has just been amazing. And, you know, what we're seeing is like people are getting applications into production using LLMs. I mean, really all this excitement just started a few months ago, with ChatGPT and other breakthroughs and the amount of activity and the amount of new systems that we're seeing hitting production already so soon after that is just unlike anything we've ever seen. So it's pretty awesome. And, you know, these language models are just, they could be applied in so many different business contexts and that it's just the amount of value that's being created is again, like unprecedented compared to anything. >> Adam, you know, you've been in this for a while, so it's an interesting point you're bringing up, and this is a good point. I was talking with my friend John Markoff, former New York Times journalist and he was talking about, there's been a lot of work been done on ethics. So there's been, it's not like it's new. It's like been, there's a lot of stuff that's been baking over many, many years and, you know, decades. So now everyone wakes up in the season, so I think that is a key point I want to get into some of your observations. But before we get into it, I want you to explain for the folks watching, just so we can kind of get a definition on the record. What's an LLM, what's a foundational model and what's generative ai? Can you just quickly explain the three things there? >> Yeah, absolutely. So an LLM or a large language model, it's just a large, they would imply a large language model that's been trained on a huge amount of data typically pulled from the internet. And it's a general purpose language model that can be built on top for all sorts of different things, that includes traditional NLP tasks like document classification and sentiment understanding. But the thing that's gotten people really excited is it's used for generative tasks. So, you know, asking it to summarize documents or asking it to answer questions. And these aren't new techniques, they've been around for a while, but what's changed is just this new class of models that's based on new architectures. They're just so much more capable that they've gone from sort of science projects to something that's actually incredibly useful in the real world. And there's a number of companies that are making them accessible to everyone so that you can build on top of them. So that's the other big thing is, this kind of access to these models that can power generative tasks has been democratized in the last few months and it's just opening up all these new possibilities. And then the third one you mentioned foundation models is sort of a broader term for the category that includes LLMs, but it's not just language models that are included. So we've actually seen this for a while in the computer vision world. So people have been building on top of computer vision models, pre-trained computer vision models for a while for image classification, object detection, that's something we've had customers doing for three or four years already. And so, you know, like you said, there are antecedents to like, everything that's happened, it's not entirely new, but it does feel like a step change. >> Yeah, I did ask ChatGPT to give me a riveting introduction to you and it gave me an interesting read. If we have time, I'll read it. It's kind of, it's fun, you get a kick out of it. "Ladies and gentlemen, today we're a privileged "to have Adam Wenchel, Founder of Arthur who's going to talk "about the exciting world of artificial intelligence." And then it goes on with some really riveting sentences. So if we have time, I'll share that, it's kind of funny. It was good. >> Okay. >> So anyway, this is what people see and this is why I think it's exciting 'cause I think people are going to start refactoring what they do. And I've been saying this on theCUBE now for about a couple months is that, you know, there's a scene in "Moneyball" where Billy Beane sits down with the Red Sox owner and the Red Sox owner says, "If people aren't rebuilding their teams on your model, "they're going to be dinosaurs." And it reminds me of what's happening right now. And I think everyone that I talk to in the business sphere is looking at this and they're connecting the dots and just saying, if we don't rebuild our business with this new wave, they're going to be out of business because there's so much efficiency, there's so much automation, not like DevOps automation, but like the generative tasks that will free up the intellect of people. Like just the simple things like do an intro or do this for me, write some code, write a countermeasure to a hack. I mean, this is kind of what people are doing. And you mentioned computer vision, again, another huge field where 5G things are coming on, it's going to accelerate. What do you say to people when they kind of are leaning towards that, I need to rethink my business? >> Yeah, it's 100% accurate and what's been amazing to watch the last few months is the speed at which, and the urgency that companies like Microsoft and Google or others are actually racing to, to do that rethinking of their business. And you know, those teams, those companies which are large and haven't always been the fastest moving companies are working around the clock. And the pace at which they're rolling out LLMs across their suite of products is just phenomenal to watch. And it's not just the big, the large tech companies as well, I mean, we're seeing the number of startups, like we get, every week a couple of new startups get in touch with us for help with their LLMs and you know, there's just a huge amount of venture capital flowing into it right now because everyone realizes the opportunities for transforming like legal and healthcare and content creation in all these different areas is just wide open. And so there's a massive gold rush going on right now, which is amazing. >> And the cloud scale, obviously horizontal scalability of the cloud brings us to another level. We've been seeing data infrastructure since the Hadoop days where big data was coined. Now you're seeing this kind of take fruit, now you have vertical specialization where data shines, large language models all of a set up perfectly for kind of this piece. And you know, as you mentioned, you've been doing it for a long time. Let's take a step back and I want to get into how you started the company, what drove you to start it? Because you know, as an entrepreneur you're probably saw this opportunity before other people like, "Hey, this is finally it, it's here." Can you share the origination story of what you guys came up with, how you started it, what was the motivation and take us through that origination story. >> Yeah, absolutely. So as I mentioned, I've been doing AI for many years. I started my career at DARPA, but it wasn't really until 2015, 2016, my previous company was acquired by Capital One. Then I started working there and shortly after I joined, I was asked to start their AI team and scale it up. And for the first time I was actually doing it, had production models that we were working with, that was at scale, right? And so there was hundreds of millions of dollars of business revenue and certainly a big group of customers who were impacted by the way these models acted. And so it got me hyper-aware of these issues of when you get models into production, it, you know. So I think people who are earlier in the AI maturity look at that as a finish line, but it's really just the beginning and there's this constant drive to make them better, make sure they're not degrading, make sure you can explain what they're doing, if they're impacting people, making sure they're not biased. And so at that time, there really weren't any tools to exist to do this, there wasn't open source, there wasn't anything. And so after a few years there, I really started talking to other people in the industry and there was a really clear theme that this needed to be addressed. And so, I joined with my Co-Founder John Dickerson, who was on the faculty in University of Maryland and he'd been doing a lot of research in these areas. And so we ended up joining up together and starting Arthur. >> Awesome. Well, let's get into what you guys do. Can you explain the value proposition? What are people using you for now? Where's the action? What's the customers look like? What do prospects look like? Obviously you mentioned production, this has been the theme. It's not like people woke up one day and said, "Hey, I'm going to put stuff into production." This has kind of been happening. There's been companies that have been doing this at scale and then yet there's a whole follower model coming on mainstream enterprise and businesses. So there's kind of the early adopters are there now in production. What do you guys do? I mean, 'cause I think about just driving the car off the lot is not, you got to manage operations. I mean, that's a big thing. So what do you guys do? Talk about the value proposition and how you guys make money? >> Yeah, so what we do is, listen, when you go to validate ahead of deploying these models in production, starts at that point, right? So you want to make sure that if you're going to be upgrading a model, if you're going to replacing one that's currently in production, that you've proven that it's going to perform well, that it's going to be perform ethically and that you can explain what it's doing. And then when you launch it into production, traditionally data scientists would spend 25, 30% of their time just manually checking in on their model day-to-day babysitting as we call it, just to make sure that the data hasn't drifted, the model performance hasn't degraded, that a programmer did make a change in an upstream data system. You know, there's all sorts of reasons why the world changes and that can have a real adverse effect on these models. And so what we do is bring the same kind of automation that you have for other kinds of, let's say infrastructure monitoring, application monitoring, we bring that to your AI systems. And that way if there ever is an issue, it's not like weeks or months till you find it and you find it before it has an effect on your P&L and your balance sheet, which is too often before they had tools like Arthur, that was the way they were detected. >> You know, I was talking to Swami at Amazon who I've known for a long time for 13 years and been on theCUBE multiple times and you know, I watched Amazon try to pick up that sting with stage maker about six years ago and so much has happened since then. And he and I were talking about this wave, and I kind of brought up this analogy to how when cloud started, it was, Hey, I don't need a data center. 'Cause when I did my startup that time when Amazon, one of my startups at that time, my choice was put a box in the colo, get all the configuration before I could write over the line of code. So the cloud became the benefit for that and you can stand up stuff quickly and then it grew from there. Here it's kind of the same dynamic, you don't want to have to provision a large language model or do all this heavy lifting. So that seeing companies coming out there saying, you can get started faster, there's like a new way to get it going. So it's kind of like the same vibe of limiting that heavy lifting. >> Absolutely. >> How do you look at that because this seems to be a wave that's going to be coming in and how do you guys help companies who are going to move quickly and start developing? >> Yeah, so I think in the race to this kind of gold rush mentality, race to get these models into production, there's starting to see more sort of examples and evidence that there are a lot of risks that go along with it. Either your model says things, your system says things that are just wrong, you know, whether it's hallucination or just making things up, there's lots of examples. If you go on Twitter and the news, you can read about those, as well as sort of times when there could be toxic content coming out of things like that. And so there's a lot of risks there that you need to think about and be thoughtful about when you're deploying these systems. But you know, you need to balance that with the business imperative of getting these things into production and really transforming your business. And so that's where we help people, we say go ahead, put them in production, but just make sure you have the right guardrails in place so that you can do it in a smart way that's going to reflect well on you and your company. >> Let's frame the challenge for the companies now that you have, obviously there's the people who doing large scale production and then you have companies maybe like as small as us who have large linguistic databases or transcripts for example, right? So what are customers doing and why are they deploying AI right now? And is it a speed game, is it a cost game? Why have some companies been able to deploy AI at such faster rates than others? And what's a best practice to onboard new customers? >> Yeah, absolutely. So I mean, we're seeing across a bunch of different verticals, there are leaders who have really kind of started to solve this puzzle about getting AI models into production quickly and being able to iterate on them quickly. And I think those are the ones that realize that imperative that you mentioned earlier about how transformational this technology is. And you know, a lot of times, even like the CEOs or the boards are very personally kind of driving this sense of urgency around it. And so, you know, that creates a lot of movement, right? And so those companies have put in place really smart infrastructure and rails so that people can, data scientists aren't encumbered by having to like hunt down data, get access to it. They're not encumbered by having to stand up new platforms every time they want to deploy an AI system, but that stuff is already in place. There's a really nice ecosystem of products out there, including Arthur, that you can tap into. Compared to five or six years ago when I was building at a top 10 US bank, at that point you really had to build almost everything yourself and that's not the case now. And so it's really nice to have things like, you know, you mentioned AWS SageMaker and a whole host of other tools that can really accelerate things. >> What's your profile customer? Is it someone who already has a team or can people who are learning just dial into the service? What's the persona? What's the pitch, if you will, how do you align with that customer value proposition? Do people have to be built out with a team and in play or is it pre-production or can you start with people who are just getting going? >> Yeah, people do start using it pre-production for validation, but I think a lot of our customers do have a team going and they're starting to put, either close to putting something into production or about to, it's everything from large enterprises that have really sort of complicated, they have dozens of models running all over doing all sorts of use cases to tech startups that are very focused on a single problem, but that's like the lifeblood of the company and so they need to guarantee that it works well. And you know, we make it really easy to get started, especially if you're using one of the common model development platforms, you can just kind of turn key, get going and make sure that you have a nice feedback loop. So then when your models are out there, it's pointing out, areas where it's performing well, areas where it's performing less well, giving you that feedback so that you can make improvements, whether it's in training data or futurization work or algorithm selection. There's a number of, you know, depending on the symptoms, there's a number of things you can do to increase performance over time and we help guide people on that journey. >> So Adam, I have to ask, since you have such a great customer base and they're smart and they got teams and you're on the front end, I mean, early adopters is kind of an overused word, but they're killing it. They're putting stuff in the production's, not like it's a test, it's not like it's early. So as the next wave comes of fast followers, how do you see that coming online? What's your vision for that? How do you see companies that are like just waking up out of the frozen, you know, freeze of like old IT to like, okay, they got cloud, but they're not yet there. What do you see in the market? I see you're in the front end now with the top people really nailing AI and working hard. What's the- >> Yeah, I think a lot of these tools are becoming, or every year they get easier, more accessible, easier to use. And so, you know, even for that kind of like, as the market broadens, it takes less and less of a lift to put these systems in place. And the thing is, every business is unique, they have their own kind of data and so you can use these foundation models which have just been trained on generic data. They're a great starting point, a great accelerant, but then, in most cases you're either going to want to create a model or fine tune a model using data that's really kind of comes from your particular customers, the people you serve and so that it really reflects that and takes that into account. And so I do think that these, like the size of that market is expanding and its broadening as these tools just become easier to use and also the knowledge about how to build these systems becomes more widespread. >> Talk about your customer base you have now, what's the makeup, what size are they? Give a taste a little bit of a customer base you got there, what's they look like? I'll say Capital One, we know very well while you were at there, they were large scale, lot of data from fraud detection to all kinds of cool stuff. What do your customers now look like? >> Yeah, so we have a variety, but I would say one area we're really strong, we have several of the top 10 US banks, that's not surprising, that's a strength for us, but we also have Fortune 100 customers in healthcare, in manufacturing, in retail, in semiconductor and electronics. So what we find is like in any sort of these major verticals, there's typically, you know, one, two, three kind of companies that are really leading the charge and are the ones that, you know, in our opinion, those are the ones that for the next multiple decades are going to be the leaders, the ones that really kind of lead the charge on this AI transformation. And so we're very fortunate to be working with some of those. And then we have a number of startups as well who we love working with just because they're really pushing the boundaries technologically and so they provide great feedback and make sure that we're continuing to innovate and staying abreast of everything that's going on. >> You know, these early markups, even when the hyperscalers were coming online, they had to build everything themselves. That's the new, they're like the alphas out there building it. This is going to be a big wave again as that fast follower comes in. And so when you look at the scale, what advice would you give folks out there right now who want to tee it up and what's your secret sauce that will help them get there? >> Yeah, I think that the secret to teeing it up is just dive in and start like the, I think these are, there's not really a secret. I think it's amazing how accessible these are. I mean, there's all sorts of ways to access LLMs either via either API access or downloadable in some cases. And so, you know, go ahead and get started. And then our secret sauce really is the way that we provide that performance analysis of what's going on, right? So we can tell you in a very actionable way, like, hey, here's where your model is doing good things, here's where it's doing bad things. Here's something you want to take a look at, here's some potential remedies for it. We can help guide you through that. And that way when you're putting it out there, A, you're avoiding a lot of the common pitfalls that people see and B, you're able to really kind of make it better in a much faster way with that tight feedback loop. >> It's interesting, we've been kind of riffing on this supercloud idea because it was just different name than multicloud and you see apps like Snowflake built on top of AWS without even spending any CapEx, you just ride that cloud wave. This next AI, super AI wave is coming. I don't want to call AIOps because I think there's a different distinction. If you, MLOps and AIOps seem a little bit old, almost a few years back, how do you view that because everyone's is like, "Is this AIOps?" And like, "No, not kind of, but not really." How would you, you know, when someone says, just shoots off the hip, "Hey Adam, aren't you doing AIOps?" Do you say, yes we are, do you say, yes, but we do differently because it's doesn't seem like it's the same old AIOps. What's your- >> Yeah, it's a good question. AIOps has been a term that was co-opted for other things and MLOps also has people have used it for different meanings. So I like the term just AI infrastructure, I think it kind of like describes it really well and succinctly. >> But you guys are doing the ops. I mean that's the kind of ironic thing, it's like the next level, it's like NextGen ops, but it's not, you don't want to be put in that bucket. >> Yeah, no, it's very operationally focused platform that we have, I mean, it fires alerts, people can action off them. If you're familiar with like the way people run security operations centers or network operations centers, we do that for data science, right? So think of it as a DSOC, a Data Science Operations Center where all your models, you might have hundreds of models running across your organization, you may have five, but as problems are detected, alerts can be fired and you can actually work the case, make sure they're resolved, escalate them as necessary. And so there is a very strong operational aspect to it, you're right. >> You know, one of the things I think is interesting is, is that, if you don't mind commenting on it, is that the aspect of scale is huge and it feels like that was made up and now you have scale and production. What's your reaction to that when people say, how does scale impact this? >> Yeah, scale is huge for some of, you know, I think, I think look, the highest leverage business areas to apply these to, are generally going to be the ones at the biggest scale, right? And I think that's one of the advantages we have. Several of us come from enterprise backgrounds and we're used to doing things enterprise grade at scale and so, you know, we're seeing more and more companies, I think they started out deploying AI and sort of, you know, important but not necessarily like the crown jewel area of their business, but now they're deploying AI right in the heart of things and yeah, the scale that some of our companies are operating at is pretty impressive. >> John: Well, super exciting, great to have you on and congratulations. I got a final question for you, just random. What are you most excited about right now? Because I mean, you got to be pretty pumped right now with the way the world is going and again, I think this is just the beginning. What's your personal view? How do you feel right now? >> Yeah, the thing I'm really excited about for the next couple years now, you touched on it a little bit earlier, but is a sort of convergence of AI and AI systems with sort of turning into AI native businesses. And so, as you sort of do more, get good further along this transformation curve with AI, it turns out that like the better the performance of your AI systems, the better the performance of your business. Because these models are really starting to underpin all these key areas that cumulatively drive your P&L. And so one of the things that we work a lot with our customers is to do is just understand, you know, take these really esoteric data science notions and performance and tie them to all their business KPIs so that way you really are, it's kind of like the operating system for running your AI native business. And we're starting to see more and more companies get farther along that maturity curve and starting to think that way, which is really exciting. >> I love the AI native. I haven't heard any startup yet say AI first, although we kind of use the term, but I guarantee that's going to come in all the pitch decks, we're an AI first company, it's going to be great run. Adam, congratulations on your success to you and the team. Hey, if we do a few more interviews, we'll get the linguistics down. We can have bots just interact with you directly and ask you, have an interview directly. >> That sounds good, I'm going to go hang out on the beach, right? So, sounds good. >> Thanks for coming on, really appreciate the conversation. Super exciting, really important area and you guys doing great work. Thanks for coming on. >> Adam: Yeah, thanks John. >> Again, this is Cube Conversation. I'm John Furrier here in Palo Alto, AI going next gen. This is legit, this is going to a whole nother level that's going to open up huge opportunities for startups, that's going to use opportunities for investors and the value to the users and the experience will come in, in ways I think no one will ever see. So keep an eye out for more coverage on siliconangle.com and theCUBE.net, thanks for watching. (bright upbeat music)

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>Mhm >>Welcome to the cube where we're celebrating the EC 2/15 birthday anniversary. My name is Dave Volonte and we're joined right now by Sandy carter, Vice President of AWS. Welcome Sandy, it's great to see you again, >>David. So great to see you too. Thanks for having me on the show today. >>Very welcome. We were last physically together. I think it was reinvent 2019. Hopefully I'll see you before 2022. But first happy birthday to EC two. I mean, it's hard to imagine back in 2006, the degree to which EC two would impact our industry. Sandy, >>I totally agree. You know, I joined a W S about 4.5 years ago in EC two and it's, it's even amazing to see what's just happened in the last 4.5 years. So I'm with you. Nobody really expected the momentum, but EC two has really shone brightly in value to our customers. >>You know, we've done the public sector summit, you know, many times. It's a great event. Things are a little different in public sector as you well know. So talk about the public sector momentum with EC two and that journey. What have you seen? >>Yeah, so it's a great question day. So I had to go back in the time vault. You know, public sector was founded in 2010 and we were actually founded by the amazon process writing a paper setting up a two pizza team, which happened to be six people. And that journey really started with a lot of our public sector customers thinking that we don't know about the cloud. So we might want to do a pilot or just look at non mission critical workloads now public sector and I know you know this day but public sector is more than just government, it has education, not for profit healthcare and now space. But everybody at that time was very skeptical. So we had to really work hard to migrate some workloads over. And one of our very first non mission critical workloads was the U. S. Navy. Um and what they did was the Navy Media Services actually moved images over to EC two. Now today that seems like oh that's pretty easy. But back then that was a big monumental reference. Um and we had to spend a lot of time on training and education to win the hearts and souls of our customers. So back then we had half of the floor and Herndon Washington, we just had a few people and that room really became a training room. We trained our reps, we trained our customers um research drive. A lot of our early adopters accounts like Nasa and jpl. And um then when cloud first came out and governments that started with the U. S. A. And we announced Govcloud, you know, things really picked up, we had migration of significant workloads. So if you think back to that S. A. P. And just moving media over um with the Navy, the Navy and S. A. P. Migrated their largest S A P E R P solution to the cloud in that time as well. Um, then we started international. Our journey continued with the UK International was UK and us was us. Then we added a P. J. And latin America and Canada. And then of course the partner team which you know, is very close to my heart. Partners today are about 73% of our overall public sector business. And it started out with some interesting small pro program SVS being very crucial to that, accelerating adoption. And then of course now the journey has continued with Covid. That has really accelerated that movement to the cloud. And we're seeing, you know, use of ec two to really help us drive by the cute power needed for A I N. M. L. And taking all that data in from IOT and computing that data. And are they are. Um, and we're really seeing that journey just continue and we see no end in sight. >>So if we can stay in the infancy and sort of the adolescent years of public sector, I mean, remember, I mean as analysts, we were really excited about, you know, the the the introduction of of of of EC two. But but there was a lot of skepticism in whatever industry, financial services, healthcare concerns about security, I presume it was similar in public sector, but I'm interested in how you you dealt with those challenges, how you you listen to folks, you know, how did you drive that leadership to where it is today? >>Yeah, you're right. The the first questions were what is the cloud? Doesn't amazon sell books? What is this clown thing? Um, what is easy to, what is easy to stand for and then what the heck is an instance? You know, way back when there was one instance, it didn't even have a name. And today of course we have over 400 instant types with different names for each one. Um and the big challenges you asked about challenges, the big challenges that we had to face. Dave were first and foremost, how do we educate? Um we had to educate our employees and then we had to educate our customers. So we created these really innovative hands on training programmes, white boarding um, sessions that we needed. They were wildly popular. So we really have to do that and then also prove security as you know. So you asked how we listen to our customers and of course we followed the amazon way we work backwards from where we were. So at that time, customers needed education. And so we started there um, data was really important. We needed to make customer or data for government more available as well. So for instance, we first started hosting the Census Bureau for instance. Um and that was all on EC two. So we had lots of early adopters and I think the early adopters around EC two really helped us to remember. I said that the UK was our international office for a while. So we had NIH we had a genomes project and the UK Ministry of Justice as well. And we had to prove security out. We had to prove how this drove a structured GovCloud and then we had to also prove it out with our partners with things like helping them get fed ramped or other certifications. I'll for that sort of thing as well. And so we really lead in those early days through that education and training. Um we lead with pilots to show the potential of the possible and we lead with that security setting those security standards and those compliance certifications, always listening to the customer, always listening to the partner, knowing how important the partners we're going to be. So for example, recovery dot gov was the first government wide system that moved to the cloud. Um the recovery transparency board was first overseeing that Recovery act spending, which included stimulus tracking website. I don't know if you remember that, but they hosted the recovery dot gov On amazon.com using EC two. And that site quickly made information available to a million visitors per hour and at that time, that was amazing. And the cost savings were significant. We also launched Govcloud. You'd asked about GovCloud earlier and that federal cloud computing strategy when the U. S. Government came out with cloud first and they had to consider what is really going to compel these federal agencies to consider cloud. They had Public-sector customers had 70 requirements for security and safety of the data that we came out with Govcloud to open up all those great opportunities. And I think Dave we continue to leave because we are customer obsessed uh you know, still supporting more security standards and compliance sort than any other provider. Um You know, now we lead with data not just data for census or images for the US Navy, but we've got now data in space and ground station and data at scale with customers like Finra who's now doing 100 billion financial transactions. Not just that one million from the early days. So it has been a heck of a ride for public sector and I love the way that the public sector team really used and leveraged the leadership principles. Re invent and simplify dive deep. Be obsessed with the customers start where they are. Um and make sure that you're always always always listening to what they need. >>You know, it's interesting just observing public sector. It's not uncommon, especially because of the certifications that some of the services, you know come out after they come out for the commercial sector. And I remember years ago when I was at I. D. C. I was kind of the steward of the public sector business. And that was a time when everybody was trying to focus in public sector on commercial off the shelf software. That was the big thing. And they want to understand, they wanted to look at commercial use cases and how they could apply them to government. And when I dug in a little bit and met with generals and like eight different agencies, I was struck by how many really smart people and the things that they were doing. And I said at the time, you know, a lot of my commercial clients could learn a lot from you. And so the reason I bring that up is because I saw the same thing with Govcloud because there was a lot of skepticism in various industries, particularly regulated industries, financial services, healthcare. And then when Govcloud hit and the CIA deal hit, people said, whoa CIA, they're like the most security conscious industry or organization in the world. And so I feel as though in a way public sector led that that breakthrough. So I'm wondering when you think about EC two today and the momentum that it has in the government, Are there similar things that you see? Where's the momentum today in public sector? >>You are right on target day? I mean that CIA was a monumental moment and that momentum with ever increasing adoption to the cloud has continued in public sector. In fact today, public sector is one of our fastest growing areas. So we've got um, you know, thousands of startups or multiple countries that were helping out today to really ignite that innovation. We have over 4000 government agencies, 9000 education agencies. Um 2000 public sector partners from all over the globe. 24,000 not for profit organizations. And what I see is the way that they're using EC two um is is leading the pack now, especially after Covid, you know, many of these folks accelerated their journey because of Covid. They got to the cloud faster and now they are doing some really things that no one else is doing like sending an outpost postbox into space or leveraging, you know robots and health care for sure. So that momentum continues today and I love that you were the champion of that you know way back when even when you were with I. D. C. >>So I want to ask you, you sort of touched on some interesting use cases, what are some of the more unusual ones and maybe breakthrough use cases that you see? >>Oh so yeah we have a couple. So one is um I mentioned it earlier but there is a robot now that is powered by IOT and EC two and the robot helps to take temperature and and readings for folks that are entering the hospital in latin America really helped during Covid, one of my favorites. It actually blew the socks off of verne or two and you know that's hard to do is a space startup called lunar outpost and they are synthesizing oxygen on mars now that's, that's driven by Ec two. That's crazy. Right? Um, we see state governments like new york, they've got this vision zero traffic and they're leveraging that to prevent accidents all through new york city. I used to live in new york city. So this is really needed. Um, and it continues like with education, we see university of Illinois and Splunk one of our partners, they created a boarding pass for students to get back to school. So I have a daughter in college. Um, and you know, it's really hard for her to prove that she's had the vaccine or that she's tested negative on the covid test. They came out with a past of this little boarding pass, just like you used to get on an airplane to get into different classes and labs and then a couple of my favorites and you guys actually filmed the Cherokee nation. So the Cherokee nation, the chief of the Cherokee nation was on our silicon um show and silicon angles show and the cube featured them And as the chief talked about how he preserves the Cherokee language. And if you remember the Cherokee language has been used to help out the US in many different ways and Presidio. One of our partners helped to create a game, a super cool game that links in with unity To help teach that next generation the language while they're playing a game and then last but not least axle three d out of the UK. Um, they're using easy to, to save lives. They've created a three D imaging process for people getting ready to get kidney transplants and they have just enhanced that taken the time frame down for months. Now today's that they can actually articulate whether the kidney transplant will work. And when I talked to roger their Ceo, they're doing R. O. L return on life's not return on investment. So those are just some of the unusual and breakthrough use cases that we see powered by E. C. To >>Sandy. I'll give you the last word. Your final closing comments. >>Well, my final closing comments are happy birthday to ec two celebrating 15 years. What a game changer and value added. It has been the early days of Ec two. Of course we're about education like what is the cloud? Why is a bookseller doing it. But um, easy to really help to create a new hub of value Now. We've got customers moving so fast with modernization using a I. M and M. L. Containers survivalists. Um, and all of these things are really changing the game and leveling it up as we increased that business connection. So I think the future is really bright. We've only just begun. We've only just begun with EC two and we've only just begun with public sector. You know, our next great moments are still left to come. >>Well, Sandy, thanks so much. Always Great to see you. Really appreciate your time. >>Thank you so much. Dave. I really appreciate it. And happy birthday again to E. C. To keep >>It right there were celebrating Ec 2's 15th birthday right back. >>Mhm.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020, sponsored by Intel and AWS. >>Welcome back to the cubes coverage of AWS reinvent 2020 I'm Lisa Martin. Joining me next is one of our cube alumni. Breton McMillan is back the director of us, federal for AWS. Right. It's great to see you glad that you're safe and well. >>Great. It's great to be back. Uh, I think last year when we did the cube, we were on the convention floor. It feels very different this year here at reinvent, it's gone virtual and yet it's still true to how reinvent always been. It's a learning conference and we're releasing a lot of new products and services for our customers. >>Yes. A lot of content, as you say, the one thing I think I would say about this reinvent, one of the things that's different, it's so quiet around us. Normally we're talking loudly over tens of thousands of people on the showroom floor, but great. That AWS is still able to connect in such an actually an even bigger way with its customers. So during Theresa Carlson's keynote, want to get your opinion on this or some info. She talked about the AWS open data sponsorship program, and that you guys are going to be hosting the national institutes of health, NIH sequence, read archive data, the biologist, and may former gets really excited about that. Talk to us about that because especially during the global health crisis that we're in, that sounds really promising >>Very much is I am so happy that we're working with NIH on this and multiple other initiatives. So the secret greed archive or SRA, essentially what it is, it's a very large data set of sequenced genomic data. And it's a wide variety of judge you gnomic data, and it's got a knowledge human genetic thing, but all life forms or all branches of life, um, is in a SRA to include viruses. And that's really important here during the pandemic. Um, it's one of the largest and oldest, um, gen sequence genomic data sets are out there and yet it's very modern. It has been designed for next generation sequencing. So it's growing, it's modern and it's well used. It's one of the more important ones that it's out there. One of the reasons this is so important is that we know to find cures for what a human ailments and disease and death, but by studying the gem genomic code, we can come up with the answers of these or the scientists can come up with answer for that. And that's what Amazon is doing is we're putting in the hands of the scientists, the tools so that they can help cure heart disease and diabetes and cancer and, um, depression and yes, even, um, uh, viruses that can cause pandemics. >>So making this data, sorry, I'm just going to making this data available to those scientists. Worldwide is incredibly important. Talk to us about that. >>Yeah, it is. And so, um, within NIH, we're working with, um, the, um, NCBI when you're dealing with NIH, there's a lot of acronyms, uh, and uh, at NIH, it's the national center for, um, file type technology information. And so we're working with them to make this available as an open data set. Why, why this is important is it's all about increasing the speed for scientific discovery. I personally think that in the fullness of time, the scientists will come up with cures for just about all of the human ailments that are out there. And it's our job at AWS to put into the hands of the scientists, the tools they need to make things happen quickly or in our lifetime. And I'm really excited to be working with NIH on that. When we start talking about it, there's multiple things. The scientists needs. One is access to these data sets and SRA. >>It's a very large data set. It's 45 petabytes and it's growing. I personally believe that it's going to double every year, year and a half. So it's a very large data set and it's hard to move that data around. It's so much easier if you just go into the cloud, compute against it and do your research there in the cloud. And so it's super important. 45 petabytes, give you an idea if it were all human data, that's equivalent to have a seven and a half million people or put another way 90% of everybody living in New York city. So that's how big this is. But then also what AWS is doing is we're bringing compute. So in the cloud, you can scale up your compute, scale it down, and then kind of the third they're. The third leg of the tool of the stool is giving the scientists easy access to the specialized tool sets they need. >>And we're doing that in a few different ways. One that the people would design these toolsets design a lot of them on AWS, but then we also make them available through something called AWS marketplace. So they can just go into marketplace, get a catalog, go in there and say, I want to launch this resolve work and launches the infrastructure underneath. And it speeds the ability for those scientists to come up with the cures that they need. So SRA is stored in Amazon S3, which is a very popular object store, not just in the scientific community, but virtually every industry uses S3. And by making this available on these public data sets, we're giving the scientists the ability to speed up their research. >>One of the things that Springs jumps out to me too, is it's in addition to enabling them to speed up research, it's also facilitating collaboration globally because now you've got the cloud to drive all of this, which allows researchers and completely different parts of the world to be working together almost in real time. So I can imagine the incredible power that this is going to, to provide to that community. So I have to ask you though, you talked about this being all life forms, including viruses COVID-19, what are some of the things that you think we can see? I expect this to facilitate. Yeah. >>So earlier in the year we took the, um, uh, genetic code or NIH took the genetic code and they, um, put it in an SRA like format and that's now available on AWS and, and here's, what's great about it is that you can now make it so anybody in the world can go to this open data set and start doing their research. One of our goals here is build back to a democratization of research. So it used to be that, um, get, for example, the very first, um, vaccine that came out was a small part. It's a vaccine that was done by our rural country doctor using essentially test tubes in a microscope. It's gotten hard to do that because data sets are so large, you need so much computer by using the power of the cloud. We've really democratized it and now anybody can do it. So for example, um, with the SRE data set that was done by NIH, um, organizations like the university of British Columbia, their, um, cloud innovation center is, um, doing research. And so what they've done is they've scanned, they, um, SRA database think about it. They scanned out 11 million entries for, uh, coronavirus sequencing. And that's really hard to do in a typical on-premise data center. Who's relatively easy to do on AWS. So by making this available, we can have a larger number of scientists working on the problems that we need to have solved. >>Well, and as the, as we all know in the U S operation warp speed, that warp speed alone term really signifies how quickly we all need this to be progressing forward. But this is not the first partnership that AWS has had with the NIH. Talk to me about what you guys, what some of the other things are that you're doing together. >>We've been working with NIH for a very long time. Um, back in 2012, we worked with NIH on, um, which was called the a thousand genome data set. This is another really important, um, data set and it's a large number of, uh, against sequence human genomes. And we moved that into, again, an open dataset on AWS and what's happened in the last eight years is many scientists have been able to compute about on it. And the other, the wonderful power of the cloud is over time. We continue to bring out tools to make it easier for people to work. So what they're not they're computing using our, um, our instance types. We call it elastic cloud computing. whether they're doing that, or they were doing some high performance computing using, um, uh, EMR elastic MapReduce, they can do that. And then we've brought up new things that really take it to the next layer, like level like, uh, Amazon SageMaker. >>And this is a, um, uh, makes it really easy for, um, the scientists to launch machine learning algorithms on AWS. So we've done the thousand genome, uh, dataset. Um, there's a number of other areas within NIH that we've been working on. So for example, um, over at national cancer Institute, we've been providing some expert guidance on best practices to how, how you can architect and work on these COVID related workloads. Um, NIH does things with, um, collaboration with many different universities, um, over 2,500, um, academic institutions. And, um, and they do that through grants. And so we've been working with doc office of director and they run their grant management applications in the RFA on AWS, and that allows it to scale up and to work very efficiently. Um, and then we entered in with, um, uh, NIH into this program called strides strides as a program for knowing NIH, but also all these other institutions that work within NIH to use the power of the cloud use commercial cloud for scientific discovery. And when we started that back in July of 2018, long before COVID happened, it was so great that we had that up and running because now we're able to help them out through the strides program. >>Right. Can you imagine if, uh, let's not even go there? I was going to say, um, but so, okay. So the SRA data is available through the AWS open data sponsorship program. You talked about strides. What are some of the other ways that AWS system? >>Yeah, no. So strides, uh, is, uh, you know, wide ranging through multiple different institutes. So, um, for example, over at, uh, the national heart lung and blood Institute, uh, do di NHL BI. I said, there's a lot of acronyms and I gel BI. Um, they've been working on, um, harmonizing, uh, genomic data. And so working with the university of Michigan, they've been analyzing through a program that they call top of med. Um, we've also been working with a NIH on, um, establishing best practices, making sure everything's secure. So we've been providing, um, AWS professional services that are showing them how to do this. So one portion of strides is getting the right data set and the right compute in the right tools, in the hands of the scientists. The other areas that we've been working on is making sure the scientists know how to use it. And so we've been developing these cloud learning pathways, and we started this quite a while back, and it's been so helpful here during the code. So, um, scientists can now go on and they can do self-paced online courses, which we've been really helping here during the, during the pandemic. And they can learn how to maximize their use of cloud technologies through these pathways that we've developed for them. >>Well, not education is imperative. I mean, there, you think about all of the knowledge that they have with within their scientific discipline and being able to leverage technology in a way that's easy is absolutely imperative to the timing. So, so, um, let's talk about other data sets that are available. So you've got the SRA is available. Uh, what are their data sets are available through this program? >>What about along a wide range of data sets that we're, um, uh, doing open data sets and in general, um, these data sets are, um, improving the human condition or improving the, um, the world in which we live in. And so, um, I've talked about a few things. There's a few more, uh, things. So for example, um, there's the cancer genomic Atlas that we've been working with, um, national cancer Institute, as well as the national human genomic research Institute. And, um, that's a very important data set that being computed against, um, uh, throughout the world, uh, commonly within the scientific community, that data set is called TCGA. Um, then we also have some, uh, uh, datasets are focused on certain groups. So for example, kids first is a data set. That's looking at a lot of the, um, challenges, uh, in diseases that kids get every kind of thing from very rare pediatric cancer as to heart defects, et cetera. >>And so we're working with them, but it's not just in the, um, uh, medical side. We have open data sets, um, with, uh, for example, uh, NOAA national ocean open national oceanic and atmospheric administration, um, to understand what's happening better with climate change and to slow the rate of climate change within the department of interior, they have a Landsat database that is looking at pictures of their birth cell, like pictures of the earth, so we can better understand the MCO world we live in. Uh, similarly, uh, NASA has, um, a lot of data that we put out there and, um, over in the department of energy, uh, there's data sets there, um, that we're researching against, or that the scientists are researching against to make sure that we have better clean, renewable energy sources, but it's not just government agencies that we work with when we find a dataset that's important. >>We also work with, um, nonprofit organizations, nonprofit organizations are also in, they're not flush with cash and they're trying to make every dollar work. And so we've worked with them, um, organizations like the child mind Institute or the Allen Institute for brain science. And these are largely like neuro imaging, um, data. And we made that available, um, via, um, our open data set, um, program. So there's a wide range of things that we're doing. And what's great about it is when we do it, you democratize science and you allowed many, many more science scientists to work on these problems. They're so critical for us. >>The availability is, is incredible, but also the, the breadth and depth of what you just spoke. It's not just government, for example, you've got about 30 seconds left. I'm going to ask you to summarize some of the announcements that you think are really, really critical for federal customers to be paying attention to from reinvent 2020. >>Yeah. So, um, one of the things that these federal government customers have been coming to us on is they've had to have new ways to communicate with their customer, with the public. And so we have a product that we've had for a while called on AWS connect, and it's been used very extensively throughout government customers. And it's used in industry too. We've had a number of, um, of announcements this weekend. Jasmine made multiple announcements on enhancement, say AWS connect or additional services, everything from helping to verify that that's the right person from AWS connect ID to making sure that that customer's gets a good customer experience to connect wisdom or making sure that the managers of these call centers can manage the call centers better. And so I'm really excited that we're putting in the hands of both government and industry, a cloud based solution to make their connections to the public better. >>It's all about connections these days, but I wish we had more time, cause I know we can unpack so much more with you, but thank you for joining me on the queue today, sharing some of the insights, some of the impacts and availability that AWS is enabling the scientific and other federal communities. It's incredibly important. And we appreciate your time. Thank you, Lisa, for Brett McMillan. I'm Lisa Martin. You're watching the cubes coverage of AWS reinvent 2020.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel AWS and our community partners. Welcome to the cubes Coverage of AWS reinvent 2020. The digital version I'm Lisa Martin and I'm joined by a couple of guests from Unisys. Please welcome unprompted high BP and Cloud CTO on income. Great to have you on the program. Thanks for joining me today. >>Great to be here leader >>and a new pre or a new Ram Raj, VP of Cloud Services. A new welcome. Great to have you on a swell. Great >>to be here in this virtual AWS being that great. >>Thank you. Very socially Distance We're following all the guidelines here. A new Let's start with you. I'd love to get just kind of Ah, you know, a vision of the AWS Unisys partnership. I know you guys are advanced consulting partner MSP. Tell me about that partnership. >>Absolutely. Lisa, we see our clients on a cloud journey which we accelerate with Unisys Cloud Services and AWS partnership is a big piece of that again. Way thorough. We have bean rated in aws MSP partner Come out very, very highly from those msb audited our, uh and we're investing in multiple competencies across the boat as well. So and we work very closely with AWS in terms off innovating in sharing our platform cloud 44 world map In looking at what our customers looking around the corner, what services could be co developed. So we're looking at some potential I o T engagements to jointly with AWS is, well, eso you're always co inventing and it's a great partnership with a W s >>excellent. And you let's stick with you Following on hybrid Cloud Journey you mentioned the Cloud Forte platform. I wanted to understand what that platform is, how your co developing that with AWS and how your customers are benefiting >>absolutely s. Um, every year Unisys does a cloud barometer study across thousands of our clients and and we got some interesting takeaways from that. Essentially two thirds of her clients that have started this cloud journey believe they don't really realize the benefits out of that and up thio 53% off. The the respondents said they needed some help with cloud security. And this is where I believe that Unisys Cloud Services has a strong viewpoint and can find their AWS, um, journey, no matter where, what challenges they're facing, whether it's budgetary challenges on optimizing AWS and whether it's getting I t operations right when you move your applications to AWS. Um, and is it is it getting the that I have seen cops models established? So no matter where clients are in the A journey, we look to accelerate with our set of solutions and services, and we're very proud about the fact that we respond very me to make sure our clients can innovate and achieve the business outcomes that they need. For example, with California Stink City, we were able to work with them on the AWS. John Pretty set up a native other lake and analytics on top of it so we could actually predict and influence graduation rates with students. Our scores are higher than any off are coming because of the outcomes that we deliver for our clients. And it's really about business outcomes and 40 platform, which helps us drive those outcomes. I mean, probably do you want to add on without cloud 40 platform? >>Sure, I know eso, as as I knew was saying cloud for the platform provides AH set off capabilities that allows us to create an offer highly differentiated services with Unisys Pipe and, as was mentioned earlier, our cloud solutions are are able to help customers no matter where they are in their car, in their cloud journeys, whether it's ah Greenfield opportunity, where they where the customers are intending to move to the cloud, or if it's a brownfield opportunity where they already have adopted the cloud and are looking to manage and operate and optimize their deployments. Cloud Forte Platform and our Cloud Solutions are able to provide, uh, customized solution for that customer context to really deliver the solution that addresses some of the pain points that you talked about. The keeping points really relate to security to get secured. It also relates to cost optimization and then optimizing the cloud purse, a cloud deployment hybrid cloud deployment of the key requirement. So our cloud 40 platform health drives the key use cases. The key pain points that our customers are looking for through a combination off accelerators, the number of cloud photo accelerators that enable customers to rapidly prove it provisioned customers and to rapidly migrate to the cloud with God rails so that they're the secure, their compliant. And then we've got the the Cloud Cloud 40 Cloud management platform for ensuring provisioning onda management and operations, along with cost optimization capabilities and the eyelid operations. So it's a comprehensive suite off services and solutions that addresses the key business outcomes. There are customers are are looking for >>outcomes. Focused is absolutely critical, especially these days. I knew I wanted to go back to you for a second. You talked about the Unisys Barometer study, and I like the name of that. When was that done? And I'm just wondering if there are certain things that you saw this year from a customer. Cloud journey. Need perspective because of the pandemic that have really influenced that barometer >>Wait Question. Hey said and development is study. The last version of it was done late last year, and we're still waiting on the ones from this year. So, but we're starting to see some of the trends that were influenced by the pandemic. We saw rush to cloud when the pandemic hit because business adopt to to remote workers to do more digital selling and then seeing our CEO is kind of struggle with optimizing and maximizing the results off their cloud. Spend right, So So that's a unique challenge that that we're seeing based on our tryingto interaction. So the rush to the cloud and the ask for more spend optimization and in terms of spend optimization, that's an interesting facet because, uh, it cuts through my multiple angles. It's it's cuts through having the platforms around, being able to dio right predictions on where you spend is going, and then it also it's across collaborative effort. Finn ops. As we see it, we call it as a synopsis of is that we bring to our clients it's passing with multiple organizations, including finance, to sometimes figure out. Where will this business be? Where should you spend be? What should be the reserved instance buys right. So combining cloud knowledge with financial knowledge and organizational and business knowledge. And that's the service that we bring to our clients with our phenoms services. At least a great question about how how is I kind of making the current business climate affecting our operating models? Um, like we said, there's increased ask for Finn ops is an increase. Ask for security ops because security threats have only amplified. And then the entire cloud ups model. I think hybrid cloud operations its's prompted us to rethink a lot off. How do we do? I t operations and and we're investing a lot in terms of automation and then underpinning that by ai led operation. So, um, you talked about the client management platform making sure we've got the best automation and processes which are repeatable around all the way from just doing provisioning to data operations to optimization. Just making all of that robust and repeatable um, is such a value. Add to clients because then they can see SOS can sleep at night knowing that everything is taken care off and, uh, the CIA, the CEOs can be rest assured that hey, they're not going to get that AWS bill that's going to make them hit the roof. So making sure we've got the right checks and balances and approval flow is all a part of our child management platform. And at that point, I know you really passionate AI and the role that it plays in operations and the entire cloud management platform and cloud for day platform So your thoughts in the poem? >>Yes, sir. No, thank you. But so yeah, yeah, I led operations is really part off the bigger question and the pain point that customers are faced with, which is I've reached the cloud. Now, how do I optimized and get benefits from the cloud on the benefits is around. You know, uh, utility for on demand access to resource is, uh, this cost optimization potential and the security, uh, cloud security potential that, if not managed properly, can really blow up in the face. And unfortunately, you know that in the case on the AI ops led Operation Side, that's again a huge foretell area where Unisys Investor is investing a lot off a lot off i p and creating a lot of differentiation. And the objective there is to ask Customers adopt cloud for day as they adopt Unisys Cloud services. They're able to take advantage off cost optimization capabilities, which essentially looks at historical usage on predicts future usage, based on a number off a I artificial intelligence and machine learning technologies that that is able to give you predictions that otherwise very hard to hard to get and, uh, in the cloud environment because of the sheer velocity volume and variety of the data. Doing that in a manual fashion is very, very hard. So automated machine learning driven approach is very productive is very effective on, you know, some of the outcomes that we've achieved is is just amazing. We've been able to save up to 25% off infrastructure costs through the island operations. About 40% off infrastructure incidents have bean reduced due to root cause analysis. Eso onda up to 35% off meantime, to resolution improvements in time. So huge customer benefits driven by e I led operations. The I am a approaches to following the problem. >>Let me see him If I could stick with you for a second big numbers that you just talked about and we talked a few minutes ago about outcomes. It's all about outcomes right now with this rush to cloud as as a new set. And we talked about this on the Cuba all the time. We've seen that the last eight months there is an acceleration of this digital transformation. I'm just curious una come from your perspective as the VP and CTO cloud how are you? What are some of the things that you advise customers to do if they need to rush to the cloud 21 just, you know, move their business quickly and not have the stay on life support. What are some of the things that you advise them to do when they're in this? Maybe a few months ago, when they were in the beginning of this? >>Yeah, that's that's a very interesting question, and lot off our clients are faced with that question as they either they're already in the cloud or the deciding to migrate to the cloud on the whole journey. Customer journeys for either stepping on the cloud or managing and operating the optimizing the cloud deployments is very key. So if you look at the market research that's out there and what we hear from our customers, the key challenges are really, really around. How do I migrate to the cloud without facing a lot of bottlenecks and challenges, and how do I overcome them? So that's the keeping pain point and again cloud for the advisory services and the cloud services that we offer allows customers to take up uh, toe work with us, and we work with the customer to ensure that they're able to do that on and then rapidly migrating to the cloud, managing and operating their operations. The hybrid cloud operations in optimized fashion is a huge challenge. How do they migrate? How do they migrate with security and compliance not being compromised once they're in the cloud, ensuring cloud security is and compliance is is maintained. Ensuring that the cost structure is is optimized so that they're not being mawr wants to move to the cloud compared to on premises and and then taking advantage of the whole cloud. Deployments to ensure you're looking at data are nothing the data to derive meaningful business outcomes. So if the entire end to end customer journey that needs to be looked at optimized. And that's where Unisys comes in with a cloud for the platform where we work with the customers to enhance the journeys. And in this case I want to mention CSU, which is, uh, the California State University, where the approach Unisys to really work with them to deliver uh, cloud services by enhancing the the objective was to enhance the student learning experience to enable adoption off off the technology by the students but also to achieve better performance, better adoption cost savings on we were able to deliver about 30% better performance help realize about 30 33% savings on 40% plus growth in adoption. On this was for about half a million student bodies. The 50,000 plus faculty staff spread across 23 campuses. So deploying, optimizing on and managing the infrastructure is something that Unisys does. Does that. And this is an example of that. I know you want to add anything to that. >>Absolutely Any Permanente's really well and, >>uh, >>it Z also securing, making sure securities with the >>journey >>it Z O Keefe or hybrid cloud. Um, uh, at least I'm sure you're aware of the Unisys tagline is securing tomorrow. So who better s so we really, really take that really, really seriously in terms of making sure we seek clients cloud journeys, and >>you >>probably heard the statistic from her. About 80% off cloud breaches are due to mis configuration, and this could have bean prevented. And and it doesn't. There's an element of the human angle in there. You believe strongly that can automate using our platform. So we've got 2000 plus security policies, which makes sure which again enables our clients to be compliant as well. So no matter what compliant standards, we've got several off our clients, for example, in the financial sector that are hosted on AWS and that we managed and they have to, especially the US They have to comply with Y de f s, the New York Department of Financial Services and making sure that they compliant with all the standards out there, which is next plus plus in this case. So that's part of what we do and enabling those journeys and then just keeping up with the rate of change like on different was talking about the variety and velocity of the data and and the rate of change of the applications out there, especially as businesses react to the pandemic and have to cope with the changing business paradigms out there. They have to be quick. Um, so we've got a drugmaker, one of the most premium drug makers in the US, who is who is against it on AWS, and, uh, they're racing for the cure and they are always looking at How do they get drugs quicker to the market? And that means accelerating applications. And we know that based on research by the Dora study, that if you adopt develops paradigms, you can accelerate 200 times faster than if you didn't. But then you have to underpin backward security as well. So really helping this adopt deaths are cops in all their deployments to AWS so that they can really race for the cure. That's the kind of business outcomes that we really, uh, are really, really proud to drive for our clients. >>Excellent on a pound. Let's wrap this up with you. We've just got about 30 seconds left sticking on the security front. It's such a huge topic right now. It has been for a long time, but even more so during these unprecedented times when you're talking with customers, what makes Unisys unique from a security perspective? >>So first thing is to understand what it takes to solve the hybrid cloud security problem. Like you said earlier, that's the biggest pain point that we hear from customers from our clients on. It's all over the market research all the breaches that have happened, like the zoom breach that happened that compromised about half a million, you know, user log ins. And then there was also the the Marriott breach, where about half a billion users names and credential for legal legal. So it Zaveri easy for customers, potential customers to become like a headline. And our our job really are the companies to make sure that they're not the next capital one or the next Marriott, uh, showing up in the newspaper. So we kind of look at their customer deployments situation on. We put together a comprehensive into an hybrid cloud solution, hybrid cloud security and compliance solution that includes look, securing their cloud infrastructure, their cloud workloads in terms of applications that they might have secured, and also to look at securing their applications, which may or may not be running on the cloud. So we kind of take a very holistic approach, using our homegrown solutions and partner solutions to create a comprehensive, robust hybrid cloud solution that really fits the customer context and and so we we are essentially a trusted adviser for our for our clients to create the solution, which again, at the cloud 40 ashore, which is a cloud security posture management solution. We have a cloud worker protection solution on then stealth, which is a full stack security solution if combined together with the other cloud Forte platform components on. We wrap this up in a matter of security services offering that allows US customers to have complete peace of mind as we take care off assessment remediation monitoring on, then continues Posture, posture, management. I know. Do you want to add anything to that? >>If I'm think in terms of closing, I think like you covered it well, we've got platform competence and services that run the gamut off the off the life cycle from migrations to two transformations. And one thing that I think in terms of outcomes of these, uh, when the service built around it have really helped us. Dr is, um is kind of responding especially to our public sector clients, very passionate about enabling cloud journeys for our public sector clients. And we'll take the example of Georgia Technology s So this is the G t A. Is the technology agency for all services are 14 of the agencies in Georgia and many of these public sector agencies had to quickly adopt cloud to deal with the report workers. Whether it was v D I whether it was chatbots on cloud, um, it was it was, ah, brand new world out there, the new normal. And it was just using the cloud management platform that anyone was refering to. We were able to kind of take them from taking three months. Plus to be able to provision workloads Thio thio less than 30 minutes to provision workloads. And this is this is across hybrid cloud. So and this is >>a big outcome, especially in this time where things were changing so quickly. Well, I wish we had more time, guys because I could tell you have a lot more that you can share. You're just gonna have to come back. And I like that. The tagline securing tomorrow. Adding on to what Anu Pump said So your customers don't become the next headline. I think they would all appreciate that. Thank you both. So much for joining me on the Cube today and sharing what's the latest with Unisys. We appreciate your time. Thank you. Thank >>you for having us >>aren't my pleasure for my guests. I'm Lisa Martin. And you're watching the Cube? Yeah,

>>from around the globe. It's the Cube presenting innovation for good, brought to you by on shape. >>Hello, everyone, and welcome to Innovation for Good Program, hosted by the Cuban. Brought to You by on Shape, which is a PTC company. My name is Dave Valentin. I'm coming to you from our studios outside of Boston. I'll be directing the conversations today. It's a very exciting, all live program. We're gonna look at how product innovation has evolved and where it's going and how engineers, entrepreneurs and educators are applying cutting edge, cutting edge product development techniques and technology to change our world. You know, the pandemic is, of course, profoundly impacted society and altered how individuals and organizations they're gonna be thinking about an approaching the coming decade. Leading technologists, engineers, product developers and educators have responded to the new challenges that we're facing from creating lifesaving products to helping students learn from home toe how to apply the latest product development techniques and solve the world's hardest problems. And in this program, you'll hear from some of the world's leading experts and practitioners on how product development and continuous innovation has evolved, how it's being applied toe positive positively affect society and importantly where it's going in the coming decades. So let's get started with our first session fueling Tech for good. And with me is John Hirschbeck, who is the president of the Suffers, a service division of PTC, which acquired on shape just over a year ago, where John was the CEO and co founder, and Dana Grayson is here. She is the co founder and general partner at Construct Capital, a new venture capital firm. Folks, welcome to the program. Thanks so much for coming on. >>Great to be here, Dave. >>All right, John. >>You're very welcome. Dana. Look, John, let's get into it for first Belated congratulations on the acquisition of Von Shape. That was an awesome seven year journey for your company. Tell our audience a little bit about the story of on shape, but take us back to Day zero. Why did you and your co founders start on shape? Well, >>actually, start before on shaping the You know, David, I've been in this business for almost 40 years. The business of building software tools for product developers and I had been part of some previous products in the industry and companies that had been in their era. Big changes in this market and about, you know, a little Before founding on shape, we started to see the problems product development teams were having with the traditional tools of that era years ago, and we saw the opportunity presented by Cloud Web and Mobile Technology. And we said, Hey, we could use Cloud Web and Mobile to solve the problems of product developers make their Their business is run better. But we have to build an entirely new system, an entirely new company, to do it. And that's what on shapes about. >>Well, so notwithstanding the challenges of co vid and difficulties this year, how is the first year been as, Ah, division of PTC for you guys? How's business? Anything you can share with us? >>Yeah, our first year of PTC has been awesome. It's been, you know, when you get acquired, Dave, you never You know, you have great optimism, but you never know what life will really be like. It's sort of like getting married or something, you know, until you're really doing it, you don't know. And so I'm happy to say that one year into our acquisition, um, PTC on shape is thriving. It's worked out better than I could have imagined a year ago. Along always, I mean sales are up. In Q four, our new sales rate grew 80% vs Excuse me, our fiscal Q four Q three. In the calendar year, it grew 80% compared to the year before. Our educational uses skyrocketing with around 400% growth, most recently year to year of students and teachers and co vid. And we've launched a major cloud platform using the core of on shape technology called Atlas. So, um, just tons of exciting things going on a TTC. >>That's awesome. But thank you for sharing some of those metrics. And of course, you're very humble individual. You know, people should know a little bit more about you mentioned, you know, we founded Solid Works, co founded Solid where I actually found it solid works. You had a great exit in the in the late nineties. But what I really appreciate is, you know, you're an entrepreneur. You've got a passion for the babies that you you helped birth. You stayed with the salt systems for a number of years. The company that quiet, solid works well over a decade. And and, of course, you and I have talked about how you participated in the the M I T. Blackjack team. You know, back in the day, a zai say you're very understated, for somebody was so accomplished. Well, >>that's kind of you, but I tend to I tend Thio always keep my eye more on what's ahead. You know what's next, then? And you know, I look back Sure to enjoy it and learn from it about what I can put to work making new memories, making new successes. >>Love it. Okay, let's bring Dana into the conversation. Hello, Dana. You look you're a fairly early investor in in on shape when you were with any A And and I think it was like it was a serious B, but it was very right close after the A raise. And and you were and still are a big believer in industrial transformation. So take us back. What did you see about on shape back then? That excited you. >>Thanks. Thanks for that. Yeah. I was lucky to be a early investment in shape. You know, the things that actually attracted me. Don shape were largely around John and, uh, the team. They're really setting out to do something, as John says humbly, something totally new, but really building off of their background was a large part of it. Um, but, you know, I was really intrigued by the design collaboration side of the product. Um, I would say that's frankly what originally attracted me to it. What kept me in the room, you know, in terms of the industrial world was seeing just if you start with collaboration around design what that does to the overall industrial product lifecycle accelerating manufacturing just, you know, modernizing all the manufacturing, just starting with design. So I'm really thankful to the on shape guys, because it was one of the first investments I've made that turned me on to the whole sector. And while just such a great pleasure to work with with John and the whole team there. Now see what they're doing inside PTC. >>And you just launched construct capital this year, right in the middle of a pandemic and which is awesome. I love it. And you're focused on early stage investing. Maybe tell us a little bit about construct capital. What your investment thesis is and you know, one of the big waves that you're hoping to ride. >>Sure, it construct it is literally lifting out of any what I was doing there. Um uh, for on shape, I went on to invest in companies such as desktop metal and Tulip, to name a couple of them form labs, another one in and around the manufacturing space. But our thesis that construct is broader than just, you know, manufacturing and industrial. It really incorporates all of what we'd call foundational industries that have let yet to be fully tech enabled or digitized. Manufacturing is a big piece of it. Supply chain, logistics, transportation of mobility or not, or other big pieces of it. And together they really drive, you know, half of the GDP in the US and have been very under invested. And frankly, they haven't attracted really great founders like they're on in droves. And I think that's going to change. We're seeing, um, entrepreneurs coming out of the tech world orthe Agnelli into these industries and then bringing them back into the tech world, which is which is something that needs to happen. So John and team were certainly early pioneers, and I think, you know, frankly, obviously, that voting with my feet that the next set, a really strong companies are going to come out of the space over the next decade. >>I think it's a huge opportunity to digitize the sort of traditionally non digital organizations. But Dana, you focused. I think it's it's accurate to say you're focused on even Mawr early stage investing now. And I want to understand why you feel it's important to be early. I mean, it's obviously riskier and reward e er, but what do you look for in companies and and founders like John >>Mhm, Um, you know, I think they're different styles of investing all the way up to public market investing. I've always been early stage investors, so I like to work with founders and teams when they're, you know, just starting out. Um, I happened to also think that we were just really early in the whole digital transformation of this world. You know, John and team have been, you know, back from solid works, etcetera around the space for a long time. But again, the downstream impact of what they're doing really changes the whole industry. And and so we're pretty early and in digitally transforming that market. Um, so that's another reason why I wanna invest early now, because I do really firmly believe that the next set of strong companies and strong returns for my own investors will be in the spaces. Um, you know, what I look for in Founders are people that really see the world in a different way. And, you know, sometimes some people think of founders or entrepreneurs is being very risk seeking. You know, if you asked John probably and another successful entrepreneurs, they would call themselves sort of risk averse, because by the time they start the company, they really have isolated all the risk out of it and think that they have given their expertise or what they're seeing their just so compelled to go change something, eh? So I look for that type of attitude experience a Z. You can also tell from John. He's fairly humble. So humility and just focus is also really important. Um, that there's a That's a lot of it. Frankly, >>Excellent. Thank you, John. You got such a rich history in the space. Uh, and one of you could sort of connect the dots over time. I mean, when you look back, what were the major forces that you saw in the market in in the early days? Particularly days of on shape on? And how is that evolved? And what are you seeing today? Well, >>I think I touched on it earlier. Actually, could I just reflect on what Dana said about risk taking for just a quick one and say, throughout my life, from blackjack to starting solid works on shape, it's about taking calculated risks. Yes, you try to eliminate the risk Sa's much as you can, but I always say, I don't mind taking a risk that I'm aware of, and I've calculated through as best I can. I don't like taking risks that I don't know I'm taking. That's right. You >>like to bet on >>sure things as much as you sure things, or at least where you feel you. You've done the research and you see them and you know they're there and you know, you, you you keep that in mind in the room, and I think that's great. And Dana did so much for us. Dana, I want to thank you again. For all that, you did it every step of the way, from where we started to to, you know, your journey with us ended formally but continues informally. Now back to you, Dave, I think, question about the opportunity and how it's shaped up. Well, I think I touched on it earlier when I said It's about helping product developers. You know, our customers of the people build the future off manufactured goods. Anything you think of that would be manufacturing factory. You know, the chair you're sitting in machine that made your coffee. You know, the computer you're using, the trucks that drive by on the street, all the covert product research, the equipment being used to make vaccines. All that stuff is designed by someone, and our job is given the tools to do it better. And I could see the problems that those product developers had that we're slowing them down with using the computing systems of the time. When we built solid works, that was almost 30 years ago. If people don't realize that it was in the early >>nineties and you know, we did the >>best we could for the early nineties, but what we did. We didn't anticipate the world of today. And so people were having problems with just installing the systems. Dave, you wouldn't believe how hard it is to install these systems. You need toe speck up a special windows computer, you know, and make sure you've got all the memory and graphics you need and getting to get that set up. You need to make sure the device drivers air, right, install a big piece of software. Ah, license key. I'm not making this up. They're still around. You may not even know what those are. You know, Dennis laughing because, you know, zero cool people do things like this anymore. Um, and it only runs some windows. You want a second user to use it? They need a copy. They need a code. Are they on the same version? It's a nightmare. The teams change, you know? You just say, Well, get everyone on the software. Well, who's everyone? You know, you got a new vendor today? A new customer tomorrow, a new employee. People come on and off the team. The other problem is the data stored in files, thousands of files. This isn't like a spreadsheet or word processor, where there's one file to pass around these air thousands of files to make one, even a simple product. People were tearing their hair out. John, what do we do? I've got copies everywhere. I don't know where the latest version is. We tried like, you know, locking people out so that only one person can change it At the time that works against speed, it works against innovation. We saw what was happening with Cloud Web and mobile. So what's happened in the years since is every one of the forces that product developers experience the need for speed, the need for innovation, the need to be more efficient with their people in their capital. Resource is every one of those trends have been amplified since we started on shape by a lot of forces in the world. And covert is amplified all those the need for agility and remote work cove it is amplified all that the same time, The acceptance of cloud. You know, a few years ago, people were like cloud, you know, how is that gonna work now They're saying to me, You know, increasingly, how would you ever even have done this without the cloud. How do you make solid works work without the cloud? How would that even happen? You know, once people understand what on shapes about >>and we're the >>Onley full SAS solution software >>as a service, >>full SAS solution in our industry. So what's happened in those years? Same problems we saw earlier, but turn up the gain, their bigger problems. And with cloud, we've seen skepticism of years ago turn into acceptance. And now even embracement in the cova driven new normal. >>Yeah. So a lot of friction in the previous environments cloud obviously a huge factor on, I guess. I guess Dana John could see it coming, you know, in the early days of solid works with, you know, had Salesforce, which is kind of the first major independent SAS player. Well, I guess that was late nineties. So his post solid works, but pre in shape and their work day was, you know, pre on shape in the mid two thousands. And and but But, you know, the bet was on the SAS model was right for Crick had and and product development, you know, which maybe the time wasn't a no brainer. Or maybe it was, I don't know, but Dana is there. Is there anything that you would invest in today? That's not Cloud based? >>Um, that's a great question. I mean, I think we still see things all the time in the manufacturing world that are not cloud based. I think you know, the closer you get to the shop floor in the production environment. Um e think John and the PTC folks would agree with this, too, but that it's, you know, there's reliability requirements, performance requirements. There's still this attitude of, you know, don't touch the printing press. So the cloud is still a little bit scary sometimes. And I think hybrid cloud is a real thing for those or on premise. Solutions, in some cases is still a real thing. What what we're more focused on. And, um, despite whether it's on premise or hybrid or or SAS and Cloud is a frictionless go to market model, um, in the companies we invest in so sass and cloud, or really make that easy to adopt for new users, you know, you sign up, started using a product, um, but whether it's hosted in the cloud, whether it's as you can still distribute buying power. And, um, I would I'm just encouraging customers in the customer world and the more industrial environment to entrust some of their lower level engineers with more budget discretionary spending so they can try more products and unlock innovation. >>Right? The unit economics are so compelling. So let's bring it, you know, toe today's you know, situation. John, you decided to exit about a year ago. You know? What did you see in PTC? Other than the obvious money? What was the strategic fit? >>Yeah, Well, David, I wanna be clear. I didn't exit anything. Really? You >>know, I love you and I don't like that term exit. I >>mean, Dana had exit is a shareholder on and so it's not It's not exit for me. It's just a step in the journey. What we saw in PTC was a partner. First of all, that shared our vision from the top down at PTC. Jim Hempleman, the CEO. He had a great vision for for the impact that SAS can make based on cloud technology and really is Dana of highlighted so much. It's not just the technology is how you go to market and the whole business being run and how you support and make the customers successful. So Jim shared a vision for the potential. And really, really, um said Hey, come join us and we can do this bigger, Better, faster. We expanded the vision really to include this Atlas platform for hosting other SAS applications. That P D. C. I mean, David Day arrived at PTC. I met the head of the academic program. He came over to me and I said, You know, and and how many people on your team? I thought he'd say 5 40 people on the PTC academic team. It was amazing to me because, you know, we were we were just near about 100 people were required are total company. We didn't even have a dedicated academic team and we had ah, lot of students signing up, you know, thousands and thousands. Well, now we have hundreds of thousands of students were approaching a million users and that shows you the power of this team that PTC had combined with our product and technology whom you get a big success for us and for the teachers and students to the world. We're giving them great tools. So so many good things were also putting some PTC technology from other parts of PTC back into on shape. One area, a little spoiler, little sneak peek. Working on taking generative design. Dana knows all about generative design. We couldn't acquire that technology were start up, you know, just to too much to do. But PTC owns one of the best in the business. This frustrated technology we're working on putting that into on shaping our customers. Um, will be happy to see it, hopefully in the coming year sometime. >>It's great to see that two way exchange. Now, you both know very well when you start a company, of course, a very exciting time. You know, a lot of baggage, you know, our customers pulling you in a lot of different directions and asking you for specials. You have this kind of clean slate, so to speak in it. I would think in many ways, John, despite you know, your install base, you have a bit of that dynamic occurring today especially, you know, driven by the forced march to digital transformation that cove it caused. So when you sit down with the team PTC and talk strategy. You now have more global resource is you got cohorts selling opportunities. What's the conversation like in terms of where you want to take the division? >>Well, Dave, you actually you sounds like we should have you coming in and talking about strategy because you've got the strategy down. I mean, we're doing everything said global expansion were able to reach across selling. We got some excellent PTC customers that we can reach reach now and they're finding uses for on shape. I think the plan is to, you know, just go, go, go and grow, grow, grow where we're looking for this year, priorities are expand the product. I mentioned the breath of the product with new things PTC did recently. Another technology that they acquired for on shape. We did an acquisition. It was it was small, wasn't widely announced. It, um, in an area related to interfacing with electrical cad systems. So So we're doing We're expanding the breath of on shape. We're going Maura, depth in the areas were already in. We have enormous opportunity to add more features and functions that's in the product. Go to market. You mentioned it global global presence. That's something we were a little light on a year ago. Now we have a team. Dana may not even know what we have. A non shape, dedicated team in Barcelona, based in Barcelona but throughout Europe were doing multiple languages. Um, the academic program just introduced a new product into that space that z even fueling more success and growth there. Um, and of course, continuing to to invest in customer success and this Atlas platform story I keep mentioning, we're going to soon have We're gonna soon have four other major PTC brands shipping products on our Atlas Saas platform. And so we're really excited about that. That's good for the other PTC products. It's also good for on shape because now there's there's. There's other interesting products that are on shape customers can use take advantage of very easily using, say, a common log in conventions about user experience there, used to invest of all they're SAS based, so they that makes it easier to begin with. So that's some of the exciting things going on. I think you'll see PTC, um, expanding our lead in SAS based applications for this sector for our our target, uh, sectors not just in, um, in cat and data management, but another area. PTC's Big and his augmented reality with of euphoria, product line leader and industrial uses of a R. That's a whole other story we should do. A whole nother show augmented reality. But these products are amazing. You can you can help factory workers people on, uh, people who are left out of the digital transformation. Sometimes we're standing from machine >>all day. >>They can't be sitting like we are doing Zoom. They can wear a R headset in our tools, let them create great content. This is an area Dana is invested in other companies. But what I wanted to note is the new releases of our authoring software. For this, our content getting released this month, used through the Atlas platform, the SAS components of on shape for things like revision management and collaboration on duh workflow activity. All that those are tools that we're able to share leverage. We get a lot of synergy. It's just really good. It's really fun to have a good time. That's >>awesome. And then we're gonna be talking to John MacLean later about that. Let's do a little deeper Dive on that. And, Dana, what is your involvement today with with on shape? But you're looking for you know, which of their customers air actually adopting. And they're gonna disrupt their industries. And you get good pipeline from that. How do you collaborate today? >>That sounds like a great idea. Um, Aziz, John will tell you I'm constantly just asking him for advice and impressions of other entrepreneurs and picking his brain on ideas. No formal relationship clearly, but continue to count John and and John and other people in on shaping in the circle of experts that I rely on for their opinions. >>All right, so we have some questions from the crowd here. Uh, one of the questions is for the dream team. You know, John and Dana. What's your next next collective venture? I don't think we're there yet, are we? No. >>I just say, as Dana said, we love talking to her about. You know, Dana, you just returned the compliment. We would try and give you advice and the deals you're looking at, and I'm sort of casually mentoring at least one of your portfolio entrepreneurs, and that's been a lot of fun for May on, hopefully a value to them. But also Dana. We uran important pipeline to us in the world of some new things that are happening that we wouldn't see if you know you've shown us some things that you've said. What do you think of this business? And for us, it's like, Wow, it's cool to see that's going on And that's what's supposed to work in an ecosystem like this. So we we deeply value the ongoing relationship. And no, we're not starting something new. I got a lot of work left to do with what I'm doing and really happy. But we can We can collaborate in this way on other ventures. >>I like this question to somebody asking With the cloud options like on shape, Wilmore students have stem opportunities s Oh, that's a great question. Are you because of sass and cloud? Are you able to reach? You know, more students? Much more cost effectively. >>Yeah, Dave, I'm so glad that that that I was asked about this because Yes, and it's extremely gratified us. Yes, we are because of cloud, because on shape is the only full cloud full SAS system or industry were able to reach. Stem education brings able to be part of bringing step education to students who couldn't get it otherwise. And one of most gratifying gratifying things to me is the emails were getting from teachers, um, that that really, um, on the phone calls that were they really pour their heart out and say We're able to get to students in areas that have very limited compute resource is that don't have an I T staff where they don't know what computer that the students can have at home, and they probably don't even have a computer. We're talking about being able to teach them on a phone to have an android phone a low end android phone. You can do three D modeling on there with on shape. Now you can't do it any other system, but with on shape, you could do it. And so the teacher can say to the students, They have to have Internet access, and I know there's a huge community that doesn't even have Internet access, and we're not able, unfortunately to help that. But if you have Internet and you have even an android phone, we can enable the educator to teach them. And so we have case after case of saving a stem program or expanding it into the students that need it most is the ones we're helping here. So really excited about that. And we're also able to let in addition to the run on run on whatever computing devices they have, we also offer them the tools they need for remote teaching with a much richer experience. Could you teach solid works remotely? Well, maybe if the student ran it had a windows workstation. You know, big, big, high end workstation. Maybe it could, but it would be like the difference between collaborating with on shape and collaborate with solid works. Like the difference between a zoom video call and talking on the landline phone. You know, it's a much richer experience, and that's what you need. And stem teaching stem is hard, So yeah, we're super super. Um, I'm excited about bringing stem to more students because of cloud yond >>we're talking about innovation for good, and then the discussion, John, you just had it. Really? There could be a whole another vector here. We could discuss on diversity, and I wanna end with just pointing out. So, Dana, your new firm, it's a woman led firm, too. Two women leaders, you know, going forward. So that's awesome to see, so really? Yeah, thumbs up on that. Congratulations on getting that off the ground. >>Thank you. Thank you. >>Okay, so thank you guys. Really appreciate It was a great discussion. I learned a lot and I'm sure the audience did a swell in a moment. We're gonna talk with on shaped customers to see how they're applying tech for good and some of the products that they're building. So keep it right there. I'm Dave Volonte. You're watching innovation for good on the Cube, the global leader in digital tech event coverage. Stay right there. >>Oh, yeah, it's >>yeah, yeah, around >>the globe. It's the Cube presenting innovation for good. Brought to you by on shape. >>Okay, we're back. This is Dave Volonte and you're watching innovation for good. A program on Cuba 3 65 made possible by on shape of PTC company. We're live today really live tv, which is the heritage of the Cube. And now we're gonna go to the sources and talkto on shape customers to find out how they're applying technology to create real world innovations that are changing the world. So let me introduce our panel members. Rafael Gomez Furberg is with the Chan Zuckerberg bio hub. A very big idea. And collaborative nonprofit was initiative that was funded by Mark Zuckerberg and his wife, Priscilla Chan, and really around diagnosing and curing and better managing infectious diseases. So really timely topic. Philip Tabor is also joining us. He's with silver side detectors, which develops neutron detective detection systems. Yet you want to know if early, if neutrons and radiation or in places where you don't want them, So this should be really interesting. And last but not least, Matthew Shields is with the Charlottesville schools and is gonna educate us on how he and his team are educating students in the use of modern engineering tools and techniques. Gentlemen, welcome to the Cuban to the program. This should be really interesting. Thanks for coming on. >>Hi. Or pleasure >>for having us. >>You're very welcome. Okay, let me ask each of you because you're all doing such interesting and compelling work. Let's start with Rafael. Tell us more about the bio hub and your role there, please. >>Okay. Yeah. So you said that I hope is a nonprofit research institution, um, funded by Mark Zuckerberg and his wife, Priscilla Chan. Um, and our main mission is to develop new technologies to help advance medicine and help, hopefully cure and manage diseases. Um, we also have very close collaborations with Universe California, San Francisco, Stanford University and the University California Berkeley on. We tried to bring those universities together, so they collaborate more of biomedical topics. And I manage a team of engineers. They by joining platform. Um, and we're tasked with creating instruments for the laboratory to help the scientist boats inside the organization and also in the partner universities Do their experiments in better ways in ways that they couldn't do before >>in this edition was launched Well, five years ago, >>it was announced at the end of 2016, and we actually started operation with at the beginning of 2017, which is when I joined, um, So this is our third year. >>And how's how's it going? How does it work? I mean, these things take time. >>It's been a fantastic experience. Uh, the organization works beautifully. Um, it was amazing to see it grow From the beginning, I was employee number 12, I think eso When I came in, it was just a nem P office building and empty labs. And very quickly we had something running about. It's amazing eso I'm very proud of the work that we have done to make that possible. Um And then, of course, that's you mentioned now with co vid, um, we've been able to do a lot of very cool work attire being of the pandemic in March, when there was a deficit of testing, uh, capacity in California, we spun up a testing laboratory in record time in about a week. It was crazy. It was a crazy project, Um, but but incredibly satisfying. And we ended up running all the way until the beginning of November, when the lab was finally shut down. We could process about 3000 samples a day. I think at the end of it all, we were able to test about 100 on the order of 100 and 50,000 samples from all over the state. We were providing free testing toe all of the Department of Public Health Department of Public Health in California, which at the media pandemic, had no way to do testing affordably and fast. So I think that was a great service to the state. Now the state has created that testing system that would serve those departments. So then we decided that it was unnecessary to keep going with testing in the other biopsy that would shut down. >>All right. Thank you for that. Now, Now, Philip, you What you do is mind melting. You basically helped keep the world safe. Maybe describe a little bit more about silver sod detectors and what your role is there and how it all works. >>Tour. So we make a nuclear bomb detectors and we also make water detectors. So we try and do our part thio keep the world from blowing up and make it a better place at the same time. Both of these applications use neutron radiation detectors. That's what we make. Put them out by import border crossing places like that. They can help make sure that people aren't smuggling. Shall we say very bad things. Um, there's also a burgeoning field of research and application where you can use neutrons with some pretty cool physics to find water so you could do things. Like what? A detector up in the mountains and measure snowpack. Put it out in the middle of the field and measure soil moisture content. And as you might imagine, there's some really cool applications in, uh, research and agronomy and public policy for this. >>All right, so it's OK, so it's a It's much more than, you know, whatever fighting terrorism, it's there's a riel edge or I kind of i o t application for what you guys >>do. We do both its's to plowshares. You might >>say a mat. I I look at your role is kind of scaling the brain power for for the future. Maybe tell us more about Charlottesville schools and in the mission that you're pursuing and what you do. >>Thank you. Um, I've been in Charlottesville City schools for about 11 or 12 years. I started their teaching, um, a handful of classes, math and science and things like that. But Thescore board and my administration had the crazy idea of starting an engineering program about seven years ago. My background is an engineering is an engineering. My masters is in mechanical and aerospace engineering and um, I basically spent a summer kind of coming up with what might be a fun engineering curriculum for our students. And it started with just me and 30 students about seven years ago, Um, kind of a home spun from scratch curriculum. One of my goals from the outset was to be a completely project based curriculum, and it's now grown. We probably have about six or 700 students, five or six full time teachers. We now have pre engineering going on at the 5th and 6th grade level. I now have students graduating. Uh, you know, graduating after senior year with, like, seven years of engineering under their belt and heading off to doing some pretty cool stuff. So it's It's been a lot of fun building a program and, um, and learning a lot in the process. >>That's awesome. I mean, you know, Cuba's. We've been passionate about things like women in tech, uh, diversity stem. You know, not only do we need more, more students and stem, we need mawr underrepresented women, minorities, etcetera. We were just talking to John Herstek and integrate gration about this is Do you do you feel is though you're I mean, first of all, the work that you do is awesome, but but I'll go one step further. Do you feel as though it's reaching, um, or diverse base? And how is that going? >>That's a great question. I think research shows that a lot of people get funneled into one kind of track or career path or set of interests really early on in their educational career, and sometimes that that funnel is kind of artificial. And so that's one of the reasons we keep pushing back. Um, so our school systems introducing kindergartners to programming on DSO We're trying to push back how we expose students to engineering and to stem fields as early as possible. And we've definitely seen the first of that in my program. In fact, my engineering program, uh, sprung out of an after school in Extracurricular Science Club that actually three girls started at our school. So I think that actually has helped that three girls started the club that eventually is what led to our engineering programs that sort of baked into the DNA and also our eyes a big public school. And we have about 50% of the students are under the poverty line and we e in Charlottesville, which is a big refugee town. And so I've been adamant from Day one that there are no barriers to entry into the program. There's no test you have to take. You don't have to have be taking a certain level of math or anything like that. That's been a lot of fun. To have a really diverse set of kids enter the program and be successful, >>that's final. That's great to hear. So, Philip, I wanna come back to you. You know, I think about maybe some day we'll be able to go back to a sporting events, and I know when I when I'm in there, there's somebody up on the roof looking out for me, you know, watching the crowd, and they have my back. And I think in many ways, the products that you build, you know, our similar. I may not know they're there, but they're keeping us safe or they're measuring things that that that I don't necessarily see. But I wonder if you could talk about a little bit more detail about the products you build and how they're impacting society. >>Sure, so There are certainly a lot of people who are who are watching, trying to make sure things were going well in keeping you safe that you may or may not be aware of. And we try and support ah lot of them. So we have detectors that are that are deployed in a variety of variety of uses, with a number of agencies and governments that dio like I was saying, ports and border crossing some other interesting applications that are looking for looking for signals that should not be there and working closely to fit into the operations these folks do. Onda. We also have a lot of outreach to researchers and scientists trying to help them support the work they're doing. Um, using neutron detection for soil moisture monitoring is a some really cool opportunities for doing it at large scale and with much less, um, expense or complication than would have been done. Previous technologies. Um, you know, they were talking about collaboration in the previous segment. We've been able to join a number of conferences for that, virtually including one that was supposed to be held in Boston, but another one that was held out of the University of Heidelberg in Germany. And, uh, this is sort of things that in some ways, the pandemic is pushing people towards greater collaboration than they would have been able to do. Had it all but in person. >>Yeah, we did. Uh, the cube did live works a couple years ago in Boston. It was awesome show. And I think, you know, with this whole trend toward digit, I call it the Force march to digital. Thanks to cove it I think that's just gonna continue. Thio grow. Rafael. What if you could describe the process that you use to better understand diseases? And what's your organization's involvement? Been in more detail, addressing the cove in pandemic. >>Um, so so we have the bio be structured in, Um um in a way that foster so the combination of technology and science. So we have to scientific tracks, one about infectious diseases and the other one about understanding just basic human biology, how the human body functions, and especially how the cells in the human body function on how they're organized to create tissues in the body. On Ben, it has this set of platforms. Um, mind is one of them by engineering that are all technology rated. So we have data science platform, all about data analysis, machine learning, things like that. Um, we have a mass spectrometry platform is all about mass spectrometry technologies to, um, exploit those ones in service for the scientist on. We have a genomics platform that it's all about sequencing DNA and are gonna, um and then an advanced microscopy. It's all about developing technologies, uh, to look at things with advanced microscopes and developed technologies to marry computation on microscopy. So, um, the scientists set the agenda and the platforms, we just serve their needs, support their needs, and hopefully develop technologies that help them do their experiments better, faster, or allow them to the experiment that they couldn't do in any other way before. Um And so with cove, it because we have that very strong group of scientists that work on have been working on infectious disease before, and especially in viruses, we've been able to very quickly pivot to working on that s O. For example, my team was able to build pretty quickly a machine to automatically purified proteins on is being used to purify all these different important proteins in the cove. It virus the SARS cov to virus Onda. We're sending some of those purified proteins all over the world. Two scientists that are researching the virus and trying to figure out how to develop vaccines, understand how the virus affects the body and all that. Um, so some of the machines we built are having a very direct impact on this. Um, Also for the copy testing lab, we were able to very quickly develop some very simple machines that allowed the lab to function sort of faster and more efficiently. Sort of had a little bit of automation in places where we couldn't find commercial machines that would do it. >>Um, eso Matt. I mean, you gotta be listening to this and thinking about Okay, So someday your students are gonna be working at organizations like like, like Bio Hub and Silver Side. And you know, a lot of young people they're just don't know about you guys, but like my kids, they're really passionate about changing the world. You know, there's way more important than you know, the financial angles and it z e. I gotta believe you're seeing that you're right in the front lines there. >>Really? Um, in fact, when I started the curriculum six or seven years ago, one of the first bits of feedback I got from my students is they said Okay, this is a lot of fun. So I had my students designing projects and programming microcontrollers raspberry, PiS and order we nose and things like that. The first bit of feedback I got from students was they said Okay, when do we get to impact the world? I've heard engineering >>is about >>making the world a better place, and robots are fun and all, but, you know, where is the real impact? And so um, dude, yeah, thanks to the guidance of my students, I'm baking that Maurin. Now I'm like day one of engineering one. We talk about how the things that the tools they're learning and the skills they're gaining, uh, eventually, you know, very soon could be could be used to make the world a better place. >>You know, we all probably heard that famous line by Jeff Hammer Barker. The greatest minds of my generation are trying to figure out how to get people to click on ads. I think we're really generally generationally, finally, at the point where young students and engineering a really, you know, a passionate about affecting society. I wanna get into the product, you know, side and understand how each of you are using on shape and and the value that that it brings. Maybe Raphael, you could start how long you've been using it. You know, what's your experience with it? Let's let's start there. >>I begin for about two years, and I switched to it with some trepidation. You know, I was used to always using the traditional product that you have to install on your computer, that everybody uses that. So I was kind of locked into that. But I started being very frustrated with the way it worked, um, and decided to give on ship chance. Which reputation? Because any change always, you know, causes anxiety. Um, but very quickly my engineers started loving it, Uh, just because it's it's first of all, the learning curve wasn't very difficult at all. You can transfer from one from the traditional product to entree very quickly and easily. You can learn all the concepts very, very fast. It has all the functionality that we needed and and what's best is that it allows to do things that we couldn't do before or we couldn't do easily. Now we can access the our cat documents from anywhere in the world. Um, so when we're in the lab fabricating something or testing a machine, any computer we have next to us or a tablet or on iPhone, we can pull it up and look at the cad and check things or make changes. That's something that couldn't do before because before you had to pay for every installation off the software for the computer, and I couldn't afford to have 20 installations to have some computers with the cat ready to use them like once every six months would have been very inefficient. So we love that part. And the collaboration features are fantastic, especially now with Kobe, that we have to have all the remote meetings eyes fantastic, that you can have another person drive the cad while the whole team is watching that person change the model and do things and point to things that is absolutely revolutionary. We love it. The fact that you have very, very sophisticated version control before it was always a challenge asking people, please, if you create anniversary and apart, how do we name it so that people find it? And then you end up with all these collection of files with names that nobody ever remembers, what they are, the person left. And now nobody knows which version is the right one. A mess with on shape on the version ING system it has, and the fact that you can go back in history off the document and go back to previous version so easily and then go back to the press and version and explore the history of the part that is truly, um, just world changing for us, that we can do that so easily on for me as a manager to manage this collection of information that is critical for our operations. It makes it so much easier because everything is in one place. I don't have to worry about file servers that go down that I have to administer that have to have I t taken care off that have to figure how to keep access to people to those servers when they're at home, and they need a virtual private network and all of that mess disappears. I just simply give give a person in accounting on shape and then magically, they have access to everything in the way I want. And we can manage the lower documents and everything in a way that is absolutely fantastic. >>Feel what was your what? What were some of the concerns you had mentioned? You had some trepidation. Was it a performance? Was it security? You know some of the traditional cloud stuff, and I'm curious as to how, How, whether any of those act manifested really that you had to manage. What were your concerns? >>Look, the main concern is how long is it going to take for everybody in the team to learn to use the system like it and buy into it? Because I don't want to have my engineers using tools against their will write. I want everybody to be happy because that's how they're productive. They're happy, and they enjoyed the tools they have. That was my main concern. I was a little bit worried about the whole concept of not having the files in a place where I couldn't quote unquote seat in some server and on site, but that That's kind of an outdated concept, right? So that took a little bit of a mind shift, but very quickly. Then I started thinking, Look, I have a lot of documents on Google Drive. Like, I don't worry about that. Why would I worry about my cat on on shape, right? Is the same thing. So I just needed to sort of put things in perspective that way. Um, the other, um, you know, the concern was the learning curve, right? Is like, how is he Will be for everybody to and for me to learn it on whether it had all of the features that we needed. And there were a few features that I actually discussed with, um uh, Cody at on shape on, they were actually awesome about using their scripting language in on shape to sort of mimic some of the features of the old cat, uh, in on, shaped in a way that actually works even better than the old system. So it was It was amazing. Yeah, >>Great. Thank you for that, Philip. What's your experience been? Maybe you could take us through your journey within shape. >>Sure. So we've been we've been using on shaped silver side for coming up on about four years now, and we love it. We're very happy with it. We have a very modular product line, so we make anything from detectors that would go into backpacks. Two vehicles, two very large things that a shipping container would go through and saw. Excuse me. Shape helps us to track and collaborate faster on the design. Have multiple people working a same time on a project. And it also helps us to figure out if somebody else comes to us and say, Hey, I want something new how we congrats modules from things that we already have put them together and then keep track of the design development and the different branches and ideas that we have, how they all fit together. A za design comes together, and it's just been fantastic from a mechanical engineering background. I will also say that having used a number of different systems and solid works was the greatest thing since sliced bread. Before I got using on shape, I went, Wow, this is amazing and I really don't want to design in any other platform. After after getting on Lee, a little bit familiar with it. >>You know, it's funny, right? I'll have the speed of technology progression. I was explaining to some young guns the other day how I used to have a daytime er and that was my life. And if I lost that daytime, er I was dead. And I don't know how we weigh existed without, you know, Google maps eso we get anywhere, I don't know, but, uh but so So, Matt, you know, it's interesting to think about, you know, some of the concerns that Raphael brought up, you hear? For instance, you know, all the time. Wow. You know, I get my Amazon bill at the end of the month that zip through the roof in, But the reality is that Yeah, well, maybe you are doing more, but you're doing things that you couldn't have done before. And I think about your experience in teaching and educating. I mean, you so much more limited in terms of the resource is that you would have had to be able to educate people. So what's your experience been with With on shape and what is it enabled? >>Um, yeah, it was actually talking before we went with on shape. We had a previous CAD program, and I was talking to my vendor about it, and he let me know that we were actually one of the biggest CAD shops in the state. Because if you think about it a really big program, you know, really big company might employ. 5, 10, 15, 20 cad guys, right? I mean, when I worked for a large defense contractor, I think there were probably 20 of us as the cad guys. I now have about 300 students doing cat. So there's probably more students with more hours of cat under their belt in my building than there were when I worked for the big defense contractor. Um, but like you mentioned, uh, probably our biggest hurdle is just re sources. And so we want We want one of things I've always prided myself and trying to do in this. Programs provide students with access two tools and skills that they're going to see either in college or in the real world. So it's one of the reason we went with a big professional cad program. There are, you know, sort of K 12 oriented software and programs and things. But, you know, I want my kids coding and python and using slack and using professional type of tools on DSO when it comes to cat. That's just that That was a really hurt. I mean, you know, you could spend $30,000 on one seat of, you know, professional level cad program, and then you need a $30,000 computer to run it on if you're doing a heavy assemblies, Um and so one of my dreams And it was always just a crazy dream. And I was the way I would always pitcher in my school system and say, someday I'm gonna have a kid on a school issued chromebook in subsidized housing, on public WiFi doing professional level bad and that that was a crazy statement until a couple of years ago. So we're really excited that I literally and you know, March and you said the forced march, the forced march into, you know, modernity, March 13th kids sitting in my engineering lab that we spent a lot of money on doing cad March 14th. Those kids were at home on their school issued chromebooks on public WiFi, uh, keeping their designs going and collaborating. And then, yeah, I could go on and on about some of the things you know, the features that we've learned since then they're even better. So it's not like this is some inferior, diminished version of Academy. There's so much about it. Well, I >>wanna I wanna ask you that I may be over my skis on this, but we're seeing we're starting to see the early days of the democratization of CAD and product design. It is the the citizen engineer, I mean, maybe insulting to the engineers in the room, But but is that we're beginning to see that >>I have to believe that everything moves into the cloud. Part of that is democratization that I don't need. I can whether you know, I think artists, you know, I could have a music studio in my basement with a nice enough software package. And Aiken, I could be a professional for now. My wife's a photographer. I'm not allowed to say that I could be a professional photographer with, you know, some cloud based software, and so, yeah, I do think that's part of what we're seeing is more and more technology is moving to the cloud. >>Philip. Rafael Anything you Dad, >>I think I mean, yeah, that that that combination of cloud based cat and then three d printing that is becoming more and more affordable on ubiquitous It's truly transformative, and I think for education is fantastic. I wish when I was a kid I had the opportunity to play with those kinds of things because I was always the late things. But, you know, the in a very primitive way. So, um, I think this is a dream for kids. Teoh be able to do this. And, um, yeah, there's so many other technologies coming on, like Arduino on all of these electronic things that live kids play at home very cheaply with things that back in my day would have been unthinkable. >>So we know there's a go ahead. Philip, please. >>We had a pandemic and silver site moved to a new manufacturing facility this year. I was just on the shop floor, talking with contractors, standing 6 ft apart, pointing at things. But through it all, our CAD system was completely unruffled. Nothing stopped in our development work. Nothing stopped in our support for existing systems in the field. We didn't have to think about it. We had other server issues, but none with our, you know, engineering cad, platform and product development in support world right ahead, which was cool, but also a in that's point. I think it's just really cool what you're doing with the kids. The most interesting secondary and college level engineering work that I did was project based, taken important problem to the world. Go solve it and that is what we do here. That is what my entire career has been. And I'm super excited to see. See what your students are going to be doing, uh, in there home classrooms on their chromebooks now and what they do building on that. >>Yeah, I'm super excited to see your kids coming out of college with engineering degrees because, yeah, I think that Project based experience is so much better than just sitting in a classroom, taking notes and doing math problems on day. I think it will give the kids a much better flavor. What engineering is really about Think a lot of kids get turned off by engineering because they think it's kind of dry because it's just about the math for some very abstract abstract concept on they are there. But I think the most important thing is just that hands on a building and the creativity off, making things that you can touch that you can see that you can see functioning. >>Great. So, you know, we all know the relentless pace of technology progression. So when you think about when you're sitting down with the folks that on shape and there the customer advisor for one of the things that that you want on shape to do that it doesn't do today >>I could start by saying, I just love some of the things that does do because it's such a modern platform. And I think some of these, uh, some some platforms that have a lot of legacy and a lot of history behind them. I think we're dragging some of that behind them. So it's cool to see a platform that seemed to be developed in the modern era, and so that Z it is the Google docks. And so the fact that collaboration and version ing and link sharing is and like platform agnostic abilities, the fact that that seems to be just built into the nature of the thing so far, That's super exciting. As far as things that, uh, to go from there, Um, I don't know, >>Other than price. >>You can't say >>I >>can't say lower price. >>Yeah, so far on P. D. C. S that work with us. Really? Well, so I'm not complaining. There you there, >>right? Yeah. Yeah. No gaps, guys. Whitespace, Come on. >>We've been really enjoying the three week update. Cadence. You know, there's a new version every three weeks and we don't have to install it. We just get all the latest and greatest goodies. One of the trends that we've been following and enjoying is the the help with a revision management and release work flows. Um, and I know that there's more than on shape is working on that we're very excited for, because that's a big important part about making real hardware and supporting it in the field. Something that was cool. They just integrated Cem markup capability. In the last release that took, we were doing that anyway, but we were doing it outside of on shapes. And now we get to streamline our workflow and put it in the CAD system where We're making those changes anyway when we're reviewing drawings and doing this kind of collaboration. And so I think from our perspective, we continue to look forward. Toa further progress on that. There's a lot of capability in the cloud that I think they're just kind of scratching the surface on you, >>right? I would. I mean, you're you're asking to knit. Pick. I would say one of the things that I would like to see is is faster regeneration speed. There are a few times with convicts, necessities that regenerating the document takes a little longer than I would like. It's not a serious issue, but anyway, I I'm being spoiled, >>you know? That's good. I've been doing this a long time, and I like toe ask that question of practitioners and to me, it It's a signal like when you're nit picking and that's what you're struggling to knit. Pick that to me is a sign of a successful product, and and I wonder, I don't know, uh, have the deep dive into the architecture. But are things like alternative processors. You're seeing them hit the market in a big way. Uh, you know, maybe helping address the challenge, But I'm gonna ask you the big, chewy question now. Then we maybe go to some audience questions when you think about the world's biggest problems. I mean, we're global pandemics, obviously top of mind. You think about nutrition, you know, feeding the global community. We've actually done a pretty good job of that. But it's not necessarily with the greatest nutrition, climate change, alternative energy, the economic divides. You've got geopolitical threats and social unrest. Health care is a continuing problem. What's your vision for changing the world and how product innovation for good and be applied to some of the the problems that that you all are passionate about? Big question. Who wants toe start? >>Not biased. But for years I've been saying that if you want to solve the economy, the environment, uh, global unrest, pandemics, education is the case. If you wanna. If you want to, um, make progress in those in those realms, I think funding funding education is probably gonna pay off pretty well. >>Absolutely. And I think Stam is key to that. I mean, all of the ah lot of the well being that we have today and then industrialized countries. Thanks to science and technology, right improvements in health care, improvements in communication, transportation, air conditioning. Um, every aspect of life is touched by science and technology. So I think having more kids studying and understanding that is absolutely key. Yeah, I agree, >>Philip, you got anything to add? >>I think there's some big technical problems in the world today, Raphael and ourselves there certainly working on a couple of them. Think they're also collaboration problems and getting everybody to be able to pull together instead of pulling separately and to be able to spur the ideas on words. So that's where I think the education side is really exciting. What Matt is doing and it just kind of collaboration in general when we could do provide tools to help people do good work. Uh, that is, I think, valuable. >>Yeah, I think that's a very good point. And along those lines, we have some projects that are about creating very low cost instruments for low research settings, places in Africa, Southeast Asia, South America, so that they can do, um, um, biomedical research that it's difficult to do in those place because they don't have the money to buy the fancy lab machines that cost $30,000 an hour. Um, so we're trying to sort of democratize some of those instruments. And I think thanks to tools like Kahn shape then is easier, for example, to have a conversation with somebody in Africa and show them the design that we have and discuss the details of it with them on. But it's amazing, right to have somebody, you know, 10 time zones away, Um, looking really life in real time with you about your design and discussing the details or teaching them how to build a machine, right? Because, um, you know, they have a three D printer. You can you can just give them the design and say like, you build it yourself, uh, even cheaper than and, you know, also billing and shipping it there. Um, so all that that that aspect of it is also super important. I think for any of these efforts to improve some of the hardest part was in the world for climate change. Do you say, as you say, poverty, nutrition issues? Um, you know, availability of water. You have that project at about finding water. Um, if we can also help deploy technologies that teach people remotely how to create their own technologies or how to build their own systems that will help them solve those forms locally. I think that's very powerful. >>Yeah, the point about education is right on. I think some people in the audience may be familiar with the work of Erik Brynjolfsson and Andrew McAfee, the second machine age where they sort of put forth the premise that, uh, is it laid it out. Look, for the first time in history, machines air replacing humans from a cognitive perspective. Machines have always replaced humans, but that's gonna have an impact on jobs. But the answer is not toe protect the past from the future. The answer is education and public policy that really supports that. So I couldn't agree more. I think it's a really great point. Um, we have We do have some questions from the audience. If if we could If I can ask you guys, um, you know, this one kind of stands out. How do you see artificial intelligence? I was just talking about machine intelligence. Um, how do you see that? Impacting the design space guys trying to infuse a I into your product development. Can you tell me? >>Um, absolutely, like, we're using AI for some things, including some of these very low cost instruments that will hopefully help us diagnose certain diseases, especially this is that are very prevalent in the Third World. Um, and some of those diagnostics are these days done by thes armies of technicians that are trained to look under the microscope. But, um, that's a very slow process. Is very error prone and having machine learning systems that can to the same diagnosis faster, cheaper and also little machines that can be taken to very remote places to these villages that have no access to a fancy microscope. To look at a sample from a patient that's very powerful. And I we don't do this, but I have read quite a bit about how certain places air using a Tribune attorneys to actually help them optimize designs for parts. So you get these very interesting looking parts that you would have never thought off a person would have never thought off, but that are incredibly light ink. Earlier, strong and I have all sort of properties that are interesting thanks to artificial intelligence machine learning in particular >>yet another. The advantage you get when when your work is in the cloud I've seen. I mean, there's just so many applications that so if the radiology scan is in the cloud and the radiologist is goes to bed at night, Radiologist could come in in the morning and and say, Oh, the machine while you were sleeping was using artificial intelligence to scan these 40,000 images. And here's the five that we picked out that we think you should take a closer look at. Or like Raphael said, I can design my part. My, my, my, my, my you know, mount or bracket or whatever and go to sleep. And then I wake up in the morning. The machine has improved. It for me has made it strider strider stronger and lighter. Um And so just when your when your work is in the cloud, that's just that's a really cool advantage that you get that you can have machines doing some of your design work for you. >>Yeah, we've been watching, uh, you know, this week is this month, I guess is AWS re invent and it's just amazing to see how much effort is coming around machine learning machine intelligence. You know Amazon has sage maker Google's got, you know, embedded you no ML and big query. Uh, certainly Microsoft with Azure is doing tons of stuff and machine learning. I think the point there is that that these things will be infused in tow R and D and in tow software product by the vendor community. And you all will apply that to your business and and build value through the unique data that your collecting, you know, in your ecosystems. And and that's how you add value. You don't have to be necessarily, you know, developers of artificial intelligence, but you have to be practitioners to apply that. Does that make sense to you, Philip? >>Yeah, absolutely. And I think your point about value is really well chosen. We see AI involved from the physics simulations all the way up to interpreting radiation data, and that's where the value question, I think, is really important because it's is the output of the AI giving helpful information that the people that need to be looking at it. So if it's curating a serious of radiation alert, saying, Hey, like these air the anomalies. You need to look at eyes it, doing that in a way that's going to help a good response on. In some cases, the II is only as good as the people. That sort of gave it a direction and turn it loose. And you want to make sure that you don't have biases or things like that underlying your AI that they're going to result in less than helpful outcomes coming from it. So we spend quite a lot of time thinking about how do we provide the right outcomes to people who are who are relying on our systems? >>That's a great point, right? Humans air biased and humans build models, so models are inherently biased. But then the software is hitting the market. That's gonna help us identify those biases and help us, you know? Of course. Correct. So we're entering Cem some very exciting times, guys. Great conversation. I can't thank you enough for spending the time with us and sharing with our audience the innovations that you're bringing to help the world. So thanks again. >>Thank you so much. >>Thank you. >>Okay. Welcome. Okay. When we come back, John McElheny is gonna join me. He's on shape. Co founder. And he's currently the VP of strategy at PTC. He's gonna join the program. We're gonna take a look at what's next and product innovation. I'm Dave Volonte and you're watching innovation for good on the Cube, the global leader. Digital technology event coverage. We'll be right back. >>Okay? Okay. Yeah. Okay. >>From around >>the globe, it's the Cube. Presenting innovation for good. Brought to you by on shape. >>Okay, welcome back to innovation. For good. With me is John McElheny, who is one of the co founders of On Shape and is now the VP of strategy at PTC. John, it's good to see you. Thanks for making the time to come on the program. Thanks, Dave. So we heard earlier some of the accomplishments that you've made since the acquisition. How has the acquisition affected your strategy? Maybe you could talk about what resource is PTC brought to the table that allowed you toe sort of rethink or evolve your strategy? What can you share with us? >>Sure. You know, a year ago, when when John and myself met with Jim Pepperman early on is we're we're pondering. Started joining PTC one of things became very clear is that we had a very clear shared vision about how we could take the on shape platform and really extended for, for all of the PTC products, particular sort of their augmented reality as well as their their thing works or the i o. T business and their product. And so from the very beginning there was a clear strategy about taking on shape, extending the platform and really investing, um, pretty significantly in the product development as well as go to market side of things, uh, toe to bring on shape out to not only the PTC based but sort of the broader community at large. So So So PTC has been a terrific, terrific, um, sort of partner as we've we've gonna go on after this market together. Eso We've added a lot of resource and product development side of things. Ah, lot of resource and they go to market and customer success and support. So, really, on many fronts, that's been both. Resource is as well a sort of support at the corporate level from from a strategic standpoint and then in the field, we've had wonderful interactions with many large enterprise customers as well as the PTC channels. So it's been really a great a great year. >>Well, and you think about the challenges of in your business going to SAS, which you guys, you know, took on that journey. You know, 78 years ago. Uh, it's not trivial for a lot of companies to make that transition, especially a company that's been around as long as PTC. So So I'm wondering how much you know, I was just asking you How about what PCP TC brought to the table? E gotta believe you're bringing a lot to the table to in terms of the mindset, uh, even things is, is mundane is not the right word, but things like how you compensate salespeople, how you interact with customers, the notion of a service versus a product. I wonder if you could address >>that. Yeah, it's a it's a really great point. In fact, after we had met Jim last year, John and I one of the things we walked out in the seaport area in Boston, one of things we sort of said is, you know, Jim really gets what we're trying to do here and and part of let me bring you into the thinking early on. Part of what Jim talked about is there's lots of, you know, installed base sort of software that's inside of PTC base. That's helped literally thousands of customers around the world. But the idea of moving to sass and all that it entails both from a technology standpoint but also a cultural standpoint. Like How do you not not just compensate the sales people as an example? But how do you think about customer success? In the past, it might have been that you had professional services that you bring out to a customer, help them deploy your solutions. Well, when you're thinking about a SAS based offering, it's really critical that you get customers successful with it. Otherwise, you may have turned, and you know it will be very expensive in terms of your business long term. So you've got to get customers success with software in the very beginning. So you know, Jim really looked at on shape and he said that John and I, from a cultural standpoint, you know, a lot of times companies get acquired and they've acquired technology in the past that they integrate directly into into PTC and then sort of roll it out through their products, are there just reached channel, he said. In some respects, John John, think about it as we're gonna take PTC and we want to integrate it into on shape because we want you to share with us both on the sales side and customer success on marketing on operations. You know all the things because long term, we believe the world is a SAS world, that the whole industry is gonna move too. So really, it was sort of an inverse in terms of the thought process related to normal transactions >>on That makes a lot of sense to me. You mentioned Sharon turns the silent killer of a SAS company, and you know, there's a lot of discussion, you know, in the entrepreneurial community because you live this, you know what's the best path? I mean today, You see, you know, if you watch Silicon Valley double, double, triple triple, but but there's a lot of people who believe, and I wonder, if you come in there is the best path to, you know, in the X Y axis. If if it's if it's uh, growth on one and retention on the other axis. What's the best way to get to the upper right on? Really? The the best path is probably make sure you've nailed obviously the product market fit, But make sure that you can retain customers and then throw gas on the fire. You see a lot of companies they burn out trying to grow too fast, but they haven't figured out, you know that. But there's too much churn. They haven't figured out those metrics. I mean, obviously on shape. You know, you were sort of a pioneer in here. I gotta believe you've figured out that customer retention before you really, You know, put the pedal to the >>metal. Yeah, and you know, growth growth can mask a lot of things, but getting getting customers, especially the engineering space. Nobody goes and sits there and says, Tomorrow we're gonna go and and, you know, put 100 users on this and and immediately swap out all of our existing tools. These tools are very rich and deep in terms of capability, and they become part of the operational process of how a company designs and builds products. So any time anybody is actually going through the purchasing process. Typically, they will run a try along or they'll run a project where they look at. Kind of What? What is this new solution gonna help them dio. How are we gonna orient ourselves for success? Longer term. So for us, you know, getting new customers and customer acquisition is really critical. But getting those customers to actually deploy the solution to be successful with it. You know, we like to sort of, say, the marketing or the lead generation and even some of the initial sales. That's sort of like the Kindle ing. But the fire really starts when customers deploy it and get successful. The solution because they bring other customers into the fold. And then, of course, if they're successful with it, you know, then in fact, you have negative turn which, ironically, means growth in terms of your inside of your install. Bates. >>Right? And you've seen that with some of the emerging, you know, SAS companies, where you're you're actually you know, when you calculate whatever its net retention or renew ALS, it's actually from a dollar standpoint. It's up in the high nineties or even over 100%. >>So >>and that's a trend we're gonna continue. See, I >>wonder >>if we could sort of go back. Uh, and when you guys were starting on shape, some of the things that you saw that you were trying to strategically leverage and what's changed, you know, today we were talking. I was talking to John earlier about in a way, you kinda you kinda got a blank slate is like doing another startup. >>You're >>not. Obviously you've got installed base and customers to service, but But it's a new beginning for you guys. So one of the things that you saw then you know, cloud and and sas and okay, but that's we've been there, done that. What are you seeing? You know today? >>Well, you know, So So this is a journey, of course, that that on shape on its own has gone through it had I'll sort of say, you know, several iterations, both in terms of of of, you know, how do you How do you get customers? How do you How do you get them successful? How do you grow those customers? And now that we've been part of PTC, the question becomes okay. One, There is certainly a higher level of credibility that helps us in terms of our our megaphone is much bigger than it was when we're standalone company. But on top of that now, figuring out how to work with their channel with their direct sales force, you know, they have, um, for example, you know, very large enterprises. Well, many of those customers are not gonna go in forklift out their existing solution to replace it with with on shape. However, many of them do have challenges in their supply chain and communications with contractors and vendors across the globe. And so, you know, finding our fit inside of those large enterprises as they extend out with their their customers is a very interesting area that we've really been sort of incremental to to PTC. And then, you know, they they have access to lots of other technology, like the i o. T business. And now, of course, the augmented reality business that that we can bring things to bear. For example, in the augmented reality world, they've they've got something called expert capture. And this is essentially imagine, you know, in a are ah, headset that allows you to be ableto to speak to it, but also capture images still images in video. And you could take somebody who's doing their task and capture literally the steps that they're taking its geo location and from their builds steps for new employees to be, we'll learn and understand how todo use that technology to help them do their job better. Well, when they do that, if there is replacement products or variation of of some of the tools that that they built the original design instruction set for they now have another version. Well, they have to manage multiple versions. Well, that's what on shape is really great at doing and so taking our technology and helping their solutions as well. So it's not only expanding our customer footprint, it's expanding the application footprint in terms of how we can help them and help customers. >>So that leads me to the tam discussion and again, as part of your strategist role. How do you think about that? Was just talking to some of your customers earlier about the democratization of cat and engineering? You know, I kind of joked, sort of like citizen engineering, but but so that you know, the demographics are changing the number of users potentially that can access the products because the it's so much more of a facile experience. How are you thinking about the total available market? >>It really is a great question, You know, it used to be when you when you sold boxes of software, it was how many engineers were out there. And that's the size of the market. The fact that matter is now when, When you think about access to that information, that data is simply a pane of glass. Whether it's a computer, whether it's a laptop, UH, a a cell phone or whether it's a tablet, the ability to to use different vehicles, access information and data expands the capabilities and power of a system to allow feedback and iteration. I mean, one of the one of the very interesting things is in technology is when you can take something and really unleash it to a larger audience and builds, you know, purpose built applications. You can start to iterate, get better feedback. You know there's a classic case in the clothing industry where Zara, you know, is a fast sort of turnaround. Agile manufacturer. And there was a great New York Times article written a couple years ago. My wife's a fan of Zara, and I think she justifies any purchases by saying, You know, Zara, you gotta purchase it now. Otherwise it may not be there the next time. Yet you go back to the store. They had some people in a store in New York that had this woman's throw kind of covering Shaw. And they said, Well, it would be great if we could have this little clip here so we can hook it through or something. And they sent a note back toe to the factory in Spain, and literally two weeks later they had, you know, 4000 of these things in store, and they sold out because they had a closed loop and iterative process. And so if we could take information and allow people access in multiple ways through different devices and different screens, that could be very specific information that, you know, we remove a lot of the engineering data book, bring the end user products conceptually to somebody that would have had to wait months to get the actual physical prototype, and we could get feedback well, Weaken have a better chance of making sure whatever product we're building is the right product when it ultimately gets delivered to a customer. So it's really it's a much larger market that has to be thought of rather than just the kind of selling A boxes software to an engineer. >>That's a great story. And again, it's gonna be exciting for you guys to see that with. The added resource is that you have a PTC, Um, so let's talk. I promise people we wanna talk about Atlas. Let's talk about the platform. A little bit of Atlas was announced last year. Atlas. For those who don't know it's a SAS space platform, it purports to go beyond product lifecycle management and you You're talking cloud like agility and scale to CAD and product design. But John, you could do a better job than I. What do >>we need to know about Atlas? Well, I think Atlas is a great description because it really is metaphorically sort of holding up all of the PTC applications themselves. But from the very beginning, when John and I met with Jim, part of what we were intrigued about was that he shared a vision that on shape was more than just going to be a cad authoring tool that, in fact, you know, in the past these engineering tools were very powerful, but they were very narrow in their purpose and focus. And we had specialty applications to manage the versions, etcetera. What we did in on shape is we kind of inverted that thinking. We built this collaboration and sharing engine at the core and then kind of wrap the CAD system around it. But that collaboration sharing and version ING engine is really powerful. And it was that vision that Jim had that he shared that we had from the beginning, which was, how do we take this thing to make a platform that could be used for many other applications inside of inside of any company? And so not only do we have a partner application area that is is much like the APP store or Google play store. Uh, that was sort of our first Stan Shih ation of this. This this platform. But now we're extending out to broader applications and much meatier applications. And internally, that's the thing works in the in the augmented reality. But there'll be other applications that ultimately find its way on top of this platform. And so they'll get all the benefits of of the collaboration, sharing the version ing the multi platform, multi device. And that's an extremely extremely, um, strategic leverage point for the company. >>You know, it's interesting, John, you mentioned the seaport before. So PTC, for those who don't know, built a beautiful facility down at the Seaport in Boston. And, of course, when PTC started, you know, back in the mid 19 eighties, there was nothing at the seaport s. >>So it's >>kind of kind of ironic, you know, we were way seeing the transformation of the seaport. We're seeing the transformation of industry and of course, PTC. And I'm sure someday you'll get back into that beautiful office, you know? Wait. Yeah, I'll bet. And, uh and but I wanna bring this up because I want I want you to talk about the future. How you how you see that our industry and you've observed this has moved from very product centric, uh, plat platform centric with sass and cloud. And now we're seeing ecosystems form around those products and platforms and data flowing through the ecosystem powering, you know, new innovation. I wonder if you could paint a picture for us of what the future looks like to you from your vantage point. >>Yeah, I think one of the key words you said there is data because up until now, data for companies really was sort of trapped in different applications. And it wasn't because people were nefarious and they want to keep it limited. It was just the way in which things were built. And, you know, when people use an application like on shape, what ends up happening is there their day to day interaction and everything that they do is actually captured by the platform. And, you know, we don't have access to that data. Of course it's it's the customer's data. But as as an artifact of them using the system than doing their day to day job, what's happening is they're creating huge amounts of information that can then be accessed and analyzed to help them both improve their design process, improve their efficiencies, improve their actual schedules in terms of making sure they can hit delivery times and be able to understand where there might be roadblocks in the future. So the way I see it is companies now are deploying SAS based tools like on shape and an artifact of them. Using that platform is that they have now analytics and tools to better understand and an instrument and manage their business. And then from there, I think you're going to see, because these systems are all you know extremely well. Architected allow through, you know, very structured AP. I calls to connect other SAS based applications. You're gonna start seeing closed loop sort of system. So, for example, people design using on shape, they end up going and deploying their system or installing it, or people use the end using products. People then may call back into the customers support line and report issues, problems, challenges. They'll be able to do traceability back to the underlying design. They'll be able to do trend analysis and defect analysis from the support lines and tie it back and closed loop the product design, manufacture, deployment in the field sort of cycles. In addition, you can imagine there's many things that air sort of as designed. But then when people go on site and they have to install it. There's some alterations modifications. Think about think about like a large air conditioning units for buildings. You go and you go to train and you get a large air conditioning unit that put up on top of building with a crane. They have to build all kinds of adaptors to make sure that that will fit inside of the particulars of that building. You know, with on shape and tools like this, you'll be able to not only take the design of what the air conditioning system might be, but also the all the adapter plates, but also how they installed it. So it sort of as designed as manufactured as stalled. And all these things can be traced, just like if you think about the transformation of customer service or customer contacts. In the early days, you used to have tools that were PC based tools called contact management solution, you know, kind of act or gold mine. And these were basically glorified Elektronik role in Texas. It had a customer names and they had phone numbers and whatever else. And Salesforce and Siebel, you know, these types of systems really broadened out the perspective of what a customer relationship? Waas. So it wasn't just the contact information it was, you know, How did they come to find out about you as a company? So all of the pre sort of marketing and then kind of what happens after they become a customer and it really was a 3 60 view. I think that 3 60 view gets extended to not just to the customers, but also tools and the products they use. And then, of course, the performance information that could come back to the manufacturer. So, you know, as an engineer, one of the things you learn about with systems is the following. And if you remember, when the CD first came out CDs that used to talk about four times over sampling or eight times over sampling and it was really kind of, you know, the fidelity the system. And we know from systems theory that the best way to improve the performance of a system is to actually have more feedback. The more feedback you have, the better system could be. And so that's why you get 16 60 for example, etcetera. Same thing here. The more feedback we have of different parts of a company that a better performance, The company will be better customer relationships. Better, uh, overall financial performance as well. So that's that's the view I have of how these systems all tied together. >>It's a great vision in your point about the data is I think right on. It used to be so fragmented in silos, and in order to take a system view, you've gotta have a system view of the data. Now, for years, we've optimized maybe on one little component of the system and that sometimes we lose sight of the overall outcome. And so what you just described, I think is, I think sets up. You know very well as we exit. Hopefully soon we exit this this covert era on John. I hope that you and I can sit down face to face at a PTC on shape event in the near term >>in the seaport in the >>seaport would tell you that great facility toe have have an event for sure. It >>z wonderful >>there. So So John McElhinney. Thanks so much for for participating in the program. It was really great to have you on, >>right? Thanks, Dave. >>Okay. And I want to thank everyone for participating. Today we have some great guest speakers. And remember, this is a live program. So give us a little bit of time. We're gonna flip this site over toe on demand mode so you can share it with your colleagues and you, or you can come back and and watch the sessions that you heard today. Uh, this is Dave Volonte for the Cube and on shape PTC. Thank you so much for watching innovation for good. Be well, Have a great holiday. And we'll see you next time. Yeah.

>>Yeah, yeah. >>Welcome back. I'm so excited to introduce our next session empowerment through inclusion, reimagining society and technology. This is a topic that's personally very near and dear to my heart. Did you know that there's only 2% of Latinas in technology as a Latina? I know that there's so much more we could do collectively to improve these gaps and diversity. I thought spot diversity is considered a critical element across all levels of the organization. The data shows countless times. A diverse and inclusive workforce ultimately drives innovation better performance and keeps your employees happier. That's why we're passionate about contributing to this conversation and also partnering with organizations that share our mission of improving diversity across our communities. Last beyond, we hosted the session during a breakfast and we packed the whole room. This year, we're bringing the conversation to the forefront to emphasize the importance of diversity and data and share the positive ramifications that it has for your organization. Joining us for this session are thought spots Chief Data Strategy Officer Cindy Housing and Ruhollah Benjamin, associate professor of African American Studies at Princeton University. Thank you, Paola. So many >>of you have journeyed with me for years now on our efforts to improve diversity and inclusion in the data and analytic space. And >>I would say >>over time we cautiously started commiserating, eventually sharing best practices to make ourselves and our companies better. And I do consider it a milestone. Last year, as Paola mentioned that half the room was filled with our male allies. But I remember one of our Panelists, Natalie Longhurst from Vodafone, suggesting that we move it from a side hallway conversation, early morning breakfast to the main stage. And I >>think it was >>Bill Zang from a I G in Japan. Who said Yes, please. Everyone else agreed, but more than a main stage topic, I want to ask you to think about inclusion beyond your role beyond your company toe. How Data and analytics can be used to impact inclusion and equity for the society as a whole. Are we using data to reveal patterns or to perpetuate problems leading Tobias at scale? You are the experts, the change agents, the leaders that can prevent this. I am thrilled to introduce you to the leading authority on this topic, Rou Ha Benjamin, associate professor of African studies at Princeton University and author of Multiple Books. The Latest Race After Technology. Rou ha Welcome. >>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today, and I thought I would just kick things off with some opening reflections on this really important session theme. And then we could jump into discussion. So I'd like us to as a starting point, um, wrestle with these buzzwords, empowerment and inclusion so that we can have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design in the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism, and I'll explain what that means in just a minute. Techno determinism comes in two forms. The first, on your left is the idea that technology automation, um, all of these emerging trends are going to harm us, are going to necessarily harm humanity. They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story and this is what Hollywood loves to sell us in the form of movies like The Matrix or Terminator. The other version on your right is the techno utopian story that technologies automation. The robots as a shorthand, are going to save humanity. They're gonna make everything more efficient, more equitable. And in this case, on the surface, he seemed like opposing narratives right there, telling us different stories. At least they have different endpoints. But when you pull back the screen and look a little bit more closely, you see that they share an underlying logic that technology is in the driver's seat and that human beings that social society can just respond to what's happening. But we don't really have a say in what technologies air designed and so to move beyond techno determinism the notion that technology is in the driver's seat. We have to put the human agents and agencies back into the story, the protagonists, and think carefully about what the human desires worldviews, values, assumptions are that animate the production of technology. And so we have to put the humans behind the screen back into view. And so that's a very first step and when we do that, we see, as was already mentioned, that it's a very homogeneous group right now in terms of who gets the power and the resource is to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to create more participation of those who are working behind the scenes to design technology now to dig a little more a deeper into this, I want to offer a kind of low tech example before we get to the more hi tech ones. So what you see in front of you here is a simple park bench public bench. It's located in Berkeley, California, which is where I went to graduate school and on this particular visit I was living in Boston, and so I was back in California. It was February. It was freezing where I was coming from, and so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D, and I quickly realized, actually, I couldn't lay down on this bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought. Okay, the the armrest have, ah functional reason why they're there. I mean, you could literally rest your elbows there or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. When I was nine months pregnant, it could help me get up and down or for the elderly, the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are homeless from sleeping on the bench. And this is the Bay area that we were talking about where, in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown within equity because we haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, Okay, this crisis is growing in this area, and so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the where they're implemented and So this is what we might call structural inequity. By the way something is designed. It has certain effects that exclude or harm different people. And so it may not necessarily be the intense, but that's the effect. And I did a little digging, and I found, in fact, it's a global phenomenon, this thing that architects called hostile architecture. Er, I found single occupancy benches in Helsinki, so only one booty at a time no laying down there. I found caged benches in France. And in this particular town. What's interesting here is that the mayor put these benches out in this little shopping plaza, and within 24 hours the people in the town rallied together and had them removed. So we see here that just because we have, uh, discriminatory design in our public space doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the meter bench. In this case, this bench is designed with spikes in them, and to get the spikes to retreat into the bench, you have to feed the meter you have to put some coins in, and I think it buys you about 15 or 20 minutes. Then the spikes come back up. And so you'll be happy to know that in this case, this was designed by a German artists to get people to think critically about issues of design, not just the design of physical space but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harm, whether we're talking about education or health care. And the meter bench also presents something interesting. For those of us who care about technology, it creates a technical fix for a social problem. In fact, it started out his art. But some municipalities in different parts of the world have actually adopted this in their public spaces in their parks in order to deter so called lawyers from using that space. And so, by a technical fix, we mean something that creates a short term effect, right. It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside in the first place. And so, in addition to techno determinism, we have to think critically about technical fixes that don't address the underlying issues that technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with or that we create. And the question we really have to continuously ask ourselves is, What values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something. This is the way things have always been done. So we inherit the spike bench. What is our responsibility when we noticed that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem? What is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for Apple, the Apple card and, in one case, a husband and wife applied and the husband, the husband received a much higher limit almost 20 times the limit as his wife, even though they shared bank accounts, they lived in Common Law State. And so the question. There was not only the fact that the husband was receiving a much better interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know what the factors were that they were being judged that was creating this form of discrimination. So in terms of financial technologies, it's not simply the outcome that's the issue. Or that could be discriminatory, but the process that black boxes, all of the decision making that makes it so that consumers and the general public have no way to question it. No way to understand how they're being judged adversely, and so it's the process not only the product that we have to care a lot about. And so the case of the apple cart is part of a much broader phenomenon of, um, racist and sexist robots. This is how the headlines framed it a few years ago, and I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creator's biases. And now I think we've entered a phase of attempts to override and address the default settings of so called racist and sexist robots, for better or worse. And here robots is just a kind of shorthand, that the way people are talking about automation and emerging technologies more broadly. And so as I was encountering these headlines, I was thinking about how these air, not problems simply brought on by machine learning or AI. They're not all brand new, and so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the New Jim Code, which plays on the phrase Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era, and I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim Code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those were the kinds of inequalities tech mediated inequalities that we can generally see coming. They're kind of obvious. But then we go down the line and we see it becomes harder to detect. It's happening in our own backyards. It's happening around us, and we don't really have a view into the black box, and so it becomes more insidious. And so in the remaining couple minutes, I'm just just going to give you a taste of the last three of these, and then a move towards conclusion that we can start chatting. So when it comes to default discrimination. This is the way that social inequalities become embedded in emerging technologies because designers of these technologies aren't thinking carefully about history and sociology. Ah, great example of this came Thio headlines last fall when it was found that widely used healthcare algorithm affecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm healthcare algorithm does not explicitly take note of race. That is to say, it is race neutral by using cost to predict healthcare needs. This digital triaging system unwittingly reproduces health disparities because, on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect, and so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code coded exposure, there's attention because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera or on the computer. And so coated exposure names this tension between wanting to be seen and included and recognized, whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over included. When you're surveiled when you're to centered. And so we should note that it's not simply in being left out, that's the problem. But it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point. It's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim Code. That means the the most insidious, let's say, is technologies that are touted as helping US address bias, so they're not simply including people, but they're actively working to address bias. And so in this case, There are a lot of different companies that are using AI to hire, create hiring software and hiring algorithms, including this one higher view. And the idea is that there there's a lot that AI can keep track of that human beings might miss. And so so the software can make data driven talent decisions. After all, the problem of employment discrimination is widespread and well documented. So the logic goes, Wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the look of the idea of techno benevolence trying to do good without fully reckoning with what? How technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. E. And what they found was that in fact, the algorithm associating black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003, before all of the emerging technologies were on the scene where two University of Chicago economists sent out thousands of resumes to employers in Boston and Chicago, and all they did was change the names on those resumes. All of the other work history education were the same, and then they waited to see who would get called back. And the applicants, the fictional applicants with white sounding names received 50% more callbacks than the black applicants. So if you're presented with that study, you might be tempted to say, Well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So, too, with gender coded words and names Amazon learned a couple years ago when its own hiring algorithm was found discriminating against women. Nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If Onley there was a way to slay centuries of racist and sexist demons with a social justice box beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment but a curse from any jobseekers, as this headline puts it, your next interview could be with a racist spot, bringing us back to that problem space we started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers test and creating fake applications as informal audits of their own. In terms of a more collective response, there's a federation of European Trade unions call you and I Global that's developed a charter of digital rights for work, others that touches on automated and a I based decisions to be included in bargaining agreements. And so this is one of many efforts to change their ecosystem to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the US There's the algorithmic accountability bill that's been presented, and it's one effort to create some more protections around this ubiquity of automated decisions, and I think we should all be calling from more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag Tech won't build it. Among other statements that they have made and walking out and petitioning their companies. Who one group said, as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can we can grow a more historically and socially literate approach to tech design. And this is just one, um, resource that you all can download, Um, by developed by some wonderful colleagues at the Data and Society Research Institute in New York and the goal of this interventionist threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies, not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations, and a commitment to take action to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer a couple last provocations. The first is for us to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to a I systems without historical or social depth, is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. The last provocation is this. If, as I suggested at the start, inequity is woven into the very fabric of our society, it's built into the design of our. Our policies are physical infrastructures and now even our digital infrastructures. That means that each twist, coil and code is a chance for us toe. We've new patterns, practices and politics. The vastness of the problems that we're up against will be their undoing. Once we accept that we're pattern makers. So what does that look like? It looks like refusing color blindness as an anecdote to tech media discrimination rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting. And how do we create a more participatory rather than predatory form of inclusion? And ultimately, it also means owning our own power in these systems so that we can change the patterns of the past. If we're if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more just and equitable technologies. So with that, I look forward to our conversation. >>Thank you, Ruth. Ha. That was I expected it to be amazing, as I have been devouring your book in the last few weeks. So I knew that would be impactful. I know we will never think about park benches again. How it's art. And you laid down the gauntlet. Oh, my goodness. That tech won't build it. Well, I would say if the thoughts about team has any saying that we absolutely will build it and will continue toe educate ourselves. So you made a few points that it doesn't matter if it was intentional or not. So unintentional has as big an impact. Um, how do we address that does it just start with awareness building or how do we address that? >>Yeah, so it's important. I mean, it's important. I have good intentions. And so, by saying that intentions are not the end, all be all. It doesn't mean that we're throwing intentions out. But it is saying that there's so many things that happened in the world, happened unwittingly without someone sitting down to to make it good or bad. And so this goes on both ends. The analogy that I often use is if I'm parked outside and I see someone, you know breaking into my car, I don't run out there and say Now, do you feel Do you feel in your heart that you're a thief? Do you intend to be a thief? I don't go and grill their identity or their intention. Thio harm me, but I look at the effect of their actions, and so in terms of art, the teams that we work on, I think one of the things that we can do again is to have a range of perspectives around the table that can think ahead like chess, about how things might play out, but also once we've sort of created something and it's, you know, it's entered into, you know, the world. We need to have, ah, regular audits and check ins to see when it's going off track just because we intended to do good and set it out when it goes sideways, we need mechanisms, formal mechanisms that actually are built into the process that can get it back on track or even remove it entirely if we find And we see that with different products, right that get re called. And so we need that to be formalized rather than putting the burden on the people that are using these things toe have to raise the awareness or have to come to us like with the apple card, Right? To say this thing is not fair. Why don't we have that built into the process to begin with? >>Yeah, so a couple things. So my dad used to say the road to hell is paved with good intentions, so that's >>yes on. In fact, in the book, I say the road to hell is paved with technical fixes. So they're me and your dad are on the same page, >>and I I love your point about bringing different perspectives. And I often say this is why diversity is not just about business benefits. It's your best recipe for for identifying the early biases in the data sets in the way we build things. And yet it's such a thorny problem to address bringing new people in from tech. So in the absence of that, what do we do? Is it the outside review boards? Or do you think regulation is the best bet as you mentioned a >>few? Yeah, yeah, we need really need a combination of things. I mean, we need So on the one hand, we need something like a do no harm, um, ethos. So with that we see in medicine so that it becomes part of the fabric and the culture of organizations that that those values, the social values, have equal or more weight than the other kinds of economic imperatives. Right. So we have toe have a reckoning in house, but we can't leave it to people who are designing and have a vested interest in getting things to market to regulate themselves. We also need independent accountability. So we need a combination of this and going back just to your point about just thinking about like, the diversity on teams. One really cautionary example comes to mind from last fall, when Google's New Pixel four phone was about to come out and it had a kind of facial recognition component to it that you could open the phone and they had been following this research that shows that facial recognition systems don't work as well on darker skin individuals, right? And so they wanted Thio get a head start. They wanted to prevent that, right? So they had good intentions. They didn't want their phone toe block out darker skin, you know, users from from using it. And so what they did was they were trying to diversify their training data so that the system would work better and they hired contract workers, and they told these contract workers to engage black people, tell them to use the phone play with, you know, some kind of app, take a selfie so that their faces would populate that the training set, But they didn't. They did not tell the people what their faces were gonna be used for, so they withheld some information. They didn't tell them. It was being used for the spatial recognition system, and the contract workers went to the media and said Something's not right. Why are we being told? Withhold information? And in fact, they told them, going back to the park bench example. To give people who are homeless $5 gift cards to play with the phone and get their images in this. And so this all came to light and Google withdrew this research and this process because it was so in line with a long history of using marginalized, most vulnerable people and populations to make technologies better when those technologies are likely going toe, harm them in terms of surveillance and other things. And so I think I bring this up here to go back to our question of how the composition of teams might help address this. I think often about who is in that room making that decision about sending, creating this process of the contract workers and who the selfies and so on. Perhaps it was a racially homogeneous group where people didn't want really sensitive to how this could be experienced or seen, but maybe it was a diverse, racially diverse group and perhaps the history of harm when it comes to science and technology. Maybe they didn't have that disciplinary knowledge. And so it could also be a function of what people knew in the room, how they could do that chest in their head and think how this is gonna play out. It's not gonna play out very well. And the last thing is that maybe there was disciplinary diversity. Maybe there was racial ethnic diversity, but maybe the workplace culture made it to those people. Didn't feel like they could speak up right so you could have all the diversity in the world. But if you don't create a context in which people who have those insights feel like they can speak up and be respected and heard, then you're basically sitting on a reservoir of resource is and you're not tapping into it to ensure T to do right by your company. And so it's one of those cautionary tales I think that we can all learn from to try to create an environment where we can elicit those insights from our team and our and our coworkers, >>your point about the culture. This is really inclusion very different from just diversity and thought. Eso I like to end on a hopeful note. A prescriptive note. You have some of the most influential data and analytics leaders and experts attending virtually here. So if you imagine the way we use data and housing is a great example, mortgage lending has not been equitable for African Americans in particular. But if you imagine the right way to use data, what is the future hold when we've gotten better at this? More aware >>of this? Thank you for that question on DSO. You know, there's a few things that come to mind for me one. And I think mortgage environment is really the perfect sort of context in which to think through the the both. The problem where the solutions may lie. One of the most powerful ways I see data being used by different organizations and groups is to shine a light on the past and ongoing inequities. And so oftentimes, when people see the bias, let's say when it came to like the the hiring algorithm or the language out, they see the names associated with negative or positive words that tends toe have, ah, bigger impact because they think well, Wow, The technology is reflecting these biases. It really must be true. Never mind that people might have been raising the issues in other ways before. But I think one of the most powerful ways we can use data and technology is as a mirror onto existing forms of inequality That then can motivate us to try to address those things. The caution is that we cannot just address those once we come to grips with the problem, the solution is not simply going to be a technical solution. And so we have to understand both the promise of data and the limits of data. So when it comes to, let's say, a software program, let's say Ah, hiring algorithm that now is trained toe look for diversity as opposed to homogeneity and say I get hired through one of those algorithms in a new workplace. I can get through the door and be hired. But if nothing else about that workplace has changed and on a day to day basis I'm still experiencing microaggressions. I'm still experiencing all kinds of issues. Then that technology just gave me access to ah harmful environment, you see, and so this is the idea that we can't simply expect the technology to solve all of our problems. We have to do the hard work. And so I would encourage everyone listening to both except the promise of these tools, but really crucially, um, Thio, understand that the rial kinds of changes that we need to make are gonna be messy. They're not gonna be quick fixes. If you think about how long it took our society to create the kinds of inequities that that we now it lived with, we should expect to do our part, do the work and pass the baton. We're not going to magically like Fairy does create a wonderful algorithm that's gonna help us bypass these issues. It can expose them. But then it's up to us to actually do the hard work of changing our social relations are changing the culture of not just our workplaces but our schools. Our healthcare systems are neighborhoods so that they reflect our better values. >>Yeah. Ha. So beautifully said I think all of us are willing to do the hard work. And I like your point about using it is a mirror and thought spot. We like to say a fact driven world is a better world. It can give us that transparency. So on behalf of everyone, thank you so much for your passion for your hard work and for talking to us. >>Thank you, Cindy. Thank you so much for inviting me. Hey, I live back to you. >>Thank you, Cindy and rou ha. For this fascinating exploration of our society and technology, we're just about ready to move on to our final session of the day. So make sure to tune in for this customer case study session with executives from Sienna and Accenture on driving digital transformation with certain AI.

>>welcome back. And thanks for joining us for our second session. External data, your new leading indicators. We'll be hearing from industry leaders as they share best practices and challenges in leveraging external data. This panel will be a true conversation on the part of the possible. All right, let's get to >>it >>today. We're excited to be joined by thought spots. Chief Data Strategy Officer Cindy Housing Deloitte's chief data officer Manteo, the founder and CEO of Eagle Alfa. And it Kilduff and Snowflakes, VP of data marketplace and customer product strategy. Matt Glickman. Cindy. Without further ado, the floor is yours. >>Thank you, Mallory. And I am thrilled to have this brilliant team joining us from around the world. And they really bring each a very unique perspective. So I'm going to start from further away. Emmett, Welcome. Where you joining us from? >>Thanks for having us, Cindy. I'm joining from Dublin, Ireland, >>great. And and tell us a little bit about Eagle Alfa. What do you dio >>from a company's perspective? Think of Eagle Alfa as an aggregator off all the external data sets on a word I'll use a few times. Today is a big advantage we could bring companies is we have a data concierge service. There's so much data we can help identify the right data sets depending on the specific needs of the company. >>Yeah. And so, Emma, you know, people think I was a little I kind of shocked the industry. Going from gardener to a tech startup. Um, you have had a brave journey as well, Going from financial services to starting this company, really pioneering it with I think the most data sets of any of thes is that right? >>Yes, it was. It was a big jump to go from Morgan Stanley. Uh, leave the comforts of that environment Thio, PowerPoint deck and myself raising funding eight years ago s So it was a big jump on. We were very early in our market. It's in the last few years where there's been real momentum and adoption by various types of verticals. The hedge funds were first, maybe then private equity, but corporate sar are following quite quickly from behind. That will be the biggest users, in our view, by by a significant distance. >>Yeah, great. Thank um, it So we're going to go a little farther a field now, but back to the U. S. So, Juan, where you joining us from? >>Hey, Cindy. Thanks for having me. I'm joining you from Houston, Texas. >>Great. Used to be my home. Yeah, probably see Rice University back there. And you have a distinct perspective serving both Deloitte customers externally, but also internally. Can you tell us about that? >>Yeah, absolutely. So I serve as the Lord consultants, chief data officer, and as a professional service firm, I have the responsibility for overseeing our overall data agenda, which includes both the way we use data and insights to run and operate our own business, but also in how we develop data and insights services that we then take to market and how we serve our dealers and clients. >>Great. Thank you, Juan. And last but not least, Matt Glickman. Kind of in my own backyard in New York. Right, Matt? >>Correct. Joining I haven't been into the city and many months, but yes, um, based in New York. >>Okay. Great. And so, Matt, you and Emmett also, you know, brave pioneers in this space, and I'm remembering a conversation you and I shared when you were still a J. P. Morgan, I believe. And you're Goldman Sachs. Sorry. Sorry. Goldman. Can you Can you share that with us? >>Sure. I made the move back in 2015. Um, when everyone thought, you know, my wife, my wife included that I was crazy. I don't know if I would call it Comfortable was emitted, but particularly had been there for a long time on git suffered in some ways. A lot of the pains we're talking about today, given the number of data, says that the amount of of new data sets that are always demand for having run analytics teams at Goldman, seeing the pain and realizing that this pain was not unique to Goldman Sachs, it was being replicated everywhere across the industry, um, in a mind boggling way and and the fortuitous, um, luck to have one of snowflakes. Founders come to pitch snowflake to Goldman a little bit early. Um, they became a customer later, but a little bit early in 2014. And, you know, I realized that this was clearly, you know, the answer from first principles on bond. If I ever was going to leave, this was a problem. I was acutely aware of. And I also was aware of how much the man that was in financial services for a better solution and how the cloud could really solve this problem in particular the ability to not have to move data in and out of these organizations. And this was something that I saw the future of. Thank you, Andi, that this was, you know, sort of the pain that people just expected to pay. Um, this price if you need a data, there was method you had thio. You had to use you either ftp data in and out. You had data that was being, you know, dropped off and, you know, maybe in in in a new ways and cloud buckets or a P i s You have to suck all this data down and reconstruct it. And God forbid the formats change. It was, you know, a nightmare. And then having issues with data, you had a what you were seeing internally. You look nothing like what the data vendors were seeing because they want a completely different system, maybe model completely differently. Um, but this was just the way things were. Everyone had firewalls. Everyone had their own data centers. There was no other way on git was super costly. And you know this. I won't even share the the details of you know, the errors that would occur in the pain that would come from that, Um what I realized it was confirmed. What I saw it snowflake at the time was once everyone moves to run their actual workloads in this in the cloud right where you're now beyond your firewall, you'll have all this scale. But on top of that, you'll be able to point at data from these vendors were not there the traditional data vendors. Or, you know, this new wave of alternative data vendors, for example, like the ones that eagle out for brings together And bring these all these data sets together with your own internal data without moving it. Yeah, this was a fundamental shift of what you know, it's in some ways, it was a side effect of everyone moving to the cloud for costs and scale and elasticity. But as a side effect of that is what we talked about, You know it snowflake summit, you know, yesterday was this notion of a data cloud that would connect data between regions between cloud vendors between customers in a way where you could now reference data. Just like your reference websites today, I don't download CNN dot com. I point at it, and it points me to something else. I'm always seeing the latest version, obviously, and we can, you know, all collaborate on what I'm seeing on that website. That's the same thing that now can happen with data. So And I saw this as what was possible, and I distinctly asked the question, you know, the CEO of the time Is this possible? And not only was it possible it was a fundamental construct that was built into the way that snowflake was delivered. And then, lastly, this is what we learned. And I think this is what you know. M It also has been touting is that it's all great if data is out there and even if you lower that bar of access where data doesn't have to move, how do I know? Right? If I'm back to sitting at Goldman Sachs, how do I know what data is available to me now in this this you know, connected data network eso we released our data marketplace, which was a very different kind of marketplace than these of the past. Where for us, it was really like a global catalog that would elect a consumer data consumer. Noah data was available, but also level the playing field. Now we're now, you know, Eagle, Alfa, or even, you know, a new alternative data vendor build something in their in their basement can now publish that data set so that the world could see and consume and be aligned to, you know, snowflakes, core business, and not where we wouldn't have to be competing or having to take, um, any kind of custody of that data. So adding that catalog to this now ubiquitous access, um really changed the game and, you know, and then now I seem like a genius for making this move. But back then, like I said, we've seen I seem like instant. I was insane. >>Well, given, given that snowflake was the hottest aipo like ever, you were a genius. Uh, doing this, you know, six years in advance. E think we all agree on that, But, you know, a lot of this is still visionary. Um, you know, some of the most leading companies are already doing this. But one What? What is your take our Are you best in class customers still moving the data? Or is this like they're at least thinking about data monetization? What are you seeing from your perspective? >>Yeah, I mean, I did you know, the overall appreciation and understanding of you know, one. I got to get my house in order around my data, um, has something that has been, you know, understood and acted upon. Andi, I do agree that there is a shift now that says, you know, data silos alone aren't necessarily gonna bring me, you know, new and unique insights on dso enriching that with external third party data is absolutely, you know, sort of the the ship that we're seeing our customers undergo. Um, what I find extremely interesting in this space and what some of the most mature clients are doing is, you know, really taking advantage of these data marketplaces. But building data partnerships right there from what mutually exclusive, where there is a win win scenario for for you know, that organization and that could be, you know, retail customers or life science customers like with pandemic, right the way we saw companies that weren't naturally sharing information are now building these data partnership right that are going are going into mutually benefit, you know, all organizations that are sort of part of that value to Andi. I think that's the sort of really important criteria. And how we're seeing our clients that are extremely successful at this is that partnership has benefits on both sides of that equation, right? Both the data provider and then the consumer of that. And there has to be, you know, some way to ensure that both parties are are are learning right, gaining you insights to support, you know, whatever their business organization going on. >>Yeah, great one. So those data partnerships getting across the full value chain of sharing data and analytics Emmett, you work on both sides of the equation here, helping companies. Let's say let's say data providers maybe, like, you know, cast with human mobility monetize that. But then also people that are new to it. Where you seeing the top use cases? Well, >>interestingly, I agree with one of the supply side. One of the interesting trends is we're seeing a lot more data coming from large Corporates. Whether they're listed are private equity backed, as opposed to maybe data startups that are earning money just through data monetization. I think that's a great trend. I think that means a lot of the best. Data said it data is yet to come, um, in terms off the tough economy and how that's changed. I think the category that's had the most momentum and your references is Geo location data. It's that was the category at our conference in December 2000 and 12 that was pipped as the category to watch in 2019. On it didn't become that at all. Um, there were some regulatory concerns for certain types of geo data, but with with covert 19, it's Bean absolutely critical for governments, ministries of finance, central banks, municipalities, Thio crunch that data to understand what's happening in a real time basis. But from a company perspective, it's obviously critical as well. In terms of planning when customers might be back in the High Street on DSO, fourth traditionally consumer transaction data of all the 26 categories in our taxonomy has been the most popular. But Geo is definitely catching up your slide. Talked about being a tough economy. Just one point to contradict that for certain pockets of our clients, e commerce companies are having a field day, obviously, on they are very data driven and tech literate on day are they are really good client base for us because they're incredibly hungry, firm or data to help drive various, uh, decision making. >>Yeah, So fair enough. Some sectors of the economy e commerce, electron, ICS, healthcare are doing great. Others travel, hospitality, Um, super challenging. So I like your quote. The best is yet to come, >>but >>that's data sets is yet to come. And I do think the cloud is enabling that because we could get rid of some of the messy manual data flows that Matt you talked about, but nonetheless, Still, one of the hardest things is the data map. Things combining internal and external >>when >>you might not even have good master data. Common keys on your internal data. So any advice for this? Anyone who wants to take that? >>Sure I can. I can I can start. That's okay. I do think you know, one of the first problems is just a cataloging of the information that's out there. Um, you know, at least within our organization. When I took on this role, we were, you know, a large buyer of third party data. But our organization as a whole didn't necessarily have full visibility into what was being bought and for what purpose. And so having a catalog that helps us internally navigate what data we have and how we're gonna use it was sort of step number one. Um, so I think that's absolutely important. Um, I would say if we could go from having that catalog, you know, created manually to more automated to me, that's sort of the next step in our evolution, because everyone is saying right, the ongoing, uh, you know, creation of new external data sets. It's only going to get richer on DSO. We wanna be able to take advantage of that, you know, at the at the pacing speed, that data is being created. So going from Emanuel catalog to anonymous >>data >>catalog, I think, is a key capability for us. But then you know, to your second point, Cindy is how doe I then connect that to our own internal data to drive greater greater insights and how we run our business or how we serve our customers. Andi, that one you know really is a It's a tricky is a tricky, uh, question because I think it just depends on what data we're looking toe leverage. You know, we have this concept just around. Not not all data is created equal. And when you think about governance and you think about the management of your master data, your internal nomenclature on how you define and run your business, you know that that entire ecosystem begins to get extremely massive and it gets very broad and very deep on DSO for us. You know, government and master data management is absolutely important. But we took a very sort of prioritized approach on which domains do we really need to get right that drive the greatest results for our organization on dso mapping those domains like client data or employee data to these external third party data sources across this catalog was really the the unlocked for us versus trying to create this, you know, massive connection between all the external data that we're, uh, leveraging as well as all of our own internal data eso for us. I think it was very. It was a very tailored, prioritized approach to connecting internal data to external data based on the domains that matter most to our business. >>So if the domains so customer important domain and maybe that's looking at things, um, you know, whether it's social media data or customer transactions, you prioritized first by that, Is that right? >>That's correct. That's correct. >>And so, then, Matt, I'm going to throw it back to you because snowflake is in a unique position. You actually get to see what are the most popular data sets is is that playing out what one described are you seeing that play out? >>I I'd say Watch this space. Like like you said. I mean this. We've you know, I think we start with the data club. We solve that that movement problem, which I think was really the barrier that you tended to not even have a chance to focus on this mapping problem. Um, this notion of concordance, I think this is where I see the big next momentum in this space is going to be a flurry of traditional and new startups who deliver this concordance or knowledge graph as a service where this is no longer a problem that I have to solve internal to my organization. The notion of mastering which is again when everyone has to do in every organization like they used to have to do with moving data into the organization goes away. And this becomes like, I find the best of breed for the different scopes of data that I have. And it's delivered to me as a, you know, as a cloud service that just takes my data. My internal data maps it to these 2nd and 3rd party data sets. Um, all delivered to me, you know, a service. >>Yeah, well, that would be brilliant concordance as a service or or clean clean master data as a service. Um, using augmented data prep would be brilliant. So let's hope we get there. Um, you know, so 2020 has been a wild ride for everyone. If I could ask each of you imagine what is the art of the possible or looking ahead to the next to your and that you are you already mentioned the best is yet to come. Can you want to drill down on that. What what part of the best is yet to come or what is your already two possible? >>Just just a brief comment on mapping. Just this week we published a white paper on mapping, which is available for for anyone on eagle alfa dot com. It's It's a massive challenge. It's very difficult to solve. Just with technology Onda people have tried to solve it and get a certain level of accuracy, but can't get to 100% which which, which, which makes it difficult to solve it. If if if there is a new service coming out against 100% I'm all ears and that there will be a massive step forward for the entire data industry, even if it comes in a few years time, let alone next year, I think going back to the comment on data Cindy. Yes, I think boards of companies are Mawr and Mawr. Viewing data as an asset as opposed to an expense are a cost center on bond. They are looking therefore to get their internal house in order, as one was saying, but also monetize the data they are sitting on lots of companies. They're sitting on potentially valuable data. It's not all valuable on a lot of cases. They think it's worth a lot more than it is being frank. But in some cases there is valuable data on bond. If monetized, it can drop to the bottom line on. So I think that bodes well right across the world. A lot of the best date is yet to come on. I think a lot of firms like Deloitte are very well positioned to help drive that adoption because they are the trusted advisor to a lot of these Corporates. Um, so that's one thing. I think, from a company perspective. It's still we're still at the first base. It's quite frustrating how slow a lot of companies are to move and adopt, and some of them are haven't hired CDO. Some of them don't have their internal house in order. I think that has to change next year. I think if we have this conference at this time next year, I would expect that would hopefully be close to the tipping point for Corporates to use external data. And the Malcolm Gladwell tipping point on the final point I make is I think, that will hopefully start to see multi department use as opposed to silos again. Parliaments and silos, hopefully will be more coordinated on the company's side. Data could be used by marketing by sales by r and D by strategy by finance holds external data. So it really, hopefully will be coordinated by this time next year. >>Yeah, Thank you. So, to your point, there recently was an article to about one of the airlines that their data actually has more value than the company itself now. So I know, I know. We're counting on, you know, integrators trusted advisers like Deloitte to help us get there. Uh, one what? What do you think? And if I can also drill down, you know, financial services was early toe all of this because they needed the early signals. And and we talk about, you know, is is external data now more valuable than internal? Because we need those early signals in just such a different economy. >>Yeah, I think you know, for me, it's it's the seamless integration of all these external data sources and and the signals that organizations need and how to bring those into, you know, the day to day operations of your organization, right? So how do you bring those into, You know, you're planning process. How do you bring that into your sales process on DSO? I think for me success or or where I see the that the use and adoption of this is it's got to get down to that level off of operations for organizations. For this to continue to move at the pace and deliver the value that you know, we're all describing. I think we're going to get there. But I think until organizations truly get down to that level of operations and how they're using this data, it'll sort of seem like a Bolton, right? So for me, I think it's all about Mawr, the seamless integration. And I think to what Matt mentioned just around services that could help connect external data with internal data. I'll take that one step beyond and say, How can we have the data connect itself? Eso I had references Thio, you know, automation and machine learning. Um, there's significant advances in terms of how we're seeing, you know, mapping to occur in a auto generated fashion. I think this specific space and again the connection between external and internal data is a prime example of where we need to disrupt that, you know, sort of traditional data pipeline on. Try to automate that as much as possible. And let's have the data, you know, connect itself because it then sort of supports. You know, the first concept which waas How do we make it more seamless and integrated into, you know, the business processes of the organization's >>Yeah, great ones. So you two are thinking those automated, more intelligent data pipelines will get us there faster. Matt, you already gave us one. Great, Uh, look ahead, Any more to add to >>it, I'll give you I'll give you two more. One is a bit controversial, but I'll throw that you anyway, um, going back to the point that one made about data partnerships What you were saying Cindy about, you know, the value. These companies, you know, tends to be somehow sometimes more about the data they have than the actual service they provide. I predict you're going to see a wave of mergers and acquisitions. Um, that it's solely about locking down access to data as opposed to having data open up. Um to the broader, you know, economy, if I can, whether that be a retailer or, you know, insurance company was thes prime data assets. Um, you know, they could try to monetize that themselves, But if someone could acquire them and get exclusive access that data, I think that's going to be a wave of, um, in a that is gonna be like, Well, we bought this for this amount of money because of their data assets s. So I think that's gonna be a big wave. And it'll be maybe under the guise of data partnerships. But it really be about, you know, get locking down exclusive access to valuable data as opposed to trying toe monetize it itself number one. And then lastly, you know. Now, did you have this kind of ubiquity of data in this interconnected data network? Well, we're starting to see, and I think going to see a big wave of is hyper personalization of applications where instead of having the application have the data itself Have me Matt at Snowflake. Bring my data graph to applications. Right? This decoupling of we always talk about how you get data out of these applications. It's sort of the reverse was saying Now I want to bring all of my data access that I have 1st, 2nd and 3rd party into my application. Instead of having to think about getting all the data out of these applications, I think about it how when you you know, using a workout app in the consumer space, right? I can connect my Spotify or connect my apple music into that app to personalize the experience and bring my music list to that. Imagine if I could do that, you know, in a in a CRM. Imagine I could do that in a risk management. Imagine I could do that in a marketing app where I can bring my entire data graph with me and personalize that experience for, you know, for given what I have. And I think again, you know, partners like thoughts. But I think in a unique position to help enable that capability, you know, for this next wave of of applications that really take advantage of this decoupling of data. But having data flow into the app tied to me as opposed to having the APP have to know about my data ahead of time, >>Yeah, yeah, So that is very forward thinking. So I'll end with a prediction and a best practice. I am predicting that the organizations that really leverage external data, new data sources, not just whether or what have you and modernize those data flows will outperform the organizations that don't. And as a best practice to getting there, I the CDOs that own this have at least visibility into everything they're purchasing can save millions of dollars in duplicate spend. So, Thio, get their three key takeaways. Identify the leading indicators and market signals The data you need Thio. Better identify that. Consolidate those purchases and please explore the data sets the range of data sets data providers that we have on the thought spot. Atlas Marketplace Mallory over to you. >>Wow. Thank you. That was incredible. Thank you. To all of our Panelists for being here and sharing that wisdom. We really appreciate it. For those of you at home, stay close by. Our third session is coming right up and we'll be joined by our partner AWS and get to see how you can leverage the full power of your data cloud complete with the demo. Make sure to tune in to see you >>then

>> Announcer: From around the globe, it's theCUBE, with digital coverage of AWS re:Invent 2020. Special coverage sponsored by AWS Worldwide Public Sector. >> Welcome to theCUBE Virtual and our coverage of AWS re:Invent 2020, the specialized programming for Worldwide Public Sector. I'm Lisa Martin. I'm joined by Nick Speece the chief federal technologist for Snowflake. Nick, welcome to theCUBE. >> Thank you, Lisa. It's great to be here. >> Likewise, chief federal technologist, that's the first time I've ever heard of that title. Tell me a little bit about that. >> It's probably the last time you'll hear it. So chief federal technologist is really somebody in the company who is focused on bringing the needs of the federal government back to our corporate headquarters, making sure that the product as it's developed and evolves has the federal requirements in mind. >> Excellent. So the last couple of months for Snowflake big, biggest software IPO and software history your market cap right now is at 66 billion 515 data workloads running on Snowflake's platform every day 250 petabytes of data under management, a lot is going on. Let's talk about Snowflake. You guys operate only in the cloud, why was that decision made and how does that impact businesses analysis of data? >> Yeah, so great question and the answer is actually in the opening that you gave for us and thank you for that reinforcement. Snowflake can't exist anywhere, but the cloud. Technology over the last five to 10 years has really seen a move from what the cloud originally was, which was I have a virtual machine in my data center, I'm going to run it on your stuff not mine, into more comprehensive service offerings like Snowflake. We can't reach the kind of scale that Snowflake operates at every day and that our customers demand without the technology of clouds like AWS. The technology has to be there, the underlying and underpinning architecture has to be there, otherwise our customers get left in the dark and we can't can't have that. >> And especially today as data volumes are massively increasing and we know that that's only going to go up. We know that IT is only going to be more complex but when we talk to businesses in any industry the value of the data is in the insights the ability to extract that data in real time glean insights from it so that businesses can make data-based decisions that pivot their business, especially critical during the year that we have now known as 2020. Talk to me a little bit about though digging into your marketing material, everyone, there's all these terms, right, that everyone uses and you guys use single source of truth. What does it actually mean for single source, for stuff like? >> Yeah, so we talk about cloud, we talk about single source of truth and when you're looking at data problems, the problem and the solution are the same thing. A massive amount of data is a raw resource, that's all it is. And trying to refine that raw resource into something that is insightful or something that is useful to a business process is a challenge that every customer in every market, in every region undergoes. And how you overcome that is critical. And one of the primary focuses of Snowflake is to evolve the data cloud. Snowflake platform is the underlying technology for the data cloud but the data cloud is where we're going. And what I mean by data cloud. If you have a data set, your internal data, that is your truth, but it might not be the truth. So in Snowflake we encourage our customers to collaborate on data sets. For example, if you want to know how many people are living in a certain borough in New York City you could go around with a clicker and count everyone, or you could just ask the Census Bureau. That's the nature of the data cloud and what we're talking about here. Going to the subject matter experts who have the data that you need, using our marketplace, using our private exchanges, using our data sharing to build your own data cloud and become part of the next gen architecture for data sharing and collaboration, to get to the source of the truth, to make better decisions, to gain better insights. It's great to combine your data with enrich data from other sources, especially when it comes to making federal decisions and governance decisions. >> Absolutely that's critical. That the biggest challenge customers have is being able to sort through all that and find. I like how you put this as their single source of truth. Can you give us some examples of some federal agencies maybe even just anonymously that are using the power of Snowflake to do just that? >> Absolutely. We've got customers in the healthcare space and in some of the law enforcement spaces and especially in public education that are trying to increase the awareness of the folks that are subscribing to their services, for example, folks that are looking for healthcare help. If you're filing claims for a certain healthcare providers or certain care facilities, we want to make sure that those claims that are forwarded to those entities are legitimate, first of all, for example, if you're filing a claim for knee surgery in Florida, you probably didn't have one in California, three hours later. So those kinds of enforcement activities, and not just trying to do audits but also to benefit everybody who's receiving care. There's a lot of push now about genetic sequencing, DNA and RNA vaccination is huge with COVID-19, getting access to massive amounts of data to do analysis against and figure out the best approach, that's critical for where we go in the next 10 to 15 years in healthcare. Snowflake is very, very honored and happy to be propelling that move in the healthcare space. >> It is that's going to be absolutely critical but we're also seeing it, you know, everywhere else, such as for universities and education, suddenly this need, the last few months for real-time learning. Talk to me about data analysis. Can Snowflake help companies, you talked about enriching data sets so not just companies sources of data but additional data sets that they can add in and evaluate and analyze to make great decisions, but from a historical real-time perspective, talk to me how Snowflake helps with that data analysis. >> Yeah, sure. Right. So Snowflake in and of itself can do some analysis work. We've got some great visualization tools in our new UI that was released recently on public preview. So there's some analysis tools built into Snowflake but really where the value comes from is in taking your tools that you already use today and connecting it to a data source or platform that can wrangle that data, that can move that data through automated pipelines to give you a model view of that data that's beneficial. For example, data scientists and data engineers spend 80% of their time, and I know a lot of statistics are made up on the spot, that was not a promise, but trying to move this data through and refine it and build features to get to the point where you can ask a question is 80% of these very valuable professionals time. Shortening those timelines is what Snowflake really aims to do in the analysis space. We're not trying to replace the analysis tools that you use today, we work fine with all of them. The big difference is presenting them with enough data volume to give you real insights and eliminate bias as much as we can in data sets. >> What are some of the things that differentiates Snowflake from data warehouses and other folks in the market? >> Yeah. Great question. The big difference is Snowflake was built natively for the cloud. We weren't adapted to the cloud, we didn't adopt the cloud at some point in the future, Snowflake was built from scratch to be in the cloud. And since this is the appropriate show to mention it the primary difference between us is we were built to use object storage foundationally underneath our technology. And I know that sounds really nerdy and it is, but it adds a tremendous amount of value. If you think about how we used to collaborate 10 years ago we'd have a spreadsheet that if I open that spreadsheet for my share drive and you tried to open it at the same time, you'd get locked out. You're told you couldn't have it. And if tradition stays true I would probably be on vacation for two weeks. Contrast that now with the massive Google Doc platform and Office 365, object storage has changed the way that we collaborate on the same kinds of documents. Multiple people interacting with one thing at one time without contention, that's the reason why Snowflake has to operate in the cloud. We bring that same paradigm, multiple actors on a single object and give you that source of truth the truth that you absolutely need to make decisions. >> And that's critical these days as we know. We're in living in uncertain times and one of the things I think we can expect is the uncertainty to continue, but also for many industries people to stay remote or some big percentage for quite a while. So the ability to have those collaboration tools and be able to collaborate in real time is table stakes for so many companies. But when we're talking about some of the things going on this year, security, we can't not talk about security. You know, all these folks from home accessing corporate networks, you know, maybe not through VPNs or behind firewalls, the cloud is paramount to that. How does Snowflake address the security issue? >> Absolutely. So I'll start by saying our security is inherited from the wonderful security platform that AWS has underneath it. So we inherit all the security around data storage the EC Compute, all of the different entities and end points that AWS already secures Snowflake takes the same precautions. More than that, we've also built and rolled this access control to ensure that people are getting access only to the data that they should be getting access to, we recently implemented data masking as well, so certain roles are not able to see unmasked data, but they can still do queries that use the underlying data to filter. So there's a lot of different capabilities built in, encryption at rest, encryption in flight, AES-256 encryption keys used in a hierarchial model. These are phenomenal security architectures that are paramount to the security of the folks that are using our platform. Because we know at the end of the day the first day we have a leak in Snowflake is probably our last day in business. We got to be good at that which is why it's our top priority. >> I didn't, to ever talk about security as an inherited, I must be a dominant trait if we're going to be talking about, you know, genetics and chromosomes and mRNA and things like that. So walk me through last question, a government organization, or say they're an AWS customer or they want to start using Snowflake, what's that process? How do they go about doing that to leverage those inherited security capabilities that you talked about? >> Well, thankfully AWS has helped us put a FedRAMP moderate certified Snowflake region together in AWS, East commercial, so we're very happy to have a FedRAMP moderate region. They can access Snowflake through the AWS Marketplace or from Snowflake.com, you can start a trial in just a couple of minutes. Our security is built into all of our regions although the FedRAMP regions are specialized in some of the encryption technology we use, but we always, always always protect our users' data, regardless of where it is. >> You make it sound easy, I got to say. (laughing) >> That's because it is. (laughing) Thank you cloud. >> That's good. And well, that's good and it should be, especially because there's so much complexity and uncertainty everywhere else in the world right now. Last question for you. As I mentioned in the beginning, the biggest IPO in software history, just a couple of months ago during probably one of the most strangest time of any of us have ever, and our relatives ever witnessed, what can we expect from Snowflake in 2021? Are you going to bring all the good vibes that we all need? (laughing) >> Well, good vibes is our business model. You know, Snowflake is a phenomenal platform. We've had a ton of success driven by the success of our cloud provider partners, driven by the success of our wonderful customers. We have over 4,000 people using Snowflake now to great effect. You can look for more features, you can look for more functions, but really the evolution of the data cloud, our big push is to help our customers get into the data cloud, get the truth out of their data and make better decisions every day. And you'll see more of that from us as time continues. >> One more question I wanted to sneak in, how did you work with those customers to evolve the data cloud? What's that feedback loop like? >> It's, a lot of it comes down to silos that the customers have built up over years and years and years of operation. That's the first step. In Snowflake there isn't such thing really as a data silo there's data put into Snowflake, everything is unified, you can do queries across databases, that's the first thing. The second thing is browsing our data marketplace. It's just like an App Store for your phone but instead it's data sets and the data sets are published by the experts who know that material better than anyone. I mentioned earlier bringing in everything from housing evaluation data to COVID-19 data from California and Boston, bringing World Health Organization data, John Hopkins University data, joining that with the data that you already use today along with weather and population counts, the main thing here, the strategy is almost endless. More and more data sets are being published over every day. We have over a hundred contributors in the marketplace now. >> That's exciting that we have the technology and the power like this to help the world re, you know, recover from such a crazy time. It's nice to know that, that there was the power of that behind that, and the smart folks like you chief federal technologists, helping to fine tune that and really ensure that organizations across the government can maximize the value of data and find their single source of truth. Nick, it's been a blast having you on theCUBE. Thank you for joining me. >> Thank you for having me. >> For next piece, I'm Lisa Martin. You're watching theCUBE Virtual. (upbeat music)

>>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today. And I thought I would just kick things off with some opening reflections on this really important session theme, and then we can jump into discussion. So I'd like us to, as a starting point, um, wrestle with these buzz words, empowerment and inclusion so that we can, um, have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design and the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism and I'll explain what that means in just a minute. And techno determinism comes in two forms. The first on your left is the idea that technology automate. Um, all of these emerging trends are going to harm us are going to necessarily, um, harm humanity. >>They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story. And this is what Hollywood loves to sell us in the form of movies like the matrix or Terminator. The other version on your right is the techno utopian story that technologies automation, the robots, as a shorthand are going to save humanity. They're going to make everything more efficient, more equitable. And in this case, on the surface, they seem like opposing narratives, right? They're telling us different stories. At least they have different endpoints, but when you pull back the screen and look a little bit more closely, you see that they share an underlying logic, that technology is in the driver's seat and that human beings, that social society can just respond to what's happening. But we don't, I really have a say in what technologies are designed. >>And so to move beyond techno determinism, the notion that technology is in the driver's seat, we have to put the human agents and agencies back into the story protagonists and think carefully about what the human desires, worldviews values assumptions are that animate the production of technology. We have to put the humans behind the screen back into view. And so that's a very first step in when we do that. We see as was already mentioned that it's a very homogenous group right now in terms of who gets the power and the resources to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to, to create more participation of those who are working behind the scenes to design technology. Now, to dig a little more deeper into this, I want to offer a kind of low tech example before we get to the more high tech ones. >>So what you see in front of you here is a simple park bench public it's located in Berkeley, California, which is where I went to graduate school. And on this one particular visit, I was living in Boston. And so I was back in California, it was February, it was freezing where I was coming from. And so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D. And I quickly realized actually I couldn't lay down on the bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought, okay, th th the armrests have a functional reason why they're there. I mean, you could literally rest your elbows there, or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. >>Um, when I was nine months pregnant, it could help me get up and down or for the elderly the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are, are homeless from sleeping on the bench. And this is the Bay area that we're talking about, where in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown with inequity because we have, I haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, okay, this crisis is growing in this area. And so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the, where they're implemented. And so this is what we might call structural inequity, by the way something is designed. >>It has certain yeah. Affects that exclude or harm different people. And so it may not necessarily be the intent, but that's the effect. And I did a little digging and I found, in fact, it's a global phenomenon, this thing that architect next call, hostile architecture around single occupancy, benches and Helsinki. So only one booty at a time, no Nolan down there. I've found caged benches in France. Yeah. And in this particular town, what's interesting here is that the mayor put these benches out in this little shopping Plaza and within 24 hours, the people in the town rally together and have them removed. So we see here that just because we, we have a discriminatory design in our public space, doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the metered bench. >>And then this case, this bench is designed with spikes in them and to get the spikes to retreat into the bench, you have to feed the meter. You have to put some coins in, and I think it buys you about 15, 20 minutes, then the spikes come back up. And so you will be happy to know that in this case, uh, this was designed by a German artist to get people to think critically about issues of design, not the design of physical space, but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harmed. Whether we're talking about education or healthcare. And the meter bench also presents something interesting for those of us who care about technology, it creates a technical fix for a social problem. >>In fact, it started out as art, but some municipalities in different parts of the world have actually adopted this in their public spaces, in their parks in order to deter so-called loiters from using that space. And so by a technical fix, we mean something that creates a short-term effect, right? It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside of the first place. And so, in addition to techno determinism, we have to think critically about technical fixes, that don't address the underlying issues that the tech tech technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with, or that we create. >>And the question we really have to continuously ask ourselves is what values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something, this is the way things have always been done. So we inherit the spiked bench. What is our responsibility? When we notice that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem, what is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for apples, >>The Apple card. And in one case, a husband and wife applied, and the husband, the husband received a much higher limit, almost 20 times the limit as his, >>His wife, even though they shared bank accounts, they lived in common law state. Yeah. >>And so the question there was not only the fact that >>The husband was receiving a much better rate and a high and a better >>The interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know how, what the factors were that they were being judged that was creating this form of discrimination. So >>In terms of financial technologies, it's not simply the outcome, that's the issue, or that can be discriminatory, >>But the process that black box is all of the decision-making that makes it so that consumers and the general public have no way to question it, no way to understand how they're being judged adversely. And so it's the process, not only the product that we have to care a lot about. And so the case of the Apple card is part of a much broader phenomenon >>Of, um, races >>And sexist robots. This is how the headlines framed it a few years ago. And I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect, that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creators biases. And now I think we've entered a phase of attempts to override and address the default settings of so-called racist and sexist robots for better or worse than here. Robots is just a kind of shorthand that the way that people are talking about automation and emerging technologies more broadly. And so, as I was encountering these headlines, I was thinking about how these are not problems simply brought on by machine learning or AI. They're not all brand new. And so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the new Jim code, >>Which plays on the phrase, >>Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era. And I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those are the kinds of inequalities tech mediated in the qualities that we can generally see coming. They're kind of obvious, but then we go down the line and we see it becomes harder to detect it's happening in our own backyards, it's happening around us. And we don't really have a view into the black box. And so it becomes more insidious. And so in the remaining couple of minutes, I'm just, just going to give you a taste of the last three of these, and then a move towards conclusion. Then we can start chatting. So when it comes to default discrimination, this is the way that social inequalities >>Become embedded in emerging technologies because designers of these technologies, aren't thinking carefully about history and sociology. A great example of this, uh, came to, um, uh, the headlines last fall when it was found that widely used healthcare algorithm, effecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm, healthcare algorithm does not explicitly take note of race. That is to say it is race neutral by using cost to predict healthcare needs this digital triaging system unwittingly reproduces health disparities, because on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study, by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect. >>And so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code, coded exposure, there's a tension because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera, on the computer. And so coded exposure names, this tension between wanting to be seen and included and recognized whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over, it >>Included when you're surveilled, when you're >>Too centered. And so we should note that it's not simply in being left out, that's the problem, but it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point, it's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim code. That means the, the most insidious let's say is technologies that are touted as helping us address bias. So they're not simply including people, but they're actively working to address bias. And so in this case, there are a lot of different companies that are using AI to hire, uh, create hiring, um, software and hiring algorithms, including this one higher view. >>And the idea is that there there's a lot that, um, AI can keep track of that human beings might miss. And so, so the software can make data-driven talent decisions after all the problem of employment discrimination is widespread and well-documented, so the logic goes, wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the idea of techno benevolence, trying to do good without fully reckoning with what, how technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. And what they found was that in fact, the algorithm associated black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003 before all of the emerging technologies were on the scene where two university of Chicago economists sent out thousands of resumes to employers in Boston and Chicago. >>And all they did was change the names on those resumes. All of the other work history education were the same. And then they waited to see who would get called back and the applicants, the fictional applicants with white sounding names received 50% more callbacks than the, the black applicants. So if you're presented with that study, you might be tempted to say, well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So two with gender coded words and names as Amazon learned a couple years ago, when its own hiring algorithm was found discriminating against women, nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If only there was a way to slay centuries of racist and sexist demons with a social justice bot beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment, but a curse from any job seekers as this headline puts it. >>Your next interview could be with a racist bot, bringing us back to that problem space. We started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers tests and creating fake applications as informal audits of their own. In terms of a more collective response. There's a Federation of European trade unions call you and I global that's developed a charter of digital rights for workers that touches on automated and AI based decisions to be included in bargaining agreements. And so this is one of many efforts to change the ecosystem, to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the U S there's the algorithmic accountability bill that's been presented. And it's one effort to create some more protections around this ubiquity of automated decisions. >>And I think we should all be calling for more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag tech, won't build it among other statements that they've made and walking out and petitioning their companies. One group said as the, at Google at Microsoft wrote as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can, we can grow a more historically and socially literate approach to tech design. And this is just one resource that you all can download, um, by developed by some wonderful colleagues at the data and society research Institute in New York. >>And the, the goal of this intervention is threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations and a commitment to take action, to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer, uh, a couple last provocations. The first is pressed to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to AI systems without historical or social depth is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. >>The last provocation is this. If as I suggested at the start in the inequity is woven into the very fabric of our society. It's built into the design of our, our policies, our physical infrastructures, and now even our digital infrastructures. That means that each twist coil and code is a chance for us to weave new patterns, practices, and politics. The vastness of the problems that we're up against will be their undoing. Once we, that we are pattern makers. So what does that look like? It looks like refusing colorblindness as an anecdote to tech media discrimination, rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating. Have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting and how do we create a more participatory rather than predatory form of inclusion. And ultimately it also means owning our own power in these systems so that we can change the patterns of the past. If we're, if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more, just an equitable technologies. So with that, I look forward to our conversation.

>>from around the globe. It's the Cube covering space and cybersecurity. Symposium 2020 hosted by Cal Poly. Yeah, Lauren, Welcome to the Space and Cybersecurity Symposium 2020 put on by Cal Poly and hosted with Silicon Angle acute here in Palo Alto, California for a virtual conference. Couldn't happen in person this year. I'm John for a year. Host the intersection of space and cybersecurity. I'll see critical topics, great conversations. We got a great guest here to talk about the addressing the cybersecurity workforce gap, and we have a great guest, a feature speaker. Stewart Knox, the undersecretary with California's Labor and Workforce Development Office. Stewart Thanks for joining us today. >>Thank you so much, John. Appreciate your time today and listening to a little bit of our quandaries with making sure that we have the security that's necessary for the state of California and making sure that we have the work force that is necessary for cybersecurity in space. >>Great, I'd love to get started. I got a couple questions for you, but first take a few minutes for an opening statement to set the stage. >>Sure, realizing that in California we lead the nation in much of cybersecurity based on Department of Defense contractors within the Santa California leading the nation with over $160 billion within the industry just here in California alone and having over 800,000 bus workers. Full time employment in the state of California is paramount for us to make sure that we face, um, defense manufacturers approximate 700,000 jobs that are necessary to be filled. There's over 37,000 vacancies that we know of in California, just alone in cybersecurity. And so we look forward to making sure that California Workforce Development Agency is leading the charge to make sure that we have equity in those jobs and that we are also leading in a way that brings good jobs to California and to the people of California, a good education system that is developed in a way that those skills are necessarily met for the for the employers here in California and the nation, >>One of the exciting things about California is obviously look at Silicon Valley, Hewlett Packard in the garage, storied history space. It's been a space state. Many people recognize California. You mentioned defense contractors. It's well rooted with with history, um, just breakthroughs bases, technology companies in California. And now you've got technology. This is the cybersecurity angle. Um, take >>them into >>Gets more commentary to that because that's really notable. And as the workforce changes, these two worlds are coming together, and sometimes they're in the same place. Sometimes they're not. This is super exciting and a new dynamic that's driving opportunities. Could you share, um, some color commentary on that dynamic? >>Absolutely. And you're so correct. I think in California we lead the nation in the way that we developed programs that are companies lead in the nation in so many ways around, uh, cyberspace cybersecurity, Uh, in so many different areas for which in the Silicon Valley is just, uh, such a leader in those companies are good qualified companies to do so. Obviously, one of the places we play a role is to make sure that those companies have a skilled workforce. Andi, also that the security of those, uh, systems are in place for our defense contractors onda For the theater companies, those those outlying entities that are providing such key resource is to those companies are also leading on the cutting edge for the future. Also again realizing that we need to expand our training on skills to make sure that those California companies continue to lead is just, um, a great initiative. And I think through apprenticeship training programs on By looking at our community college systems, I think that we will continue to lead the nation as we move forward. >>You know, we've had many conversations here in this symposium, virtually certainly around. The everyday life of consumer is impacted by space. You know, we get our car service Uber lyft. We have maps. We have all this technology that was born out of defense contracts and r and D that really changed generations and create a lot of great societal value. Okay, now, with space kind of on the next generation is easier to get stuff into space. The security of the systems is now gonna be not only paramount for quality of life, but defending that and the skills are needed in cybersecurity to defend that. And the gap is there. What >>can we >>do to highlight the opportunities for career paths? It used to be the day when you get a mechanical engineering degree or aerospace and you graduated. You go get a job. Not anymore. There's a variety of of of paths career wise. What can we do to highlight this career path? >>Absolutely correct. And I think it starts, you know, k through 12 system on. I know a lot of the work that you know, with this bow and other entities we're doing currently, uh, this is where we need to bring our youth into an age where they're teaching us right as we become older on the uses of technology. But it's also teaching, um, where the levels of those education can take them k through 12. But it's also looking at how the community college system links to that, and then the university system links above and beyond. But it's also engage in our employers. You know, One of the key components, obviously, is the employers player role for which we can start to develop strategies that best meet their needs quickly. I think that's one of the comments we hear the most labor agency is how we don't provide a change as fast as we should, especially in technology. You know, we buy computers today, and they're outdated. Tomorrow it's the same with the technology that's in those computers is that those students are going to be the leaders within that to really develop how those structures are in place. S O. K. Through 12 is probably primary place to start, but also continuing. That passed the K 12 system and I bring up the employers and I bring them up in a way, because many times when we've had conversations with employers around what their skills needs were and how do we develop those better? One of the pieces that of that that I think is really should be recognized that many times they recognized that they wanted a four year degree, potentially or five year, six year degree. But then, when we really looked at the skill sets, someone coming out of the community college system could meet those skill sets. And I think we need to have those conversations to make sure not that they shouldn't be continue their education. They absolutely should. Uh, but how do we get those skill sets built into this into 12 plus the two year plus the four year person? >>You know, I love the democratization of these new skills because again. There's no pattern matching because they weren't around before, right? So you gotta look at the exposure to your point K through 12 exposure. But then there's an exploration piece of whether it's community, college or whatever progression. And sometimes it's nonlinear, right? I mean, people are learning different ways, combining the exposure and the exploration. That's a big topic. Can you share your view on this because this now opens up mawr doors for people choice. You got new avenues. You got online clock and get a cloud computing degree now from Amazon and walk in and help. I could be, you know, security clearance, possibly in in college. So you know you get exposure. Is there certain things you see? Is it early on middle school? And then I'll see the exploration Those air two important concepts. Can you unpack that a little bit exposure and exploration of skills? >>Absolutely. And I think this takes place, you know, not only in in the K 12 because somebody takes place in our community colleges and universities is that that connection with those employers is such a key component that if there's a way we could build in internships where experiences what we call on the job training programs apprenticeship training pre apprenticeship training programs into a design where those students at all levels are getting an exposure to the opportunities within the Space and Cybersecurity Avenue. I think that right there alone will start to solve a problem of having 37 plus 1000 openings at any one time in California. Also, I get that there's there's a burden on employers. Thio do that, and I think that's a piece that we have to acknowledge. And I think that's where education to play a larger role That's a place we had. Labor, Workforce, Development Agency, player role With our apprenticeship training programs are pre apprenticeship training programs. I could go on all day of all of our training programs that we have within the state of California. Many of the list of your partners on this endeavor are partners with Employment Training Panel, which I used to be the director of the Brown administration of um, That program alone does incumbent worker training on DSO. That also is an exposure place where ah worker, maybe, you know, you know, use the old adage of sweeping the floors one day and potentially, you know, running a large portion of the business, you know, within years. But it's that exposure that that employee gets through training programs on band. Acknowledging those skill sets and where their opportunities are, is what's valid and important. I think that's where our students we need to play a larger role in the K 12. That's a really thio Get that pushed out there. >>It's funny here in California you're the robotics clubs in high school or like a varsity sport. You're seeing kids exposed early on with programming. But you know, this whole topic of cybersecurity in space intersection around workforce and the gaps and skills is not just for the young. Certainly the young generations gotta be exposed to the what the careers could be and what the possible jobs and societal impact and contributions what they could be. But also it's people who are already out there. You know, you have retraining re Skilling is plays an important role. I know you guys do a lot of thinking on this is the under secretary. You have to look at this because you know you don't wanna have a label old and antiquated um systems. And then a lot of them are, and they're evolving and they're being modernized by digital transformation. So what does the role of retraining and skill development these programs play? Can you share what you guys are working on in your vision for that? >>Absolutely. That's a great question. And I think that is where we play a large role, obviously in California and with Kobe, 19 is we're faced with today that we've never seen before, at least in my 27 years of running program. Similar Thio, of course, in economic development, we're having such a large number of people displaced currently that it's unprecedented with unemployment rates to where we are. We're really looking at How do we take? And we're also going to see industries not return to the level for which they stood at one point in time. Uh, you know, entertainment industries, restaurants, all the alike, uh, really looking at how do we move people from those jobs that were middle skill jobs, topper skilled jobs? But the pay points maybe weren't great, potentially, and there's an opportunity for us to skill people into jobs that are there today. It may take training, obviously, but we have dollars to do that generally, especially within our K 12 and are que 14 systems and our universities. But we really wanna look at where those skill sets are are at currently. And we want to take people from that point in time where they said today, and try to give them that exposure to your point. Earlier question is, how do we get them exposed to a system for which there are job means that pay well with benefit packages with companies that care about their employees? Because that's what our goal is. >>You know. You know, I don't know if you have some visibility on this or ah opinion, but one observation that I've had and talking to whether it's a commercial or public sector is that with co vid uh, there have been a lot of awareness of the situation. We're adequately prepared. There's, um, readiness. But as everyone kind of deals with it, they're also starting to think about what to do. Post covert as we come out of it, Ah, growth strategy for a company or someone's career, um, people starting to have that on the top of their minds So I have to ask you, Is there anything that you see that they say? Okay, certain areas, maybe not doubling down on other areas. We're gonna double down on because we've seen some best practices on a trajectory of value for coming out of co vid with, you know, well, armed skills or certain things because you because that's what a lot of people are thinking right now. It's probably cyber is I mean, how many jobs are open? So you got well, that that's kind of maybe not something double down on here are areas we see that are working. Can you share your current visibility to that dynamic? >>Absolutely. Another great question. One of the key components that we look at Labor Workforce Development Agency. And so look at industries and growth modes and ones that are in decline boats. Now Kobe has changed that greatly. We were in a growth rate for last 78 years. We saw almost every industry might miss a few. You know that we're all in growth in one way or enough, obviously, that has changed. Our landscape is completely different than we saw 67 months ago. So today we're looking at cybersecurity, obviously with 30 plus 1000 jobs cos we're looking at Defense Department contractor is obviously with federal government contracts. We were looking at the supply chains within those we're looking at. Health care, which has always been one, obviously are large one of our large entities that has has grown over the years. But it's also changed with covered 19. We're looking at the way protective equipment is manufactured in the way that that will continue to grow over time. We're looking at the service industry. I mean, it will come back, but it won't come back the way we've seen it, probably in the past, but where the opportunities that we develop programs that we're making sure that the skill sets of those folks are transferrable to other industries with one of the issues that we face constant labor and were forced moment programs is understanding that over the period of time, especially in today's world again, with technology that people skill sets way, don't see is my Parents Day that you worked at a job for 45 years and you retired out of one job. Potentially, that is, that's been gone for 25 years, but now, at the pace for which we're seeing systems change. This is going to continue to amp up. I will stay youth of today. My 12 year old nephew is in the room next door to me on a classroom right now online. And so you know, there. It's a totally different atmosphere, and he's, you know, enjoying actually being in helping learning from on all online system. I would not have been able to learn that way, but I think we do see through the K Through 12 system where we're moving, um, people's interest will change, and I think that they will start to see things in a different way than we have in the past. They were forced systems. We are an old system been around since the thirties. Some even will say prior to the thirties came out of the Great Depression in some ways, and that system we have to change the way we develop our programs are should not be constant, and it should be an evolving system. >>It's interesting a lot of the conversation between the private and public partnerships and industry. You're seeing an agile mind set where it's a growth mindset. It's also reality based mindset and certainly space kind of forces. This conversation with cyber security of being faster, faster, more relevant, more modern. You mentioned some of those points, and with co vid impact the workforce development, it's certainly going to put a lot of pressure on faster learning. And then you mentioned online learning. This has become a big thing. It's not just putting education online per se. There's new touch points. You know you got APS, you got digital. This digital transformation is also accelerating. How do you guys view the workforce development? Because it's going to be open. It's gonna be evolving. There's new data coming in, and maybe kids don't want to stare at a video conference. Is there some game aspect to it? Is there how do you integrate thes new things that are coming really fast? And it's happening kind of in real time in front of our eyes. So I love to get your thoughts on how you guys see that, because it will certainly impact their ability to compete for jobs and or to itself learn. >>I think one of the key components of California's our innovation right and So I think one of the things that we pride ourselves in California is around that, um that said, that is the piece that I think the Silicon Valley and there's many areas in California that that have done the same, um, or trying to do the same, at least in their economy, is to build in innovation. And I think that's part of the K through 12 system with our with our our state universities and our UCS is to be able to bridge that. I think that you we see that within universities, um, that really instill an innovative approach to teaching but also instill innovation within their students. I'm not sure there yet with our fully with our K 12 system. And I think that's a place that either our community colleges could be a bridge, too, as well. Eso that's one component of workforce development I think that we look at as being a key. A key piece you brought up something that's really interesting to me is when you talk about agile on day, one of the things that even in state government on this, is gonna be shocking to you. But we have not been an agile system, Aziz. Well, I think one of the things that the Newsome administration Governor Newsom's administration has brought is. And when I talk about agile systems, I actually mean agile systems. We've gone from Kobol Systems, which are old and clunky, still operating. But at the same time, we're looking at upgrading all of our systems in a way that even our technology in the state of California should be matching the technology that our great state has within our our state. So, um, there in lies. It's also challenges of finding the qualified staff that we need in the state of California for all of our systems and servers and everything that we have. Um, currently. So you know, not only are we looking at external users, users of labor, workforce development, but we're looking at internal users that the way we redevelop our systems so that we are more agile in two different ways. >>You just got me. I triggered with COBOL. I programmed in the eighties with COBOL is only one credit lab in college. Never touched it again. Thank God. But this. But this >>is the >>benefit of cloud computing. I think this is at the heart, and this is the undertone of the conference and symposium is cloud computing. You can you can actually leverage existing resource is whether there legacy systems because they are running. They're doing a great job, and they do a certain work load extremely well. Doesn't make sense to replace what does a job, but you can integrate it in this. What cloud does this is Opening up? Can mawr more and more capabilities and workloads? This is kind of the space industry is pointing to when they say we need people that can code. And that could solve data problems. Not just a computer scientist, but a large range of people. Creative, um, data, science, everything. How does California's workforce solve the needs of America's space industry? This is because it's a space state. How do you see that? Let your workforce meeting those needs. >>Yeah, I think I think it's an investment. Obviously, it's an investment on our part. It's an investment with our college partners. It's an investment from our K 12 system to make sure that that we are allocating dollars in a way through meeting the demand of industry Onda, we do look at industry specific around there needs. Obviously, there's a large one. We wanna be very receptive and work with our employers and our employee groups to make sure that we need that demand. I think it's putting our money where our mouth is and and designing and working with employer groups to make sure that the training meets their needs. Um, it's also working with our employer groups to make sure that the employees are taken care of. That equity is built within the systems, Um, that we keep people employed in California on their able to afford a home, and they're able to afford a life here in California. But it's also again, and I brought up the innovation component. I think it's building an innovation within systems for which they are employers but are also our incoming employees are incumbent workers. And you brought this up earlier. People that already employed and people that are unemployed currently with the skill set that might match up, is how do we bridge those folks into employment that they maybe have not thought about. We have a whole career network of systems out throughout the city, California with the Americans job Centers of California on day will be working, and they already are working with a lot of dislocated workers on day. One of the key components of that is to really look at how do we, um, take what their current skills that might be and then expose them to a system for which we have 37 plus 1000 job openings to Andi? How do we actually get those books employed? It's paying for potentially through those that local Workforce Innovation Opportunity Act, funding for Americans job centers, um, to pay for some on the job, training it Z to be able to pay for work experiences. It's to be able to pay for internships for students, um, to get that opportunity with our employers and also partner with our employers that they're paying obviously a percentage of that, too. >>You know, one of the things I've observed over my, um, career 54 times around the sun is you know, in the old days when I was in college in school, you had career people have longer jobs, as you mentioned. Not like that anymore. But also I knew someone I'm gonna be in line to get that job, maybe nepotism or things of that nature. Now the jobs have no historical thing or someone worked longer in a job and has more seniority. Ah, >>lot of these >>jobs. Stewart don't HAVA requirements like no one's done them before. So the ability for someone who, um, is jumping in either from any college, there's no riel. It's all level set. It's like complete upside down script here. It's not like, Oh, I went to school. Therefore I get the job you could be Anyone could walk into these careers because the jobs air so new. So it's not where you came from or what school you went to or your nationality or gender. The jobs have been democratized. They're not discriminating against people with skills. So this opens up mawr. How >>do you >>see that? Because this really is an opportunity for this next generation to be more diverse and to be mawr contributed because diversity brings expertise and different perspectives. Your thoughts on that? >>Absolutely. And that was one of the things we welcome. Obviously we want to make sure that that everybody is treated equally and that the employers view everyone as employer employer of choice but an employee of choices. Well, we've also been looking at, as I mentioned before on the COVITZ situation, looking at ways that books that are maybe any stuck in jobs that are don't have a huge career pathway or they don't have a pathway out of poverty. I mean, we have a lot of working for people in the state of California, Um, that may now do to cope and lost their employment. Uh, this, you know, Let's let's turn back to the old, you know? Let's try, eliminate, eliminate, eliminate. How do we take those folks and get them employed into jobs that do have a good career pathway? And it's not about just who you knew or who you might have an in with to get that job. It is based on skills, I think, though that said there we need to have a better way to actually match those jobs up with those employers. And I think those are the long, ongoing conversations with those employer groups to make sure that one that they see those skill sets is valid and important. Um, they're helping design this crew sets with us, eh? So that they do match up and that were quickly matching up those close skills. That so that we're not training people for yesterday skills. >>I think the employer angles super important, but also the educators as well. One of the things that was asked in another question by the gas they they said. She said The real question to ask is, how early do you start exposing the next generation? You mentioned K through 12. Do you have any data or insight into or intuition or best practice of where that insertion point is without exposure? Point is, is that middle school is a elementary, obviously high school. Once you're in high school, you got your training. Wheels are off, you're off to the races. But is there a best practice? What's your thoughts? Stewart On exposure level to these kinds of new cyber and technical careers? >>Sure, absolutely. I I would say kindergarten. We San Bernardino has a program that they've been running for a little bit of time, and they're exposing students K through 12 but really starting in kindergarten. One is the exposure Thio. What a job Looks like Andi actually have. I've gone down to that local area and I've had three opportunity to see you know, second graders in a health care facility, Basically that they have on campus, built in on dear going from one workstation as a second grader, Uh, looking at what those skills would be and what that job would entail from a nurse to a Dr Teoh physician's assistant in really looking at what that is. Um you know, obviously they're not getting the training that the doctor gets, but they are getting the exposure of what that would be. Andi, I think that is amazing. And I think it's the right place to start. Um, it was really interesting because I left. This was pre covet, but I jumped on the plane to come back up north. I was thinking to myself, How do we get this to all school district in California, where we see that opportunity, um, to expose jobs and skill sets to kids throughout the system and develop the skill set so that they do understand that they have an opportunity. >>We're here at Cal Poly Space and Cybersecurity Symposium. We have educators. We have, um, students. We have industry and employers and government together. What's your advice to them all watching and listening about the future of work. Let's work force. What can people do? What do you think you're enabling? What can maybe the private sector help with And what are you trying to do? Can you share your thoughts on that? Because we have a range from the dorm room to the boardroom here at this event. Love to get your thoughts on the workforce development view of this. >>Yeah, absolutely. I think that's the mix. I mean, I think it's going to take industry to lead A in a lot of ways, in terms of understanding what their needs are and what their needs are today and what they will be tomorrow. I think it takes education, toe listen, and to understand and labor and workforce development also listen and understand what those needs will look like. And then how do we move systems? How do we move systems quickly? How do we move systems in a way that meets those needs? How do we, uh, put money into systems where the most need is, but also looking at trends? What is that trend going to look like in two years? What does that train gonna look like in five years. But that's again listening to those employers. Um, it's also the music community based organizations. I think, obviously some of our best students are also linked to CBS. And one way or another, it may be for services. It maybe for, uh, faith based. It may be anything, but I think we also need to bring in the CBS is Well, ah, lot of outreach goes through those systems in conjunction with, but I think that's the key component is to make sure that our employers are heard on. But they sit at the table like you said to the boardroom of understanding, and I think bringing students into that so that they get a true understanding of what that looks like a well, um, is a key piece of this. >>So one of the things I want to bring up with you is maybe a bit more about the research side of it. But, um, John Markoff, who was a former New York Times reporter with author of the book What the Dormouse, said It was a book about the counter culture of the sixties and the computer revolution, and really there was about how government defense spending drove the computer revolution that we now saw with Apple and PC, and then the rest is history in California has really participated. Stanford, uh, Berkeley and the University of California School system and all the education community colleges around it. That moment, the enablement. And now you're seeing space kind of bringing that that are a lot of research coming in and you eat a lot of billionaires putting money in. You got employers playing a role. You have this new focus space systems, cybersecurity, defending and making it open and and not congested and peaceful is going to enable quickly new inflection points for opportunities. E want to get your thoughts on that? Because California is participate in drove these revolutions that created massive value This next wave seems to be coming upon us. >>Yeah, absolutely. And again, Nazis covered again as too much of ah starting point to this. But I think that is also an opportunity to actually, because I think one of the things that we were seeing seven months ago was a skill shortage, and we still see the skills shortage, obviously. But I think a key piece to that is we saw people shortage. Not only was it skills shortage, but we didn't have enough people really to fill positions in addition to and I think that people also felt they were already paying the bills and they were making ends meet and they didn't have the opportunities. Thio get additional skills This again is where we're looking at. You know that our world has changed. It changed in the sixties based on what you're you're just expressing in terms of California leading the way. Let's like California lead the way again in developing a system from which labor, workforce development with our universities are, you know, are amazing universities and community college system and structure of how do we get students back into school? You know, a lot of graduates may already have a degree, but how do they now take a skill so that they already have and develop that further with the idea that they those jobs have changed? Whales have a lot of folks that don't have a degree, and that's okay. But how do we make that connection to a system that may have failed? Ah, lot of our people over the years, um, and our students who didn't make it through the school system. How do we develop in adult training school? How do we develop contract education through our community college system with our employer sets that we developed cohorts within those systems of of workers that have amazing talents and abilities to start to fill these needs? And I think that's the key components of hearing Agency, Labor, Workforce Development Agency. We work with our community. Colleges are UCS in our state universities t develop and figure that piece out, and I think it is our opportunity for the future. >>That's such a great point. I want to call that out This whole opportunity to retrain people that are out there because these air new jobs, I think that's a huge opportunity, and and I hope you keep building and investing in those programs. That's that's really worth calling out. Thank you for doing that. And, yeah, it's a great opportunity. Thes jobs they pay well to cyber security is a good job, and you don't really need to have that classical degree. You can learn pretty quickly if you're smart. So again, great call out there question for you on geography, Um, mentioned co vid we're talking about Covic. Virtualization were virtual with this conference. We couldn't be in person. People are learning virtually, but people are starting to relocate virtually. And so one observation that I have is the space state that California is there space clusters of areas where space people hang out or space spaces and whatnot. Then you got, like, the tech community cybersecurity market. You know, Silicon Valley is a talented in these hubs, and sometimes cyber is not always in the same hubs of space. Maybe Silicon Valley has some space here, Um, and some cyber. But that's not generally the case. This is an opportunity potentially to intersect. What's your thoughts on this? Because this is This is something that we're seeing where your space has historical, you know, geography ease. Now, with borderless communication, the work boat is not so much. You have to move the space area. You know what I'm saying? So okay. What's your thoughts on this? How do you guys look at this? Is on your radar On how you're viewing this this dynamic? >>It's absolute on our radar, Like you said, you know, here we are talking virtually on and, you know, 75% of all of our staff currently in some of our department that 80% of our staff are now virtual. Um you know, seven months ago, uh, we were not were government again being slow move, we quickly transitioned. Obviously, Thio being able to have a tele work capacity. We know employers move probably even quickly, more quickly than we did, but we see that as an opportunity for our rural areas. Are Central Valley are north state um, inland Empire that you're absolutely correct. I mean, if you didn't move to a city or to a location for which these jobs were really housed, um, you didn't have an opportunity like you do today. I think that's a piece that we really need to work with our education partners on of to be able to see how much this has changed. Labor agency absolutely recognizes this. We are investing funding in the Central Valley. We're investing funding in the North State and empire to really look a youth populations of how the new capacity that we have today is gonna be utilized for the future for employers. But we also have to engage our universities around. This is well, but mostly are employers. I know that they're already very well aware. I know that a lot of our large employers with, um, Silicon Valley have already done their doing almost 100% tele work policies. Um, but the affordability toe live in rural areas in California. Also, it enables us to have, ah, way thio make products more affordable is, well, potentially in the future. But we want to keep California businesses healthy and whole in California. Of course, on that's another way we can We can expand and keep California home to our 40 plus million people, >>most to a great, great work. And congratulations for doing such a great job. Keep it up. I gotta ask about the governor. I've been following his career since he's been office. A za political figure. Um, he's progressive. He's cutting edge. He likes toe rock the boat a little bit here and there, but he's also pragmatic. Um, you're starting to see government workers starting to get more of a tech vibe. Um um just curious from your perspective. How does the governor look at? I mean, the old, almost the old guard. But like you know, used to be. You become a lawyer, become a lawmaker Now a tech savvy lawmaker is a premium candidates, a premium person in government, you know, knowing what COBOL is. A start. I mean, these are the things. As we transform and evolve our society, we need thinkers who can figure out which side the streets, self driving cars go on. I mean, who does that? I mean, it's a whole another generation off thinking. How does the Governor how do you see this developing? Because this is the challenge for society. How does California lead? How do you guys talk about the leadership vision of Why California and how will you lead the future? >>Absolutely no governor that I'm aware of that I've been around for 26 27 years of workforce development has led with an innovation background, as this governor has a special around technology and the use of technology. Uh, you know, he's read a book about the use of technology when he was lieutenant governor, and I think it's really important for him that we, as his his staff are also on the leading edge of technology. I brought a badge. I'll systems. Earlier, when I was under the Brown administration, we had moved to where I was at a time employment training panel. We moved to an agile system and deported that one of the first within within the state to do that and coming off of an old legacy system that was an antique. Um, I will say it is challenging. It's challenging on a lot of levels. Mostly the skill sets that are folks have sometimes are not open to a new, agile system to an open source system is also an issue in government. But this governor, absolutely. I mean, he has established three Office of Digital Innovation, which is part of California and department technology, Um, in partnership with and that just shows how much he wants. Thio push our limits to make sure that we are meeting the needs of Californians. But it's also looking at, you know, Silicon Valley being at the heart of our state. How do we best utilize systems that already there? How do we better utilize the talent from those those folks is well, we don't always pay as well as they dio in the state. But we do have great benefit packages. Everybody does eso If anybody's looking for a job, we're always looking for technology. Folks is well on DSO I would say that this governor, absolute leads in terms of making sure that we will be on cutting edge of technology for the nation, >>you know, and, you know, talk about pay. I mean, I know it's expensive to live in some parts of California, but there's a huge young population that wants a mission driven job and serving, um, government for the governments. Awesome. Ah, final parting question for you, Stuart, is, as you look at, um, workforce. Ah, lot of people are passionate about this, and it's, you know, you you can't go anywhere without people saying, You know, we got to do education this way and that way there's an opinion everywhere you go. Cybersecurity is a little bit peaked and focused, but there are people who are paying attention to education. So I have to ask you, what creative ways can people get involved and contribute to workforce development? Whether it's stem underrepresented minorities, people are looking for new, innovative ways to contribute. What advice would you give these people who have the passion to contribute to the next cyber workforce. >>Yeah, I appreciate that question, because I think is one of the key components. But my secretary, Julie Sue, secretary of Labor and Workforce Development Agency, talks about often, and a couple of us always have these conversations around. One is getting people with that passion to work in government one or on. I brought it up community based organizations. I think I think so many times, um, that we didn't work with our CBS to the level of in government we should. This administration is very big on working with CBS and philanthropy groups to make sure that thing engagement those entities are at the highest level. So I would say, You know, students have opportunities. Thio also engage with local CBS and be that mission what their values really drives them towards Andi. That gives them a couple of things to do right. One is to look at what ways that we're helping society in one way or another through the organizations, but it also links them thio their own mission and how they could develop those skills around that. But I think the other piece to that is in a lot of these companies that you are working with and that we work with have their own foundations. So those foundations are amazing. We work with them now, especially in the new administration. More than we ever have, these foundations are really starting to help develop are strategies. My secretary works with a large number of foundations already. Andi, when we do is well in terms of strategy, really looking at, how do we develop young people's attitudes towards the future but also skills towards the future? >>Well, you got a pressure cooker of a job. I know how hard it is. I know you're working hard, appreciate you what you do and and we wish you the best of luck. Thank you for sharing this great insight on workforce development. And you guys working hard. Thank you for what you do. Appreciate it. >>Thank you so much. Thistle's >>three cube coverage and co production of the space and cybersecurity supposed in 2020 Cal Poly. I'm John for with silicon angle dot com and the Cube. Thanks for watching

>>from around the globe. It's the Cube covering >>space and cybersecurity. Symposium 2020 hosted by Cal Poly >>Over On Welcome to this Special virtual conference. The Space and Cybersecurity Symposium 2020 put on by Cal Poly with support from the Cube. I'm John for your host and master of ceremonies. Got a great topic today in this session. Really? The intersection of space and cybersecurity. This topic and this conversation is the cybersecurity workforce development through public and private partnerships. And we've got a great lineup. We have Jeff Armstrong's the president of California Polytechnic State University, also known as Cal Poly Jeffrey. Thanks for jumping on and Bang. Go ahead. The second director of C four s R Division. And he's joining us from the office of the Under Secretary of Defense for the acquisition Sustainment Department of Defense, D O D. And, of course, Steve Jake's executive director, founder, National Security Space Association and managing partner at Bello's. Gentlemen, thank you for joining me for this session. We got an hour conversation. Thanks for coming on. >>Thank you. >>So we got a virtual event here. We've got an hour, have a great conversation and love for you guys do? In opening statement on how you see the development through public and private partnerships around cybersecurity in space, Jeff will start with you. >>Well, thanks very much, John. It's great to be on with all of you. Uh, on behalf Cal Poly Welcome, everyone. Educating the workforce of tomorrow is our mission to Cal Poly. Whether that means traditional undergraduates, master students are increasingly mid career professionals looking toe up, skill or re skill. Our signature pedagogy is learn by doing, which means that our graduates arrive at employers ready Day one with practical skills and experience. We have long thought of ourselves is lucky to be on California's beautiful central Coast. But in recent years, as we have developed closer relationships with Vandenberg Air Force Base, hopefully the future permanent headquarters of the United States Space Command with Vandenberg and other regional partners, we have discovered that our location is even more advantages than we thought. We're just 50 miles away from Vandenberg, a little closer than u C. Santa Barbara, and the base represents the southern border of what we have come to think of as the central coast region. Cal Poly and Vandenberg Air force base have partner to support regional economic development to encourage the development of a commercial spaceport toe advocate for the space Command headquarters coming to Vandenberg and other ventures. These partnerships have been possible because because both parties stand to benefit Vandenberg by securing new streams of revenue, workforce and local supply chain and Cal Poly by helping to grow local jobs for graduates, internship opportunities for students, and research and entrepreneurship opportunities for faculty and staff. Crucially, what's good for Vandenberg Air Force Base and for Cal Poly is also good for the Central Coast and the US, creating new head of household jobs, infrastructure and opportunity. Our goal is that these new jobs bring more diversity and sustainability for the region. This regional economic development has taken on a life of its own, spawning a new nonprofit called Reach, which coordinates development efforts from Vandenberg Air Force Base in the South to camp to Camp Roberts in the North. Another factor that is facilitated our relationship with Vandenberg Air Force Base is that we have some of the same friends. For example, Northrop Grumman has has long been an important defense contractor, an important partner to Cal poly funding scholarships and facilities that have allowed us to stay current with technology in it to attract highly qualified students for whom Cal Poly's costs would otherwise be prohibitive. For almost 20 years north of grimness funded scholarships for Cal Poly students this year, their funding 64 scholarships, some directly in our College of Engineering and most through our Cal Poly Scholars program, Cal Poly Scholars, a support both incoming freshman is transfer students. These air especially important because it allows us to provide additional support and opportunities to a group of students who are mostly first generation, low income and underrepresented and who otherwise might not choose to attend Cal Poly. They also allow us to recruit from partner high schools with large populations of underrepresented minority students, including the Fortune High School in Elk Grove, which we developed a deep and lasting connection. We know that the best work is done by balanced teams that include multiple and diverse perspectives. These scholarships help us achieve that goal, and I'm sure you know Northrop Grumman was recently awarded a very large contract to modernized the U. S. I. C B M Armory with some of the work being done at Vandenberg Air Force Base, thus supporting the local economy and protecting protecting our efforts in space requires partnerships in the digital realm. How Polly is partnered with many private companies, such as AWS. Our partnerships with Amazon Web services has enabled us to train our students with next generation cloud engineering skills, in part through our jointly created digital transformation hub. Another partnership example is among Cal Poly's California Cybersecurity Institute, College of Engineering and the California National Guard. This partnership is focused on preparing a cyber ready workforce by providing faculty and students with a hands on research and learning environment, side by side with military, law enforcement professionals and cyber experts. We also have a long standing partnership with PG and E, most recently focused on workforce development and redevelopment. Many of our graduates do indeed go on to careers in aerospace and defense industry as a rough approximation. More than 4500 Cal Poly graduates list aerospace and defense as their employment sector on linked in, and it's not just our engineers and computer sciences. When I was speaking to our fellow Panelists not too long ago, >>are >>speaking to bang, we learned that Rachel sins, one of our liberal arts arts majors, is working in his office. So shout out to you, Rachel. And then finally, of course, some of our graduates sword extraordinary heights such as Commander Victor Glover, who will be heading to the International space station later this year as I close. All of which is to say that we're deeply committed the workforce, development and redevelopment that we understand the value of public private partnerships and that were eager to find new ways in which to benefit everyone from this further cooperation. So we're committed to the region, the state in the nation and our past efforts in space, cybersecurity and links to our partners at as I indicated, aerospace industry and governmental partners provides a unique position for us to move forward in the interface of space and cybersecurity. Thank you so much, John. >>President, I'm sure thank you very much for the comments and congratulations to Cal Poly for being on the forefront of innovation and really taking a unique progressive. You and wanna tip your hat to you guys over there. Thank you very much for those comments. Appreciate it. Bahng. Department of Defense. Exciting you gotta defend the nation spaces Global. Your opening statement. >>Yes, sir. Thanks, John. Appreciate that day. Thank you, everybody. I'm honored to be this panel along with President Armstrong, Cal Poly in my long longtime friend and colleague Steve Jakes of the National Security Space Association, to discuss a very important topic of cybersecurity workforce development, as President Armstrong alluded to, I'll tell you both of these organizations, Cal Poly and the N S. A have done and continue to do an exceptional job at finding talent, recruiting them in training current and future leaders and technical professionals that we vitally need for our nation's growing space programs. A swell Asare collective National security Earlier today, during Session three high, along with my colleague Chris Hansen discussed space, cyber Security and how the space domain is changing the landscape of future conflicts. I discussed the rapid emergence of commercial space with the proliferations of hundreds, if not thousands, of satellites providing a variety of services, including communications allowing for global Internet connectivity. S one example within the O. D. We continue to look at how we can leverage this opportunity. I'll tell you one of the enabling technologies eyes the use of small satellites, which are inherently cheaper and perhaps more flexible than the traditional bigger systems that we have historically used unemployed for the U. D. Certainly not lost on Me is the fact that Cal Poly Pioneer Cube SATs 2020 some years ago, and they set the standard for the use of these systems today. So they saw the valiant benefit gained way ahead of everybody else, it seems, and Cal Poly's focus on training and education is commendable. I especially impressed by the efforts of another of Steve's I colleague, current CEO Mr Bill Britain, with his high energy push to attract the next generation of innovators. Uh, earlier this year, I had planned on participating in this year's Cyber Innovation Challenge. In June works Cal Poly host California Mill and high school students and challenge them with situations to test their cyber knowledge. I tell you, I wish I had that kind of opportunity when I was a kid. Unfortunately, the pandemic change the plan. Why I truly look forward. Thio feature events such as these Thio participating. Now I want to recognize my good friend Steve Jakes, whom I've known for perhaps too long of a time here over two decades or so, who was in acknowledge space expert and personally, I truly applaud him for having the foresight of years back to form the National Security Space Association to help the entire space enterprise navigate through not only technology but Polly policy issues and challenges and paved the way for operational izing space. Space is our newest horrifying domain. That's not a secret anymore. Uh, and while it is a unique area, it shares a lot of common traits with the other domains such as land, air and sea, obviously all of strategically important to the defense of the United States. In conflict they will need to be. They will all be contested and therefore they all need to be defended. One domain alone will not win future conflicts in a joint operation. We must succeed. All to defending space is critical as critical is defending our other operational domains. Funny space is no longer the sanctuary available only to the government. Increasingly, as I discussed in the previous session, commercial space is taking the lead a lot of different areas, including R and D, A so called new space, so cyber security threat is even more demanding and even more challenging. Three US considers and federal access to and freedom to operate in space vital to advancing security, economic prosperity, prosperity and scientific knowledge of the country. That's making cyberspace an inseparable component. America's financial, social government and political life. We stood up US Space force ah, year ago or so as the newest military service is like the other services. Its mission is to organize, train and equip space forces in order to protect us and allied interest in space and to provide space capabilities to the joint force. Imagine combining that US space force with the U. S. Cyber Command to unify the direction of space and cyberspace operation strengthened U D capabilities and integrate and bolster d o d cyber experience. Now, of course, to enable all of this requires had trained and professional cadre of cyber security experts, combining a good mix of policy as well as high technical skill set much like we're seeing in stem, we need to attract more people to this growing field. Now the D. O. D. Is recognized the importance of the cybersecurity workforce, and we have implemented policies to encourage his growth Back in 2013 the deputy secretary of defense signed the D. O d cyberspace workforce strategy to create a comprehensive, well equipped cyber security team to respond to national security concerns. Now this strategy also created a program that encourages collaboration between the D. O. D and private sector employees. We call this the Cyber Information Technology Exchange program or site up. It's an exchange programs, which is very interesting, in which a private sector employees can naturally work for the D. O. D. In a cyber security position that spans across multiple mission critical areas are important to the d. O. D. A key responsibility of cybersecurity community is military leaders on the related threats and cyber security actions we need to have to defeat these threats. We talk about rapid that position, agile business processes and practices to speed up innovation. Likewise, cybersecurity must keep up with this challenge to cyber security. Needs to be right there with the challenges and changes, and this requires exceptional personnel. We need to attract talent investing the people now to grow a robust cybersecurity, workforce, streets, future. I look forward to the panel discussion, John. Thank you. >>Thank you so much bomb for those comments and you know, new challenges and new opportunities and new possibilities and free freedom Operating space. Critical. Thank you for those comments. Looking forward. Toa chatting further. Steve Jakes, executive director of N. S. S. A Europe opening statement. >>Thank you, John. And echoing bangs thanks to Cal Poly for pulling these this important event together and frankly, for allowing the National Security Space Association be a part of it. Likewise, we on behalf the association delighted and honored Thio be on this panel with President Armstrong along with my friend and colleague Bonneau Glue Mahad Something for you all to know about Bomb. He spent the 1st 20 years of his career in the Air Force doing space programs. He then went into industry for several years and then came back into government to serve. Very few people do that. So bang on behalf of the space community, we thank you for your long life long devotion to service to our nation. We really appreciate that and I also echo a bang shot out to that guy Bill Britain, who has been a long time co conspirator of ours for a long time and you're doing great work there in the cyber program at Cal Poly Bill, keep it up. But professor arms trying to keep a close eye on him. Uh, I would like to offer a little extra context to the great comments made by by President Armstrong and bahng. Uh, in our view, the timing of this conference really could not be any better. Um, we all recently reflected again on that tragic 9 11 surprise attack on our homeland. And it's an appropriate time, we think, to take pause while the percentage of you in the audience here weren't even born or babies then For the most of us, it still feels like yesterday. And moreover, a tragedy like 9 11 has taught us a lot to include to be more vigilant, always keep our collective eyes and ears open to include those quote eyes and ears from space, making sure nothing like this ever happens again. So this conference is a key aspect. Protecting our nation requires we work in a cybersecurity environment at all times. But, you know, the fascinating thing about space systems is we can't see him. No, sir, We see Space launches man there's nothing more invigorating than that. But after launch, they become invisible. So what are they really doing up there? What are they doing to enable our quality of life in the United States and in the world? Well, to illustrate, I'd like to paraphrase elements of an article in Forbes magazine by Bonds and my good friend Chuck Beans. Chuck. It's a space guy, actually had Bonds job a fuse in the Pentagon. He is now chairman and chief strategy officer at York Space Systems, and in his spare time he's chairman of the small satellites. Chuck speaks in words that everyone can understand. So I'd like to give you some of his words out of his article. Uh, they're afraid somewhat. So these are Chuck's words. Let's talk about average Joe and playing Jane. Before heading to the airport for a business trip to New York City, Joe checks the weather forecast informed by Noah's weather satellites to see what pack for the trip. He then calls an uber that space app. Everybody uses it matches riders with drivers via GPS to take into the airport, So Joe has lunch of the airport. Unbeknownst to him, his organic lunch is made with the help of precision farming made possible through optimized irrigation and fertilization, with remote spectral sensing coming from space and GPS on the plane, the pilot navigates around weather, aided by GPS and nose weather satellites. And Joe makes his meeting on time to join his New York colleagues in a video call with a key customer in Singapore made possible by telecommunication satellites. Around to his next meeting, Joe receives notice changing the location of the meeting to another to the other side of town. So he calmly tells Syria to adjust the destination, and his satellite guided Google maps redirects him to the new location. That evening, Joe watches the news broadcast via satellite. The report details a meeting among world leaders discussing the developing crisis in Syria. As it turns out, various forms of quote remotely sensed. Information collected from satellites indicate that yet another band, chemical weapon, may have been used on its own people. Before going to bed, Joe decides to call his parents and congratulate them for their wedding anniversary as they cruise across the Atlantic, made possible again by communications satellites and Joe's parents can enjoy the call without even wondering how it happened the next morning. Back home, Joe's wife, Jane, is involved in a car accident. Her vehicle skids off the road. She's knocked unconscious, but because of her satellite equipped on star system, the crash is detected immediately and first responders show up on the scene. In time, Joe receives the news books. An early trip home sends flowers to his wife as he orders another uber to the airport. Over that 24 hours, Joe and Jane used space system applications for nearly every part of their day. Imagine the consequences if at any point they were somehow denied these services, whether they be by natural causes or a foreign hostility. And each of these satellite applications used in this case were initially developed for military purposes and continue to be, but also have remarkable application on our way of life. Just many people just don't know that. So, ladies and gentlemen, now you know, thanks to chuck beans, well, the United States has a proud heritage being the world's leading space faring nation, dating back to the Eisenhower and Kennedy years. Today we have mature and robust systems operating from space, providing overhead reconnaissance to quote, wash and listen, provide missile warning, communications, positioning, navigation and timing from our GPS system. Much of what you heard in Lieutenant General J. T. Thompson earlier speech. These systems are not only integral to our national security, but also our also to our quality of life is Chuck told us. We simply no longer could live without these systems as a nation and for that matter, as a world. But over the years, adversary like adversaries like China, Russia and other countries have come to realize the value of space systems and are aggressively playing ketchup while also pursuing capabilities that will challenge our systems. As many of you know, in 2000 and seven, China demonstrated it's a set system by actually shooting down is one of its own satellites and has been aggressively developing counter space systems to disrupt hours. So in a heavily congested space environment, our systems are now being contested like never before and will continue to bay well as Bond mentioned, the United States has responded to these changing threats. In addition to adding ways to protect our system, the administration and in Congress recently created the United States Space Force and the operational you United States Space Command, the latter of which you heard President Armstrong and other Californians hope is going to be located. Vandenberg Air Force Base Combined with our intelligence community today, we have focused military and civilian leadership now in space. And that's a very, very good thing. Commence, really. On the industry side, we did create the National Security Space Association devoted solely to supporting the national security Space Enterprise. We're based here in the D C area, but we have arms and legs across the country, and we are loaded with extraordinary talent. In scores of Forman, former government executives, So S s a is joined at the hip with our government customers to serve and to support. We're busy with a multitude of activities underway ranging from a number of thought provoking policy. Papers are recurring space time Webcast supporting Congress's Space Power Caucus and other main serious efforts. Check us out at NSS. A space dot org's One of our strategic priorities in central to today's events is to actively promote and nurture the workforce development. Just like cow calling. We will work with our U. S. Government customers, industry leaders and academia to attract and recruit students to join the space world, whether in government or industry and two assistant mentoring and training as their careers. Progress on that point, we're delighted. Be delighted to be working with Cal Poly as we hopefully will undertake a new pilot program with him very soon. So students stay tuned something I can tell you Space is really cool. While our nation's satellite systems are technical and complex, our nation's government and industry work force is highly diverse, with a combination of engineers, physicists, method and mathematicians, but also with a large non technical expertise as well. Think about how government gets things thes systems designed, manufactured, launching into orbit and operating. They do this via contracts with our aerospace industry, requiring talents across the board from cost estimating cost analysis, budgeting, procurement, legal and many other support. Tasker Integral to the mission. Many thousands of people work in the space workforce tens of billions of dollars every year. This is really cool stuff, no matter what your education background, a great career to be part of. When summary as bang had mentioned Aziz, well, there is a great deal of exciting challenges ahead we will see a new renaissance in space in the years ahead, and in some cases it's already begun. Billionaires like Jeff Bezos, Elon Musk, Sir Richard Richard Branson are in the game, stimulating new ideas in business models, other private investors and start up companies. Space companies are now coming in from all angles. The exponential advancement of technology and microelectronics now allows the potential for a plethora of small SAT systems to possibly replace older satellites the size of a Greyhound bus. It's getting better by the day and central to this conference, cybersecurity is paramount to our nation's critical infrastructure in space. So once again, thanks very much, and I look forward to the further conversation. >>Steve, thank you very much. Space is cool. It's relevant. But it's important, as you pointed out, and you're awesome story about how it impacts our life every day. So I really appreciate that great story. I'm glad you took the time Thio share that you forgot the part about the drone coming over in the crime scene and, you know, mapping it out for you. But that would add that to the story later. Great stuff. My first question is let's get into the conversations because I think this is super important. President Armstrong like you to talk about some of the points that was teased out by Bang and Steve. One in particular is the comment around how military research was important in developing all these capabilities, which is impacting all of our lives. Through that story. It was the military research that has enabled a generation and generation of value for consumers. This is kind of this workforce conversation. There are opportunities now with with research and grants, and this is, ah, funding of innovation that it's highly accelerate. It's happening very quickly. Can you comment on how research and the partnerships to get that funding into the universities is critical? >>Yeah, I really appreciate that And appreciate the comments of my colleagues on it really boils down to me to partnerships, public private partnerships. You mentioned Northrop Grumman, but we have partnerships with Lockie Martin, Boeing, Raytheon Space six JPL, also member of organization called Business Higher Education Forum, which brings together university presidents and CEOs of companies. There's been focused on cybersecurity and data science, and I hope that we can spill into cybersecurity in space but those partnerships in the past have really brought a lot forward at Cal Poly Aziz mentioned we've been involved with Cube set. Uh, we've have some secure work and we want to plan to do more of that in the future. Uh, those partnerships are essential not only for getting the r and d done, but also the students, the faculty, whether masters or undergraduate, can be involved with that work. Uh, they get that real life experience, whether it's on campus or virtually now during Covic or at the location with the partner, whether it may be governmental or our industry. Uh, and then they're even better equipped, uh, to hit the ground running. And of course, we'd love to see even more of our students graduate with clearance so that they could do some of that a secure work as well. So these partnerships are absolutely critical, and it's also in the context of trying to bring the best and the brightest and all demographics of California and the US into this field, uh, to really be successful. So these partnerships are essential, and our goal is to grow them just like I know other colleagues and C. S u and the U C are planning to dio, >>you know, just as my age I've seen I grew up in the eighties, in college and during that systems generation and that the generation before me, they really kind of pioneered the space that spawned the computer revolution. I mean, you look at these key inflection points in our lives. They were really funded through these kinds of real deep research. Bond talk about that because, you know, we're living in an age of cloud. And Bezos was mentioned. Elon Musk. Sir Richard Branson. You got new ideas coming in from the outside. You have an accelerated clock now on terms of the innovation cycles, and so you got to react differently. You guys have programs to go outside >>of >>the Defense Department. How important is this? Because the workforce that air in schools and our folks re skilling are out there and you've been on both sides of the table. So share your thoughts. >>No, thanks, John. Thanks for the opportunity responded. And that's what you hit on the notes back in the eighties, R and D in space especially, was dominated by my government funding. Uh, contracts and so on. But things have changed. As Steve pointed out, A lot of these commercial entities funded by billionaires are coming out of the woodwork funding R and D. So they're taking the lead. So what we can do within the deal, the in government is truly take advantage of the work they've done on. Uh, since they're they're, you know, paving the way to new new approaches and new way of doing things. And I think we can We could certainly learn from that. And leverage off of that saves us money from an R and D standpoint while benefiting from from the product that they deliver, you know, within the O D Talking about workforce development Way have prioritized we have policies now to attract and retain talent. We need I I had the folks do some research and and looks like from a cybersecurity workforce standpoint. A recent study done, I think, last year in 2019 found that the cybersecurity workforce gap in the U. S. Is nearing half a million people, even though it is a growing industry. So the pipeline needs to be strengthened off getting people through, you know, starting young and through college, like assess a professor Armstrong indicated, because we're gonna need them to be in place. Uh, you know, in a period of about maybe a decade or so, Uh, on top of that, of course, is the continuing issue we have with the gap with with stamps students, we can't afford not to have expertise in place to support all the things we're doing within the with the not only deal with the but the commercial side as well. Thank you. >>How's the gap? Get? Get filled. I mean, this is the this is again. You got cybersecurity. I mean, with space. It's a whole another kind of surface area, if you will, in early surface area. But it is. It is an I o t. Device if you think about it. But it does have the same challenges. That's kind of current and and progressive with cybersecurity. Where's the gap Get filled, Steve Or President Armstrong? I mean, how do you solve the problem and address this gap in the workforce? What is some solutions and what approaches do we need to put in place? >>Steve, go ahead. I'll follow up. >>Okay. Thanks. I'll let you correct. May, uh, it's a really good question, and it's the way I would. The way I would approach it is to focus on it holistically and to acknowledge it up front. And it comes with our teaching, etcetera across the board and from from an industry perspective, I mean, we see it. We've gotta have secure systems with everything we do and promoting this and getting students at early ages and mentoring them and throwing internships at them. Eyes is so paramount to the whole the whole cycle, and and that's kind of and it really takes focused attention. And we continue to use the word focus from an NSS, a perspective. We know the challenges that are out there. There are such talented people in the workforce on the government side, but not nearly enough of them. And likewise on industry side. We could use Maura's well, but when you get down to it, you know we can connect dots. You know that the the aspect That's a Professor Armstrong talked about earlier toe where you continue to work partnerships as much as you possibly can. We hope to be a part of that. That network at that ecosystem the will of taking common objectives and working together to kind of make these things happen and to bring the power not just of one or two companies, but our our entire membership to help out >>President >>Trump. Yeah, I would. I would also add it again. It's back to partnerships that I talked about earlier. One of our partners is high schools and schools fortune Margaret Fortune, who worked in a couple of, uh, administrations in California across party lines and education. Their fifth graders all visit Cal Poly and visit our learned by doing lab and you, you've got to get students interested in stem at a early age. We also need the partnerships, the scholarships, the financial aid so the students can graduate with minimal to no debt to really hit the ground running. And that's exacerbated and really stress. Now, with this covert induced recession, California supports higher education at a higher rate than most states in the nation. But that is that has dropped this year or reasons. We all understand, uh, due to Kobe, and so our partnerships, our creativity on making sure that we help those that need the most help financially uh, that's really key, because the gaps air huge eyes. My colleagues indicated, you know, half of half a million jobs and you need to look at the the students that are in the pipeline. We've got to enhance that. Uh, it's the in the placement rates are amazing. Once the students get to a place like Cal Poly or some of our other amazing CSU and UC campuses, uh, placement rates are like 94%. >>Many of our >>engineers, they have jobs lined up a year before they graduate. So it's just gonna take key partnerships working together. Uh, and that continued partnership with government, local, of course, our state of CSU on partners like we have here today, both Stephen Bang So partnerships the thing >>e could add, you know, the collaboration with universities one that we, uh, put a lot of emphasis, and it may not be well known fact, but as an example of national security agencies, uh, National Centers of Academic Excellence in Cyber, the Fast works with over 270 colleges and universities across the United States to educate its 45 future cyber first responders as an example, so that Zatz vibrant and healthy and something that we ought Teoh Teik, banjo >>off. Well, I got the brain trust here on this topic. I want to get your thoughts on this one point. I'd like to define what is a public private partnership because the theme that's coming out of the symposium is the script has been flipped. It's a modern error. Things air accelerated get you got security. So you get all these things kind of happen is a modern approach and you're seeing a digital transformation play out all over the world in business. Andi in the public sector. So >>what is what >>is a modern public private partnership? What does it look like today? Because people are learning differently, Covert has pointed out, which was that we're seeing right now. How people the progressions of knowledge and learning truth. It's all changing. How do you guys view the modern version of public private partnership and some some examples and improve points? Can you can you guys share that? We'll start with the Professor Armstrong. >>Yeah. A zai indicated earlier. We've had on guy could give other examples, but Northup Grumman, uh, they helped us with cyber lab. Many years ago. That is maintained, uh, directly the software, the connection outside its its own unit so that students can learn the hack, they can learn to penetrate defenses, and I know that that has already had some considerations of space. But that's a benefit to both parties. So a good public private partnership has benefits to both entities. Uh, in the common factor for universities with a lot of these partnerships is the is the talent, the talent that is, that is needed, what we've been working on for years of the, you know, that undergraduate or master's or PhD programs. But now it's also spilling into Skilling and re Skilling. As you know, Jobs. Uh, you know, folks were in jobs today that didn't exist two years, three years, five years ago. But it also spills into other aspects that can expand even mawr. We're very fortunate. We have land, there's opportunities. We have one tech part project. We're expanding our tech park. I think we'll see opportunities for that, and it'll it'll be adjusted thio, due to the virtual world that we're all learning more and more about it, which we were in before Cove it. But I also think that that person to person is going to be important. Um, I wanna make sure that I'm driving across the bridge. Or or that that satellites being launched by the engineer that's had at least some in person training, uh, to do that and that experience, especially as a first time freshman coming on a campus, getting that experience expanding and as adult. And we're gonna need those public private partnerships in order to continue to fund those at a level that is at the excellence we need for these stem and engineering fields. >>It's interesting People in technology can work together in these partnerships in a new way. Bank Steve Reaction Thio the modern version of what a public, successful private partnership looks like. >>If I could jump in John, I think, you know, historically, Dodi's has have had, ah, high bar thio, uh, to overcome, if you will, in terms of getting rapid pulling in your company. This is the fault, if you will and not rely heavily in are the usual suspects of vendors and like and I think the deal is done a good job over the last couple of years off trying to reduce the burden on working with us. You know, the Air Force. I think they're pioneering this idea around pitch days where companies come in, do a two hour pitch and immediately notified of a wooden award without having to wait a long time. Thio get feedback on on the quality of the product and so on. So I think we're trying to do our best. Thio strengthen that partnership with companies outside the main group of people that we typically use. >>Steve, any reaction? Comment to add? >>Yeah, I would add a couple of these air. Very excellent thoughts. Uh, it zits about taking a little gamble by coming out of your comfort zone. You know, the world that Bond and Bond lives in and I used to live in in the past has been quite structured. It's really about we know what the threat is. We need to go fix it, will design it says we go make it happen, we'll fly it. Um, life is so much more complicated than that. And so it's it's really to me. I mean, you take you take an example of the pitch days of bond talks about I think I think taking a gamble by attempting to just do a lot of pilot programs, uh, work the trust factor between government folks and the industry folks in academia. Because we are all in this together in a lot of ways, for example. I mean, we just sent the paper to the White House of their requests about, you know, what would we do from a workforce development perspective? And we hope Thio embellish on this over time once the the initiative matures. But we have a piece of it, for example, is the thing we call clear for success getting back Thio Uh, President Armstrong's comments at the collegiate level. You know, high, high, high quality folks are in high demand. So why don't we put together a program they grabbed kids in their their underclass years identifies folks that are interested in doing something like this. Get them scholarships. Um, um, I have a job waiting for them that their contract ID for before they graduate, and when they graduate, they walk with S C I clearance. We believe that could be done so, and that's an example of ways in which the public private partnerships can happen to where you now have a talented kid ready to go on Day one. We think those kind of things can happen. It just gets back down to being focused on specific initiatives, give them giving them a chance and run as many pilot programs as you can like these days. >>That's a great point, E. President. >>I just want to jump in and echo both the bank and Steve's comments. But Steve, that you know your point of, you know, our graduates. We consider them ready Day one. Well, they need to be ready Day one and ready to go secure. We totally support that and and love to follow up offline with you on that. That's that's exciting, uh, and needed very much needed mawr of it. Some of it's happening, but way certainly have been thinking a lot about that and making some plans, >>and that's a great example of good Segway. My next question. This kind of reimagining sees work flows, eyes kind of breaking down the old the old way and bringing in kind of a new way accelerated all kind of new things. There are creative ways to address this workforce issue, and this is the next topic. How can we employ new creative solutions? Because, let's face it, you know, it's not the days of get your engineering degree and and go interview for a job and then get slotted in and get the intern. You know the programs you get you particularly through the system. This is this is multiple disciplines. Cybersecurity points at that. You could be smart and math and have, ah, degree in anthropology and even the best cyber talents on the planet. So this is a new new world. What are some creative approaches that >>you know, we're >>in the workforce >>is quite good, John. One of the things I think that za challenge to us is you know, we got somehow we got me working for with the government, sexy, right? The part of the challenge we have is attracting the right right level of skill sets and personnel. But, you know, we're competing oftentimes with the commercial side, the gaming industry as examples of a big deal. And those are the same talents. We need to support a lot of programs we have in the U. D. So somehow we have to do a better job to Steve's point off, making the work within the U. D within the government something that they would be interested early on. So I tracked him early. I kind of talked about Cal Poly's, uh, challenge program that they were gonna have in June inviting high school kid. We're excited about the whole idea of space and cyber security, and so on those air something. So I think we have to do it. Continue to do what were the course the next several years. >>Awesome. Any other creative approaches that you guys see working or might be on idea, or just a kind of stoked the ideation out their internship. So obviously internships are known, but like there's gotta be new ways. >>I think you can take what Steve was talking about earlier getting students in high school, uh, and aligning them sometimes. Uh, that intern first internship, not just between the freshman sophomore year, but before they inter cal poly per se. And they're they're involved s So I think that's, uh, absolutely key. Getting them involved many other ways. Um, we have an example of of up Skilling a redeveloped work redevelopment here in the Central Coast. PG and e Diablo nuclear plant as going to decommission in around 2020 24. And so we have a ongoing partnership toe work on reposition those employees for for the future. So that's, you know, engineering and beyond. Uh, but think about that just in the manner that you were talking about. So the up skilling and re Skilling uh, on I think that's where you know, we were talking about that Purdue University. Other California universities have been dealing with online programs before cove it and now with co vid uh, so many more faculty or were pushed into that area. There's going to be much more going and talk about workforce development and up Skilling and Re Skilling The amount of training and education of our faculty across the country, uh, in in virtual, uh, and delivery has been huge. So there's always a silver linings in the cloud. >>I want to get your guys thoughts on one final question as we in the in the segment. And we've seen on the commercial side with cloud computing on these highly accelerated environments where you know, SAS business model subscription. That's on the business side. But >>one of The >>things that's clear in this trend is technology, and people work together and technology augments the people components. So I'd love to get your thoughts as we look at the world now we're living in co vid um, Cal Poly. You guys have remote learning Right now. It's a infancy. It's a whole new disruption, if you will, but also an opportunity to enable new ways to collaborate, Right? So if you look at people and technology, can you guys share your view and vision on how communities can be developed? How these digital technologies and people can work together faster to get to the truth or make a discovery higher to build the workforce? These air opportunities? How do you guys view this new digital transformation? >>Well, I think there's there's a huge opportunities and just what we're doing with this symposium. We're filming this on one day, and it's going to stream live, and then the three of us, the four of us, can participate and chat with participants while it's going on. That's amazing. And I appreciate you, John, you bringing that to this this symposium, I think there's more and more that we can do from a Cal poly perspective with our pedagogy. So you know, linked to learn by doing in person will always be important to us. But we see virtual. We see partnerships like this can expand and enhance our ability and minimize the in person time, decrease the time to degree enhanced graduation rate, eliminate opportunity gaps or students that don't have the same advantages. S so I think the technological aspect of this is tremendous. Then on the up Skilling and Re Skilling, where employees air all over, they can be reached virtually then maybe they come to a location or really advanced technology allows them to get hands on virtually, or they come to that location and get it in a hybrid format. Eso I'm I'm very excited about the future and what we can do, and it's gonna be different with every university with every partnership. It's one. Size does not fit all. >>It's so many possibilities. Bond. I could almost imagine a social network that has a verified, you know, secure clearance. I can jump in, have a little cloak of secrecy and collaborate with the d o. D. Possibly in the future. But >>these are the >>kind of kind of crazy ideas that are needed. Are your thoughts on this whole digital transformation cross policy? >>I think technology is gonna be revolutionary here, John. You know, we're focusing lately on what we call digital engineering to quicken the pace off, delivering capability to warfighter. As an example, I think a I machine language all that's gonna have a major play and how we operate in the future. We're embracing five G technologies writing ability Thio zero latency or I o t More automation off the supply chain. That sort of thing, I think, uh, the future ahead of us is is very encouraging. Thing is gonna do a lot for for national defense on certainly the security of the country. >>Steve, your final thoughts. Space systems are systems, and they're connected to other systems that are connected to people. Your thoughts on this digital transformation opportunity >>Such a great question in such a fun, great challenge ahead of us. Um echoing are my colleague's sentiments. I would add to it. You know, a lot of this has I think we should do some focusing on campaigning so that people can feel comfortable to include the Congress to do things a little bit differently. Um, you know, we're not attuned to doing things fast. Uh, but the dramatic You know, the way technology is just going like crazy right now. I think it ties back Thio hoping Thio, convince some of our senior leaders on what I call both sides of the Potomac River that it's worth taking these gamble. We do need to take some of these things very way. And I'm very confident, confident and excited and comfortable. They're just gonna be a great time ahead and all for the better. >>You know, e talk about D. C. Because I'm not a lawyer, and I'm not a political person, but I always say less lawyers, more techies in Congress and Senate. So I was getting job when I say that. Sorry. Presidential. Go ahead. >>Yeah, I know. Just one other point. Uh, and and Steve's alluded to this in bonded as well. I mean, we've got to be less risk averse in these partnerships. That doesn't mean reckless, but we have to be less risk averse. And I would also I have a zoo. You talk about technology. I have to reflect on something that happened in, uh, you both talked a bit about Bill Britton and his impact on Cal Poly and what we're doing. But we were faced a few years ago of replacing a traditional data a data warehouse, data storage data center, and we partner with a W S. And thank goodness we had that in progress on it enhanced our bandwidth on our campus before Cove. It hit on with this partnership with the digital transformation hub. So there is a great example where, uh, we we had that going. That's not something we could have started. Oh, covitz hit. Let's flip that switch. And so we have to be proactive on. We also have thio not be risk averse and do some things differently. Eyes that that is really salvage the experience for for students. Right now, as things are flowing, well, we only have about 12% of our courses in person. Uh, those essential courses, uh, and just grateful for those partnerships that have talked about today. >>Yeah, and it's a shining example of how being agile, continuous operations, these air themes that expand into space and the next workforce needs to be built. Gentlemen, thank you. very much for sharing your insights. I know. Bang, You're gonna go into the defense side of space and your other sessions. Thank you, gentlemen, for your time for great session. Appreciate it. >>Thank you. Thank you. >>Thank you. >>Thank you. Thank you. Thank you all. >>I'm John Furry with the Cube here in Palo Alto, California Covering and hosting with Cal Poly The Space and Cybersecurity Symposium 2020. Thanks for watching.