>> Narrator: TheCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Greetings from the MGM Grand Hotel in beautiful Las Vegas. It's theCUBE Live Day two of our coverage of Palo Alto Networks, ignite 22. Lisa Martin, Dave Vellante. Dave, what can I say? This has been a great couple of days. The amount of content we have created and shared with our viewers on theCUBE is second to none. >> Well, the cloud has completely changed the way that people think about security. >> Yeah. You know at first it was like, oh, the cloud, how can that be secure? And they realized, wow actually cloud is pretty secure if we do it right. And so shared responsibility model and partnerships are critical. >> Partnerships are critical, especially as more and more organizations are multicloud by default. Right? These days we're going to be bring Google into the conversation. Josh Haslet joins us. Strategic Partnership Manager at Google. Welcome. Great to have you Josh. >> Hi Lisa, thanks for having me here. >> So you are a secret squirrel from Palo Alto Networks. Talk to me a little bit about your background and about your role at Google in terms of partnership management. >> Sure, I feel like we need to add that to my title. [Lisa] You should, secret squirrel. >> Great. Yeah, so as a matter of fact, I've been at Google for two and a half years. Prior to that, I was at Palo Alto Networks. I was managing the business development relationship with Google, and I was kind of at the inception of when the cash came in and, and decided that we needed to think about how to do security in a new way from a platform standpoint, right? And so it was exciting because when I started with the partnership, we were focusing on still securing you know, workloads in the cloud with next generation firewall. And then as we went through acquisitions the Palo Alto added it expanded the capabilities of what we could do from cloud security. And so it was very exciting, you know, to, to make sure that we could onboard with Google Cloud, take a look at how not only Palo Alto was enhancing their solutions as they built those and delivered those from Google Cloud. But then how did we help customers adopt cloud in a more easy fashion by making things, you know more tightly integrated? And so that's really been a lot of what I've been involved in, which has been exciting to see the growth of both organizations as we see customers shifting to cloud transformation. And then how do they deploy these new methodologies and tools from a security perspective to embrace this new way of working and this new way of, you know creating applications and doing digital transformation. >> Important, since work is no longer a place, it's an activity. Organizations have have to be able to cater to the distributed workforce. Of course, the, the, the workforce has to be able to access everything that they need to, but it has to be done in a secure way regardless of what kind of company you are. >> Yeah, you're right, Lisa. It's interesting. I mean, the pandemic has really changed and accelerated that transformation. I think, you know really remote working has started previous to that. And I think Nikesh called that out in the keynote too right? He, he really said that this has been ongoing for a while, but I think, you know organizations had to figure out how to scale and that was something that they weren't as prepared for. And a lot of the technology that was deployed for VPN connectivity or supporting remote work that was fixed hardware. And so cloud deployment and cloud architecture specifically with Prisma access really enabled this transformation to happen in a much faster, you know, manner. And where we've come together is how do we make sure that customers, no matter what device, what user what application you're accessing. As we take a look at ZTNA, Zero Trust Network Access 2.0, how can we come together to partner to make sure the customers have that wide range of coverage and capability? >> How, how do you how would you describe Josh Google's partner strategy generally and specifically, you know, in the world of cyber and what makes it unique and different? >> Yeah, so that's a great question. I think, you know, from Google Cloud perspective we heard TK mention this in the keynote with Nikesh. You know, we focus on on building a secure platform first and foremost, right? We want to be a trusted cloud for customers to deploy on. And so, you know, we find that as customers do one of two things, they're looking at, you know, reducing cost as they move to cloud and consolidate workloads or as they embrace innovation and look at, you know leveraging things like BigQuery for analytics and you know machine learning for the way that they want to innovate and stay ahead of the competition. They have to think about how do they secure in a new way. And so, not only do we work on how do we secure our own platform, we work with trusted partners to make sure that customers have you mentioned it earlier, Dave the shared security model, right? How do they take a look at their applications and their workloads and this new way of working as they go to CI/CD pipelines, they start thinking about DevSecOps. How do they integrate tooling that is frictionless and seamless for their, for their teams to deploy but allows them to quickly embrace that cloud transformation journey. And so, yes, partners are critical to that. The other thing is, you know we find that, you mentioned earlier, Lisa that customers are multicloud, right? That's kind of the the new normal as we look at enterprises today. And so Google Cloud's going to do a great job at securing our platform, but we need partners that can help customers deploy policy that embraces not only the things that they put in Google Cloud but as they're in their transformation journey. How that embraces the estates that are in data centers the things that are still on-prem. And really this is about making sure that the applications no matter where they are, the databases no matter where they are, and the users no matter where they are are all secure in that new framework of deploying and embracing innovation on public cloud. >> One of the things that almost everybody from Palo Alto Networks talks about is their partnering strategy their acquisition strategy integrations. And I was doing some research. There's over 50 joint integrations that Google Cloud and Palo Alto Networks. Have you talked about Zero Trust Network Access 2.0 that was announced yesterday. >> Correct. >> Give us a flavor of what that is and what does it deliver that 1.0 did not? >> Well, great. And what I'd like to do is touch a little bit on those 50 integrations because it's been, you know, a a building rolling thunder, shall we say as far as how have we taken a look at customers embracing the cloud. The first thing was we took a look at at how do we make sure that Palo Alto solutions are easier for customers to deploy and to orchestrate in Google Cloud making their journey to embracing cloud seamless and easy. The second thing was how could we make that deployment and the infrastructure even more easy to adopt by doing first party integrations? So earlier this year we announced cloud IDS intrusion detection system where we actually have first party directly in our console of customers being able to simply select, they want to turn on inspection of the traffic that's running on Google Cloud and it leverages the threat detection capability from Palo Alto Networks. So we've gone from third party integration alone to first party integration. And that really takes us to, you know, the direction of what we're seeing customers need to embrace now which is, this is your Zero Trusts strategy and Zero Trust 2.0 helps customers do a number of things. The first is, you know, we don't want to just verify a user and their access into the environment once. It needs to be continuous inspection, right? Cause their state could change. I think, you know, the, the teams we're talking about some really good ways of addressing, you know for instance, TSA checkpoints, right? And how does that experience look? We need to make sure that we're constantly evaluating that user's access into the environment and then we need to make sure that the content that's being accessed or, you know, loaded into the environment is inspected. So we need continuous content inspection. And that's where our partnership really comes together very well, is not only can we take care of any app any device, any user, and especially as we take a look at you know, embracing contractor like use cases for instance where we have managed devices and unmanaged devices we bring together beyond Corp and Prisma access to take a look at how can we make sure any device, any user any application is secure throughout. And then we've got content inspection of how that ZTNA 2.0 experience looks like. >> Josh, that threat data that you just talked about. >> Yeah. >> Who has access to that? Is it available to any partner, any customer, how... it seems like there's gold in them, NAR hills, so. >> There is. But, this could be gold going both ways. So how, how do you adjudicate and, how do you make sure that first of all that that data's accessible for, for good and not in how do you protect it against, you know, wrong use? >> Well, this is one of the great things about partnering with Palo Alto because technically the the threat intelligence is coming from their ingestion of malware, known threats, and unknown threats right into their technology. Wildfire, for instance, is a tremendous example of this where unit 42 does, you know, analysis on unknown threats based upon what Nikesh said on stage. They've taken their I think he said 27 days to identification and remediation down to less than a minute, right? So they've been able to take the intelligence of what they ingest from all of their existing customers the unknown vulnerabilities that are identified quickly assessing what those look like, and then pushing out information to the rest of their customers so that they can remediate and protect against those threats. So we get this shared intelligence from the way that Palo Alto leverages that capability and we've brought that natively into Google Cloud with cloud intrusion detection. >> So, okay, so I'm, I'm I dunno why I have high frequency trading in my mind cause it used to be, you know, like the norm was, oh it's going to take a year to identify an intrusion. And, and, and now it's down to, you know take was down to 27 days. Now it's down to a minute. Now it's not. That's best practice. And I'm, again, I'm thinking high frequency trading how do I beat the speed of light? And that's kind of where we're headed, right? >> Right. >> And so that's why he said one minute's not enough. We have to keep going. >> That's right. >> So guys got your best people working on that? >> Well, as a matter of fact, so Palo Alto Networks, you know when we take a look at what Nikesh said from stage, he talked about using machine learning and AI to get ahead of what we what they look at as far as predictability not only about behaviors in the environment so things that are not necessarily known threats but things that aren't behaving properly in the environment. And you can start to detect based on that. The second piece of it then is a lot of that technology is built on Google Cloud. So we're leveraging, their leveraging the capabilities that come together with you know, aggregation of, of logs the file stitching across the entire environment from the endpoint through to cloud operations the things that they detect for network content inspection putting all those files together to understand, you know where has the threat vector entered how has it gone lateral inside the environment? And then how do you make sure that you remediate all of those points of intrusion. And so yeah it's been exciting to see how our product teams have worked together to continue to advance the capabilities for speed for customers. >> And secure speed is critical. We had the opportunity this morning to speak with Lee Claridge, the chief product officer, and you know one of the things that I had heard about Lee is that despite all of the challenges in cybersecurity and the amorphous expansion of the threat network and the sophistication of the adversaries he's really optimistic about what it's going to enable organizations to do. I see you smiling. Do you share that optimism? >> I, I do. I think, you know, when you bring, when you bring leaders together to tackle big problems, I think, you know we've got the right teams working on the right things and we understand the problems that the customers are facing. And so, you know, from a a Google cloud perspective we understand that partnering with Palo Alto Networks helps to make sure that that optimism continues. You know, we work on continuous innovation when it comes to Google Cloud security framework, but then partnering with Palo Alto brings additional capabilities to the table. >> Vision for the, for the partnership. Where do you want to see it go? What's... we're two to five years down the road, what's it look like? Maybe two to three years. Let's go. >> Well, it was interesting. I, I think neer was the one that mentioned on stage about, you know how AI is going to start replacing us in our main jobs, right? I I think there's a lot of truth to that. I think as we look forward, we see that our teams are going to continue to help with automation remediation and we're going to have the humans working on things that are more interesting and important. And so that's an exciting place to go because today the reality is that we are understaffed in cybersecurity across the industry and we just can't hire enough people to make sure that we can detect, remediate and secure, you know every user endpoint and environment out there. So it's exciting to see that we've got a capability to move in a direction to where we can make sure that we get ahead of the threat actors. >> Yeah. So he said within five years your SOC will be AI based and and basically he elaborated saying there's a lot of stuff that you're doing today that you're not going to be doing tomorrow. >> That's true. >> And that's going to continue to be a moving target I would think Google is probably ahead in that game and ahead of most, right? I mean, you guys were there early. I mean, I remember when Hadoop was all the rage like just at the beginning you guys like, yeah, you know Google's like, no, no, no, we're not doing Hadoop anymore. That's like old news. So you tended to be, I don't know, at least five maybe seven years ahead of the industry. So I imagine you using a lot of those AI techniques in your own business today. >> Absolutely. I mean, I think you see it in our consumer products, and you certainly see it in the the capabilities we make available to enterprise as far as how they can innovate on our cloud. And we want to make sure that we continue to provide those capabilities, you know not only for the tools that we build but the tools that customers use. >> What's the, as we kind of get towards the end of our conversation here, we we talk about zero trust as, as a journey, as an approach. It's not a product, it's not a tool. What is the, who's involved in the zero trust journey from the customers perspective? Is this solely with the CSO, CSO, CIOs or is this at the CEO level going, we have to be a data company but we have to be a secure data company 24/7. >> It's interesting as you've seen malware, phishing, ransomware attacks. >> Yeah. >> This is not only just a CSO CIO conversation it's a board level conversation. And so, you know the way to address this new way of working where we have very distributed environments where you can't create a perimeter anymore. You need to strategize with zero trust. And so continuously, when we're talking to customers we're hearing that as a main initiative, you know from the CIO's office and from the board level. >> Got it, last question. The upgrade path for existing customers from 1., ZTNA 1.0 to 2.0. How simple is that? >> It's easy. You know, when we take- >> Is there an easy button? >> So here's the great thing [Dave] If you're feeling lucky. [Lisa] Yeah. (group laughs) >> Well, Palo Alto, right? Billing prisma access has really taken what was traditional security that was an on-prem or a data center deployed strategy to cloud-based. And so we've worked with customers like Princeton University who had to quickly transition from in-person learning to distance learning find a way to ramp their staff their faculty and their students. And we were able to, you know Palo Alto deploy it on Google Cloud's, you know network that solution in very quick order and had those, you know, everybody back up and running. So deployment and upgrade path is, is simple when you look at cloud deployed architectures to address zero trusts network. >> That's awesome. Some of those, some of those use cases that came out of the pandemic were mind blowing but also really set the table for other organizations to go, yes, this can be done. And it doesn't have to take forever because frankly where security is concerned, we don't have time. >> That's right. And it's so much faster than traditional architectures where you had to procure hardware. >> Yeah. >> Deploy it, configure it, and then, you know push agents out to all the endpoints and and get your users provisioned. In this case, we're talking about cloud delivered, right? So I've seen, you know, with Palo Alto deploying for customers that run on Google Cloud they've deployed tens of thousands of users in a very short order. You know, we're talking It was, it's not months anymore. It's not weeks anymore. It's days >> Has to be days. Josh, it's been such a pleasure having you on the program. Thank you for stopping by and talking with Dave and me about Google Cloud, Palo Alto Networks in in addition to secret squirrel. I feel like when you were describing your background that you're like the love child of Palo Alto Networks and Google Cloud, you might put that on your cartoon. >> That is a huge compliment. I really appreciate that, Lisa, thank you so much. >> Thanks so much, Josh. [Josh] It's been a pleasure being here with you. [Dave] Thank you >> Oh, likewise. For Josh Haslett and Dave, I'm Lisa Martin. You're watching theCUBE, the leader in live coverage for emerging and enterprise tech. (upbeat outro music)

(upbeat music) >> Hey everyone. Welcome back to theCUBE's live coverage of Women in Data Science, WiDS 2022, coming to live from Stanford University. I'm Lisa Martin. My next guest is here. Nandi Leslie, Doctor Nandi Leslie, Senior Engineering Fellow at Raytheon Technologies. Nandi, it's great to have you on the program. >> Oh it's my pleasure, thank you. >> This is your first WiDS you were saying before we went live. >> That's right. >> What's your take so far? >> I'm absolutely loving it. I love the comradery and the community of women in data science. You know, what more can you say? It's amazing. >> It is. It's amazing what they built since 2015, that this is now reaching 100,000 people 200 online event. It's a hybrid event. Of course, here we are in person, and the online event going on, but it's always an inspiring, energy-filled experience in my experience of WiDS. >> I'm thoroughly impressed at what the organizers have been able to accomplish. And it's amazing, that you know, you've been involved from the beginning. >> Yeah, yeah. Talk to me, so you're Senior Engineering Fellow at Raytheon. Talk to me a little bit about your role there and what you're doing. >> Well, my role is really to think about our customer's most challenging problems, primarily at the intersection of data science, and you know, the intersectional fields of applied mathematics, machine learning, cybersecurity. And then we have a plethora of government clients and commercial clients. And so what their needs are beyond those sub-fields as well, I address. >> And your background is mathematics. >> Yes. >> Have you always been a math fan? >> I have, I actually have loved math for many, many years. My dad is a mathematician, and he introduced me to, you know mathematical research and the sciences at a very early age. And so, yeah, I went on, I studied in a math degree at Howard undergrad, and then I went on to do my PhD at Princeton in applied math. And later did a postdoc in the math department at University of Maryland. >> And how long have you been with Raytheon? >> I've been with Raytheon about six years. Yeah, and before Raytheon, I worked at a small to midsize defense company, defense contracting company in the DC area, systems planning and analysis. And then prior to that, I taught in a math department where I also did my postdoc, at University of Maryland College Park. >> You have a really interesting background. I was doing some reading on you, and you have worked with the Navy. You've worked with very interesting organizations. Talk to the audience a little bit about your diverse background. >> Awesome yeah, I've worked with the Navy on submarine force security, and submarine tracking, and localization, sensor performance. Also with the Army and the Army Research Laboratory during research at the intersection of machine learning and cyber security. Also looking at game theoretic and graph theoretic approaches to understand network resilience and robustness. I've also supported Department of Homeland Security, and other government agencies, other governments, NATO. Yeah, so I've really been excited by the diverse problems that our various customers have you know, brought to us. >> Well, you get such great experience when you are able to work in different industries and different fields. And that really just really probably helps you have such a much diverse kind of diversity of thought with what you're doing even now with Raytheon. >> Yeah, it definitely does help me build like a portfolio of topics that I can address. And then when new problems emerge, then I can pull from a toolbox of capabilities. And, you know, the solutions that have previously been developed to address those wide array of problems, but then also innovate new solutions based on those experiences. So I've been really blessed to have those experiences. >> Talk to me about one of the things I heard this morning in the session I was able to attend before we came to set was about mentors and sponsors. And, you know, I actually didn't know the difference between that until a few years ago. But it's so important. Talk to me about some of the mentors you've had along the way that really helped you find your voice in research and development. >> Definitely, I mean, beyond just the mentorship of my my family and my parents, I've had amazing opportunities to meet with wonderful people, who've helped me navigate my career. One in particular, I can think of as and I'll name a number of folks, but Dr. Carlos Castillo-Chavez was one of my earlier mentors. I was an undergrad at Howard University. He encouraged me to apply to his summer research program in mathematical and theoretical biology, which was then at Cornell. And, you know, he just really developed an enthusiasm with me for applied mathematics. And for how it can be, mathematics that is, can be applied to epidemiological and theoretical immunological problems. And then I had an amazing mentor in my PhD advisor, Dr. Simon Levin at Princeton, who just continued to inspire me, in how to leverage mathematical approaches and computational thinking for ecological conservation problems. And then since then, I've had amazing mentors, you know through just a variety of people that I've met, through customers, who've inspired me to write these papers that you mentioned in the beginning. >> Yeah, you've written 55 different publications so far. 55 and counting I'm sure, right? >> Well, I hope so. I hope to continue to contribute to the conversation and the community, you know, within research, and specifically research that is computationally driven. That really is applicable to problems that we face, whether it's cyber security, or machine learning problems, or others in data science. >> What are some of the things, you're giving a a tech vision talk this afternoon. Talk to me a little bit about that, and maybe the top three takeaways you want the audience to leave with. >> Yeah, so my talk is entitled "Unsupervised Learning for Network Security, or Network Intrusion Detection" I believe. And essentially three key areas I want to convey are the following. That unsupervised learning, that is the mathematical and statistical approach, which tries to derive patterns from unlabeled data is a powerful one. And one can still innovate new algorithms in this area. Secondly, that network security, and specifically, anomaly detection, and anomaly-based methods can be really useful to discerning and ensuring, that there is information confidentiality, availability, and integrity in our data >> A CIA triad. >> There you go, you know. And so in addition to that, you know there is this wealth of data that's out there. It's coming at us quickly. You know, there are millions of packets to represent communications. And that data has, it's mixed, in terms of there's categorical or qualitative data, text data, along with numerical data. And it is streaming, right. And so we need methods that are efficient, and that are capable of being deployed real time, in order to detect these anomalies, which we hope are representative of malicious activities, and so that we can therefore alert on them and thwart them. >> It's so interesting that, you know, the amount of data that's being generated and collected is growing exponentially. There's also, you know, some concerning challenges, not just with respect to data that's reinforcing social biases, but also with cyber warfare. I mean, that's a huge challenge right now. We've seen from a cybersecurity perspective in the last couple of years during the pandemic, a massive explosion in anomalies, and in social engineering. And companies in every industry have to be super vigilant, and help the people understand how to interact with it, right. There's a human component. >> Oh, for sure. There's a huge human component. You know, there are these phishing attacks that are really a huge source of the vulnerability that corporations, governments, and universities face. And so to be able to close that gap and the understanding that each individual plays in the vulnerability of a network is key. And then also seeing the link between the network activities or the cyber realm, and physical systems, right. And so, you know, especially in cyber warfare as a remote cyber attack, unauthorized network activities can have real implications for physical systems. They can, you know, stop a vehicle from running properly in an autonomous vehicle. They can impact a SCADA system that's, you know there to provide HVAC for example. And much more grievous implications. And so, you know, definitely there's the human component. >> Yes, and humans being so vulnerable to those social engineering that goes on in those phishing attacks. And we've seen them get more and more personal, which is challenging. You talking about, you know, sensitive data, personally identifiable data, using that against someone in cyber warfare is a huge challenge. >> Oh yeah, certainly. And it's one that computational thinking and mathematics can be leveraged to better understand and to predict those patterns. And that's a very rich area for innovation. >> What would you say is the power of computational thinking in the industry? >> In industry at-large? >> At large. >> Yes, I think that it is such a benefit to, you know, a burgeoning scientist, if they want to get into industry. There's so many opportunities, because computational thinking is needed. We need to be more objective, and it provides that objectivity, and it's so needed right now. Especially with the emergence of data, and you know, across industries. So there are so many opportunities for data scientists, whether it's in aerospace and defense, like Raytheon or in the health industry. And we saw with the pandemic, the utility of mathematical modeling. There are just so many opportunities. >> Yeah, there's a lot of opportunities, and that's one of the themes I think, of WiDS, is just the opportunities, not just in data science, and for women. And there's obviously even high school girls that are here, which is so nice to see those young, fresh faces, but opportunities to build your own network and your own personal board of directors, your mentors, your sponsors. There's tremendous opportunity in data science, and it's really all encompassing, at least from my seat. >> Oh yeah, no I completely agree with that. >> What are some of the things that you've heard at this WiDS event that inspire you going, we're going in the right direction. If we think about International Women's Day tomorrow, "Breaking the Bias" is the theme, do you think we're on our way to breaking that bias? >> Definitely, you know, there was a panel today talking about the bias in data, and in a variety of fields, and how we are, you know discovering that bias, and creating solutions to address it. So there was that panel. There was another talk by a speaker from Pinterest, who had presented some solutions that her, and her team had derived to address bias there, in you know, image recognition and search. And so I think that we've realized this bias, and, you know, in AI ethics, not only in these topics that I've mentioned, but also in the implications for like getting a loan, so economic implications, as well. And so we're realizing those issues and bias now in AI, and we're addressing them. So I definitely am optimistic. I feel encouraged by the talks today at WiDS that you know, not only are we recognizing the issues, but we're creating solutions >> Right taking steps to remediate those, so that ultimately going forward. You know, we know it's not possible to have unbiased data. That's not humanly possible, or probably mathematically possible. But the steps that they're taking, they're going in the right direction. And a lot of it starts with awareness. >> Exactly. >> Of understanding there is bias in this data, regardless. All the people that are interacting with it, and touching it, and transforming it, and cleaning it, for example, that's all influencing the veracity of it. >> Oh, for sure. Exactly, you know, and I think that there are for sure solutions are being discussed here, papers written by some of the speakers here, that are driving the solutions to the mitigation of this bias and data problem. So I agree a hundred percent with you, that awareness is you know, half the battle, if not more. And then, you know, that drives creation of solutions >> And that's what we need the creation of solutions. Nandi, thank you so much for joining me today. It was a pleasure talking with you about what you're doing with Raytheon, what you've done and your path with mathematics, and what excites you about data science going forward. We appreciate your insights. >> Thank you so much. It was my pleasure. >> Good, for Nandi Leslie, I'm Lisa Martin. You're watching theCUBE's coverage of Women in Data Science 2022. Stick around, I'll be right back with my next guest. (upbeat flowing music)

[Music] okay we're here with ceres vitale who's the chief product officer at alteryx and adam wilson the ceo of trifacta now of course part of alteryx just closed this quarter gentlemen welcome great to be here okay so rush let me start with you in my opening remarks i talked about alteryx's traditional position serving business analysts and how the hyperanna acquisition brought you deeper into the business user space what does trifacta bring to your portfolio why'd you buy the company yeah thank you thank you for the question um you know we see a we see a massive opportunity of helping brands democratize the use of analytics across their business every knowledge worker every individual in the company should have access to analytics it's no longer optional as they navigate their businesses with that in mind you know we know designer and our the products that alteryx has been selling the past decade or so do a really great job addressing the business analysts with hyper rana now kind of renamed alteryx auto insights we even speak with the business owner the line of business owner who's looking for insights that aren't revealed in traditional dashboards and so on um but we see this opportunity of really helping the data engineering teams and i.t organizations to also make better use of analytics and that's where trifacta comes in for us trifacta has the best data engineering cloud in the planet they have an established track record of working across multiple cloud platforms and helping data engineers um do better data pipelining and work better with this massive kind of cloud transformation that's happening in every business um and so trifecta made so much sense for us yeah thank you for that i mean look you could have built it yourself would have taken you know who knows how long you know but uh so definitely a great time to market move adam i wonder if we could dig into trifacta some more i mean i remember interviewing joe hellerstein in the early days you've talked about this as well on thecube coming at the problem of taking data from raw refined to an experience point of view and joe in the early days talked about flipping the model and starting with data visualization something jeff herr was expert at so maybe explain how we got here we used to have this cumbersome process of etl and you maybe and some others change that model with you know el and then t explain how trifacta really changed the data engineering game yeah that's exactly right uh dave and it's been a really interesting journey for us because i think the original hypothesis coming out of the campus research at berkeley and stanford that really birthed trifacta was you know why is it that the people who know the data best can't do the work you know why is this become the exclusive purview the highly technical and you know can we rethink this and make this a user experience problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those so so a broader set of users can can really see for themselves and help themselves and and i think that um there was a lot of pent up frustration out there because people have been told for you know for a decade now to be more data driven and then the whole time they're saying well then give me the data you know in the shape that i can use it with the right level of quality and i'm happy to be but don't tell me to be more data driven and they'll don't then and and not empower me um to to get in there and to actually start to work with the data in meaningful ways and so um that was really you know what you know the origin story of the company and i think as as we saw over the course of the last five six seven years that um you know a real uh excitement to embrace this idea of of trying to think about data engineering differently trying to democratize the the etl process and to also leverage all these exciting new uh engines and platforms that are out there that allow for you know processing you know ever more diverse data sets ever larger data sets and new and interesting ways and that's where a lot of the push down or the elt approaches uh you know i think it really won the day um and that and that for us was a hallmark of the solution from the very beginning yeah this is a huge point that you're making this is first of all there's a large business probably about a hundred billion dollar tam uh and and the the point you're making is we look we've contextualized most of our operational systems but the big data pipelines hasn't gotten there but and maybe we could talk about that a little bit because democratizing data is nirvana but it's been historically very difficult you've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome but it's been hard and so what's going to be different about alteryx as you bring these puzzle pieces together how is this going to impact your customers who would like to take that one yeah maybe maybe i'll take a crack at it and adam will add on um you know there hasn't been a single platform [Music] for analytics automation in the enterprise right people have relied on different products to solve kind of smaller problems across this analytics automation data transformation domain and i think uniquely alteryx has that opportunity we've got 7000 plus customers who rely on analytics for data management for analytics for ai and ml for transformations for reporting and visualization for automated insights and so on and so by bringing trifecta we have the opportunity to scale this even further and solve for more use cases expand the scenarios where angles gets applied and serve multiple personas um and now we just talked about the data engineers they are really a growing stakeholder in this transformation of data analytics yeah good maybe we can stay on this for a minute because you're right you bring it together now at least three personas the business analyst the end user size business user and now the data engineer which is really out of an i.t role in a lot of companies and you've used this term the data engineering cloud what is that how is it going to integrate in with or support these other personas and and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores yeah you know that's great uh you know i think for us we really looked at this and said you know we want to build an open and interactive you know cloud platform for data engineers you know to collaboratively profile pipeline um and prepare data for analysis and and that really meant collaborating with the analysts that were in the line of business and so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that what i would describe as collaborative curation you know of the data together so that you're starting to see um uh you know increasing returns to scale as this uh as this rolls out i just think that is an incredibly uh powerful combination and frankly something that the market has not cracked the code on yet and so um i think when we when i sat down with surash and with mark and and the team at ultrix that was really part of the the big idea the big vision that that was painted and and got us really energized um about the acquisition and about the the potential of the combination yeah and you're really you're obviously riding the cloud and the cloud native wave um and but specifically we're seeing you know i almost don't even want to call it a data warehouse anyway because when you look at what princeton snowflake is doing of course their marketing is around the data cloud but i i actually think there's real justification for that because it's not like the traditional data warehouse right it's it's simplified get there fast don't necessarily have to go through this central organization to share data uh and and but it's really all about simplification right isn't that really what the democratization comes down to yeah it's simplification and collaboration right i don't want to i want to kind of just uh what what adam said resonates with me deeply um analytics is one of those massive disciplines inside an enterprise that's really had the weakest of tools um and weakest of interfaces to collaborate with and i think truly this was alteryx's end of superpower was helping the analysts get more out of their data get more out of the analytics like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources understanding data models better i think curating those insights i borrowing adam's phrase again i think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data we're still in such early phases of this journey so how should we think about designer cloud which is from alteryx it's really been the on-prem the server or desktop you know offering and of course trifecta is about cloud cloud data warehouses right um how should we think about those two products yeah i think i think you should think about them and as very complementary right designer cloud really shares a lot of dna and heritage with designer desktop the low code tooling and the interface that really appeals to the business analysts and gets a lot of the things that they do well we've also built it with interoperability in mind right so if you started building your workflows in designer desktop you want to share that with designer cloud we want to make it super easy for you to do that and i think over time now we're only a week into this alliance with uh with trifacta i think we have to get deeper and start to think about what does the data engineer really need what business analysts really need and how to design a cloud and try factor really support both of those requirements uh while kind of continue to build on the trifecta on the amazing trifecta cloud platform you know and i think let's go ahead i'm just to say i think that's one of the things that um you know creates a lot of opportunity as we go forward because ultimately you know trifacta took a platform uh first mentality to everything that we built so thinking about openness and extensibility and um and how over time people could build things on top of trifacta that are a variety of analytic tool chain or analytic applications and so when you think about um alteryx now starting to uh to move some of its capabilities or to provide additional capabilities uh in the cloud um you know trifacta becomes uh a a platform that can accelerate you know all of that work and create a cohesive set of of cloud-based services that share a common platform and that maintains independence because both companies um have been uh you know fiercely independent uh in really giving people choice um so making sure that whether you're uh you know picking one cloud platform another whether you're running things on the desktop uh whether you're running in hybrid environments that no matter what your decision you're always in a position to be able to get out your data you're always in a position to be able to cleanse transform shape structure that data and ultimately to deliver uh the analytics that you need and so i think in in that sense um uh you know this this again is another reason why the combination you know fits so well together giving people um the choice um and as they as they think about their analytics strategy and and their platform strategy going forward you know i make a chuckle but one of the reasons i always liked alteryx is because you kind of did did a little end run on i.t i.t can be a blocker sometimes but that created problems right because the organization said wow this big data stuff is taken off but we need security we need governance and and it's interesting because you got you know etl has been complex whereas the visualization tools they really you know really weren't great at governance and security it took some time there so that's not not their heritage you're bringing those worlds together and i'm interested you guys just had your sales kickoff you know what was the reaction like uh maybe suresh you could start off and maybe adam you could bring us home yeah um thanks for asking about our sales kickoff so we met uh for the first time in kind of two years right for as it is for many of us um in person uh um which i think was a was a real breakthrough as alteryx has been on its transformation journey uh we had a try factor to um the the party such as it were um and getting all of our sales teams and product organizations um to meet in person in one location i thought that was very powerful for us as a company but then i tell you um the reception for trifecta was beyond anything i could have imagined uh we were working adam and i were working so hard on on the the deal and the core hypotheses and so on and then you step back and kind of share the vision with the field organization and it blows you away the energy that it creates among our sellers our partners and i'm sure adam and his team were mobbed every single day with questions and opportunities to bring them in but adam maybe you should share yeah no it was uh it was through the roof i mean uh the uh the amount of energy the uh when so certainly how welcoming everybody was uh you know just i think the story makes so much sense together i think culturally the companies are very aligned um and uh it was a real uh real capstone moment uh to be able to complete the acquisition and to and to close and announce you know at the kickoff event and um i think you know for us when we really thought about it you know when we and the story that we told was just you have this opportunity to really cater to what the end users you know care about which is a lot about interactivity and self-service and at the same time and that's and that's a lot of the goodness that um that alteryx is has brought you know through you know you know years and years of of building a very vibrant community of you know thousands hundreds of thousands of users and on the other side you know trifecta bringing in this data engineering focus that's really about uh the governance things that you mentioned and the openness that that it cares deeply about and all of a sudden now you have a chance to put that together into a complete story where the data engineering cloud and analytics automation you know come together and um and i just think you know the lights went on um you know for people instantaneously and you know this is a story that um that i think the market is really hungry for and and certainly the reception we got from from the broader team at kickoff was uh was a great indication of that well i think the story hangs together really well you know one of the better ones i've seen in this space um and and you guys coming off a really really strong quarter so congratulations on that gents we have to leave it there really appreciate your time today yeah take a look at this short video and when we come back we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses you're watching the cube your leader in enterprise tech coverage [Music]

>>Yeah, yeah. >>Welcome back to our final session in cultivating a data fluent culture track earlier today, we heard from experts like Valerie from the Data Lodge who shared best practices that you can apply to build that data flew into culture in your organization and tips on how to become the next analyst of the future from Yasmin at Comcast and Steve at all Terex. Then we heard from a captivating session with Cindy Hausen and Ruhollah Benjamin, professor at Princeton, on how now is our chance to change the patterns of injustice that we see have been woven into the fabric of society. If you do not have a chance to see today's content, I highly recommend that you check it out on demand. There's a lot of great information that you could start applying today. Now I'm excited to introduce our next session, which will take a look at how the democratization of data is powering digital transformation in the insurance industry. We have two prestigious guests joining us today. First Jim Bramblett, managing director of North America insurance practice, lead at its center. Throughout Jim's career, he's been focused on large scale transformation from large to midsize insurance carriers. His direct experience with clients has traditionally been in the intersection of technology, platform transformation and operating remodel redesign. We also have Michael cast Onus, executive VP and chief operating officer at DNA. He's responsible for all information technology, analytics and operating functions across the organization. Michael has led major initiatives to launch digital programs and incorporating modern AP I architectures ER, which was primarily deployed in the cloud. Jim, please take it away. >>Great. Thanks, Paula E thought we'd cover a few things today around around data. This is some of the trends we see in data within the insurance sector. And then I'll hand it over to Michael Teoh, take you through his story. You know, I think at the macro level, as we think about data and we think about data in the context of the insurance sector, it's interesting because the entire history of the insurance sector has been built on data and yet, at the same time, the entire future of it relies on that same data or similar similar themes for data. But but different. Right? So we think about the history, what has existed in an insurance companies. Four walls was often very enough, very enough to compete, right? So if you think about your customer data, claims, data, CRM, data, digital data, all all the data that was yeah, contained within the four walls of your company was enough to compete on. And you're able to do that for hundreds of years. But as we we think about now as we think about the future and the ability to kind of compete on data, this data comes from many more places just than inside your four walls. It comes from every device, every human, every vehicle, every property, every every digital interaction. Um in upon this data is what we believe insurers need to pivot to. To compete right. They need to be able to consume this data at scale. They need to be able to turn through this data to drive analytics, and they serve up insights based on those analytics really at the desktop of insurance professionals. And by the way, that has to be in the natural transition of national transaction. Of that employees work day. So an underwriter at a desktop claim him on the desktop, the sales associate of desktop. Those insights need to be served up at that point in time when most relevant. And you know. So if we think about how insurance companies are leveraging data, we see this really on kind of three horizons and starting from the left hand side of the page here, this is really brilliant basics. So how my leveraging core core data and core applied intelligence to monetize your existing strategy? And I think this brilliant based, brilliant basics concept is where most of most of my clients, at least within insurance are are today. You know, how are we leveraging data in the most effective way and putting it in the hands of business decision makers to make decisions largely through reporting and some applied intelligence? Um, Horizon two. We see, you know, definitely other industries blazing a trail here, and this is really about How do we integrate ecosystems and partners Now? I think within insurance, you know, we've had data providers forever, right? Whether it's NPR data, credit data risk data, you know, data aggregators and data providers have been a critical part of the insurance sector for for decades. I think what's different about this this ecosystem and partnership model is that it's much more Oneto one and it's much more, you know, kind of. How do we integrate more tightly and how do we become more embedded in each other's transactions? I think that we see some emergence of this, um, in insurance with automotive manufacturers with building management systems. But I think in the grand scheme of things, this is really very, very nascent for us as a sector. And I think the third horizon is is, you know, how do we fundamentally think about data differently to drive new business models? And I, you know, I don't know that we haven't ensure here in North America that's really doing this at any sort of scale. We certainly see pilots and proofs of concepts. We see some carriers in Europe farther down this path, but it's really it's really very new for us. A Z Think about these three horizons for insurance. So you know what's what's behind all this and what's behind. You know, the next powering of digital transformation and and we think at the end of the exercise, its data data will be the next engine that powers digital transformation. So in this exhibit, you know we see the three horizons across the top. You know, data is activated and activating digital transformation. And this, you know, this purple 3rd, 3rd road here is we think some of the foundational building blocks required to kind of get this right. But I think what's most important about about this this purple third bar here is the far right box, which is business adoption. Because you can build this infrastructure, you can have. You know, this great scalable cloud capability. Um, you can create a bunch of applications and intelligence, but unless it's adopted by the business, unless it's democratized, unless those insights and decisions air served up in the natural course of business, you're gonna have trouble really driving value. So that way, I think this is a really interesting time for data. We think this is kind of the next horizon to power the next age of digital transformation for insurance companies. With that brief prelude, I am, I'm honored. Thio, turn it over to Michael Stone Is the Cielo at CNN Insurance? >>Thanks, Jim, for that intro and very exciting Thio be here is part of part of beyond when I think a digital transformation within the context of insurance, actually look at it through the lens of competing in an era of near perfect information. So in order to be able to deliver all of the potential value that we talked about with regard to data and changing ecosystem and changing demands, the question becomes, How do you actually harness the information that's available to everybody to fundamentally change the business? So if you'll indulge me a bit here, let me tell you just a little bit more for those that don't know about insurance, what it really is. And I use a very long run on sentence to do that. It's a business model where capital is placed against risk in the form of products and associated services sold the customers through channels two companies to generate a return. Now, this sounds like a lot of other businesses in across multiple industries that were there watching today. But the difference within insurance is that every major word in that long run on sentence is changing sources of capital that we could draw on to be able to underwrite risk of going away. The nature of risk itself is changing from the perspective of policies that live six months to a year, the policies that could last six minutes. The products that we're creating are changing every day for our ability to actually put a satellite up in the air or ensure against the next pandemic. Our customers are not just companies or individuals, but they could be governments completely different entities than we would have been in sharing in the past and channels were changing. We sell direct, we sell through brokers and products are actually being embedded in other products. So you may buy something and not even know that insurance is a part of it. And what's most interesting here is the last word which is around return In the old world. Insurance was a cash flow business in which we could bring the premium in and get a level of interest income and being able to use that money to be able thio buffer the underwriting results that we would have. But those returns or dramatically reduced because of the interest income scenario, So we have to generate a higher rate of return. So what do we need to do? Is an insurance company in through this digital transformation to be able to get there? Well, fundamentally, we need to rethink how we're using information, and this is where thought spot and the cloud coming for us. We have two basic problems that we're looking to solve with information. The first one is information veracity. Do we believe it? When we get it? Can we actually trust it? Do we know what it means when we say that this is a policy in force or this is a new customer where this is the amount of attention or rate that we're going to get? Do we actually believe in that piece of data? The second is information velocity. Can we get it fast enough to be able to capitalize upon it? So in other words, we're We're working in a situation where the feedback loop is closing quickly and it's operating at a speed that we've never worked in before. So if we can't solve veracity and velocity, then we're never going to be able to get to where we need to go. So when we think of something like hot spot, what do we use it for? We use it to be able to put it in the hands of our business years so that they could ask the key questions about how the business is running. How much profit of my generating this month? What brokers do I need to talk? Thio. What is my rate retention? Look like what? The trends that I'm seeing. And we're using that mechanism not just to present nice visualizations, but to enable that really quick, dynamic question and answer and social, socially enabled search, which completely puts us in a different position of being able to respond to the market conditions. In addition, we're using it for pattern recognition. Were using it for artificial intelligence. We're gonna be capitalizing on the social aspect of of search that's that's enabled through thought spot and also connecting it into our advanced machine learning models and other capabilities that we currently have. But without it solving the two fundamental problems of veracity and velocity, we would be handicapped. So let me give you some advice about if I were in your position and you don't need to be in sleepy old industry like insurance to be able to do this, I'll leave you with three things. The first one is picking water holes so What are the things that you really want to be good at? What are the pieces of information that you really need to know more about? I mean, in insurance, its customers, it's businesses, locations, it's behavior. There are only a few water also really understand and pick those water holes that you're going to be really good at. The second is stand on the shoulders of giants. You know, in the world of technology, there's often a philosophy that says, Well, I can build it something better than somebody else create if I have it in house. But I'm happy to stand on the shoulders of giants like Thought Spot and Google and others to be able to create this capability because guess what? They're gonna out innovate any of the internal shops all day and every day. So don't be afraid. Thio. Stand side by side on the shoulders of giants as part of your journey. Unless you've got to build these organizations not just the technology for rapid experimentation and learning, because guess what? The moment you deliver insight, it begs another question, which also could change the business process, which could change the business model and If your organization the broader organization of business technology, analytics, customer service operations, etcetera is not built in a way that could be dynamic and flexible based on where the market is or is going, then you're gonna miss out on the opportunity. So again, I'm proud to be part of the fast black community. Really love the technology. And if if you look too, have the same kind of issues with your given industry about how you can actually speed up decision making, deliver insights and deliver this kind of search and recommended to use it. And with that, let's go to some questions. >>Awesome. Thank you so much, Michael and Jim for that in depth perspective and those tangible takeaways for our audience. We have a few minutes left and would love to ask a few questions. So here's the first one for Michael Michael. What are some of the most important things that you know now that you didn't know before you started this process? I think one of >>the things that's a great question. I think one of the things that really struck me is that, you know, traditional thinking would be very use case centric or pain point centric Show me, uh, this particular model or a particular question you want me to answer that can build your own analytics to do that or show me a deficiency in the system and I can go and develop a quick head that will do well, then you know, wallpaper over that particular issue. But what we've really learned is the foundation matters. So when we think about building things is building the things that are below the waterline, the pipes and plumbing about how you move data around how the engines work and how it all connects together gives you the above the waterline features that you could deliver to. You know, your employees into your customers much faster chasing use cases across the top above the waterline and ignoring what's below the water line to me. Is it really, uh, easy recipe too quick? Get your way to nothing. So again, focus on the foundation bill below the water line and then iterated above the water line that z what the lessons we've learned. It has been very effective for us. >>I think that's a very great advice for all those watching today on. But Here's one for Jim. Jim. What skills would you say are required for teams to truly adopt this digital transformation process? >>Yeah, well, I think that's a really good question, and I think I'd start with it's It's never one. Well, our experience has shown us number a one person show, right? So So we think to kind of drive some of the value that that that Michael spoke about. We really looked across disciplinary teams, which is a an amalgamation of skills and and team members, right? So if you think about the data science skills required, just kinda under under understand how toe toe work with data and drive insights, Sometimes that's high end analytic skills. Um, where you gonna find value? So some value architectural skills Thio really articulate, you know, Is this gonna move the needle for my business? I think there's a couple of critical critical components of this team. One is, you know, the operation. Whatever. That operation maybe has to be embedded, right, because they designed this is gonna look at a piece of data that seems interesting in the business Leader is going to say that that actually means nothing to me in my operation. So and then I think the last the last type of skill would be would be a data translator. Um, sitting between sometimes the technology in the business so that this amalgamation of skills is important. You know, something that Michael talked about briefly that I think is critical is You know, once you deliver insight, it leads to 10 more questions. So just in a intellectual curiosity and an understanding of, you know, if I find something here, here, the implications downstream from my business are really important. So in an environment of experimenting and learning thes thes cross discipline teams, we have found to be most effective. And I think we thought spot, you know, the platform is wired to support that type of analysis and wired to support that type of teaming. >>Definitely. I think that's though there's some really great skills. That's for people to keep in mind while they are going through this process. Okay, Michael, we have another question for you. What are some of the key changes you've had to make in your environment to make this digital transformation happen? >>That's a great question. I think if you look at our environment. We've got a mixture of, you know, space agent Stone age. We've got old legacy systems. We have all sorts of different storage. We have, you know, smatterings of things that were in cloud. The first thing that we needed to do was make a strong commitment to the cloud. So Google is our partner for for the cloud platform on unabashedly. The second thing that we needed to dio was really rethink the interplay between analytics systems in operational systems. So traditionally, you've got a large data warehouses that sit out over here that, you know, we've got some kind of extract and low that occurs, and we've got transactional operational systems that run the business, and we're thinking about them very differently from the perspective of bringing them together. How Doe I actually take advantage of data emotion that's in the cloud. So then I can actually serve up analytics, and I can also change business process as it's happening for the people that are transacting business. And in the meantime, I can also serve the multiple masters of total cost and consumption. So again, I didn't applications are two ships that pass in the night and never be in the world of Sienna. When you look at them is very much interrelated, especially as we want to get our analytics right. We want to get our A i m all right, and we want to get operational systems right By capturing that dated motion force across that architecture er that was an important point. Commit to the cloud, rethink the way we think analytics systems, work and operational systems work and then move them in tandem, as opposed to doing one without the other one in the vacuum. >>That's that's great advice, Michael. I think it's very important those key elements you just hit one question that we have final question we have for Jim. Jim, how do you see your client sustain the benefits that they've gained through this process? >>Yeah, it's a really good question. Um, you know, I think about some of the major themes around around beyond right, data fluency is one of them, right? And as I think about fluency, you only attain fluency through using the language every single day. They were day, week, over week, month over month. So you know, I think that applies to this. This problem too. You know, we see a lot of clients have to change probably two things at the same time. Number one is mindset, and number two is is structure. So if you want to turn these data projects from projects into processes, right, so so move away from spinning up teams, getting getting results and winding down. You wanna move away from that Teoh process, which is this is just the way working for these teams. Um, you have to change the mindset and often times you have to marry that with orb structure change. So So I'm gonna spin up these teams, but this team is going to deliver a set of insights on day. Then we're gonna be continuous improvement teams that that persist over time. So I think this shifting from project teams to persistent teams coupled with mindset coupled with with or structure changed, you know, a lot of times has to be in place for a period of time to get to get the fluency and achieve the fluency that that most organizations need. >>Thanks, Jim, for that well thought out answer. It really goes to show that the transformation process really varies when it comes to organizations, but I think this is a great way to close out today's track. I like to think Jim, Michael, as well as all the experts that you heard earlier today for sharing. There's best practice as to how you all can start transforming your organization's by building a data fluent culture, Um, and really empowering your employees to understand what data means and how to take actions with it. As we wrap up and get ready for the next session, I'd like to leave you all with just a couple of things. Number one if you miss anything or would like to watch any of the other tracks. Don't worry. We have everything available after this event on demand number two. If you want to ask more questions from the experts that you heard earlier today, you have a chance to do so. At the Meet The Experts Roundtable, make sure to attend the one for track four in cultivating a data fluent culture. Now, as we get ready for the product roadmap, go take a sip of water. This is something you do not want to miss. If you love what you heard yesterday, you're gonna like what you hear today. I hear there's some type of Indiana Jones theme to it all, so I won't say anything else, but I'll see you there.

>>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.

>>Yeah, yeah. >>Hello and welcome back. I hope you're ready for our next session. Become the analyst of the future. We'll hear the customer's perspective about their increasingly strategic role and the potential career growth that comes with it. Joining us today are Nate Weaver, director of product marketing at Thought Spot. Yasmin Natasa, senior director of national sales strategy and insights over at Comcast and Steve Would Ledge VP of customer and partner initiatives. Oughta Terex. We're so happy to have you all here today. I'll hand things over to meet to kick things off. >>Yeah, thanks, Paula. I'd like to start with a personal story that might resonate with our audience, says an analyst. Early in my career, I was the intermediary between the business and what we called I t right. Basically database administrators. I was responsible for understanding business logic gathering requirements, Ringling data building dashboards for executives and, in my case, 100 plus sales reps. Every request that came through the business intelligence team. We owned everything, right? Indexing databases for speed, S s. I s packages for data transfer maintaining Department of Data Lakes all out cubes, etcetera. We were busy. Now we were constantly building or updating something. The worst part is an analyst, If you ask the business, every request took too long. It was slow. Well, from an analyst perspective, it was slow because it's a complex process with many moving parts. So as an analyst fresh out of grad school often felt overeducated, sometimes underappreciated, like a report writer, we were constantly overwhelmed by never ending ad hoc request, even though we had hundreds of reports and robust dashboards that would answer 90% of the questions. If the end user had an analytical foundation like I did right, if they knew where to look and how to navigate dimensions and hierarchies, etcetera. So anyway, point is, we had to build everything through this complex and slow, um, process. So for the first decade of my career, I had this gut feeling there had to be a better way, and today we're going to talk about how thought SWAT and all tricks are empowering the analysts of the future by reimagining the entire data pipeline. This paradigm shift allows businesses and data teams thio, connect, transform, model and, most importantly, automate what used to be this terribly complex data analysis process. With that, I'd like to hand it over to Steve to describe the all tricks analytic process automation platform and how they help analysts create more robust data sets that enable non technical end users toe ask and answer their own questions, but also more sophisticated business questions. Using Search and AI Analytics in Thoughts Fire Steve over to you. >>Thanks for that really relevant example. Nate and Hi, everyone. I'm Steve. Will it have been in the market for about 20 years, and then Data Analytics and I can completely I can completely appreciate what they was talking about. And what I think is unique about all tricks is how we not only bring people to the data for a self service environment, but I think what's often missed in analytics is the automation and figure out. What is the business process that needs to be repeated and connecting the dots between the date of the process and the people To speed up those insights, uh, to not only give people to self service, access to information, to do data prep and blending, but more advanced analytics, and then driving that into the business in terms of outcomes. And I'll show you what that looks like when you talk about the analytic process automation platform on the next slide. What we've done is we've created this end to end workflow where data is on the left, outcomes around the right and within the ultras environment, we unify data prep and blend analytics, data science and process automation. In this continuous process, so is analysis or an end user. I can go ahead and grab whatever data is made available to me by i t. You have got 80 plus different inputs and a p i s that we connect to. You have this drag and drop environment where you conjoined the data together, apply filters, do some descriptive analytics, even do things like grab text documents and do sentiments analysis through that with text, mining and natural language processing. As people get more used to the platform and want to do more advanced analytics and process automation, we also have things like assisted machine learning and predictive analytics out of the box directly within it as well and typically within organizations. These would be different departments and different tools doing this and we try to bring all this together in one system. So there's 260 different automation building blocks again and drag a drop environment. And then those outcomes could be published into a place where thoughts about visualizes that makes it accessible to the business users to do additional search based B I and analytics directly from their browser. And it's not just the insights that you would get from thought spot, but a lot of automation is also driving unattended, unattended or automated actions within operational systems. If you take an example of one of our customers that's in the telecommunications world, they drive customer insights around likeliness to turn or next best offers, and they deliver that within a salesforce applications. So when you walk into a retail store for your cell phone provider, they will know more about you in terms of what services you might be interested in. And if you're not happy at the time and things like that. So it's about how do we connect all those components within the business process? And what this looks like is on this screen and I won't go through in detail, but it's ah, dragon drop environment, where everything from the input data, whether it's cloud on Prem or even a local file that you might have for a spreadsheet. Uh, I t wants to have this environment where it's governed, and there's sort of components that you're allowed to have access to so that you could do that data crept and blending and not just data within your organization, but also then being able to blend in third party demographic data or firm a graphic information from different third party data providers that we have joined that data together and then do more advanced analytics on it. So you could have a predictive score or something like that being applied and blending that with other information about your customer and then sharing those insights through thought spots and more and more users throughout the organization. And bring that to life. In addition to you, as we know, is gonna talk about her experience of Comcast. Given the world that we're in right now, uh, hospital care and the ability to have enough staff and and take care of all of our people is a really important thing. So one of our customers, a large healthcare network in the South was using all tricks to give not only analyst with the organization, but even nurses were being trained on how to use all tricks and do things like improve observation. Wait time eso that when you come in, the nurse was actually using all tricks to look at the different time stamps out of ethic and create a process for the understands. What are all the causes for weight in three observation room and identify outliers of people that are trying to come in for a certain type of care that may wait much longer than on average. And they're actually able to reduce their wait time by 22%. And the outliers were reduced by about 50% because they did a better job of staffing. And overall staffing is a big issue if you can imagine trying to have a predictive idea of how many staff you need in the different medical facilities around the network, they were bringing in data around the attrition of healthcare workers, the volume of patient load, the scheduled holidays that people have and being able to predict 4 to 6 months out. What are the staff that they need to prepare toe have on on site and ready so they could take care of the patients as they're coming in. In this case, they used in our module within all tricks to do that, planning to give HR and finance a view of what's required, and they could do a drop, a drop down by department and understand between physicians, nurses and different facilities. What is the predicted need in terms of staffing within that organization? So you go to the next slide done, you know, aside from technology, the number one thing for the analysts of the future is being able to focus on higher value business initiatives. So it's not just giving those analysts the ability to do this self service dragon drop data prep and blend and analytics, but also what are the the common problems that we've solved as a community? We have 150,000 people in the alter its community. We've been in business for over 23 years, so you could go toe this gallery and not only get things like the thought spot tools that we have to connect so you can do direct query through T Q l and pushed it into thought spot in Falcon memory and other things. But look at things like the example here is the healthcare District, where we have some of our third party partners that have built out templates and solutions around predictive staffing and tracking the complicating conditions around Cove. It as an example on different KPs that you might have in healthcare, environment and retail, you know, over 150 different solution templates, tens of thousands of different posts across different industries, custom return and other problems that we can solve, and bringing that to the community that help up level, that collective knowledge, that we have this business analyst to solve business problems and not just move data, and then finally, you know, as part of that community, part of my role in all tricks is not only working with partners like thought spot, but I also share our C suite advisory board, which we just happen to have this morning, as a matter of fact, and the number one thing we heard and discussed at that customer advisory board is a round up Skilling, particularly in this virtual world where you can't do in classroom learning how do we game if I and give additional skills to our staff so that they can digitize and automate more and more analytic processes in their organization? I won't go through all this, but we do have learning paths for both beginners. A swell as advanced people that want to get more into the data science world. And we've also given back to our community. There's an initiative called Adapt where we've essentially donated 125 hours of free training free access to our products. Within the first two weeks, we've had over 9000 people participate in that get certified across 100 different companies and then get jobs in this new world where they've got additional skills now around analytics. So I encourage you to check that out, learn what all tricks could do for you in up Skilling your journey becoming that analysts of the future And thanks for having me today thoughts fun looking forward to the rest of conversation with the Azmin. >>Yeah, thanks. I'm gonna jump in real quick here because you just mentioned something that again as an analyst, is incredibly important. That's, you know, empowering Mia's an analyst to answer those more sophisticated business questions. There's a few things that you touched on that would be my personal top three. Right? Is an analyst. You talked about data cleansing because everyone has data quality problems enhancing the data sets. I came from a supply chain analytics background. So things like using Dun and Bradstreet in your examples at risk profiles to my supplier data and, of course, predictive analytics, like creating a forecast to estimate future demand. These are things that I think is an analyst. I could truly provide additional value. I'd like to show you a quick example, if I may, of the type of ad hoc request that I would often get from the business. And it's fairly complex, but with a combination of all tricks and thought spots very easy to answer. Crest. The request would look something like this. I'd like to see my spend this year versus last year to date. Uh, maybe look at that monthly for Onley, my area of responsibility. But I only want to focus on my top five suppliers from this year, right? And that's like an end statement. I saw that in one of your slides and so in thoughts about that's answering or asking a simple question, you're getting the answer in maybe 30 seconds. And that's because behind the scenes, the last part is answering those complexities for you. And if I were to have to write this out in sequel is an analyst, it could take me upwards, maybe oven our because I've got to get into the right environment in the database and think about the filters and the time stamps, and there's a lot going on. So again, thoughts about removes that curiosity tax, which when becoming the analysts of the future again, if I don't have to focus on the small details that allows me to focus on higher value business initiatives, right. And I want to empower the business users to ask and answer their own questions. That does come with up Skilling, the business users as well, by improving data fluency through education and to expand on this idea. I wanna invite Yasmin from Comcast to kind of tell her personal story. A zit relates to analysts of the future inside Comcast. >>Well, thank you for having me. It's such a pleasure. And Steve, thank you so much for starting and setting the groundwork for this amazing conversation. You hit the nail on the head. I mean, data is a Trojan horse off analytics, and our ability to generate that inside is eyes busy is anchored on how well we can understand the data on get the data clean It and tools, like all tricks, are definitely at the forefront off ability to accelerate the I'll speak to incite, which is what hot spot brings to the table. Eso My story with Thought spot started about a year and a half ago as I'm part of the Sales Analytics team that Comcast all group is officially named, uh, compensation strategy and insight. We are part of the Consumer Service, uh, Consumer Service expected Consumer Service group in the cell of Residential Sales Organization, and we were created to provide insight to the Comcast sells channel leaders Thio make sure that they have database insight to drive sales performance, increased revenue. We When we started the function, we were really doing a lot of data wrangling, right? It wasn't just a self performance. It waas understanding who are customers were pulling a data on productivity. Uh, so we were going into HR systems are really going doing the E T l process, but manually sometimes. And we took a pause at one point because we realized that we're spending a good 70% of our time just doing that and maybe 5% of our time storytelling. Now our strength was the storytelling. And so you see how that balance wasn't really there. And eso Jim, my leader pause. It pulls the challenge of Is there a better way of doing this on DSO? We scan the industry, and that's how we came across that spot. And the first time I saw the tool, I fell in love. There's not a way for me to describe it. I fell in love because I love the I love the the innovation that it brought in terms of removing the middleman off, having to create all these layers between the data and me. I want to touch the data. I want to feel it, and I want to ask questions directly to it, and that's what that's what does for us. So when we launched when we launch thoughts about for our team, we immediately saw the difference in our ability to provide our stakeholders with better answers faster. And the combination of the two makes us actually quite dangerous right on. But it has been It has been a great great journey altogether are inter plantation was done on the cloud because at the time, uh, the the we had access to AWS account and I love to be at the edge of technology, So I figured it would be a good excuse for me to learn more about cloud technology on its been things. Video has been a great journey. Um, my, my background, uh, into analytics comes from science. And so, for me, uh, you know, we are really just stretching the surface off. What is possible in terms off the how well remind data to answer business questions on Do you know, tools like thought spot in combination with technologies. Like all trades, eyes really are really the way to go about it. And the up skilling, um the up skilling off the analysts that comes with it is really, really, really exciting because people who love data want to be able to, um want to be efficient about how they spend time with data. Andi and that's what? That's what I spend a lot of my Korea I'd Comcast and before Comcast doing so It gives me a lot of ah, a lot of pleasure to, um to bring that to my organization and to walk with colleagues outside off. We didn't Comcast to do so The way we the way we use stops, that's what we did not seem is varies. One of the things that I'm really excited about is integrating it with all the tools that we have in our analytics portfolio, and and I think about it as the over the top strategy. Right. Uh, group, like many other groups, wouldn't Comcast and with our organizations also used to be I tools. And it is not, um, you choose on a mutually exclusive strategies, right? Eso In our world, we build decision making, uh, decision making tools from the analysis that we generate. When we have the read out with the cells channel leaders, we we talk about the insight, and invariably there's some components off those insight that they want to see on a regular basis. That becomes a reporting activity. We're not in a reporting team. We partner with reporting team for them to think that input and and and put it on and create a regular cadence for it. Uh, the over the top strategy for me is, um, are working with the reporting team to then embed the link to talk spot within the report so that the questions that can be answered by the reports left dashboard are answered within the dashboard. But we make sure that we replicate the data source that feeds that report into thought spot so that the additional questions can then be insert in that spot. It and it works really well because it creates a great collaboration with our partners on the on the reporting side of the house on it also helps of our end the end users do the cell service in along the analytic spectrum, right? You go to the report when you can, when all you need is dropped down the filters and when the questions become more sophisticated, you still have a platform in the place to go to ask the questions directly and do things that are a bit funk here, like, you know, use for like you because you don't know what you're looking for. But you know that there's there's something there to find. >>Yeah, so yeah, I mean, a quick question. Our think would be on this year's analytics meet Cloud open for everyone and your experience. What does that mean to you? Including in the context of the thought spot community inside Comcast? >>Oh yes, it's the Comcast community. The passport commedia Comcast is very vibrant. My peers are actually our colleagues, who I have in my analytics village prior to us getting on board with hot spot and has been a great experience for us. So have thoughts, but as an additional kind of topic Thio to connect on. So my team was the second at Comcast to implement that spot. The first waas, the product team led by Skylar, and he did his instance on Prem. Um, he the way that he brings his data is, is through a sequel server. When I came what, as I mentioned earlier, I went on the cloud because, as I mentioned earlier, I like to be on the edge of technology and at the time thought spot was moving towards towards the cloud. So I wanted to be part of that wave. There's Ah, mobile team has a new instance that is on the cloud thing. The of the compliance team uses all tricks, right? And the S O that that community to me is really how the intellectual capital that we're building, uh, using thought spot is really, really growing on by what happens to me. And the power of being on the cloud is that if we are all using the same tool, right and we are all kind of bringing our data together, um, we are collaborating in ways that make the answer to the business questions that the C suite is asking much better, much richer. They don't always come to us at the same time, right? Each function has his own analytics group, Andi. Sometimes if we are not careful, we're working silo. But the community allows us to know about what each other are working on. And the fact that we're using the same tool creates a common language that translates into opportunities for collaboration, which will translate into, as I mentioned earlier, richer better on what comprehensive answers to the business. So analyst Nick the cloud means better, better business and better business answers and and better experiences for customers at the end of the day, so I'm all for it. >>That's great. Yeah. Comcast is obviously a very large enterprise. Lots of data sources, lots of data movement. It's cool to hear that you have a bit of a hybrid architecture, er thought spot both on premise. Stand in the cloud and you did bring up one other thing that I think is an important question for Steve. Most people may just think of all tricks as an E T l tool, but I know customers like Comcast use it for way more than just that. Can you expand upon the differences between what people think of a detail tool and what all tricks is today? >>Yeah, I think of E. T L tools as sort of production class source to target mapping with transformations and data pipelines that air typically built by I t. To service, you know, major areas within the business, and that's super valuable. One doesn't go away, and in all tricks can provide some of that. But really, it's about the end user empowerment. So going back to some of guys means examples where you know there may be some new information that you receive from a third party or even a spreadsheet that you develop something on. You wanna start to play around that information so you can think of all the tricks as a data lab or data science workbench, in fact, that you know, we're in the Gartner Magic Quadrant for data science and machine learning platforms. Because a lot of that innovation is gonna happen at the individual level we're trying to solve. And over time, you might want to take that learning and then have I t production eyes it within another system. But you know, there's this trade off between the agility that end users need and sort of the governance that I t needs to bring. So we work best in a environment where you have that in user autonomy. You could do E tail workloads, data prep and Glenn bringing your own information on then work with i t. To get that into the right server based environment to scale out in the thought spot and other applications that you develop new insights for the business. So I see it is ah, two sides of the same coin. In many ways, a home. And >>with that we're gonna hand it back over to a Paula. >>Thank you, Nate, Yasmin and Steve for the insights into the journey of the analyst of the future. Next up in a couple minutes, is our third session of today with Ruhollah Benjamin, professor of African American Studies at Princeton University, and our chief data strategy officer, Cindy House, in do a couple of jumping jacks or grab a glass of water and don't miss out on the next important discussion about diversity and data.

>>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.

(upbeat music) >> From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE. We're in our Palo Alto studios today. We're having a CUBE Conversation around a really interesting topic. It's applied AI, applied machine learning. You know, we hear a lot about artificial intelligence and machine learning in kind of the generic sense, but I think really, where we're going to see a lot of the activity is when that's applied to specific solutions and specific applications. And we're really excited to have our next guest. He's applying AI and machine learning in a really interesting and important space. So joining us from San Francisco is Bryton Shang. He's the founder and CEO of Aquabyte. Bryton great to see you. >> Yeah, Jeff. Great to be here. >> I can't believe it's been almost a year since we met at a Kosta Noah event. I looked it up June of last year. Wow, how time flies. But before we get into it, give everyone just kind of the quick overview of what you guys are up to at Aquabyte. >> Aquabyte's a company, we're building software to be able to help fish farmers. It's computer vision and machine learning software based on a camera that takes pictures of a fish in a fish pen, analyzes those images and helps the farmer understand the health of the fish, the weight of the fish, how much to feed and generally better manage their farms. >> It's such a great story. So for those people that haven't seen it, I encourage you to jump on the internet and look up the AWS special that Werner did on Aquabyte last year. It's a really nice piece, really gets into the technology and a lot of the fun part of the story. I really enjoyed it and you know, congratulations to you for getting featured in that AWS piece. But let's go to how did you get here? I mean, you're really interesting guy. You're a multiple company founder coming out of Princeton, in most of your startup role, your startups are all about, Applied Mathematics and Statistics but you've been in everything from finance and trading to looking at cells in the context of Cancer. How did you get to Aquabyte? Was it the technology? And then you found a cool solution? Or did you hear about, you know, an interesting problem and you thought, you know, I have just the trick to help attack that problem. >> Well, so I had studied Operations Research and Financial Engineering at Princeton, which I guess we would call nowadays, like modern day machine learning and data science. So that was something as you mentioned, first I'd apply it to algorithmic trading, and then got on to more general applications of computer vision for example, in cancer detection. The idea to apply machine learning talk to aquaculture, came from a number of different sources. One was from a previous co-founder who had been doing some investigation in the fish farming space, had a business school classmate who owned a fish farm. And also growing up in Ithaca, New York near to Cornell I had a family friend who is a professor of aquaculture. And really just to learn about fish farming and overfishing and the idea that over half the fish we eat nowadays are coming from fish farms and that you could use machine learning and computer vision to make these farms more efficient. That being very interesting and compelling. >> So it's really interesting. One of the things that jumped out from me when I watched the piece with Werner was the amazing efficiency on the feed to protein output in fish farming. I had no idea that it was so high, it's basically approaching one to one really interesting opportunity. And I had no idea to that, as you said over 50% of the world's seafood that's consumed was commercially farmed. So really a giant opportunity and so great space to be in a lot of environmental impacts. So but how did you decide to find an entree? We know where to find an entree for machine learning to make a big impact in this industry. >> So it came from a couple different angles. First, there's been applications of machine learning computer vision and other industries that served as good parallels where we're using cameras to be able to take images and then use computer vision to derive insight from those images. For example, just take aquaculture where you're using cameras to spray weeds to understand crop yield. And so there's good parallels and other industries. aquaculture specifically, I was also looking at what was coming out in the machine learning literature in terms of using cameras to size fish. And so the idea that you could use cameras to size fish was very interesting because then you can use that to figure out growth rates and feeding. And as I developed my idea, it really became clear that you could use computer vision and machine learning to do a wide range of things at the farm and so, it started with this idea about using cameras to size fish and then it became monitoring health and sea lice and parasites and then ultimately, all the aspects of the farm that you would want to manage. >> And correct me for wrong, but do you guys identify individual fish within the population within that big net and then you're basically tracking individuals and then aggregating that to see the health of the whole population. >> That's right, the spot pattern on the fish is unique and we have an algorithm that's able to use that to determine each individual fish via the spot pattern. >> Wow. And then how long once, once you kind of got together with the farmers to really start to say, wow, we can use this application for, as you said, worrying about lice and disease control and oh wow, we can use this application to measure growth. So now we know the health of the environment or wow, now we know the size so we can impact our harvest depending on what our customers are looking for. I assume there's all kinds of ways you can slice and dice the data that comes out of the system into actual information that can be applied in lots of different ways. >> Right So I started the company back in 2017. And if you think about aquaculture, it's actually a hugely international industry 99% outside the US, and within aquaculture, very quickly zeroed in on salmon farming, and specifically salmon farming in Norway. Norway produces about half of the world's farmed salmon and ended up going there for a conference Aqua Nor August of 2017 and whilst there had my idea and a prototype for sizing the fish with a camera, but then also realized in Norway they have recently passed regulations around counting sea lice on the fish so this is parasite that attaches to the fish and is regulated and pretty much every country that grows fish in the ocean and farmers asked me then, okay, if you could use the camera to size fish, can you also count sea lice? And can you also detect the appetite? And then it just turned into this more platform approach where this single camera could do a wide variety of application. >> That's awesome. And I'm just curious to get your take on, the acceptance and really the excitement around, you know, kind of application of machine learning in this computer vision in terms of the digital transformation of commercial fish farming, because once it sounds like once they discovered the power of this thing, they very quickly saw lots of different applications, and I assume continue to see kind of new applications to apply this to transform their business. >> Right, I would say fish farming itself is already fairly highly mechanized. So you're dealing with fairly rough conditions in the ocean. And a lot of the equipment there is already mechanized. So you have automatic feeders, you have feeding systems. That said, there isn't too much computer vision machine learning in the industry. Today, a lot of that is fairly new to the farmers. That said they were open to trying out the technology, especially when it helps save labor at the farm. And it's something that they have familiarity with, with some of the applications for example, with Tesla with their autopilot and other examples that you could point to in common day use. >> That's interesting that you brought up Tesla, I was going to say that the Tesla had an autonomous driving day presentation. I don't know, it's probably been a year or so now but really long in-depth presentations by some of his key technical people around the microprocessor and AI and machine learning and a whole thing about computer vision. And, you know, there's this great debate about, can you can you have an autonomous car without Lidar and I love the great quote from that thing was you "Lions don't have Lidar "and they chase down gazelles all day long." So, we can do a lot with our vision. I'm curious, some of the specific challenges within working in your environment within working in water and working with all kinds of crazy light conditions. It's funny on that Tesla, they talked about really some of the more challenging environments being like a tunnel, inside of a tunnel with wet pavement. So, kind of reflections and these kind of metric conditions that make it much harder. What are some of the special challenges you guys had to overcome? And how much, is it really the technology? Or is it really being done in the software and the algorithms and the analyzing or is it basically a bunch of pixel dots? >> Right. The basic technology is based on similar, it's a serial camera that takes images of the fish. Now, a lot of the special challenges we deal with relate to the underwater domain. So underwater, you're dealing with a rough environment, there could be particles in the water, specularity some reflections underwater, you're dealing with practical challenges such as algae, but even the behavior of the fish, are they swimming by the camera? Or do you want to position your camera in the pen. Also, water itself has interesting optical properties. So the deeper you go, it affects the wavelength that's hitting the camera. And also you have specialized optics where the focal length and other aspects of the optics are affected underwater. And so a lot of the specific expertise we've developed is understanding how to sense properly underwater. Some of that is handled by the mechanical design. A lot of it is also handled by the software, where on the camera we have GPUs that are processing the images and using deep learning computer vision algorithms to identify fish parts and sea lice and other aspects of the fish. >> It's crazy, and how many fish are in one you know, individuals are in one of these nets. >> So single pen can have as much as 100,000. Where actually in one pen, which is I think it's the largest salmon farm in Norway based on an oil rig called the ocean farm where they have 2 million fish in a single pen. >> 2 million fish, and you're in that one. >> Right, yes. >> And you've identified all 2 million fish or do you work on some sampling? Or how do you make sure every fish eventually swims by the camera? Or does the camera move around inside that population? That's an amazing amount of fish. >> So I think we'll eventually get to the point where we can identify every single fish in the pen and use that to track individual health and growth. Well we practice what we use the individual recognition algorithm the deal is to de-duplicate fish. So a common question we get asked is okay, what if the same fish swims by the camera twice, and so it's used to de-duplicate fish But I think eventually you'd be able to survey the entire population. >> That's crazy. So where do you guys go next Bryton, again you've brought your analytical brain to a number of problems. Do you see kind of expanding the use within the fish industry and kind of a vertical player? Do you see really a horizontal play in different parts of agriculture and beyond to apply some of the techniques and the IP that you guys have built up so far? >> Well, starting with Norwegian salmon, we want to bring this to other countries around the world for other species. So we've expanded to our second species, which is a rainbow trout. We also are, starting with computer vision are building this very interesting data set which we can use to enable other applications. Eventually, we'll get to the point where that data allows us to run fully autonomous fish farms. Right now the limitations of fish farming is that it needs to be close to the shore. So you can have people go to the farms. And once you have fully autonomous fish farms, then you can have fish farms in the open ocean, fish farms on land. And with the world being 70% water, we're only producing about 5% of the protein from the oceans. And so it presents a massive opportunity for us to be able to increase the amount of world's demand for protein. Also given that we're running out of land to grow crops. >> Wow, that's amazing. We're only getting 5% of our food protein out of the ocean at this stage? >> Right, right. >> That is crazy. I thought it would be much higher than that. Well, certainly a really cool opportunity and, a kind of a really awesome little documentary by Werner and the team, definitely go watch it if you haven't seen it. So I just give you the last word as you've been in this industry and really seen kind of the transformative potential of something like computer vision in commercial fishing and who would have even thought that, six or seven years ago? How does that help you kind of think forward, kind of the opportunity really to use these types of applications like computer vision and machine learning to advance something so important, like food creation for our world. >> I think there's definitely a lot of opportunities to be able to use machine learning computer vision, similar technologies to help make these industries a lot more efficient. Also a lot more environmentally sustainable. I'd say something like this industry, like aquaculture, it's not so apparent just if you're in the valley, and even in the US just because 99% of it happens outside the US and so to be able to be familiar with the industry to know that it exists and to build applications itself is a bit of a challenge. I would say that is changing. One of the things that actually came out a couple weeks ago was an executive order to actually start kick starting offshore aquaculture in the US. So it is starting in the US. But more generally, I do think there's a massive opportunity to be able to apply machine and computer vision in new industries that previously haven't been addressed. >> Yeah, that's great. And I just love how you got kind of a single source of data, but really the information that you can apply and the applications you can apply are actually quite broad. It's a super use case. Well, Bryton, thanks for spending a few minutes. I've really enjoyed the story. Congratulations on your funding rounds and your continued success. >> Thanks, and really appreciate to be on and yeah, hope to continue to help bring the world more sustainable seafood. >> Absolutely. Well, thanks a lot Bryton. So he's Bryton and I'm Jeff. You're watching theCUBE. We'll see you next time, thanks for watching. (upbeat music)

by from Las Vegas it's the cube coverage AWS executive summary brought to you by Accenture hello everyone and welcome back to the cubes live coverage of the Accenture executive summit here at the Venetian in Las Vegas part of aw reinvent of course I'm your host Rebecca Knight we have two guests for this segment we have Leandro Nunez he is the vice president product development at MasterCard thank you so much for coming on the show thanks for having me and Christine Leung she is the managing director Accenture blockchain and biometrics thank you so much you so sustainability is a hot topic in the industry too in all industry today and especially here at AWS reinvent I want to talk to you about circular supply chain which was an idea that germinated in your brain a couple of years ago but it's really just sort of launched a year ago tell us more about why you started Cs sure we started this actually because a couple of things I we drink coffee every single day and we go into every coffee shop and we think about well you know you see packets saying this is my single origin coffee this is I pay extra for this and it's sustainably grown and yet you see news saying that you know the rain forest is being burnt down and animals are being killed and so about two years ago I looked at this and I thought wow you know how do I know this is really sustainable what I'm drinking the extra five books that I'm hanging at my coffee shop is it really is it really sink origin is it really going to the right people is it really killing the orangutans and the rainforest and then I see a statistic success well for every coffee a cup of coffee that we drink a square inch of rain forests get burnt down and I mean I drink at least five cups of coffee a day and working actually with MasterCard at the time I'm doing a and still do actually doing a lot of work with MasterCard in around identity and biometrics and I thought well you know how can we combine some of these capabilities we have with blockchain identity to say to put our money where our mouths is to change incentives as the base of the pyramid where you know performers produces smallholders if I can say to them that I really won't care but you don't burn a fat forest out that you produce in a good way and they just tried to survive they're not bad people if they're just hand-to-mouth but if they we can say right will hate you more as consumers and we know it's definitely going to that right person then maybe we can help to change some of you know and not have the rain force and don't have my guilty cup of coffee right so even if we don't drink quite as much coffee as you we are as a as a group consumers are more socially conscious than they ever have been what are some of the statistics here that people just care more about this stuff in general and they're willing to pay a premium for it so for example the green trade is estimated and this came out for Unilever at two trillion dollars a year by the by next year actually a growing statistic and let's just see I mean more and more on social media or literally you know every platform that you can see sustainability is a huge topic with you know sort of the the recent sort of UN climate discussions I mean it's this week with next week we're in Madrid this a big topic that we should all as a responsible consumers care about so Leander what do you see as the benefits of CSE to to small actors well it's a great point because when you see that just think about it do you usually say a lot about consumers in the big brands and now we're protecting the big brands but just think about the sourcing side of the supply chain right the small communities the ones that are growing the coffee the ones that are the farm the farmers over there or the fishermen now these ones are there's meaning for a while they're just been because it squeezed by the whole supply chain it's but the whole business right you think like let's remove a little bit of their margin let's put in something else now when they have the circular supply chain because consumers and this new generation is so interested in knowing where the product comes from you know if you're then doing the right thing now it has a change that you can pay them back it's all about come up with incentive model that's why we should in a MasterCard right when you create a network like that which the blockchain solution is a big network so how we can gain traction how we can gain adoption one thing is you need to establish incentives through all of the parties that you have at a network so if you're just taking care of the brand and they're gonna say bran mandate to your suppliers that needs to do that this is not going to work what it works is what is the incentive the farmers gonna have what's the sourcing so we don't mean it so don't don't you think the farmers wants to do the right thing of course they do but do they have incentives for that if it's just a letter if you're just someone mandating they're not gonna do it but if you come with the idea of hey I pay you back your your coffee or whatever your products you're doing we can help you can have a premium so we can it's going to be sustainable to your family as well your business can be more profitable so they you see okay I want to be part of it so it's creating incentives for people to for the for the for the producers themselves to grow things more sustainably it's all about that it's not only them and then you go to the suppliers you go to the logistic transportation companies how do you creative you give them the visibility they always complain about how can I have the visibility of my supply chain why can you create the visibility you give the transparency that you create the trust in and if you ask people in a supply chain business what the big problem is supply chain is trust they don't trust each other but they have to trade things and they don't trust each other you do business with people you don't trust every single day it's not a good thing so if we bring this visibility you facilitate this and they see there's an incentive to be also part of it so Christine what are the kind of technologies that are bad that are that are powering the CSC and and how are we how does it create that trust i cultivate that trust um and Leandra is for Honor's and in terms of trust it's about trusting the people but trusting the data and trusting the entities that I put in some of this data there are components of blockchain of course the surface the traceability aspects of the any of the product blockchain also helps with the decentralized identity capability that we've put in we've made also biometrics for the for the individual but this is optional depending on how you know in terms of using it very responsibly payments of course digital payments and you know having the ability to actually direct payments through the MasterCard rails and then of course with you know the power of AWS and then hosting on the cloud and be able to have that anywhere and the different aspects of including a iot so we know that let's say for fisheries this product is actually really came from displays you know the sensors we know that it's kept the right temperature we did that therefore you know insurance payers and things like that would be right and tracked all the way through and knowing that the product is really fresh and really kept you know intact throughout the journey so a whole bunch of different technology totally great projects with blockchain only would tend not to succeed and the reason is because you need to come up with you need to nurture the ecosystem so how you bring the IOT yes to the table how you doing you know payments how you bring AI so you get at all these solutions together and then you create what this visibility that's trust we need so companies are trying to do one side you know which is just a blockchain they're not going anywhere the reason that I like it our alliance with Accenture and AWS is because we know that we needed to do this end-to-end and this can be broader than just talk about watching and it's about the people because you have the ultimate is the consumer and the the base of pyramid producer both have identities and if we are able to say this is the identity of the person I can then help to influence their their livelihoods so it's putting a real face on the supply chain for the end consumer I mean at a time where consumers are demanding more transparency in the supply chain demanding to know more about the source of the goods that they put the products that they're buying what has been the reception and and what are you hearing back I think we've had great receptions we launched at Davos earlier this year we've had a huge amount of interest and now slowly we're gaining sort of traction in terms of getting the pilots I'm putting them in place and I think it's also something that we'll need to UM in initially it's a little bit of Education understanding well how does this actually all work you know is it just traceability is it just identity well it's actually all those things are understanding the use cases and embracing that there are it's not just one way of doing something and this is really a concept that embracing better business through better technology and innovation can actually be more sustainable and responsible businesses so the traction has been great and we've had a we have a number of pilots in the pipeline yeah well we will in the past I used to believe that some things we should stop doing or stop eating because of the sustainable part of it and I have learned that it's not the case you can do the right thing you can make sure that they're doing the right thing and you can eat with no guilt that's why everybody wants right so so this is this is the the type of you know visibility when to give from the consumer side but not from the from the company side of I like the brands are ok I'm safe because brands they have a very good visibility from the distributor on but they don't know what's going on behind that you know products the this is so globalized now they so fragmented you know it comes from so many different places Princeton that there's no way that they can control it if they don't have this you know there's this view so that's why we're trying to bring together so when so when this when this fully does launch and a consumer is then seeing the face of the coffee grower in Brazil or in Kenya and saying ok so then what what happens then how are they able to to to incentivize that farmer to do the right thing as you say there's a digital payment channel of powered by monster cop that you can then so sue speaks if the farmer donate money and actually say well there's multiple ways of doing things right so for example if I'm the consumer scanning the the product and there is we have a whole lego city built upstairs that can show cases and say right this is how it works and you know scan the product and what I can save right I want to be able to donate an extra dollar for this farmer because I really like the fact that you are sustainable and not burning the rainforests and protecting the orangutan or elephant so the the the birds so great I'm going to give you an extra dollar so this is how it's going to work on the app and there are other consequences well there's so many organic products nowadays they're not really organic so you can prove with the organic so the farmers would feel more motivated to really grow that as a organic product because there is a premium so it's not only the the tea that you give it to them but also the fact that you can create a premium price situation that will motivate others to do the same so brands would grieve the differentiator farmers would feel like okay if I do this way how to get will be more profitable and consumers will benefit from that from a real organic or a real product what the sustainability you know behind it consumers can trust more so how do what are some of the I mean this is such a cool concept what is what are some of the biggest challenges in in really launching and making it a reality what is keeping you up at night I think some of it is actually just education and getting it out there and understanding that this is it's a lot of stakeholders so from consumer brands all the way down to the the smallholder providers so it's a lot of people to link up and a lot of organizations to talk to so some of it is just getting through that process and getting people to understand and also actually hopefully we'll get consumers understand that this is something that they will want to do yeah and that this whole integration I Christine said it's in it's important right so you understand all the key stakeholders don't need to beat all of them at the beginning but at least the key stakeholders in the supply chain and how you can create this business incentive in a dissented model for them to be part of that so it's a mapping exercise which is we are getting there and in intestine we gain adoption and and if you gather the consumer side doing this as well so it creates a network effect and that's why we try to do in a MasterCard assist in our DNA like building networks right everybody knows that so we wanted to bring this to you know >> to the ecosystem to contribute okay so how can I create a network effect that they can it exponentially scale you know for for the whole market share for the whole you know marketplace so I want to ask you a personal question you've been in technology for a really long time time and now but in terms of the kinds of projects you've worked on and the kinds of ways you're thinking about technology and then this particular project at a time where climate change is a monumental challenge the fate of our planet really hangs in the balance with what with the decisions that we're making policymakers and consumers are making today wait how what is it like to work on this kind of products a great question I yeah I was for this all these years so go to work with this business mentality you know we're gonna make more money for someone else we're gonna work for a big company and see some friends and family doing things for the society and say oh my gosh there's something like that and now I feel like I can do both right we're talking you know it's a business it's it's a great solution but makes it so well for the you know for the whole society you know it makes me feel really every day going to work and say oh this is what I want to do you know this is so cool I mean I'm helping I'm benefiting myself as I go to the supermarket I'm gonna be the one who's gonna tip the farmer I'm gonna be the one who's gonna check where my shrimp comes from right so so I'm doing this for my family my kids are like I hope they can live in a better planet that know exactly where the products come from and the family that you have it's not even been born yet so that's the other generation that's amazing really doing things that we never know thank you so much Lee under and Christine for coming on the cube a really fun and fascinating conversation thank you thank you I'm Rebecca night stay tuned for more of the cubes live coverage at the Accenture executive summit coming up after lunch [Music]

(upbeat music) >> Hello there and welcome to a special Cube conversation here at The Cube's Palo Alto studios, I'm John Furrier. Join with me for this special Cube Conference, Stu Miniman with Wikibon and The Cube co-host as well just up at Amazon Web Services Summit. Stu, great to see you again. Our next guest is Ben Golub, who's the executive chairman and interim CEO of Storj, pronounced storage. So it's a really hot cryptocurrency, blockchain based storage solution. I should say decentralized storage, not necessarily cryptocurrency, but tokens are involved, encryption. Great to see you. >> Great to see you, it's good to be back. >> Formerly Docker CEO and now advising at Mayfield Fund as a venture partner and also interim CEO of a hot-- >> Yeah really exciting company. And I'm really excited to talk to you about it today. >> So let's just jump into it. So obviously the ICO craze is awesome and we've always speculated that the blockchain and the decentralized applications are coming is going to be the real action. But yet it's going to create efficiencies where there's inefficiencies. >> Sure. >> Venture capital is one of them and that's why the ICO craze is going. People are raising a boatload of money that they probably wouldn't have gotten that amount. >> Wouldn't have gotten, yeah no dilution, things like that. It's interesting yeah. >> So give us an update on Storj or storage. How much in ICO did they raised, whitepapers out there? It's peer to peer, give a quick, take a minute to explain what the company's doing. >> Yeah well I guess that I should probably start by saying that I think that blockchain is bigger than just cryptocurrency, and decentralized is bigger than blockchain, and Storj is primarily a decentralized storage company. So we're about decentralized apps and the whole thing would absolutely work even if we were just using dollars. But I think it does make it a whole lot more exciting. And so the company, kind of unique in the crypto space in that we actually had a running service that was providing real value, before we did the large token sale. And the token sale raised about $30 million. Fortunately they took about 10 of that in Ethereum and Bitcoin which rose up. So there's a good deal more than that in the bank account right now. >> John: Hopefully they converted to fiat currency. >> And then they converted to fiat along the way. >> It's at an all-time high of $20,000 right now. It's like $7,000, something like that. >> Yeah, so you know, didn't sell everything at the peak, but didn't sell at the-- >> Yeah, so we've been having many blockchain and crypto or token-based economic kind of things. But the real question is what's happening? Now we know the action's been on the infrastructure side. We look at all the top hedge funds, Polychain, amongst others. They love these deals because it's infrastructure. Is that where the action is and how are you guys looking at that because at the same time, there's a wave of decentralized applications also known as Dapps coming on. So there's a relationship going on between how fast the infrastructure can go, and then how applications are going to work with either on chain or off chain dynamics. >> Sure, sure. So maybe it would be helpful to give you a sense of what it is that we do. 'Cause I think that if you do that, then I think it makes sense in the context of decentralized infrastructure, decentralized apps, but also actually traditional infrastructure as well. I've always been searching for a company that I could describe at Thanksgiving. I've never succeeded, so I always end up saying that I'm in computers, and fixing somebody's printer. (laughing) But I guess if I were to describe Storj at Thanksgiving, I'd say it's basically the Airbnb of storage, or the Airbnb of disc drives. So Airbnb, people have lots of condos or vacation properties that aren't being used all the time, and so Airbnb brings them together with people who want to rent those, and they're the largest hotel company in the world, without owning a single property. And we're kind of doing the same thing with Storj, in that there is, first of all, this explosion in the amount of data that's getting created. It would fill a stack of CD-ROMs to Mars and back this year. Yet the price of cloud storage hasn't come down. And 90% of all the disc drives that are out there are only about 10% utilized. So seems like a problem that needs a solution. And that's what we've done. We've basically brought together a very large network of individuals and companies that have spare storage capacity and matched them up with people who need storage. The really cool aspect, there are many cool aspects about it, but one of them is that basically if you want to store on the Storj network, we take your file, you encrypt it, so we never hold the keys. You encrypt it, it's all scrambled up, we break it up into between 20 and 80 pieces, and we spread those out across 150,000 or so nodes that we have in our network. So it's super cheap, but it's also super secure. Great performance because the data's way out at the edge. And super available because there's no storm or power outage or idiot tripping over a power cord that can take out your storage. >> So, Ben, you touched on, first question I was going to ask, of course, trust and security. Storage I absolutely have to worry about, so it sounds like that's at the core, but there's a number of dynamics going on in the industry. Object storage was great, let's spread it out, let's make it more decentralized, but most of the core storage industry is speeds and feeds and latency's super important, and even when you start getting to distributed architecture, I worry about that latency. So what are kind of the use cases, what are some of the key customer issues? Is price a big piece of it? Or what solutions does Storj solve that others can't? >> I always said when I was at Cluster, which was a storage company that there were four things that mattered in storage. There's certainly price; there was security; as in I don't want anybody to be able to access it; there's availability, I never want to drop or lose files; and finally there's performance, how fast I can get it. And so for a huge range of use cases that involve files, basically everything that object storage is kind of used for today, the design of our system is actually much better because we've encrypted it locally and then spread it out, you really can't attack it. First of all, you'd have to figure out... So a would-be attacker who wanted to find one of your files in the storage network would have to figure out which of the 80 or the 20 nodes out of 150,000 it's located on. If they found one of those, and they got the small portion of the file that's there, they wouldn't be able to do anything with it 'cause it's encrypted. Even if they were somehow able to decrypt it by stealing the key from you, not from us... >> So encryption and immutability... >> And immutability, right. So you get all of that. So for the security piece, it's great. For the availability piece, I never lose a file. It's really, really good, because if you just look at the math, the chances that somehow... You can basically lose 10 out of 20 nodes and still be able to recover your files. And all of our nodes are run by different people, different power supply. >> So let's take a step back. How many nodes are on the network now, you said? >> 150,000 now, run by 70,000 farmers, is what we call them. They're not miners, 'cause they're not just solving that problem, they're just producing something of value. 70,000 farmers, and then we have on the network right now, over 50 petabytes of data, which is a really large amount, and yet, we don't run a single data center. >> Have you guys raised any venture at all, or is it all ICO proceeds? >> There was a small seed round that was done, before the ICO craze. But other than that, it's all-- >> And how many people are working on the company? >> 25. >> So you guys are a classic startup. The working product, how does that look now? Is it on the blockchain, is it off the chain, how's it working, Bitcoin? >> So I've described to you what the product does. So far nothing I've described to you involves blockchain. The way the economics work is that as a user, somebody who wants to store on our network, we quote a price in dollars. You can either pay us in dollars or in the Storj token, and as a farmer, you get compensated with a Storj token. And that's done, of course, using blockchain we're actually part of Ethereum. >> Is that ERC-20 token? >> ERC-20 token, yeah. There are also interesting things that we are working on using blockchain for things like you just mentioned, data integrity, so I can make sure that if I'm doing a snapshot of a database, and I want to make sure that it's exactly what it is, nobody can tamper with it, et cetera, then that's a perfect use of blockchain. But using blockchain for the stuff I was talking about before, like figuring out where the shards are and making sure that they're uptime and reliable, that's actually stuff where blockchain isn't the best answer. >> Ben, tell us a little bit about the customers that you find there, 'cause storage administrators, that role's been changing a lot, but the typical storage administrator, if you tell them, "Oh yeah, I'm doing some distributed thing, "somewhere else, and paying in crypto-currency," they'd be like, are you kidding me? I want this thing that I can lock and hold and guard with a gun. >> This is like anything else, there's an adoption curve, and right now it's clearly very much early adopters. And actually similarly to Docker and similar to the cloud in general, it's developers who are leading the way. Developers are saying, oh, wow, I can write to the storage network in the same way that I would have written to S3, only it's cheaper, for many use cases, more performing, and not centralized, so I'm not trusting one cloud provider. So for certain use cases, this is fantastic. >> Are there certain cloud native apps that you're finding have strong affinity here? >> Yeah, so basically what we have affinity with right now, and let's be clear, this is early days. I wouldn't recommend that people store their most sensitive data on this, but-- >> Not Oracle certified yet, is what you're saying? >> We're not Oracle certified, no. (laughing) Basically anything involving a large file that you're not writing to very frequently, but you're reading a lot, or that's getting read by lots of people around the world, we're a really good solution. It's one of the things I think I mentioned to you. So we've got 150,000 nodes. They're located in I think it's now 180 countries, and all over the U.S. So if you want to get your data close to the edge, the people who are consuming your data are really close to the edge, this is actually really good. And because it's spread across so many, you get the benefit of parallelism, so it's super fast, in addition to being super safe and super secure. >> How does it work for the farmers? Because we have video files, so we would love to spread our video files on the Storj network. So let's just say... >> I'd do a special deal for you, too, you know. >> Of course, yeah, get a little token action going on both sides, Cube coins. But the availability thing is concerning. Whose computers is it being stored on? Is it extra capacity? Is it servers? Is it people's home computers? What's the, is it that kind of model? >> Sure, so basically yeah, we, just as Airbnb measures reputation, we measure reputation, too. And so if you don't have a good reputation, certain characteristics, we won't send data to you. What it basically means is you've got to have dedicated hardware and a dedicated connection. So we do have people who are running things in their home, but it's not a laptop, it's not on your phone. But if you have a disc drive that's connected with reasonably high capacity and reasonably well connected, then you'll establish good reputation. But what we are seeing is we are seeing a lot of universities, a lot of small businesses, some data center operators who have spare capacity or just want to use us as like, be both a farmer and a user. So backup and get stuff on their capacity as a good idea. And interestingly enough, we also are getting a lot of people who were Bitcoin miners and bought equipment, which is good quality equipment, but there's such an arms race in doing that. >> So they abandoned, because it was too hard for them to get coins. >> It's too hard to make money, right, and very expensive, specialized equipment, and in our case, basically general high quality equipment works well. >> What's the profit model? How do the farmers make money? Take our Cube videos, as an example, so I'm paying you guys, and you're distributing those tokens? >> You're paying us and you're paying us either in dollars or tokens. And then farmers get compensated in tokens. Right now, about 60 cents on every dollar goes to farmers. And farmers get more storage based off of their reputation. We charge people based on both how much you're storing as well as how much bandwidth egress that you're doing, and we compensate farmers exactly the same way. >> It's handled through a consensus protocol that you guys have? >> Yeah, yeah, so the payment and assessing reputation we actually use good distributed blockchain as well there, right, so you're not counting on Storj to be in the middle there. Now, with the remaining 40 cents, which I think is actually the really interesting part, we keep some of that, we put some back into the network, but what I'm really excited about is that this is now a way for us to economically empower demand partners as well. The first thing we announced was FileZilla, but we have lots of other open source projects waiting in the wings, and we're happy to share with them. So as opposed to centralized cloud, where it's really hard to make money as an open source company, we're not an open source project in our case, right? We're happy if you're sending us users and data, to give you a really meaningful percentage. >> Any kind of freemium model you guys are playing with? I can imagine this being pretty interesting, because S3 democratized and lowered the cost barrier, obviously with cloud. >> S3 has been great for many things. >> How low are you in terms of the disruption? You guys are probably going to have to come in and undercut S3, is that the strategy? Or is that the price value? >> I think what I learned from my time in storage, is price is important but you have to be really safe and available and reliable, 'cause people's data is really important. But we looked across a pretty broad set of use cases, in comparing us to the traditional cloud providers we're probably a third. And we could go lower. What I think is really interesting in our case is that the economics just work really well. So from our perspective, if you're a farmer, you've already got, it's spare capacity, you don't need any more electricity to run this thing, you've got bandwidth, right? You don't need to hire any more people. So it's almost pure margin for a farmer, which is great for them. And so we can give economic value to farmers, we can give economic value to our customers, we can give economic value to partners. >> Any kind of economic models you can share in terms of what someone would make? Let's just say that I had this big music library that's not being used anymore, and I had a-- >> Well, as a customer of course, if you've got data that you want to store on our network, you'll save a lot of money, and it's probably a third of what you might pay. >> But is there any kind of, if I'm a farmer, I want to join the network? >> But if you're a farmer. >> How much am I going to make? >> It really depends on how much you're storing and how good your connection is, but as a farmer, I think you can make decent money. This could probably be I don't know off the top of my head, $20, $30 a month per drive, which isn't bad, and certainly much easier than making money-- >> So it kind of depends like the Airbnb model, depends how well you're using-- >> How well you're used. So some people earn less, some people earn more. And again, for most of the farmers, this is pure margin. >> Great, we got a couple back to back rooms, Stu. We should get some drives up there and get on board. We could pay for the cameras. >> And look, I think for videos, you guys would actually be a perfect use case with a lot of the stuff that's going to be coming out later this year. You get both storage and CDN like things for free, in the sense that because-- >> I'm really glad you brought that up, 'cause I want to ask you about Videocoin, 'cause Halsey Minor has Videocoin, another ICO, he raised $50 million. We covered that on Silicon Angle. But he's trying to democratize Acromi. Is that similar to what you guys are doing? >> I guess you could say yeah, we're further democratizing object storage, democratizing S3, but I think we can also democratize Acromi, we can democratize Isilon, there's certain other really exciting things that are-- >> What other services, you mentioned CDN, so it's not just storing the information, but that global dispersion, what does that enable? >> It used to be that people had a really big difference between archival which is slow, hard to get at, and CDN, right? And but actually, given the way that we're doing this thing, we can be pretty seamless. Pay archival for stuff that's staying in archival, but go up market if you're going to be having a lot of people read it. >> So I got to ask you about the, obviously, security. You're looking at it for additional services around redundancy, I can see that being a nice headroom for you. On a personal note, you've been involved in a lot of industry companies that have done very well, entrepreneurial success. >> Ben: Why am I doing this? (laughing) >> I can tell you're having fun. How could you not have fun, it's a whole 'nother generation of innovation, disruption coming, a whole 'nother price point. So what's it like, are you having fun? And if you could talk to your 22-year-old self right now, 'cause I wish I was 22 right now in this market-- >> Are you saying I'm not 22? >> How do you explain this? And when you go to parties, even in the Valley, and people say, "Man, you're crazy, it's a fricken' "scam out there," how do you explain to 'em this revolution? Because this is like a special, unique wave. How would you talk about that? >> Actually I describe it the same way to people in the Valley the same way that I described at the beginning, which is that blockchain is bigger than cryptocurrency, and decentralized is much bigger than blockchain. And Storj is first and foremost decentralized. It's about decentralized computing, decentralized storage, supporting decentralized apps, keeping the internet from ending up in the hands of just three people, three companies, which I think is really important. But also I feel very good that, to the extent that Storj does touch on cryptocurrency, that we've done it the right way. We had the service working first before we did the token sale. We raised what now appears to be a modest amount in the token sale, tried to be very transparent and at the forefront. >> You probably could've gotten more if you wanted to. >> Probably, right? But we were trying to be forefront in terms of governance and transparency, and I think that it'll probably be a good thing, just as it was kind of a good thing that the bubble burst in the late '90s and you got rid of a lot of such not great companies and not such great operators. I think that the current corrections, or whatever, in the crypto market I think will-- >> Like pets.com is gone, but DogeCoin still exists. (laughing) >> So I'm sure that somebody has a crypto base pets.com or webvan lurking in the wings somewhere. Kodak just did it. >> I got to ask you, you're super smart. You went to some really good schools, I think Princeton, Harvard Business School. So you got a good education, so I got to get your take on the whole token economics vision. 'Cause this is, if you look at outside the tech trends, there's actually new economic models that are coming out. Have you looked at token economics? New liquidity on the one side, you've got sovereignty, you've got consensus. These are not just tech issues, these are society issues. What's your vision around that? How are you viewing it? What's the upside? How is this shaping the future? >> Yeah, I think if you're a token network, you sort of have to have some central bank chops as well, right? And we actually have a central banker. >> John: So you have a chief economic officer? >> So we don't, no, we have an advisor-- >> John: Public policy. >> I actually had a degree in public policy at one point. But we need to think about the token supply in the same way you'd think about the money supply. We're backed by something real, so it's sort of like having currencies backed by gold. We need to make sure that the market grows and the network grows. And my fundamental belief is that the more the network grows, the more people use it, the more value that we're able to provide, that'll be good for token economics in the long run. In the short run, though, what we've done, is again, we price based off of dollars, and we compensate farmers based off the token based off of the spot price. So for farmers, we've tried to remove any need to worry about volatility or things like that. >> So I want your reaction-- >> Or the price. >> I've said on The Cube multiple times that in the old days of venture startups, the CTO was everything. You had to have a great CTO or VP of engineering and great senior executive team on the entrepreneurial team. Now it's almost like the chief economic officer is a critical piece, 'cause you've got public policy intersecting with economics. You've got new kinds of math that's not technical algorithm but it's kind of business algorithms. >> It is, business algorithms. Just like any economy, the money supply matters. And people's trust in that money matters. And the supply matters. All that stuff like that, and stability matters. So I think absolutely this new breed of network based token companies will have to worry about that, and probably should think about a chief economics officer, but it doesn't mean that you don't also have to have a great CTO and great technology, 'cause that's how you make the network valuable and grow. And one of the reasons that gave me both excitement and comfort about going to Storj is that the economic model works, fundamentally, even if the crypto's not there. >> John: 'Cause technology is decentralized. >> Decentralized storage makes sense even if you're buying and selling it with dollars or pounds or rubles, or whatever. >> Ben, great to see you, thanks for coming in and sharing the Ben Golub School of Economics, Public Policy for Tokens. You can give a class at Stanford on that soon, although that's the competition's school. >> Maybe, yes. Slightly different. We still like them. >> Great to see you, congratulations. Storj, pronounced storage. Great, successful ICO, hot startup, really, an example of the infrastructure opportunities of a new decentralized infrastructure that can be and will soon, we think, it will be critical infrastructure in a whole new way. Great to see you. >> Ben: Really good to see you, great to be back with you. >> It's the Cube Conversation, I'm John Furrier, Stu Miniman, thanks for watching. (upbeat music)

>> Narrator: Live from Nassau in the Bahamas. It's The Cube! Covering PolyCon 18. Brought to you by PolyMath. >> Welcome back everyone, we're live here in the Bahamas with The Cube's exclusive coverage of PolyCon 18, I'm John Furrier with my co-host Dave Vellante, both co-founders of SiliconANGLE. We start our coverage of the crypto-currency ICO, blockchain, decentralized world internet that it is becoming. It's the beginning of our tour, 2018. Our next guest is Hartej Sawhney who's the advisor at Pink Sky Capital, but also the co-founder of Hosho.io. Welcome to The Cube. >> Thank you so much. >> Hey thanks for coming on. Thanks for coming on. >> Thanks guys. >> We had a great chat last night, and you do some real good work. You're one of the smartest guys in the business. Got a great reputation. A lot of good stuff going on. So, take a minute to talk about who you are, what you're working on, what you're doing, and the projects you're involved in. >> So first of all, thank you so much for having me, it's really exciting to see the progress of high-quality content being created in the space. So my name is Hartej Sawhney. We have a team based in Las Vegas. I've been based in Las Vegas for about five years. But I was born and raised in central New Jersey, in Princeton. And my co-founder is Yo Sup Quan. We started this company about seven months ago and my co-founder's background was he's the co-founder of Coin Sighter in Exchange out of New York, which exited to Kraken. After that he started Launch Key which exited to Iovation. And prior to this company, my previous company was Zuldi, Z-U-L-D-I .com where we had a mobile point of sale system specifically for high volume food and beverage companies and businesses. So we were focused on Fintech and mobile point of sale and payment processing. So both of us have a unique background in both Fintech and cyber-security and my co-founder Yo, he's a managing partner of a crypto hedge fund named Pink Sky Capital. And he was doing diligence for Pink Sky, and he realized that the quality of the smart contracts he was seeing for deals that he wanted to participate as an investor in, and I'm an advisor in that hedge fund, we both realized that essentially the quality of these smart contracts is extremely low. And that there was nobody in this space that we saw laser focused on just blockchain security. And all the solutions that would be entailed in there. And so we began focusing on just auditing smart contracts, doing a line-by-line code review of each smart contract that's written, conducting a GAS analysis, and conducting a static analysis, making sure that the smart contract does what the white paper says, and then putting a seal of approval on that smart contract to mitigate risk. So that the code has not been changed once we've done an analysis of it, that there's no security vulnerabilities in this code, and that we can mitigate the risks for exchanges and for investors that someone has done a thorough code analysis of this. That there's no chance that this is going to be hacked, that money won't be stolen, money won't be lost, and that there's no chance of a security vulnerability on this. And we put our company's name and reputation on this. >> And what was the problem that is the alternative to that? Was there just poorly written code? Was it updated code? Was it gas was too expensive? They were doing off-chain transactions. I mean what are some of the dynamics that lead you guys down this path? I mean this makes sense. You're kind of underwriting the code, or you're ensuring it or I don't know what you call it, but essentially verifying it. What was the problem? And what were some of the use cases of problems? >> I would say that the underlying problem today in this whole industry, of the blockchain space, is that the most commonly found blockchain is Ethereum. The language behind Ethereum is called Solidity. Solidity is a brand new software language that very few people in the world are sufficient programmers in Solidity. On top of that, Solidity is updated, as a language on a weekly basis. So there are a very limited number of engineers in the world who are full-stack engineers, that have studied and understand Solidity, that have a security background, and have a QA mindset. Everything that I just said does exist on this Earth today and if it does, there's a chance that that person has made too much money to want to get out of bed. Because Ethereum's price has gone up. So the quality of smart contracts that we're seeing being written by even development shops, the developers building them are actually not full-stack engineers, they're web developers who have learned the language Solidity and so thus we believe that the quality of the code has been significantly low. We're finding lots of critical vulnerabilities. In fact, 100% of the time that Hosho has audited code for a smart contract, we have found at least a couple of vulnerabilities. Even as a second or the third auditor after other companies conduct an audit, we always find a vulnerability. >> And is it correct that Solidity is much more easy to work with than say, Bitcoin scripting language, so you can do a lot more with it, so you're getting a lot more, I don't want to say rogue code, but maybe that's what it is. Is that right? Is that the nature of the theory? >> Compared to Bitcoin script, yes. But compared to JavaScript, no. Because Fortune 500 companies have rooms full of Java engineers, Java developers. And now the newer blockchains are being written, are being written on in block JavaScript, right? So you have IBM's Hyperledger program, you have EOS, you have ICX, Cardano, Stellar, Waves, Neo, there's so many new projects that are coming, that all of them are flexing about the same thing. Including Rootstock, RSK. RSK is a project where they're allowing smart contracts to be tied to the Bitcoin blockchain for the first time ever. Right, so Fortune 500 companies may take advantage of the fact that they have Java developers to take advantage of already, that already work for them, who could easily write to a new blockchain, and possibly these new blockchains are more enterprise grade and able to take more institutional capital. But only time will tell. And us as the auditor, we want to see more code from these newer blockchains, and we want to see more developers actually put in commits. Because it's what matters the most, is where are the developers putting in commits and right now maximum developers are on the Ethereum blockchain. >> Is that, the numbers I mean. Just take a step there. So the theory of blockchain. Percentage of developers vis-a-vis other platforms percentages-- >> By far the most is on developed on Ethereum. >> And in terms of code, obviously the efficiencies that are not yet realized, 'cause there's not enough cycles of coding going on, it's evolution, right? >> Yes. >> Seems to be the problem, wouldn't you say? So a combination of full-stack developer requirements, >> Yes. >> To people who aren't proficient in all levels of the stack. >> Yes. >> Just are inefficient in the coding. It's not a ding on the developers, it's just they're writing code and they miss something, right? Or maybe they're not sufficient in the language-- >> It's a new language. The functions are being updated on a weekly basis, so sometimes you copied and pasted a part of another contract, that came from a very sophisticated project, so they'll say to us, well we copied and pasted this portion from EOS, so it should be great. But what that's leading to is either A, they're using a function that's now outdated, or B, by copying and pasting someone else's code from their smart contract, this smart contract is no longer doing what you intended it to do. >> So now Hartej, how much of your capability is human versus machine? >> Yeah I was going to ask that. >> ML, AI type stuff? >> So we're increasingly becoming automated, but because of the over, there's so much demand in the space. And we've had so much demand to consistently conduct audits, it's tough to pull my engineers away from conducting an audit to work on the tooling to automate the audit, right? And so we are building a lot of proprietary tooling to speed up the process, to automate conducting a GAS analysis, where we make sure you're not clogging up the blockchain by using too much GAS. Static analysis, we're trying to automate that as fast as possible. But what's a bit more difficult to automate, at least right now, is when we have a qualified full-stack engineer read the white paper or the source of truth and make sure the smart contract actually does it, that is, it's a bit longer tail where you're leveraging machine learning and AI to make that fully automated. (talking over each other) >> But maybe is that, I'm sorry John. Is that the long term model or do you think you can actually, I mean there's people that say augmented intelligence is going to be a combination of humans and machines, what do you think? >> I think it's going to be a combination for a long time. Every single day that we audit code, our process gets faster and faster and faster because once we find a vulnerability, finding that same vulnerability next time will be faster and easier and faster and easier. And so as time goes on, we see it as, since the bundle of our work today is ICOs, token generation events, there are ERC 20 tokens on the Ethereum blockchain. And we don't know how long this party will last. Like maybe in a couple years or a couple months, we have a big twist in the ICO space that the numbers will drastically go down. The long tail of Hosho's business for us, is to keep track of people writing smart contracts, period. But we think they are going to become more functional smart contracts where the entire business is on a smart contract and they've cut out sophisticated middle men. Right and it may be less ICOs, and in those cases I mean, if you're a publicly traded company, and you're going from R&D phase where you wrote a smart contract and now actually going to deploy it, I think the publicly traded company's going to do three to five audits. They're going to do multiple audits and take security as a very major concern. And in the space today, security is not being discussed nearly as much as it should. We have the best hedge funds cutting checks into companies, before the smart contract is even written, let alone audited. And so we're trying to partner with all the biggest hedge funds and tell the hedge funds to mandate that if you cut a check into a company that is going to do a token generation event, that they need to guarantee that they're going to at least value security, both in-house for the company and for the smart contract that's going to be written. >> How much do you charge for this? I mean just ballpark. Is it a range of purchase price, sales price? What's the average engagement go for, is it on a scope of work? Statement of work? Or is it license? I mean how does it work? >> So first it depends is it a penetration test of the website or the exchange? Penetration testing of exchanges are far more complex than just a website. Or if it's a smart contract audit, is it an ICO or is it a functional smart contract? In either case for the smart contract audit, we have to build a long set of custom tooling to attack each and every smart contract. So it's definitely very case-by-case. But a ballpark that we could maybe give is somewhere around the lines of 10 to 15 thousand dollars per 100 lines of functional code. And we ask for about three weeks of lead time for both a smart contract audit and a penetration test. And surprisingly in this space, some of the highest caliber companies and high caliber projects with the best teams, are coming to us far too late to get a security audit and a penetration test. So after months of fundraising and a private pre-sale and another pre-sale, and going and throwing parties and events and conferences to increase the excitement for participating in their token sale, what we think is the most important part, the security audit for a smart contract is left to the last week before your ICO. And a ridiculous number of companies are coming to us within seven days of the token sale, >> John: Scrambling. >> Scrambling, and we're saying but we've seen you at seven conferences, I think that we need to delay your ICO by two or three weeks. We can assure you that all of your investors will say thank you for valuing security, because this is irreversible. Once this goes live and the smart contract is deployed. >> Horse is out of the barn. >> It's irreversible. >> Right right. >> And once we seal the code, no one should touch it. >> It's always the case with security, it's bolted on at the last minute. >> It's like back road recovery too, oh we'll just back it up. It's an architectural decision we should have made that months ago. So question for you, the smart contract, because again I'm just getting my wires crossed, 'cause there's levels of smart contracts. So if we, hypothetical ICO or we're doing smart contracts for our audience that's going to come out soon. But see that's more transactional. There's security token sales, >> Yes. >> That are essentially, can be ERC 20 tokens, and that's not huge numbers. It could be big, but not massive. Not a lot transaction costs. That's a contract, right? That's a smart contract? >> People are writing smart contracts to conduct a token generational event, most commonly for an ERC 20 token, that's correct. >> Okay so that's the big, I call that the big enchilada. That's the big-- >> Right now that is the most important, the most common. >> Okay so as you go in the future, I can envision a day where in our community, people going to be doing smart contracts peer-to-peer. >> Sure. >> How does that work? Is that a boiler plate? Is is audited, then it's going to be audited every time? Do the smart contracts get smaller? I mean what's your vision on that? Because we are envisioning a day where people in our audience will say hey Hartej, let's do a white paper together, let's write it together, have a handshake, do a smart contract click, click. Lock it in. And charge a dollar a download, get a million downloads, we split it. >> I envision a day where you can have a more drag and drop smart contract and not need a technical developer to be a full-stack engineer to have to write your smart contract. Yes I totally envision that day. >> John: But that's not today. >> We are very far from that today. >> Dave, kill that project. >> We're so far, we're very far from that. We're light years far from that. >> Okay well look. If we can't eliminate the full-stack engineers, I'm okay with that. Can we eliminate the lawyers? At least minimize them. >> We can minimize them possibly, but we have five stacks of lawyers for our company, I don't see them going anywhere. We need lawyers all the time. >> I see that in the press sometimes, yeah it's going to get disrupted. I don't see it happening. Okay we were having a great conversation off-camera about what makes a good ICO. You see, you have a huge observation space. And you were very opinionated. A lot of companies are out there just floating a token because they're trying to raise money. And they could do the same thing with Ethereum or Bitcoin. >> That's correct. >> Your thoughts? >> My thoughts are that it's very important for companies who are sophisticated, I think, to start by giving away a little bit of equity in the business. And that if you want to be in the blockchain space, and you really firmly believe you have a model to have a token within a decentralized application, I would still start by finding quality investors in the space, in the world. They might be still in Silicon Valley. Silicon Valley didn't just disappear overnight now that the blockchain is out. I am all for the fact that Silicon Valley no longer has as much of a grip on tech because of their blockchain world. And they're not seeing as much deal flow, and there's not as much reliance on venture capitalists, that's exciting to me. But let's not forget the value, that top-tier VCs like Andreessen Horowitz and Vinod Khosla. and Fintech VCs like Commerce Ventures and Nyca Partners in New York, Propel VC, these are good Fintech VC arms that continue to time and time again add immense value to companies. >> And they have networks. They add value. >> They have strong-valued networks, but they're just not going to disappear. And those VCs, if they've invested into a company, took a board seat, fostered their growth, taught them what it means to actually be a real business that's growing at 7-15% week over week, maybe two years down the line, after they've given away a board seat to someone like Nyca Partners, I would be interested in understanding what your token economics look like. Now that you have a revenue generating business, how you've placed a token model into this already running business that makes 25 to 50 grand a month and you have a team of 10, self-sustaining themselves off of revenue. Much more intriguing of a conversation. What's happening today in the space is, hey my buddy Jim and Steve and I came up with an idea for this business. There's going to be a token, and we're starting a private pre-sale tomorrow. I'm going to give you 300% bonus and will you be my advisor? And they're going to start raising capital because of an idea. You know what we used to say in the Silicon Valley startup world, you can raise on just a PowerPoint. I think in the blockchain world, you could raise on just an idea? And then maybe a white paper? And the white paper is one page? And so you've raised a bunch of capital, you have a white paper. >> Now you got to build it. >> Now you got to build, you got to write a smart contract, you got to build it, you got to do it, and then everyone loses excitement and it goes back to our previous conversation the development talent. So, another thing not being discussed in the space is company employee retention, right? So if you have a growing number of ICOs, that have very large budgets because investors have found a way to sink millions of dollars into a company early, you've got $5 million in the hands of a company to start, well this company can afford to pay someone a very ridiculous salary to come join them to write the smart contract now. So they could offer an engineer 500 Eth a month to come join them for three months. So you have good engineers just bouncing from one ICO to the next and as soon as the ICO goes live, they quit. This is a problem to companies who are-- >> It's migration, out migration. >> How do you retain, even capital? >> Companies like Hosho, ShapeShift, companies that are selling picks and shovels of the industry, that want to be household names in the space, we have to really think about how we're going to retain our employees in the space. >> So the recruitment and bringing on the new generation, we were also talking off camera about Bill Tye and the younger generation and kind of riffing on the notion that, because there is a new set of mission-driven developers and builders, on the business side as well. Your thoughts and reaction to what you see and what you see that's good and what you see that we need more of? >> So the most powerful thing in the blockchain space that I think is so exciting is that you have a lot of people between the age of 25 and 35 that don't come from money, that didn't go to Stanford, didn't go to Y Combinator, they're probably not white, from-- >> John: Ivy League schools. >> Ivy League schools. I'm not trying to make it about race, but if you're a white male and went to Stanford and went to Y Combinator, chances of you raising VC money on sand hill are a lot higher, right? And you have a guy looking like me who didn't go to Stanford, doesn't come from money, running up and down sand hill, I have personally faced that battle and it wasn't easy. And we were based in Vegas and so being based in Vegas, I'd also have to deal with so why do you live in Vegas? When are you going to move to Silicon Valley? And if we invest in you, you're going to open an office in sand hill right? And now in the blockchain world, what's exciting is you have so many heavy-hitters running as founders, some of the most successful companies in the space, who don't come from money and a big prestigious background, but they're honest, they're hard-working, they're putting in 12 to 15 hours of work every single day, seven days a week. And to space, six weeks is like six years. And we all have a level of trust that goes back to times when we were all running struggling startups. And so our bond is, to me, even more significant than what must have been between Keith Rabois and Peter Thiel in the PayPal Mafia. We have our own mafias being formed of much stronger bonds of younger people who will be able to share much more significant deal flow so if the PayPal Mafia was able to join forces to punch out companies like eBay and Square, wait 'til companies in this space, we have young, heavy-hitters right now who are non-reliant on some of the more traditional older folks. Wait 'til you see what happens in the next couple years. >> Hartej, great conversation. And I want to get one more question in. We've seen Keiretsu Forum, mafias, teams more than ever as community becomes an integral part of vetting and by the way trust, you have unwritten rules. I mean baseball, Dave and I used to do sports analogies. >> Self-governance. >> Reggie Jackson talked about unwritten rules and it works. If you beam the batter, the other guy, your best star, your side's going to get beamed. That's an unwritten rule. These are what keeps things going, balanced through the course of a season. What are the unwritten rules in the Ethos right now? >> Honesty, transparency, and that's the key. We need self-governance. This is a very unregulated market. There's rules being broken by people who are ignorant to the rules. The most common rule I've seen being broken is by people who are not broker dealers, running around fundraising capital, they don't even know what an institutional advisor license is. They don't know what a Series 7 and a Series 63 is. I asked a guy just last night, he said I'm pooling capital, I'm syndicating, let me know if you want in on the deal. And I said when did you take your Series 7? He goes what's that? Get away from me. You're an American, you need to look up what US securities laws are and make sure that you're playing by the rules and if someone who doesn't know the rules has entered our inner circle of investors, of advisors, of people sharing deal flow, we have a good network of people that are closing the loop for companies, whether it's lawyers, investors, exchanges, security auditors, people who write smart contracts, dev shops, people who write white papers, PR marketing, people who do the road show, there's a full circle-- >> So people are actually doing work to put into the community, to know your neighbor if you will, know the deals that are going down, to identify potential trip wires that are being established by either bad actors or-- >> KYC, AML, this is a new space that's also attracting people that have a criminal background. Right? And that's just a harsh reality of the space. That in the United States if you have a felony on your record, maybe getting a job has become really difficult and you figured let's do an ICO, no one's going to check my record. That is a reality of the space. Another reality is the money that was invested into this entire ICO clean. Right, that's a massive issue for the US government right now. It's been less than 15 hours since the SEC has issued actually subpoenas to people on this exact topic, today. >> This is a great topic, we'd like to do more on. >> Dozens of them. >> We'd like to continue to keep in touch with you on The Cube. Obviously you're welcome anytime, loved your insight. Certainly we'd love to have you be an advisor on our mission, you're welcome anytime. >> For sure, let's talk about it. Come out to Las Vegas. Hosho's always happy to host you. >> John And Dave: We're there all the time. >> The Cube lives at the sands. >> It's our second home. >> Come by Hosho's office and let us know. Vegas is our home. We are hosting a conference in Vegas after DEFCON. So DEFCON is the biggest security conference in the world. You have the best black hats and white hats show up as security experts in Vegas and right on the tail end of it, Hosho's going to host a very exclusive invite-only conference. >> What's it called? Just Hosho Conference? >> Just Blockchain. It'll be called the just, it'll be by the Just Blockchain Group and Hosho's the main backer behind it. >> Well we appreciate your integrity and your sharing here on The Cube, and again you're paying it forward in the community, that's great. Ethos we love that. That's our mission here, paying it forward content. Here in the Bahamas. Live coverage here at PolyCon 18. We're talking about securitized token, a decentralized future for awesome things happening. I'm Jeff Furrier, Dave Vellante. We'll be back with more after this short break. (upbeat music)