(upbeat music) >> Hey everyone. Welcome back to "The Cube"'s live coverage of "Women in Data Science 2023". As every year we are here live at Stanford University, profiling some amazing women and men in the fields of data science. I have my co-host for this segment is Hannah Freitag. Hannah is from Stanford's Data Journalism program, really interesting, check it out. We're very pleased to welcome our first guest of the day fresh from the keynote stage, Gayatree Ganu, the VP of Data Science at Meta. Gayatree, It's great to have you on the program. >> Likewise, Thank you for having me. >> So you have a PhD in Computer Science. You shared some really cool stuff. Everyone knows Facebook, everyone uses it. I think my mom might be one of the biggest users (Gayatree laughs) and she's probably watching right now. People don't realize there's so much data behind that and data that drives decisions that we engage with. But talk to me a little bit about you first, PhD in Computer Science, were you always, were you like a STEM kid? Little Gayatree, little STEM, >> Yeah, I was a STEM kid. I grew up in Mumbai, India. My parents are actually pharmacists, so they were not like math or stats or anything like that, but I was always a STEM kid. I don't know, I think it, I think I was in sixth grade when we got our first personal computer and I obviously used it as a Pacman playing machine. >> Oh, that's okay. (all laugh) >> But I was so good at, and I, I honestly believe I think being good at games kind of got me more familiar and comfortable with computers. Yeah. I think I always liked computers, I, yeah. >> And so now you lead, I'm looking at my notes here, the Engagement Ecosystem and Monetization Data Science teams at Facebook, Meta. Talk about those, what are the missions of those teams and how does it impact the everyday user? >> Yeah, so the engagement is basically users coming back to our platform more, there's, no better way for users to tell us that they are finding value on the things that we are doing on Facebook, Instagram, WhatsApp, all the other products than coming back to our platform more. So the Engagement Ecosystem team is looking at trends, looking at where there are needs, looking at how users are changing their behaviors, and you know, helping build strategy for the long term, using that data knowledge. Monetization is very different. You know, obviously the top, top apex goal is have a sustainable business so that we can continue building products for our users. And so, but you know, I said this in my keynote today, it's not about making money, our mission statement is not, you know, maximize as much money as you can make. It's about building a meaningful connection between businesses, customers, users, and, you know especially in these last two or three funky, post-pandemic years, it's been such a big, an important thing to do for small businesses all over all, all around the world for users to find like goods and services and products that they care about and that they can connect to. So, you know, there is truly an connection between my engagement world and the monetization world. And you know, it's not very clear always till you go in to, like, you peel the layers. Everything we do in the ads world is also always first with users as our, you know, guiding principle. >> Yeah, you mentioned how you supported especially small businesses also during the pandemic. You touched a bit upon it in the keynote speech. Can you tell our audience what were like special or certain specific programs you implemented to support especially small businesses during these times? >> Yeah, so there are 200 million businesses on our platform. A lot of them small businesses, 10 million of them run ads. So there is a large number of like businesses on our platform who, you know use the power of social media to connect to the customers that matter to them, to like you, you know use the free products that we built. In the post-pandemic years, we built a lot of stuff very quickly when Covid first hit for business to get the word out, right? Like, they had to announce when special shopping hours existed for at-risk populations, or when certain goods and services were available versus not. We had grants, there's $100 million grant that we gave out to small businesses. Users could show sort of, you know show their support with a bunch of campaigns that we ran, and of course we continue running ads. Our ads are very effective, I guess, and, you know getting a very reliable connection with from the customer to the business. And so, you know, we've run all these studies. We support, I talked about two examples today. One of them is the largest black-owned, woman black-owned wine company, and how they needed to move to an online program and, you know, we gave them a grant, and supported them through their ads campaign and, you know, they saw 60% lift in purchases, or something like that. So, a lot of good stories, small stories, you know, on a scale of 200 million, that really sort of made me feel proud about the work we do. And you know, now more than ever before, I think people can connect so directly with businesses. You can WhatsApp them, I come from India, every business is on WhatsApp. And you can, you know, WhatsApp them, you can send them Facebook messages, and you can build this like direct connection with things that matter to you. >> We have this expectation that we can be connected anywhere. I was just at Mobile World Congress for MWC last week, where, obviously talking about connectivity. We want to be able to do any transaction, whether it's post on Facebook or call an Uber, or watch on Netflix if you're on the road, we expect that we're going to be connected. >> Yeah. >> And what we, I think a lot of us don't realize I mean, those of us in tech do, but how much data science is a facilitator of all of those interactions. >> Yeah! >> As we, Gayatree, as we talk about, like, any business, whether it is the black women-owned wine business, >> Yeah. >> great business, or a a grocer or a car dealer, everybody has to become data-driven. >> Yes. >> Because the consumer has the expectation. >> Yes. >> Talk about data science as a facilitator of just pretty much everything we are doing and conducting in our daily lives. >> Yeah, I think that's a great question. I think data science as a field wasn't really defined like maybe 15 years ago, right? So this is all in our lifetimes that we are seeing this. Even in data science today, People come from so many different backgrounds and bring their own expertise here. And I think we, you know, this conference, all of us get to define what that means and how we can bring data to do good in the world. Everything you do, as you said, there is a lot of data. Facebook has a lot of data, Meta has a lot of data, and how do we responsibly use this data? How do we use this data to make sure that we're, you know representing all diversity? You know, minorities? Like machine learning algorithms don't do well with small data, they do well with big data, but the small data matters. And how do you like, you know, bring that into algorithms? Yeah, so everything we do at Meta is very, very data-driven. I feel proud about that, to be honest, because while data gets a bad rap sometimes, having no data and making decisions in the blind is just the absolute worst thing you can do. And so, you know, we, the job as a data scientist at Facebook is to make sure that we use this data, use this responsibly, make sure that we are representing every aspect of the, you know, 3 billion users who come to our platform. Yeah, data serves all the products that we build here. >> The responsibility factor is, is huge. You know, we can't talk about AI without talking about ethics. One of the things that I was talking with Hannah and our other co-host, Tracy, about during our opening is something I just learned over the weekend. And that is that the CTO of ChatGPT is a woman. (Gayatree laughs) I didn't know that. And I thought, why isn't she getting more awareness? There's a lot of conversations with their CEO. >> Yeah. >> Everyone's using it, playing around with it. I actually asked it yesterday, "What's hot in Data Science?" (all laugh) I was like, should I have asked that to let itself in, what's hot? (Gayatree laughs) But it, I thought that was phenomenal, and we need to be talking about this more. >> Yeah. >> This is something that they're likening to the launch of the iPhone, which has transformed our lives. >> I know, it is. >> ChatGPT, and its chief technologist is a female, how great is that? >> And I don't know whether you, I don't know the stats around this, but I think CTO is even less, it's even more rare to have a woman there, like you have women CEOs because I mean, we are building upon years and years of women not choosing technical fields and not choosing STEM, and it's going to take some time, but yeah, yeah, she's a woman. Isn't it amazing? It's wonderful. >> Yes, there was a great, there's a great "Fast Company" article on her that I was looking at yesterday and I just thought, we need to do what we can to help spread, Mira Murati is her name, because what she's doing is, one of the biggest technological breakthroughs we may ever see in our lifetime. It gives me goosebumps just thinking about it. (Gayatree laughs) I also wanted to share some stats, oh, sorry, go ahead, Hannah. >> Yeah, I was going to follow up on the thing that you mentioned that we had many years with like not enough women choosing a career path in STEM and that we have to overcome this trend. What are some, like what is some advice you have like as the Vice-President Data Science? Like what can we do to make this feel more, you know, approachable and >> Yeah. >> accessible for women? >> Yeah, I, there's so much that we have done already and you know, want to continue, keep doing. Of course conferences like these were, you know and I think there are high school students here there are students from my Alma Mater's undergrad year. It's amazing to like get all these women together to get them to see what success could look like. >> Yeah. >> What being a woman leader in this space could look like. So that's, you know, that's one, at Meta I lead recruiting at Meta and we've done a bunch to sort of open up the thinking around data science and technical jobs for women. Simple things like what you write in your job description. I don't know whether you know this, or this is a story you've heard before, when you see, when you have a job description and there are like 10 things that you need to, you know be good at to apply to this job, a woman sees those 10 and says, okay, I don't meet the qualifications of one of them and she doesn't apply. And a man sees one that he meets the qualifications to and he applies. And so, you know, there's small things you can do, and just how you write your job description, what goals you set for diversity and inclusion for your own organization. We have goals, Facebook's always been pretty up there in like, you know, speaking out for diversity and Sheryl Sandberg has been our Chief Business Officer for a very long time and she's been, like, amazing at like pushing from more women. So yeah, every step of the way, I think, we made a lot of progress, to be honest. I do think women choose STEM fields a lot more than they did. When I did my Computer Science I was often one of one or two women in the Computer Science class. It takes some time to, for it to percolate all the way to like having more CTOs and CEOs, >> Yeah. >> but it's going to happen in our lifetime, and you know, three of us know this, women are going to rule the world, and it (laughs) >> Drop the mic, girl! >> And it's going to happen in our lifetime, so I'm excited about it. >> And we have responsibility in helping make that happen. You know, I'm curious, you were in STEM, you talked about Computer Science, being one of the only females. One of the things that the nadb.org data from 2022 showed, some good numbers, the number of women in technical roles is now 27.6%, I believe, so up from 25, it's up in '22, which is good, more hiring of women. >> Yeah. >> One of the biggest challenges is attrition. What keeps you motivated? >> Yeah. >> To stay what, where you are doing what you're doing, managing a family and helping to drive these experiences at Facebook that we all expect are just going to happen? >> Yeah, two things come to mind. It does take a village. You do need people around you. You know, I'm grateful for my husband. You talked about managing a family, I did the very Indian thing and my parents live with us, and they help take care of the kids. >> Right! (laughs) >> (laughs) My kids are young, six and four, and I definitely needed help over the last few years. It takes mentors, it takes other people that you look up to, who've gone through all of those same challenges and can, you know, advise you to sort of continue working in the field. I remember when my kid was born when he was six months old, I was considering quitting. And my husband's like, to be a good role model for your children, you need to continue working. Like, just being a mother is not enough. And so, you know, so that's one. You know, the village that you build around you your supporters, your mentors who keep encouraging you. Sheryl Sandberg said this to me in my second month at Facebook. She said that women drop out of technical fields, they become managers, they become sort of administrative more, in their nature of their work, and her advice was, "Don't do that, Don't stop the technical". And I think that's the other thing I'd say to a lot of women. Technical stuff is hard, but you know, keeping up with that and keeping sort of on top of it actually does help you in the long run. And it's definitely helped me in my career at Facebook. >> I think one of the things, and Hannah and I and Tracy talked about this in the open, and I think you'll agree with us, is the whole saying of you can't be what you can't see, and I like to way, "Well, you can be what you can see". That visibility, the great thing that WiDS did, of having you on the stage as a speaker this morning so people can understand, everyone, like I said, everyone knows Meta, >> Yeah. >> everyone uses Facebook. And so it's important to bring that connection, >> Yeah. >> of how data is driving the experiences, the fact that it's User First, but we need to be able to see women in positions, >> Yes. >> like you, especially with Sheryl stepping down moving on to something else, or people that are like YouTube influencers, that have no idea that the head of YouTube for a very long time, Susan Wojcicki is a woman. >> (laughs) Yes. Who pioneered streaming, and I mean how often do you are you on YouTube every day? >> Yep, every day. >> But we have to be able to see and and raise the profile of these women and learn from them and be inspired, >> Absolutely. >> to keep going and going. I like what I do, I'm making a difference here. >> Yeah, yeah, absolutely. >> And I can be the, the sponsor or the mentor for somebody down the road. >> Absolutely. >> Yeah, and then referring back to what we talked in the beginning, show that data science is so diverse and it doesn't mean if you're like in IT, you're like sitting in your dark room, >> Right. (laughs) >> coding all day, but you know, >> (laughs) Right! >> to show the different facets of this job and >> Right! >> make this appealing to women, >> Yeah. for sure. >> And I said this in my keynote too, you know, one of the things that helped me most is complimenting the data and the techniques and the algorithms with how you work with people, and you know, empathy and alignment building and leadership, strategic thinking. And I think honestly, I think women do a lot of this stuff really well. We know how to work with people and so, you know, I've seen this at Meta for sure, like, you know, all of these skills soft skills, as we call them, go a long way, and like, you know, doing the right things and having a lasting impact. And like I said, women are going to rule the world, you know, in our lifetimes. (laughs) >> Oh, I can't, I can't wait to see that happen. There's some interesting female candidates that are already throwing their hats in the ring for the next presidential election. >> Yes. >> So we'll have to see where that goes. But some of the things that are so interesting to me, here we are in California and Palo Alto, technically Stanford is its own zip code, I believe. And we're in California, we're freaking out because we've gotten so much rain, it's absolutely unprecedented. We need it, we had a massive drought, an extreme drought, technically, for many years. I've got friends that live up in Tahoe, I've been getting pictures this morning of windows that are >> (laughs) that are covered? >> Yes, actually, yes. (Gayatree laughs) That, where windows like second-story windows are covered in snow. >> Yeah. >> Climate change. >> Climate change. >> There's so much that data science is doing to power and power our understanding of climate change whether it's that, or police violence. >> Yeah. (all talk together) >> We had talk today on that it was amazing. >> Yes. So I want more people to know what data science is really facilitating, that impacts all of us, whether you're in a technical role or not. >> And data wins arguments. >> Yes, I love that! >> I said this is my slide today, like, you know, there's always going to be doubters and naysayers and I mean, but there's hard evidence, there's hard data like, yeah. In all of these fields, I mean the data that climate change, the data science that we have done in the environmental and climate change areas and medical, and you know, medicine professions just so much, so much more opportunity, and like, how much we can learn more about the world. >> Yeah. >> Yeah, it's a pretty exciting time to be a data scientist. >> I feel like, we're just scratching the surface. >> Yeah. >> With the potential and the global impact that we can make with data science. Gayatree, it's been so great having you on theCUBE, thank you. >> Right, >> Thank you so much, Gayatree. >> So much, I love, >> Thank you. >> I'm going to take Data WiD's arguments into my personal life. (Gayatree laughs) I was actually just, just a quick anecdote, funny story. I was listening to the radio this morning and there was a commercial from an insurance company and I guess the joke is, it's an argument between two spouses, and the the voiceover comes in and says, "Let's watch a replay". I'm like, if only they, then they got the data that helped the woman win the argument. (laughs) >> (laughs) I will warn you it doesn't always help with arguments I have with my husband. (laughs) >> Okay, I'm going to keep it in the middle of my mind. >> Yes! >> Gayatree, thank you so much. >> Thank you so much, >> for sharing, >> Thank you both for the opportunity. >> And being a great female that we can look up to, we really appreciate your insights >> Oh, likewise. >> and your time. >> Thank you. >> All right, for our guest, for Hannah Freitag, I'm Lisa Martin, live at Stanford University covering "Women in Data Science '23". Stick around, our next guest joins us in just a minute. (upbeat music) I have been in the software and technology industry for over 12 years now, so I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCUBE. (upbeat music) Being a host on theCUBE has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, "Hey, I'm really interested in this. I love talking with customers, gimme a shot, let me come into the studio and do an interview and see if we can work together". I think where I really impact theCUBE is being a female in technology. We interview a lot of females in tech, we do a lot of women in technology events and one of the things I.

>>live from Seattle, Washington. It's the key nude covering smartsheet engaged 2019. Brought to you by smartsheet. >>Welcome back, everyone to the cubes Live coverage of smartsheet engaged here in Seattle, Washington. I'm your host, Rebecca Knight coasting alongside Jeff. Rick. We're joined by Mark Mater. He is the CEO of smartsheet. Thank you so much for coming, Warren. Thank you. So great job up there on the keynote way. No, this is the third annual conference of 4000 people from 39 different countries. The theme is achieved more, and the theme is actually tied to a very special announcement you made today about the about the achieve as one alliance. What can you tell our viewers a little bit about that? Yeah. The chief is >>one alliance is really figuring out how to take the cultural changes that Aaron flight right now and marrying those with the people in the technology. And we think that it's important as things like concepts that are intimidating people. Aye, aye. And ml worker replacement say whoa, whoa, whoa. These are things where we actually think technology and people should work together as opposed to being a replacement for and I think there's a lot of education that needs to take place. So we plan on doing is doing research through this alliance and then publishing that work is I think a huge part of this is educating the market and giving them confidence and take that step. It's a >>different way to treat people. We're in this weird spot where you know, the super low unemployment. And yet in that you know so many things. Air service is and a lot of your assets walk out the door every single night. You hope they come back the next day. So you're trying to give them meaning. You're trying to do more than just kind of the core function of the business. You had a great hackathon yesterday for good. So it's a really challenging, people challenging time for employers to keep the workforce engaged. And you're really trying to help them kind of move some of the roadblocks and may be easier for them to keep those folks and gay >>it is Jeff and what we're seeing is and you see the studies come out where there's never been a higher percentage of people who feel disconnected from their work, and I don't think that's just giving them good tooling. They actually want to know who is being benefited. Ultimately, what's the endpoint benefit? And if they can somehow feel connected to something purposeful, that is a mechanism for feeling connected to work. So we want our team. We want our customers showing up to their offices, everyday organizations feeling motivated. And I think absent that human dimension absent knowing who you're helping, I think makes it feel a bit hollow. That's one of these about engaging Brings this together. You see it firsthand, very invigorating. >>Talk a little bit about the customers that you had upon the main stage, telling their smartsheet stories and one of the ones that you find most inspiring and and most sort of life affirming to you as the CEO of this company. >>Well, the thing that that never gets old for me, Rebecca, is when somebody felt something one day was completely unattainable. And then they have that unlocked moment like Holy smokes. I pulled it off, and what's even more exciting when they pull that off with very few resource is they didn't have to go to I t. At every turn. They didn't have to mobilize on a big budget ass. They just got it done. So one of the real memorable moments from me this year was when I visited Syngenta out in North Carolina and I spoke with a head of health and safety and she said we mobilized on Smarty. We enabled all of our team members to submit issues safety concerns they had. How do you simplify the process of taking a picture of a potential issue getting into a queue getting it responded to? They saw a 500% increase in the number of people who are saying, I think that you could use improvement. I think that could use improvement. And it's 65% faster resolution time. So she is convinced that people's lives are being materially impacted to the positive. Because of this, I mean, how can she not feel empowered that it's a pretty big? That's a pretty amazing feeling, so that's one that really stands out to me >>in terms of the other customer stories. One of the things that also struck me was just how adrenaline pumping the main stage show talk a little bit about what it means to put up the customers who have these very compelling, visually interesting stories, from outdoor clothier Sze to travel destinations, and also what it means for smartsheet employees to be in the audience hearing these stories about what they're doing to help their customers. I >>think I think we all want to wake up every day feeling like whatever we do matters right, whether that's individually or with your family or with your business. And when you see someone like an Arc Terex or a Spartan race, or a Vulcan, which is helping do census on elephants, elephants and preserving that species coupled right alongside it with Cisco that is protecting our networks, which are more complex than ever before on your participating in that site. Okay, that can again back to that connectedness, right? Andi, I think I think diversity and who we serve also keeps it interesting. You never know who you're going to serve next. One day at Cisco the next day, it's agriculture. The next day it's saving elephants. That diversity keeps things fresh. >>One of the things that struck me in the keynote is there was a story of this guy. I guess it's gonna fly around the world in London plane in five days or eight days, but on the one of the test flights are a significant change. Was trying to fly to why there was equipment failure and he had to divert. And, you know, when you see the screen grabs and people working and smart, she looks super detailed. It's like a project plan, and there's resource is that died research, legalization. But in this case, they had to be able to flip on a dime. They had to be completely at around. I think she said, that eight teams around the world, I presume, where the stops are. That's a really interesting dichotomy of the tool that you guys were delivering to, to have the detail to be. Numbers focus. Okay, I focused, but at the same time be really right. In the real world, stuff doesn't always go as planned, >>be Rio and do it instantly. So if we have an issue with the plane, we're not gonna host a summit to talk about with how to get back on track way got to do it now. So the thing that's that's also need that example is you're talking about 8 to 10 people across multiple continents who have to work Right now. There is no mobilization. There is note, as they said, Summit and I think being able to do meaningful things quickly. That is a fairly rare combination, right? Very often meaningful stuff is heavy, Complex taste time. Eso again. I'm this constant pursuit of faster, more meaningful, more depth, more value >>in this kind of cross silo collaboration to I mean, that's the theme that comes up over and over again. Is that you need contributions from loss of people and lots of know, formally silo departments is maybe what they're gonna be called in the future to get to resolution so that you can move forward >>on. I think the thing that we spoke to in one of the product announcements was We're so inundated with information and Mark, I need a faster I need a faster yet again. It's a holy Rebecca. I can't actually process it. Also, one of the things we're trying to do is how do you also improve the context within within which people see things, right? So if you ask me a question and I don't have toe tab out to another application. I can actually see your question in the context of that work. And that's when I think one of the real big breakthroughs were releasing this This engaged. >>I mean, when you think about the the current status of work and you really and you really see it from where you sit, I mean, is it almost shockingly abysmal about how bad things can get at companies in terms of how many silos there are, how the number of communication breakdowns, the way the communication breaks down? Because, as you said, you could just be working >>on a different version. Great question, Rebecca. And the reason I would say it's not shockingly business because we've been doing this for years. It's like it's the norm >>Bates, >>the cost of doing business. So what our job is to how do we get people to get that spark to elevate a basic? Oh, my goodness, there is a better way, and it takes a lot to change people's behavior. You can't just say, Well, there's a better way. They have to experience it, right? So we're in that in that pursuit of how do you get more people to clear that hurdle the first time. Because the norm is it's hard. The Norma's is distributed enormous. I don't know what. So that's what we're trying to unlock for folks. >>And you said in the Kino, once they get that spark, and then achievement becomes the new norm that that has its own momentum to >>Yes, it's the you know Jeff does something amazing and I'm I I want in. Jeff doesn't have a monopoly on that on that. That's the viral effect. And it's not so much a vendor saying, Hey, Jeff did something now you should be motivated. You should feel that way, Rebecca And that's what we see at this conference. This is 4000 people who weren't told to go to the conference. These air 4000 people who want in, and that is a really special part >>of the conference for us. Shift gears a little bit on a on artificial intelligence machine, learning. We hear about it all the time, and I think everyone now has kind of figured out that it's not going to be a company delivering an ML. It's really applied a i N M l within an application When you guys look at the opportunities, especially with the data flow that you have and you know your sass application, where do you see some of the the short term winds and opportunities using A. I even better, you know, eliminate some of this redundant, painful work. >>I think part of it starts with educating people on the potential benefits of it. And then I'm an experiential learner. I think many people are so instead of talking about the theory, demonstrate how it could help. So we've already started doing saying things like recommending to people certain things based on actions they take. It's also very important. As a vendor, we have made a commitment to be very clear that for more advanced types of a I people need to opt in. So again, part of this what's happening to my data? Who's working it well, that's part of our platform. And when I look at the future, it's the first step I think is really how do you drive? Convenience improvement recommendation? How do you let someone take better advantage of the systems they're already using? And what people don't have to appreciate today is by exhibiting this behavior. But in taking information, structuring and reporting out, the system will observe a pattern. And ultimately, should they choose to opt in, the system will get to a point where we'll be able to make recommendations, recommendations and derive insights. Um, but again, a lot of this is fairly theoretical. We're in the early innings of this, Jeff. People are just starting to figure out I can automate something. So, you know, I think there's a much like people said 10 years ago. You know, the future is now. The future is kind of showing up today, and then the next phase is still a couple of years out, but it's a very exciting. It's a very exciting prospect. >>So those recommendations then, can become best practices because I'd like to get it back to this. This achieve as one alliance and sort of how you're going to take that research and educate the market and then use it to implement these new technologies. And best practices of this is how we can get more done and achieve more together. You and >>I think by showing examples of how a I n. M. L can contribute to someone's performance as opposed to you. Did these 10 things the machine is taking over those 10 things? What's my role in it? That's not a very exciting conversation to have. So I think, by demonstrating how somebody's game can innocence slow down. So if that machine can help me further inspect more deeply, assess have that next moment of insight that's contributing, not taking away. And again we need to show examples as an industry that happening until we show it. It's sort of all for not so. I'm really excited about about helping our customers through that journey. >>Yeah, there's so much opportunity, and the the other one that comes up in other times is unplanned downtime, Right? So a lot of talk always about unplanned, unplanned downtime machines, right? It's completely disruptive. You don't want it scheduled maintenance, but no one really talks about unplanned downtime of people not necessarily in the way being sick but being distracted by often mundane, often road often anticipated task. I won't even call it work that suddenly get dumped into your lap that you have to take care of, and those really think huge opportunities to add some automation and get those things kind of off the plate. >>You think about the breakthrough ideas you've had in your lives? Does it happen when you're like feverishly working ways? No. It's usually when there's a moment of just peace before you're able to process. That's when the breakthrough happens. So one of the things we talked about today was how, also his leaders. We need to empower our teams to not just drive for more yielding throughput. Take that extra benefit in. Actually, look at the board, process the board and think about what we're going to do next. And I think again, you need to exhibit. You need to give people the permission to work that way because we're all feeling this this pressure to innovate. You got to give people time to do it >>and do more with less to do you think it's realistic? Do you think leaders are going to be able to do that? And >>I think the leaders of successful companies will do that >>and role model. That, too, because they can also be worried about their own throughput. As you said, >>right, Right? Yeah. I mean, as Gabby, have you reset at the end of her talk. You have to exhibit the behavior. You want others to practice. So I think that was a wise, wise statement. >>Well, I really loved the outdoor clothing company who, you know, specifically said, We want our people out doing the things that our customers are doing, experiencing what they're experiencing and really bacon that into the culture, not just saying it, but get outside and go run around on and do what we want our customers to do and what our customers do do a very different approach. >>It is, It is. I think, again, back to back toe us, understanding what our customers are doing. This is equivalent to our Super Bowl every year, right, we get 4000 these people coming in here and there is no substitute for that in the flesh interaction. And that's again one of the reasons why it's everyone's such a positive, engaged mood right now, >>so they're not only interacting with a smartsheet folks, but they're interacting with each other at learning how each company uses smart cheat. >>I mean, when you think that 1/2 of all collaboration that takes place on our platform is cross company, it's not a surprise that people interact with another. It is it is happening. We have companies who interact with hundreds of brands outside of their own. So we service that cross connect for companies, and that's the modern company. I don't know of a company that is completely insular. So if you can help promote that safely, that za real advantage for company >>wrapping up, what do you think you're going to be? The themes for next year's conference? What is what are sort of what's on your plate? What are you thinking about? What are the big challenges that you're gnawing on right now? >>Yeah, I think the I think the continue shift from efficiency to effectiveness people. I think most people are still measured on the output goal. How many units did I do? And while that may serve you well in the quarter in the next quarter, it does not prepare you for years two and three. So you have to be very committed to the investments today that may not pay off in that 6 to 12 month window. You have to, and I think stories will come out as people are learning new ways. Toe work of examples of Here's what we did in 2019 which ended up being a home run in 2021. So it's back to effectiveness, effectiveness versus efficiency. That is gonna be, I think, one of the themes we speak to next year. >>Thanks, Mark. A pleasure having you on the show. I'm Rebecca Knight for Jeff. Rick. Stay tuned of more of the cubes. Live coverage of smartsheet engage.

>> Live from Barcelona, Spain. It's the cue covering Sisqo Live Europe, brought to you by Cisco and its ecosystem partners. Welcome back >> to Barcelona. Everybody would adapt. Wrapping up day one of Sisqo live Barcelona Cube coverage. I'm David. Long day. He's stupid men. You're watching the Cube. The leader in live tech coverage. Donnie Williams is it director at Scott Equipment out of Louisiana. And Eric hurts August back. He's the CMO of IBM storage. Gentlemen, good to see you. Welcome. >> Thank you for having us. >> You're very welcome. So tell us about Scott equipment. What do you guys do? Look, what's the company all about were >> a heavy equipment dealer, So we've been we've been in the business for eighty years, privately owned company. And so we're we're We started out and farm implement eighty years ago by the founder, Thomas Scott, which is where the name Scott equipment comes from. And so we transition over the years, Teo construction equipment, Andi were now back in two thousand fourteen, we sold all of our the farm stores that handled all of that equipment. And now we're We're strictly servicing the construction industry and petrochemical in >> history. So your dealer of exactly what equipment and your services as well? >> Yes. We service that we were primarily a rental company. First then then we We also sell what we rent. We service service it and and also parts as well. So we're talking massive? Yes, they got. If you if you think our one of our main lines is Volvo, which you have you have you seen the show? Gold rush that that Volvo equipment you see there, that's that's what we sell. So is incredible machine. Yeah, Yeah, they are. Hada chance tio to play with one. I went Teo Shippensburg, Pennsylvania. Where were their North America offices and had a chance to play with their largest excavator? That was That was >> fun. So is a lot of your Senate on sort of the maintenance business in the service business? >> Yes. So we were just mostly. Mirror is like a car dealership. If if you so we were like I said, we do sale service parts, all of that. >> So the business flow starts after the sale is made on >> exactly. Yes. We still like, Yeah, exactly. We get. We get equipment out there in the in the in the territory, and then the revenue continues tio to come in. >> So what are some of the challenges? The external challenges that are driving your business? You really >> are. The whole heavy equipment industry is It's kind of behind the times in my from a dealership perspective from from a manufacturer perspective there. They're somewhat up with technology, especially especially Volvo. But from a dealership there, there might mainly privately owned. So they're not there's not a whole lot of resource is in, and ah, in technology they don't. That's not a focus for them that they're they're focused on the business side of it. So what? We we're not When I first started the company ten, eleven years ago, now there was one guy servicing six hundred employees and and it was one eyed person, one i t person. So, as you can imagine, it was, it was a nightmare. Go. I mean, it's not the guy's fault. I don't blame him at all. Is this Is this the way that they had done business and not change bombed out, >> right? Exactly. Yeah. Guys >> find them. >> So their customer of ours for the versus stack, we have, ah, partner that they've been buying their IBM in their Cisco gear from. And then when they were doing a modernization effort, the reseller talk to Scott and said, Dani, what do you think? How about doing this? Converge infrastructure. Easier to play. It's after. So it all came through their existing channel. Part of that they were using for both IBM gear and Cisco Gear. >> So you wanted a solution. That one guy could run, right? We've now at least growing that company to house. We have six total in our in our department. So we've changed a lot since I started the eleven years ago. >> And why are they spending their time doing what? Premier >> Li? We do a lot of help desk on systems administration way do mostly, uh, are My focus is to make sure that our employees are satisfied that so they could take care of the customer, and that's that's the primary goal. And along with that comes comes systems administration. A cz. Well, so, But, >> you know, a full stack like this. I mean, the joke. You need more than one person, but it's going to be simplified. You know what you're buying, right? Predictable. And therefore, you shouldn't need to be seen on a basis. >> Yes, I like keeping things simple. Simple as possible. So that makes that makes my job easier. It makes my team's job easier. What >> kind of >> things you driving? Is it? You know, data protection, is it? You know what? What? What? What sort of, you know, use cases do you have on your stack >> on that Were from our were servicing on our with Francisco verse. Sorry versus stack. We are mostly it is all profit cloud were servicing applications. That's the supplement. Our court system. So we have reporting solutions. We were when we first bought it. The vs stack way were considering moving to another Air P system. Oh, and we would have that that infrastructure in place tio migrate to that. So we see what we still have that that actually on the table as a as an option >> for us, but the migration to a new Europe E system. Yes, we should talk afterwards. No, you >> were warning that it >> all about you. Of course, you don't want to convert if you don't have to write. But sometimes there's a business case. Sometimes it's hard to make you talk. Cloud in your in your future president were doing some that's ass stuff. >> Yeah, a little of that. I mean, anything. I mean things that that makes sense for us to to cloud I security services we're doing. Of course, probably most common is hosting email. Were doing a lot of that share point that that type of solution in the cloud >> How long you been with the company? Eleven years. Eleven years. Okay, So, thinking about the last decade, I mean, it's a lot of lot has changed. Yes. What's your What do you most proud of? What you like your biggest success that you can share with us. Oh, >> really? Building my the that dude the I T department and bringing our company into the twenty first system century from a from a technology perspective. I mean, like I said, we had one person that was that was handing. It was really impossible. I mean, you couldn't depend. Depends on one person. And and and, yeah, expect the company's or saw survive long term. Yeah, That one person had to say no a lot. Exactly. Right. Why would he? Just couldn't get everything >> done right? So that really that modernization? Yes, I know where you guys >> can. Ninety Mater, My team modernization play. The versus stack is heavily used for that. And, you know, as we said, on the earlier and every we had to see ESPN, we've also used it to do you know, to the next level from a night transformation to the future. Because in that case, as you know that was a CSP who uses it to service. You know, hundreds of customers all across the UK in a service model. And in this case, this is more of a mighty modernization. Take the old stuff, upgraded to what it was. They even have old IBM blade servers. That's how old the stuff wass old, actually, six played servers that must have been ten years old before they went to the Versus Stack. >> How many people in the company >> right now? We've actually sold off side since I've been with the company we sold off. Some of our non performing business units were probably roughly around five hundred fifty now. Okay, so I mean, we're Ah, we're actually more profitable now than we were eleven years ago from Ah, I mean, we have less employees, but our profitability is actually exceeded >> the name of simplification. Exactly. Right. So what's the biggest challenge you face Is the head of it today? The biggest, Probably >> the biggest challenge would be me wanting to implement technologies. They're not really not ready. I want it. I want tohave the competitive edge, that of the industry. I want to be able to be ahead of of the ahead of the curve. Uh, and that's probably the probably biggest challenge. And you're >> saying you can't Because the tech is ready or skills >> is just is just the industry just trying Teo. I work with vendors and getting getting them to be ready for I say, vendors, manufacturers, they're our vendors. Toe Get them Tio and other dealers as well. Teo Teo Albee. Acceptable to technology that's been there twenty years. >> What would you say is the but the top number one or the top things that IBM has done to make your life easier? And what's the one thing they could do that they're they're not doing that could make your life easier. What's the start with what they've done? You know whether successes, you know that >> really? Really. I mean, we've been a long time IBM customer. We have not, not just the versus Stack, but we also have the power system, which were actually runs are our core AARP. Um, okay. And so that we had long standing relationship with IBM, and the reliability is there. The trust is, >> there's well, a long term partnership. But what's the one thing they could do? One thing that you could If you could wave a wand and IBM will do x what would x B to make your life better? Uh, cut the price way. Go >> way. I should have prefaced that something that size >> on that topic. But you know, the power system thing brings up. You know, our friend Bob. Pity on who's running the cognitive systems group now You guys do with some stuff in a I talked about that a little bit. >> So what we've done is two things. First of all, we've been beauty inside of our system's ai ai all over the place. So, for example, we tear data which can weaken due not only to our own array, but literally two four hundred forty rays that have someone else's logo on them. It's all a eye dunce. When the data is hot, it's on the fastest here. So if you have fifteen thousand rpm drives in seventeen hundred rpm drives, it goes to fifteen thousand. When it cools off A. I automatically moves that the storage admin does nothing. You don't set policies, A takes care. We have flash and you have hard drive's same thing. It'll move around and you could have on IBM array talking to any AMC array. So all sorts of technology that we implement, that's a I in the box. Then, on top of that, what we've done is come up with a Siri's of a reference architectures for storage, as one of the critical elements in the platform. So we've done is create what we call a data pipeline. It involves not only our storage raise, but four pieces of our software spectrum scale, which is giant scale out file system, in fact, to fastest super computers in the world have almost half an exabyte of that software storage. With that software, our spectrum discover which we announced in queue for which is all about better management of metadata. So for a I workloads, big get anally work loves the data scientist doesn't prep the data. They can actually talk to what we do, and you could create all these meditate a template, then boom. They run a a ay workload on Thursday and then run a analytic workload on Friday. But all automated our archive and then our cloud objects towards. So all that is really think about it. Maura's an oval because when you're doing an A I system, you're constantly learning. So the thing you got to do is one you've got to have high performance and be ableto handle the analytics, which we do on flash. Okay, so the flashes connected, you've got to be able to move the date around. And part of thing with the spectrum Discover is that we can talk through an A P I to a piece of a AI software two piece of analytic software to piece of big data software, and they can literally go through that. AP I create templates for the metadata and then automatically suck what they need into their app and then munge it and then spirit back out and then obviously on the archives side, you want to be able to quickly recall the data, because if you think about a I system, it's like a human. So it's giving my Russian example. So I'm old enough. When I was a kid, there were bomb shelters in my neighborhood that people dug in the backyard. Then we have, you know, Nixon lightening up with the Chinese and we have Reagan and Gorbachev next, You know, the wall comes down right then. Next thing you know, there's no longer Soviet Union. All of a sudden, no, the Russians might get a little aggressive, even though they're no longer communist. And now, you see, depending on which political party. Either they're totally against us where they're totally helping us. But, you know, if they really were hacking systems whose whatever political party urine, they really were hacking our system, tried to manipulate the election pro or con. The point is, that's kind of like a cyber attack, and that's not a good thing. So we learn and it changes. So when a I system needs to understand and change constantly, learn. If all of a sudden you have flying cars, that's going to be different than a car with tires. Now, a lot of it, maybe the same, the interior, all the amenities. But the engine is going to be different. And there are companies, including the big Big three, four five who are actually working on flying cars, knows it will happen. But the A I system needs to understand and learn that and constantly learning. So the foundation has to be heavily resilient, heavily performance, heavily available, lasting one is an A I system going down on you, especially if you're in health care or big giant manufacturing. Like Volvo, his customer. When they're building those cranes and things, they must cost fifty sixty million dollars at that assembly line goes down its prey a big deal for them. So you need a I systems that always keep your other systems up and running. So you have to have that solid foundation storage underneath. >> Awesome. All right, we got to leave it there. Give the customer the last word. Donnie. First time in Barcelona, right? Yes. It ISS how you find in the show and the >> syphilis is awesome. This's my, actually my fifth, uh, Cisco lifers our first time in Europe, so yeah, enjoying it. >> Good. Good. Well, thank you, guys. For German of the >> correct. Thank you. Have you appreciate it? >> You're welcome. Alright. Keep right there, everybody. We'll be back to rap Day one. Sisqo live Barcelona watching you.

>> From Palo Alto, California, it's theCUBE. Covering the Conference Boards Sixth Annual Innovation Master Class. >> Hey, welcome back here, everybody. Jeff Frick here with theCUBE. We're at the Innovation Mater Class at Xerox PARC in Palo Alto. Really excited to be here, never been here, surprisingly, for all the shows we do just up the hill next to VMware, and Tesla. This is kind of the granddaddy of locations and innovation centers, it's been around forever. If you don't know the history, get a couple books, you'll learn it pretty fast. So we're excited to be here and our next guess is Antony Brydon, four-time founder and CEO, which is not easy to do. Again, check the math on that, most people are successful a couple times, hard to do it four times. And now he's the co-founder and CEO of Directly. So Antony, great to see you. >> It's good to be here. >> So, Directly, what is directly all about for people aren't familiar with the company? >> Most companies are excited to, and pursuing, the opportunity of automating up to 85% of their customer service. That's the ambition, and giving customers a delightful answer in their first experience. Most of those companies are falling down out of the gates because there are content gaps, and data gaps, and training gaps, and empathy gaps in the systems. So we build a CX automation platform and it puts experts at the heart of AI, letting these companies build networks of product experts and then rewarding those experts for creating content for AI systems, for training AI systems, for resolving customer questions. >> Right. So let's back up a step. So Zendesk is probably one we're all familiar with. You send in a customer service node, a lot of the times it comes back, customer service to Zendesk. >> Yes. >> But you're not building kind of a competitor of Zendesk, you're more of a partner, if I believe, for those types of applications, to help those apps do a better job. >> We are, we're a partner for Zendesk, we're a partner for Microsoft Dynamics, for Service Cloud and the like, and, essentially, are building the automation systems that make their AI systems work and work better. >> Right. >> Those are pure technology systems that often lack the data and the content to deliver AI at scale and quality, and that's where our platform and the human network, the experts in the mix, come into play. >> We could probably go for a long, long time on this topic. So what are some of the key things that make them not work now? Besides just the fact that it's kind of like the old dial-in systems. It's like, I just want to hit 0000. I just want to talk to a person. I have no confidence or faith that going through these other steps is going to get me the solution. Do you still see that on the online world as well? >> No, there are very clear gaps. There are four or five areas where systems are falling down. AI project mortality, as I refer to it. Very few companies have the structured data that systems need to work at scale. >> On the back, to feed the whole thing. >> That's right. Labeled, structured, organized data. So that doesn't exist. Many companies don't have the content. That's a second area. They may have enterprised knowledge bases, but they're five years old, they're seven years old, they're outdated, they're not accurate. Many companies don't have the signal. When a automated answer's delivered, they have to wait for a customer to rate it, and that tends to be really poor signal on whether that answer was good or not. And then last, many companies just don't have the teams to maintain these algorithms and constantly tune them. And that is where experts at the heart of a platform can come into play, by building a network of product experts who know the products inside and out. These could be Airbnb hosts for one of our customers, these could by Microsoft Excel users in the Microsoft example. Those experts can create that content, train the data, and actually resolve questions, filling those gaps, solving those problems. >> Right. I'm just curious, on the expert side, how many--? I don't know if there's best practices or if there's kind of certain buckets depending on the industry. Of those expert answers are generated by people inside the company versus a really kind of active, engaged community where you've got third-party experts that are happy to participate and help provide that info. >> Over 99% of the answers and the content is actually generated by the external network. >> 99%? >> 99%. You start with sources of enterprise knowledge, but it's a long, hard, arduous process to create those internal knowledge bases, and companies really struggle to keep up, it's Britannica. By the time you ship it it's outdated and you have to start all over again. The external expert networks work more like Wikipedia. Content constantly being organically created, the successful content is promoted, the unsuccessful content is demoted, and it's an evergreen cycle where it's constantly refreshing. Overwhelmingly external. >> Overwhelming. I mean, I could see where there's certain types of products. I was telling somebody else the other day about Harley-Davidson, one of the all-time great brands. People tattoo it on their body. Now, there aren't very many brands that people tattoo on their body. So easy to get people to talk about motorcycles or some of these types of things, but how do you do it for something that's really not that exciting? What are some of the tricks and incentives to engage that community? Or is there just always some little corps that you may or may not be aware of that are happy to jump in and so passionate about those types of products? >> There are definitely some companies where there's very little expertise and passion in the ecosystem around it. They're few and far between. If you find a product, if you find a company, you can find people that rely, love, and depend on that company. I gave some of the B to C examples, but we've also got networks for enterprise software companies, folks like SAP, folks like Autodesk. And those networks have experts that are developers, resellers, VARs, systems integrators, and the like. In the overwhelming majority of cases, the talent and the passion exists, you just have to have a simple platform to onboard and start tapping that talent and passion. >> So if I hear you right, you use kind of your Encyclopedia Britannica because that's what you have to start, to get the fly wheel moving, but as you start to collect inputs from third-party community, you can start to refine and get the better information back. And I ask specifically that way because you mentioned the human factors, and making people part of this thing, which is probably part of the problem with adoption, as I'd want confidence that there's some person behind this, even if the AI is smart. I'd want at least feel like there's some human-to-human contact when I reach out to this company. >> Yeah, that's critically important, because the empathy gap is real in almost all of the systems that are traditionally out there, which is when an automated answer's delivered, in a traditional system, it typically has a much lower CSAT than when it comes from a human being. What we found is when you have an expert author that content, when his or her face is shown next to the answer as it's presented to the user, and where he or she is there to back it up should that user still need more help, there you retain the human elements that personalize the contact, that humanize the experience, and immediately get big gains in CSAT. So It think that empathy piece is really important. >> Right. I wondered if you could share any specific examples of a customer that had an automated, kind of dumb system, I'll just use that word, compared to what they can do today, and some of the impacts when they put in some of the AI-powered systems like you guys support. >> So one of the first immediate impacts is often when we go in, a automated or unassisted system will be handling a very small percentage of the queries, and percentage of the customer questions coming in, and-- >> And people are going straight to zero, they're just like, I got to go to a person. >> Yeah, we're mostly in digital channels, so less phone, but yes, because the content there-- >> As an analogy, right. >> Because the content isn't there, it doesn't hit and resolve the question in that frequent a rate, or because the training and the signal isn't there, it's giving answers that are a little off-base. So the first and lowest hanging fruit is with a content library that's get created that can get 10, 50, 100 times broader that enterprise content pretty quickly. You're able to hit a much broader set of questions at a much higher rate. That's the first low-hanging fruit and kind of immediate impact. >> And is that helping them orchestrate, coordinate, collect data form this passionate ecosystem that's outside the four walls? Is that, essentially, what you're doing in that step? >> It essentially is. It is about companies having these ecosystems of these users, millions of hours of expertise in their head, millions of hours free time on their hands, and the ability to tap that in a systematic way. >> Wow. Shift gears a little bit, you are participating on a panel here at the event, talking about startups working with big companies and there's obviously a lot of challenges, starting with vendor viability issues, which is more kind of selling to big customers versus, necessarily, partnering with big companies. But what are some of the themes that you've seen that make that collaboration successful? Because, obviously, you've got different cultures, you got different kind of rates of the way things happen, you've got, beware the big company who eats you up in meetings all the time when you're a little start-up, they'll kill you accidentally just by scheduling so many meetings. What are some of the secrets of success that you're going to share here at the event? >> So we've got experience in that. Microsoft is a partner of ours, Microsoft Ventures is an investor. I think the single biggest key is an aligned vision and a complementary approach. The aligned vision where both the start-up and the partner are aiming for a similar point on the horizon. For example, the belief that automation can delight a very large set of customers by providing them a good, instant answer, but complementary approaches where the core skillsets of the companies round out each other and become less competitive. In this case, we've partnered with-- Microsoft is best in class AI platform and cognitive services, and we're able to tap and leverage that. We're also able to bring something unique to the equation by putting experts at the heart of it. So I think that architectural structure, in the first place, is a great example of kind of getting it right. >> Right. And your experience, that's been pretty easy to establish at the head-end of the process, so that you have kind of smooth sailing ahead? >> No, I don't think it's easy to establish at the head of the process, and I think that's where all of the good work and investment needs to happen. Upfront, on that kind of shared vision, and on that kind of complementary approach. And I think it is probably 20% building that together, but it's also 80% just finding it. The selection criteria by which a corporate partner picks a startup and the startup partner picks the corporate partner. I think just selecting right is the majority of the challenge, rather than trying to craft it kind of midstream. >> If it doesn't feel good at the beginning, it's probably not going to to work out. >> Right, it's about finding it. It's a little bit like the Venture analogy. Do they find great companies, or do they build great companies? Probably a little of both, but that finding that great company is a large part of the equation. >> Yeah, helps. So, Antony, finally get a last question. So, again, four successful startups. That does not happen very often with the same team. And look at your background, you're a psychology and philosophy major, not an engineer. So I'd just love to get kind of your thoughts about being a non-tech guy starting, running, and successfully exiting tech companies here in silicon valley. What's kind of the nice thing being from a slightly different background that you've used to really drive a number of successes? So I think the-- I think two things, I think one, coming from a non-tech and coming from a psych background has given us an appreciation of the human elements in these systems that tech alone can't do it. I'd say, personally, one of the impacts of being a non-tech founder in this valley is a heck of a lot of appreciation for what teams can do. And realizing that what teams can do is far more important than what individuals can do. And I say that because as a non-tech founder, there's literally nothing I could accomplish without being a part of a team. So that, I think, non-tech founders have that in spades. A harsh and frank realization that it's about team and they can't do anything on their own. >> Well, Antony, thanks for taking a minute out of your time. Good luck on the panel this afternoon and we'll keep an eye, watch the story unfold again. >> Yep, I appreciate it. Thanks very much. >> He's Antony, I'm Jeff, you're watching theCUBE. We're at the Master at the Master Innovation Class at Xerox PARC, thanks for watching.