( bright upbeat music) >> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube conversation. >> Hey, welcome back. You're ready, Jeff Frick here with theCUBE, we are still getting through COVID. It's a hot August day here in San Francisco Bay Area. It is 99, somebody said in the city that's hot, but we're still getting through it. We're still reaching out to the community, we're still talking to leaders in all the areas that we cover. And one of the really interesting areas is natural language processing. And it's a small kind of subset. We'll get into it a little bit more detail, we are very specific place within the applied AI world. And one of my very good friends and Cube alumni, who's really an expert in the space, he's coming back for his second startup in the space. And we're joined by Ben, he's Ben Chung, the Co-founder of Ogmagod. Did I get that right Ben, Ogmagod. >> That's correct. That's right. >> Great to see you again. >> Thank you for inviting to the show. >> Well, I love it. One of the topics that we've been covering a lot Ben is applied AI. 'Cause there's just so much kind of conversation about artificial intelligence, the machine learning is kind of this global big thing. And it kind of reminds me of kind of big data or cloud, in the generic it's interesting but it's really not that interesting, 'cause that's really not where it gets applied. Where I think what's much more interesting and why I wanted to have you back on is, where is it actually being applied in applications? And where are we seeing it in solutions? And where is it actually changing people's lives, changing people's days, changing people's behavior, and you seem to have a propensity for this stuff. It was five years ago, I looked July five years ago, we had you on and you had found Genie, which was a natural anti processing company focused on scheduling. Successful exit, sold that to Microsoft and they baked it into who knows, there probably baked in all over the place. Left there now you've done it again. So before we get into it. What so intriguing to you about natural language processing for all the different kind of opportunities that you might go after from an AI perspective? What's special about this realm that keeps drawing you back? >> Yeah, sure, I mean it, to be honest it was not anything premeditated, I kind of stumbled on it. I before this, I was more like an infrastructure guy spent a number of years at VMware and had a blast there and learned a lot. Then I kind of just stumble on it. Because when we started doing the startup, we didn't intend it to be a AI startup or anything like that. We just had a problem that my co-founder Charles Lee and I really wanted to solve, which is to help people, solve people's scheduling problem. But very shortly after getting into and start looking at some use cases, we thought that the easiest way is to communicate with people like humans do to help them do the scheduling. And that's kind of how I stumbled on it. And it wasn't until that I stumbled on it that I realized that it has a lot of attraction to me, because I throughout my whole life, I'm always very interested in the human emotions of it, how humans relate to each other. And that's always been the hidden side project thing, I do traveling to figure out stuff and get a little bit of that. But once I start getting into this field, I realized that there's a lot about it, about humanity and how humans communicate that it was kind of like a hidden interest for me. That now suddenly coming out and it kind of just got me hooked. >> Right, that's awesome. So one of the things and we'll just get into it is people are a little bit familiar with natural language processing, probably from Siri and from Google and from Alexa and increasingly some of these tools but I think, you kind of rapidly find out beyond what's the weather and play a song and tell me a joke that the functionality is relatively limited. So when people think about natural language and they have that as a reference point, how do you help them see that it's a lot more than, asking Siri for the weather. >> Yeah, there are a lot of capability but also hopefully not offensive to some of the tech visionaries. Just as a guy who is dealing with it every day, there are also lots of limitation is not nearly to the degree of refinements. Like what might being preach out there saying that the machines are going to take over everything in one day, we have a lot of struggles that are very basic stuff with machines. However, there has been definitely a lot of breakthroughs in the last few years and that's why I'm dedicating my life and my time into this area because I think that it just, there's going to be huge amount of innovation continuously going in this area. So that's at the high level, but if you talk about, in terms of artificial intelligence and in general, I think, I have my own understanding, I'm more like an apply guy, lot of academics so what I'm going to say might make some academics cringe because I'm more like a everyday practical guy and try to re conciliate these concepts myself. The way that I view is that artificial intelligence has really tried to help mimic some human capabilities that originally thought that is the domain of human, only humans are able to do it, but machines now try to demonstrate that machine can do it, like as though the humans could. So and then usually people get that mixed up with machine learning, to me is actually quite different thing. Artificial intelligence just like what I mentioned, machine learning is just a technique or a science or way of applying like to leverage this capability, machine learning capability in solving these artificial intelligent problems, to make it more achievable to raise the bar on it. So I don't think we should use them interchangeably, artificial intelligence and machine learning. Because today machine learning is the big deal that are making the progress wise, tomorrow might be something else to help improve artificial intelligence. And in the past, it was something else before machine learning. So it's a progression, the machine learning is the very powerful and popular technique right now to being used. Now within artificial intelligence, I think you mentioned that there are various different domains and topics, there is like object recognition deals with image processing, there's speech detection, there's a video and what I would call action or situation detection. And then there's natural language processing, which is the domain that I'm in that is really in that stage of where we seeing quite a bit of break through, but it's not quite there yet. Whereas versus speech detection and image processing actually has done a tremendous progress in the past. So and in you can say that like the innovation there is not as obvious or as leap frogging as the natural language processing. >> Right, so some of the other examples that we know about that are shared often for machine learning or say, the visual thing, can you identify a chihuahua from the blueberry muffin, which sounds kind of funny until you see the pictures, they actually look very, very similar. And the noise stated that Google and their Google Photos, right, has so many pictures such a huge and diverse data set in which to train the machines to identify a chihuahua versus a blueberry muffin. Or you take the case in Tesla, if you've watched any of their autonomous vehicle stuff and their computer vision process and they have the fleet, hundreds of thousands of cars that are recording across many, many cameras reporting back every night. With natural language processing you don't have that kind of a data set. So when you think about training the machine to the way that I speak, which is different than the way you speak and the little nuances, even if we're trying to say the same thing, I would imagine that the variety in the data set is so much higher and the quantity of the data set is so much lower that's got to be a kind of special machine learning challenge. >> Yes, it is. I think the people say that there is, we are at the cusp of, being able to understand language in general, I don't believe that we are very far away from that. And even if when you narrow scope to say, like focus on one single language like English, even within that, we still very far from it. So I think the reality, at least for me, speaking from the ground level, kind of person tried to make use of these capabilities is that you really have to narrow it to a very narrow domain to focus on and bound it. And my previous startup is really that our assistant to help you schedule meetings, that assistant doesn't understand anything else other than scheduling, we were only able to train it to really focus on doing scheduling, if you try to ask it about joke or ask anything else, it wouldn't be able to understand that. So, I think the reality on the ground at least from what I see of a practical application and being successful at it, you really need to like have a very narrow domain in which you apply these capabilities. And then in terms of technology being used broadly in natural language processing in my view there are two parts of it, one is the input, which is sometimes call natural language understanding. And then that part is actually very good progress. And then the other part is the natural language generation, meaning that the machine knows how to compose sentences and generate back to you, that is still very, very early days. So there is that break up and then if you go further, I don't want to bore you Jeff here with all these different nuances, but when you look at natural language understanding, there are a lot of areas like what we call topic extraction or entity extraction, event extraction. So that's to extract the right things and understand those things from the sentences, there is sentimental analysis knowing that where some a sentence expresses somebody angry or some different kinds of emotions, there is summarization, meaning that I can take sets of texts or paragraphs of text and summarize with fewer words for you. So and then there is like dialogue management, which manages the dialogue with the person. So they're like these various different fields within it. So the deeper you look, there's like the more stuff within it and there's more challenges. So it's not like a blanket statement, say like, "Hey, we could conquer on this." And if you digging deep there's some good progress in certain this area. But some areas like it's really just getting started. >> Right, well we talked about in getting ready for this call and kind of reviewing some of the high level concepts of and you brought up, what is the vocab? So first you have to just learn what is the vocabulary, which a lot of people probably think it stops there. But really then what is the meaning of the vocabulary, but even more important is the intent, right, which is all driven by context. And so the complexity, beyond vocabulary is super high and extremely nuanced. So how do you start to approach algorithmically, to start to call out these things like intent or I mean, people talk about sentiment all the time, that's kind of an old marketing thing, but when you're talking about specific details, to drive a conversation, and you're also oh, by the way, converting back and forth between voice and text to run the algorithms in a text based system, I assume inside the computer, not a voice system. How do you start to identify and programmatically define intent and context? >> Yeah, just to share a little anecdote, like one of the most interesting part of, since I started this journey six years ago and also interesting was a very frustrating part is that, especially when I was doing the scheduling system, is that how sloppy people are with their communication and how little that they say they communicate to you and expect you to understand. And when we were doing the scheduling assistant, we're constantly challenged by somebody telling us certain things and we look at it's like, well, what do they mean exactly? For example, like one of the simple thing that we used to talk a lot with new people coming on the team about is that when people say they want to schedule next week, they don't necessarily mean next week, what they mean is not this week. So it doesn't, if you like take it literally and you say, "Oh, sorry, Jeff, there is no time available next week." And actually Jeff probably not even remember that he told you to schedule next week, to what he remember, what he told you not to schedule it this week. So when you come back to them and say, "Jeff, you have nothing available this week or next week." And Jeff might say like, while your assistant is kind of dumb, like, why are you asking me this question? If there was nothing available next week, just scheduled the week after next week. But the problem is that you literally said next week, so if we took you literally, we would cause unhappiness for you. But we kind of have to guess like what exactly you mean. So don't like this a good example where they're like lot of sloppiness and lot of contextual things that we have to take into account when we communicate what humans, or when we try to understand what they say. So yeah, is exactly your point is not like mathematics is not simple logic. There are a lot of things to it. So the way that I look at it, there are really two parts of it. There's the science part and then there's art part to it. The science part is like what people normally talk about and I mentioned earlier, you have to narrow your domain to a very narrow domain. Because you cannot, you don't have the luxury of collecting infinite data set like Google does. You as a startup, or any team within a corporation, you cannot expect to have that kind of data set that Google or Microsoft or Facebook has. So without the data set, huge data set, so you want to deliver something with a smaller data set. So you have to narrow your domain. So that's one of the science part. The other part is I think people talk about all the time to be very disciplined about data collection and creating training data sets so that you have a very clean and good training data set. So these two are very important on the science part and that's expected. But I think a lot of people don't realize this, what I would call the art part of it, is really there are two parts to that. One is exactly like what you said Jeff is to narrow your domain or make some assumption within the domain, so that you can make some guesses about the context because the user is not giving it to you verbally or giving you to you into text. A lot of us we find out visually by looking at the person as we communicate with them. Or even harder we have some kind of empathetic understanding or situational understanding, meaning that there is some knowledge that we know that Jeff is in this situation, therefore, I understand what he's saying right now means this or that Jeff is a tech guy like me, therefore, he's saying certain thing, I have the empathetic understanding that he meant this as a tech guy. So that's a really hard kind of part of it to capture or make some good guesses about the context. So that's one part. The other part is that you can only guess so much. So you have to really design the user experience, you have to be very careful how you design the user experience to try what you don't know. So that it's not frustrating to the user or to put guardrails in place such that the user doesn't go out of balm and start going to the place where you are not trained for that you don't have to understand it. >> Right, because it's so interesting, 'cause we talked about that before that so much of communication, it's not hard to know that communication is really hard, emails are horrible. We have a hard time as humans, unless we're looking at each other and pick up all these nonverbal cues that add additional context and am I being heard, am I being understood? Does this person seem to understand what I'm trying to say? Is it not getting in? I mean, there's so many these kind of nonverbal cues as you've expressed, that really support the communication of ideas beyond simply the words in which we speak. So and then the other thing you got to worry about too, as you said, ultimately, it's user experience if the user experience sucks, for instance, if you're just super slow, 'cause you're not ready to make some guesses on context and it just takes for a long time, people are not going to to use the thing. So I'm curious on the presentation of the results, right? Lots of different ways that that can happen. Lots of different ways to screw it up. But how do you do it in such a way that it's actually adding value to some specific task or job and maybe this is a good segue to talk about what you're doing now at Ogmagod, I'm sorry I have to look again. I haven't memorized that yet. 'Cause what you're also doing if I recall is you're taking out an additional group of data and additional datasets in beyond simply this conversational flow. But ultimately, you've got to suck it in, as you said, you've got to do the analysis on it. But at the end of the day, it's really about effective presentation of that data in a way that people can do something with it. So tell us a little bit about what you're doing now beyond scheduling in the old days. >> Sure, yeah, I left Microsoft late last year and started a new startup. It's called Ogmagod. And what we do is to help salespeople to be more effective, understand the customer better so that they have higher probability of winning the deal or to be able to shorten the sales cycle. And oftentimes, a lot of the sales cycle got LinkedIn is because of the lack of understanding and there's also, I say, we focus on B2B sales. So for B2B salespeople, the world's really changed a lot since the internet came about. In the old days is really about, tell it to explain what your product is and so that your customer understand your product, but the new days is really about not explaining your product because the customer can find out everything about your product by looking at your website or maybe your marketing people did do such a good job, they already communicated to the customer exactly what your product does. But really to win out against other people you really like almost like a consultant to go to your customer and say, like, I have done your job, almost like I've done your job before I know about your company. And let me try to help you to fix this problem. And our product fit in as part of that, but our focus is let's fix this problem. So how would you be able to talk like that, like you've done this job before? Like you worked at this company before? How do you get at the level of information that you can present yourself that way to the customer and differentiate yourself against all the other people who try to get their attention, all the people sending them email every day automatically, how do you differentiate that? So we felt that the way that you do it is really have the depth of understanding where your customer that is unrivaled by anybody else. Now sure, you can do that, you can Google your customer all day, reorder news report, know all the leadership, could follow them on social media-- >> Right, they're supposed to be doing all this stuff, right Ben, they're supposed to be doing all this stuff and with Google and the internet there's no excuse anymore. It's like, how did you not do your homework? You just have to get the Yellow Pages. >> Yeah, why didn't you do your work? Yes, people get beat up by their management saying like, "Oh, how come you miss this? "It's right there go on Google." But the truth is that you have to be empathetic to a salesperson. A lot of people don't realize that for a salesperson, every salesperson, you might own 300 accounts in your territory. And a lot of times in terms of companies, there might be thousands of companies in your territory. Are you going to spend seven hours, follow all these 300 companies and read all tweet. Check out the thousands of employees in each of these company, their LinkedIn profiles, look at their job listings, look at all the news articles. It's impossible to do as a human, as a person. If you do that you'll be sitting in your computer all day and you never even get to the door to have a conversation with the customer. So that is the challenge so I felt like salespeople really put up impossible tasks, because all this information out there, you're expected it to know. And if you screwed up because you didn't check, then it's your fault. But then on the same time, how can they check all 300 accounts and be on top of everything? So, what we thought is that like, "Hey, we made a lot of progress "on natural language processing "and natural language understanding." And salespeople what they look for is a quite narrow domain. They are looking for some very specific thing related to what they selling, and very specific projects, pinpoints budget related to what they're selling. So it's a very narrow domain, we felt like it's not super narrow. It's a little bit broader than I would say scheduling. But it's still very narrow the kind of things that they're looking for. They're looking for those buying triggers. They're looking for problem statements within the customers that relate to what they selling. So we think that we can use, develop a bunch of machine learning models and use what's available in terms of the web. What's out there on the web, the type of information out there. And to be able to say, like, salesperson, you don't need to go and keep up and scan, all the tweets and all the news and everything else for these 300 companies that you cover, we'll scan all of them, we will put them into our machine learning pipeline and filter out all the junk, because there are lots of junk out there, like Nike, that's like, I don't know, hundreds of news release probably per week. And most of them are not relevant to you. It doesn't make sense for you to read all of those. So but how about we read all of them and we extract out, we it's difficult topic extraction, we extract out the topic that you're looking for and then we organize it and present to you. Not just we extracting out the topic. Once we get the topic how about we look up all the people that are related to that topic in the company for you so that you can call on them. So you know what you want to talk to them about, which is this topic or this pin point. And you know who to talk to, these are the people. So that's what we do. That's that's really interesting. It's been a tagline around here for a long time, right separating the signal from the noise. And I think what you have identified, right is, as you said, now we live in the age where all the information is out there. In fact, there's too much information. So you should be able to find what you're looking for. But to your point, there's too much. So how do you find the filter? How do you find the trusted kind of conduit for information so that you're not just simply overwhelmed that what you're talking about, if I hear you right, is you're actually querying publicly available data for particular types of I imagine phrases, keywords, sentences, digital transformation initiative, blah, blah, blah. And then basically then coalescing the ecosystem around that particular data point. And then how do you then present that back to the salesperson who's trying to figure out what he's going to work on today. >> B2B salespeople, they start with an opportunity. So opportunity is actually a very concrete word at least in the tech B2B sales-- >> We know, we see the 60 stories in downtown San Francisco will validate statement. (laughs) >> Yes, so yeah, so it starts with the word opportunity. So the output is a set of potential opportunity. So it speaks to the salespersons language and say, when you use us, we don't just say "Hey, Jeff, there's this news article about Twilio and you cover Twilio, that's interesting to you." "Oh, there's a guy at Twilio that matches the kind of persona that you sell into." We don't start with that, we start with, "Jeff, there are six Opportunities for you at Twilio. "Let's explain what those things are." And then explain the people behind these opportunities so that you can start qualify them. So get you started, right way in your vocabulary in a package that you understand. So that I think that's what differentiates us. >> Right, and at some point in time, would you potentially just thinking logically down the road, you have some type of Salesforce API. So it just pumps into whatever their existing system is. That they're working every day. And then it describes based on the algorithm, why the system identified this opportunity, what the attributes are that flagged it, who are the right people, et cetera. Awesome, so what kind of data are you requiring-- >> Yes, you are designing our product wise. >> (laughs) Since Dave and John, watch this. They're going to want to talk to you, I'm sure. But what type of data sets are you querying? >> There are lots of them. We learned most of it by through the process working for salespeople, meaning that we work for salespeople, we may be quote, unquote, stand behind their back and see what they're searching. They're searching LinkedIn. They're searching jobs. They're searching endless court transcripts, they're looking at 10K 10Q's, they dig up various, some people are very, very creative, digging out various parts of the web and find really good information. The challenge is that they can't do this to scale. They can't do it for 300 accounts, 'cause we're doing for one accounts very is laborious. So there are various different places that we can find information. And in terms of the pattern that we're looking for. It's not just keyword, it's really concepts. We call it a topic. We really looking for very specific topics that the salesperson looks for. And that's not just a word, because sometimes words is very misleading. For example, I tell you one of the common words in tech is called Jenkins. Jenkins is a very popular technologies, continuous delivery technologies step but Jenkins is also happens to be a very common last name for people. >> (laughs) Well, I'm always reminded of our Intel days with all the acronyms, but my favorite is ASP 'Cause you could use ASP twice in the same sentence and mean two different things, right? Average selling price or application service provider back in the days before we call them clouds, but yeah, so the nuances is so tricky. So within kind of what you're doing then and as you described working within defined data sets and keeping the UX and user experience pretty dialed in and within the rails, are there particular types of opportunities in terms of B2B types of opportunities that fit better that have kind of a richer data set, a higher efficacy in the returns what do you kind of seeing in terms of great opportunities for you guys. >> We're still early, so I can't tell you that like from a global view because we are like less than one year old experience, quite honestly. But so far we are being led by the customer. So meaning that there is an interesting customer, they ask us to look for certain topics or certain things. And we always find it to my surprise, because and that really is, like, I'm constantly surprised by how much is there out in the web, like what you were saying, like customer ask us to look for something. And I thought for sure, this thing we couldn't do it, we can find it. And we gave it a try and low and behold, there it is. It's out there. So, to be honest, I can't tell you at this point, because I have not run into any limits. But that is because we are still a very young startup. And we are not like Google. We're not trying to be all encompassing looking for everything and looking over everything. We're just looking over everything that a salesperson wants, that's it. >> So I'm going to make you jump up a couple levels. Since you've been thinking about this and working on this for a long time, there's a lot of conversation about machines taking everybody's jobs, then there's the whole kind of sidetrack launch to that, which is no, it's all about helping people do better jobs and helping people do more higher value work and less drudgery. I mean, that sounds so consistent with what you're talking about, I wonder as somebody down in the weeds of artificial intelligence, if you can kind of tell us your vision of how this is going to unfold over the next several years, is it just going to be many, many, many little applications that slowly before we know it are going to have moved, along many fronts very far, or do you still see it's such a fundamental human thing in terms of the communication that the these machines will get better at learning, but ultimately, they can kind of fulfill this promise of taking care of the drudgery and freeing up the people to make what are actually much harder decisions from a computer's point of view than maybe the things that we think about that a three year old could ascertain with very little extra effort. >> Yeah, if you take a look at what we do and hopefully it didn't sound like we're underselling our startup but a lot of it really is we taking away to time consumer and also grunt work process of the data collection and cleaning up the data. The humans, the real human intelligence should be focused on data analysis to be able to derive lots of insights of the data. So and to be able to formulate a strategy, how to win the account, how to win the deal. That's what's the human intelligence should be focused on. The other part by struggling with doing the Google search and in return 300 entries, in 30 different pages and you have to click through each one and then give up the first week, that kind of data collection data hunting work, we are really, it should not, I don't think it's worthy, quite honestly, for a very educated person to deal with. And we can invest it back in helping the human to do what the humans are really good at is that, how do I talk to Jeff? And I'm going to get a deal out to Jeff, how can I help and through helping him solving his problem, how can I take the burden of solving the problem from Jeff's head and solve the problem for him? That's what human intelligence for me as a salesperson, I would prefer to do that instead of sitting in front of my desk and doing googling, so net net what I'm trying to say using ourselves as an example is that we're not taking over the job of a salesperson, there was no way that we can close a deal for you. But what we're doing is that we're empowering you so that you look like you're on top of 300 accounts and you talk to any of those accounts, you'll be able to talk to the people, your customer, their particular customer, like you know them inside out. And without you being the superhuman to be able to do all this stuff, but as far as that customer is concerned, sounds like you were on top of all this stuff all day and that's all you do, you have no other customers, they're the only customer. In fact, you on top of 300 customers. So that's kind of the value that we see, to provide to the human is to allow you to scale by removing these grunt work that are preventing you from scaling or living up to your potential how you wanted to present yourself, how you want to deliver yourself. There's no way that we can be smarter than human, no way. I just don't see it not in my lifetime. >> I just love, we've had a lot of conversations over the years and you talking about the difficulty in training the computers on some really nuanced kind of human things versus the things that they're very very good at and keeping the AI in the right guard wheel is probably just as important as keeping the user interface in the right lane as well to make sure that it's a mutually beneficial exchange and one doesn't go off and completely miss the benefit to the other. Well, Ben, it's a great story. Really exciting place to dedicate yourself and we are just digging watching the story and we're going to enjoy watching this one unfold. So thanks for taking a few minutes in sharing your insight on natural language processing and this applied machine learning techniques. >> Thank you, Jeff. It's always a pleasure. >> Yep, all right. He's Ben, am Jeff, you're watching theCUBE. Thanks for watching. We'll see you next time. (bright upbeat music)

>> live from Miami Beach, Florida It's the que covering demon 2019. Brought to you, by the way, >> Welcome back to Vima on 2019 in Miami. Everybody, we're here at the Fountain Blue Hotel. This is Day two of our coverage of the Cube, the leader in live Tech. And I'm David Dante with Peter Bors. Pippen. Jay Raj is here. He's the vice president and CEO of Make A Wish America. Just that awesome foundation nonprofit people. Thanks for coming on the Cube. >> Thank you for having me appreciate it. >> So make a wish. Children with wishes and have terminal illnesses. You guys make them come true. It's just a great organizations. Been around for a long time, I think, since the early eighties, right, >> 39 years and going >> years and hundreds of thousands of wishes made. So just how did you get Teo make a wish that all come about >> it? It wasn't interesting journey. I was consulting in I t for multiple big companies. And, you know, two years back, it was through a recruiting channel that I got an opportunity to start some conversations as the CIA and make a wish. Uh, the thing that got me in the opportunity was predominately about enterprises and just to give you a little bit off, make official operations. Make a Wish was Founded and Phoenix, Arizona. And but we also operate a 60 chapters across the United States that it is 60 chapters each of the chapter there 501 C three companies themselves with the CEO and abort. Essentially, it is 60 plus one. The national team kind of managing. All of the chapters are helping the chapters. National does not do any wish. Granting all the wish planning happens to the chapters. But National helps the chapters with the distribution of funding models brand. And thanks for That's a couple of years back in the national board talked about in our dream and mission, which is granting every eligible child the notion ofthe enterprise. You know, working as an enterprise came into four and it being a great piece off providing shared services and thanks for that. So I was brought on board and we took on I would call as the leader today said and dashes dream off. Bringing together all the 60 chapters and the city chapter's essentially are split across 120 locations. So Wade took on a project off. You know, combining our integrating all of their infrastructure needs into one place. And Phoenix without ada, sent a provider. You know, we worked with a partner. Phoenix. Now fantastic partners >> there. We had them on the other day. >> Yep, yep. Yeah, MacLaren. I mean, and the team, they did a great job. And, you know, when we had to move all of the data, everything from the 60 chapters applications everything into a centralized data center, locations that we managed right now from Make a Wish National office and provide a service back to the chapters That gives you a little bit off. You know, from behind the scenes. What happened? >> You provide the technical overview framework for all the 60 chapters. >> It almost sounds like a franchise model. >> It's what we call a Federated model back in the nonprofit. >> But but but but because make a wish is so driven by information. Yep. Both in the application as well as the programs to deliver thie brand promise. And the brand execution has got to be very, very closely tied to the quality of a shared services you provide >> exactly. Exactly. And like I said, the reason I talked about them being a separate companies themselves is you know, as I always say to my 60 CEOs, Ah, I should be able to provide the services because they wanted, because they have a choice to go outside and have their own partner. Another thing for that which they can. But they would want to work with the national team and get my, you know, work through our services rather than having have to because of the very it's A. It's a big difference when it comes to, but I've been lucky on privileged to you have these conversations with the CEO's. When I start talking to them about the need for centralization, the enterprise society assed much, there are questions when he start leading with the mission and the business notion of why we need to do that, it's It's fantastic. Everybody is in line with that. I mean, there's no question, then, as toe Hey, guys, uh, let me do all the Operation Manisha fight and leave it to me and I'll in a handler for you, and I let you guys go to what you do best. which is granting wishes. So then it becomes it doesn't become a question off, you know, should be a shouldn't way. And of course, to back that up. But I was talking to the dean, folks, It just solutions. Like VMware, Veeam. It makes it much simpler even from a cost prospect. You not for me to manage a bigger team s so that I can take those dollars and give it back to the business to grant another wish. So it's it's pretty exciting that >> way. So you set the standards. Okay, here's what you know, we recommend and then you're you're saying that adoption has been quite strong. Yeah, I remember Peter. Don't say easy. I used to run Kitty Sports in my local town in which is small town. And there was, you know, a lot of five or six or seven sports, and I was the sort of central organization I couldn't get six sports to agree that high man is 60 different CEO's. But that's okay. So not easy. But so how were you able to talk leadership or leading as we heard from Gino Speaker today? How were you able to get those guys, you know, aligned with your vision. >> Uh, it's it's been fantastic. I've had a lot ofthe good support from our executive came from a leadership team because leadership is always very important to these big initiatives are National board, which comprises off some of the that stuff best leaders in America and I have the fortune toe be mentored by Randy Sloan, who used to be the CEO of Southwest. And before that, you see a global CEO for, uh, you know, Popsicle. You know, he always told me, but but I mean CIA job. One thing is to no the technology, but completely another thing. Toe building relationships and lead with the business conversation. And so a typical conversation with the CEO about Hey, I need to take the data that you have all the I t things that you have and then me doing it. And then there are questions about what about my staff and the's conversations. Because you know, it's a nonprofit is a very noble, nice feeling, and you wouldn't want the conversations about, you know, being rift and things like that are being reduced producing the staff and thinks of that. But you know as he walked through that and show the benefits of why we doing it. They get it. And they've been able to repurpose many off the I. D functions back in tow, revenue generation model or ofhis granting in our team. And in many cases, I've been ableto absolve some off their folks from different places, which has worked out fine for me, too, because now I have kind of a power user model across the United States through which I can manage all these 120 locations. It's very interesting, >> you know, site Reliable and Engineering Dev Ops talks about thie error budget or which is this notion of doo. You're going tohave errors. You're going to have challenges. Do you want it in the infrastructure you wanted the functions actually generating value for the business? I don't know much about Make a wish. I presume, however, that the mission of helping really sick kids achieve make achieve a wish is both very rewarding, very stressful. He's gotta be in a very emotional undertaking, and I imagine it part of your message them has got to be let's have the stress or that emotional budget be dedicated to the kids and not to the technology >> completely agree. That's that. That's been one of my subjects, as you asked about How is it going about? It's about having the conversation within the context of what we talked about business and true business. Availability of data. You know, before this enterprise project data was probably not secure enough, which is a big undertaking that we're going down the path with cyber security. And you know, that is a big notion, misplaced notion out there that in a non profits are less vulnerable. Nobody. But that's completely untrue, because people have found out that nonprofits do not probably have the securing of walls and were much more weight being targeted nonprofits as a whole, targeted for cyber security crimes and so on and so forth. So some of these that I used to, you know, quote unquote help or help the business leaders understand it, And once they understand they get it, they ableto, you know, appreciate why we doing it and it becomes the conversation gets much more easier. Other What's >> the scope of the size of the chapters is that is a highly variable or there is. >> It is highly variable, and I should probably said, That's Thesixty chapters. We look at it as four categories, so the cat ones are what we call the Big Ice, the Metro New Yorkers and Francisco Bay Area. They're called Category one chapters anywhere between 4 1 60 to 70 staff. Grant's close to around 700 wishes you so as Make a Wish America, we ran close toe 15,600 wishes a year, and cat ones do kind of close to 700 15,600 400 to 700. And then you get into care to scare threes and cat for scat force are anywhere between, you know, given example Puerto Rico or Guam territory there. Cat Force New Mexico is a cat for three staff members Gammas operated by two staff members and 20 volunteers. They grant about 3 2 20 12 to 15 which is a year, so it's kind of highly variable. And then, you know, we talk about Hawaii chapter. It's a great example. They cat once predominate because of the fact that you know, they they do. There's not a lot ofthe wishes getting originated from how I but you know, Florida, California and how your three big chapters with a grand are a vicious ist with a lot of grant, you know, wish granting. So there's a lot off, you know, traffic through those chapters >> so so very distributed on diverse. What's the relationship between data and the granting of wishes? Talk about the role of data. >> Should I? I was say this that in a and I probably race a lot of fibrosis and my first introductory session a couple of years back when I John make a wish with the CEO's uh, when we had the CEO meeting and talk to them about I leaders the days off making decisions based on guts are gone. It has to be a data driven decision because that's where the world is leading to be. Take anything for that matter. So when we talk about that, it was very imperative going back to my project that the hall we had all of the data in one place or a semblance off one single place, as opposed to 60 different places to make decisions based on wish forecast, for example, how many wishes are we going to do? How many wishes are coming in? How's the demand? Was the supply matching up one of the things that we need to do. Budget purposes, going after revenue. And thanks for that. So data becomes very important for us. The other thing, we use data for the wish journeys. Essentially, that's a storytelling. You know, when I you know, it was my first foray into for profit Sorry, nonprofit. And me coming from a full profit is definitely a big culture shock. And one of the things they ask us, what are we selling? Its emotions and story. And that's our data. That is what you know. That's huge for us if we use it for branding and marketing purposes. So having a good semblance off data being ableto access it quickly and being available all the time is huge for us. >> Yeah, and you've got videos on the site, and that's another form of data. Obviously, as we as we know here, okay. And then, from a data protection standpoint, how do you approach that? Presume you're trying to standardize on V maybe is way >> are actually invested in veeam with them for a couple of years right now, as we did the consolidation of infrastructure pieces Veeam supporters with all of the backup and stories replication models. Uh, we're thinking, like Ratmir talked about act one wi be a part of the journey right now, and we're looking at active. What that brings to us. One of the things that you know, dream does for us is we have close to 60 terabytes of data in production and close to another 400 terabytes in the back of things. And, uh, it's interesting when they look about look at me equation, you think about disaster recovery back up. Why do you need it? What? The business use cases case in point. This classic case where we recently celebrated the 10th anniversary ofthe back wish bad kid in San Francisco, we have to go back and get all the archives you know, in a quick fashion, because they're always often requests from the media folks to access some of those. They don't necessarily come in a planned manner. We do a lot of things, a lot of planning around it, but still there are, you know, how How did that come about? What's the story behind? So you know, there are times we have to quickly go back. That's one second thing is having having to replicate our data immediately. Another classic case was in Puerto Rico. There was a natural disaster happened completely. Shut off. All the officers work down. We had to replicate everything what they had into a completely different place so that they could in a vpn, into an access that other chapters and our pulled in to help. They were close to 10 wish families close to 10 which families were stranded because of that. So, you know, gaining that data knowledge of where the family is because the minute of his journey starts. Everything is on us till the witch's journey ends. So we need to make sure everything is proper. Everything goes so data becomes very crucial from those pants >> you're tracking us. I mean, if you haven't been on the make a Wish site is some amazing stories. There I went on the other day. There's a story of ah, of 13 year old girl who's got a heart condition. Who wanted to be a ballerina. A kid with leukemia five years old wants to be a You want to be a chef. My two favorites, I'll share What? It was this kid Brandon a 15 year old with cystic fibrosis. I wanted to be a Navy seal. You guys made that happen. And then there was this child. Colby was 12 years old and a spinal muscular issue. You want to be a secret agent so very creative, you know, wishes that you ran >> way had another wish a couple of years last year in Georgia, where they wish kid wanted to go to Saturn. Yes, yes, it was huge. I mean, and you know the best part about us once we start creating those ideas, it's amazing how much public support we get. The community comes together to make them wish granting process. Great. Now. So I got involved in that. They gave the wish Kato training sessions to make sure that he is equipped when he goes into. And we had a bushel reality company create the entire scene. It was fabulous. So, you know, the way you talk about data and the technology is now some of the things I'm very excited about us usage off thes next Gen technology is like our winter reality to grant a wish. I mean, how cool would that be for granting a wish kid who is not able to get out of the bed. But having able to experience a the Hawaii is swimming. Are being in Disney World enough a couple of days? That's That's another use case that we talked about. That other one is to put the donors who pay the money in that moment off granting, you know, they are big major gift, uh, donors for make a wish. Sometimes we were not able to be part of a fish, but that would be pretty cool if you can bring the technology back to them and you know not going for them. You know pretty much everybody and make the ass through that rather than a PowerPoint or a storytelling, when the storytelling has to evolve to incorporate all of that so pretty excited >> and potentially make a participatory like, say, the virtual reality and then even getting in more into the senses and the that the smells. And I mean this is the world that we're entering the machine intelligence, >> which you still have to have, But you still have to be a functioning, competent, operationally sound organization. There've been a number of charities, make a wish is often at the top of the list of good charities. But there were a number of charities where the amount of money that's dedicated to the mission is a lot less an amount of money, dedicated administration of fundraising, and they always blame it. Systems were not being able to track things. So no, it's become part of the mission to stay on top of how information's flowing because it's not your normal business model. But the services you provide is really useful. Important. >> Sure, let me percent you the business conundrum that I have personally as a 90 leader. It takes close to $10,400 on an average to grant a wish. Uh, and, uh, partly because of me. But being part of the mission, plus me as a 90 leader wanting to understand the business more, I signed up. I'm a volunteer at the local Arizona chapter. I've done couple of expanding myself, and, uh, the condom is, if asked, if you want to go, uh, you know, do the latest and greatest network upgrade for $10,400 are what do you want to, uh, you know and make the network more resilient cyber security and all that stuff. What do you want to go grant? Another wish as a 90 leader probably picked the former. But as a volunteer, I would be like, No, it needs to go to the kid. It's Ah, it's It's an interesting kind of number, you know? You have to find the right balance. I mean, you cannot be left behind in that journey because at many points of time s I talked about it being a cost center. It being a back office. I think those days have clearly gone. I mean, we we evolved to the point where it is making you steps to be a participant b A b a enabler for the top line to bring in more revenues, tow no augment solutions for revenue and things. For that sofa >> rattles the experience or exact role citizens. And in your case, it's the experience is what's being delivered to the degree that you can improve the experience administratively field by making operations cheaper. Great. But as you said, new digital technologies, they're going to make it possible to do things with the experience that we could even conceive of. Five >> wears a classic example. Williams and Beam. I couldn't have taken the data from 60 chapters 120 locations into one single location manageable, and it reduced the cost literally reduce the cost of the 60 instances in one place without technology is like, you know what Sharia virtual machines. And and then to have a backup robust backup solution in a replication off it. It's fantastic. It's amazing >> there. And that's against here. You could give back to the dash chapters and backing, But thanks so much for sharing your story. You Thank you. Thank you. You're welcome. Alright, keep it right there. Buddy. Peter and I were back with our next guest. You watching the Cube live from V mon from Miami? 2019. We're right back. Thank you.