>>Yeah, yeah. Welcome to the Cubes Presentation. Women in Global event Celebrating International Women's Day It's amazing showcase of great people and entrepreneurs, executives, really serious women in the industry, in the countries all around the world sharing their stories on International Women's Day. I'm your host of the great story here, an entrepreneur founder and C e 03 riders. Tanya A. I from New Zealand from all the way down under. Thanks for coming on. Appreciate it. >>Thanks for having me. >>I love your story. Let's stop. Let's start by. Just sit at the table about your story. Where your background from How you got into the business. Take us through quickly. That origination story. >>Sure. Um, look, I come from a low socio economic area. I grew up a new Plymouth. Um, and we didn't really have a lot of money. My mother did struggle to put food and milk on the table. And so, uh, what we did do, though. Although we didn't have money, we have the ability to drink. And so we would every day I remember as a child dream about what it would be like to one day have enough milk and bread, have enough money to be able to buy a car or even catch the bus. And so what we did was we dream about how I could achieve that. Um And so what I did was I got educated because we knew that if I got educated, then that would enable me to get a job and become financially independent. Um, but one of the key things she also made me promise Was that not only what I get educated and have enough money, um, to support myself. But then once I did that that I would give back their knowledge and understanding so that I could strength and others. >>I love this. I love the story again. Entrepreneurship is a lot like picking yourself up. Failure is part of the process. You got a grind. You got to do the hard work. And the idea is to make it happen. You've done that? You've got a building. The business is hard. Never mind for doing it as a woman as well. And you're conditions. What a dream. You found your dream. What's it like? Right now? >>It's hard work I'm not gonna do. I know that around the world of runs excited and they said, I'm going to leave my job and you know, I've had enough. And now I'm gonna stand up my own business. We've been working on my eye for almost three years now. Running standing up a business and then running it successfully once you've started up is actually a lot harder than what people think, especially being a woman as well. And a Maori, which is essentially an indigenous person of New Zealand. Um, it is a little bit harder to do that, especially when when you choose the industry to do that and which is technology, you don't have a lot of other woman. Um, there are some women coming through from indigenous background, uh, paved the way for us, but there's not a lot of us around, and so it does make it a lot more tricky. But I had a dream, and I had a vision that I was going to be able to give back what I had learned about business and about money to help others. So uh, was where it was going to be. >>Well, it certainly inspiration for many. I love the success story and entrepreneurship hard enough as it is, like I said. But being a woman and even harder, what are some examples can you give when you were coming through? Because you've got a really kind of push through and break down walls to get things done in any startup and with the corporate world with his biases. And there's also, um, people's preconceived mindset of who's who should be in a position, what founders are what entrepreneurship is. What was it like? Can you give some examples of situations that you broke through? >>Um, look, I think that immediately people underestimate you when you're a woman, especially in indigenous woman. And so, um, what I was So basically what I would do is I didn't think about what they thought. Um, what I focused on was actually where I needed to go. And so all those people didn't believe that I could get it done. They thought I was dreaming. I know people said, um, at one point they said, Are this company looks like they're doing something similar to that. Just waste $2 million. What makes you think that you're going to be even come close to being successful like they are, um, and And my response to them was that that they aren't me. They don't have money in their organization. And I think that's something really critical. Um, that woman has to understand when they're standing up an organization, especially one of the technology. We, as a woman are unique. We bring to the table a different set of values and different principles that potentially others don't also bring to the table. We have a different level of work ethic, and so I actually think that through those experiences, I was able to be more resilient and follow through in terms of what I believe it was possible. So it doesn't matter what people thought. It doesn't matter if someone was richer or had more money than we did. Well, they had more. Exactly. I remember the other thing was with They've got all these, you know, really high high performing executives from love organizations in New Zealand. Who do you have again? My response was, Well, they don't have me right, And so that makes a significant difference. Um, it's not that I'm a unicorn, but it's that I have a very strong belief system, and I have a have a dream that I've been following for almost 40 years and trying to make come through. So those two things are things that you can't underestimate. And sometimes they are actually a lot more productive and valuable than money or positional executives within your organisation. >>Yeah, that's a great, great insight. And then again, congratulations again. Great inspiration. People worry about what everyone else is doing. Like what they got. They don't focus on what they're doing, But I love the confidence, the conviction, um, preparation, education. These are all themes that are coming out of this international Women's Day around how to be successful, how to raise your hand, how to drive through how to drive, control your career, control your own destiny. This is the theme. Education plays a big part of it. And obviously you're building a company. Amazon. You're involved with Amazon. You've got education now at your fingertips on the internet. Education is out there now. You can get it instantly, and you could level up with cloud and and really factor and compete >>at any time. Yeah, absolutely. I think if you look at a W s, they gave us the opportunity to be global instantly. I mean, without that, you know, without their infrastructure and they're back in and for us to turn that on in any country that we wanted, um, we wouldn't have been able to go global. And so, you know, I really do appreciate all of the different platforms and the technologies that we can access as a c e o of attack organization so that it actually enables us to be a global and have a global footprint. >>You know, you're a great example of what I always say about cloud computing and these platforms Is there agnostic when it comes to talent? If you can write good code and you're talented, yeah, the world is yours. There's no real degree you can get from a pedigree college or university. If you have what it takes, just plug it into the cloud and your instantly global. This is this is new. This wasn't like this years ago. >>Look. And to be honest, when I first started, I I chose voice Alexa voice as one of our channels to through which I I would provide financial updates to organizations. Now I didn't know what no one in New Zealand or Australia even knew what it was three years ago. And so, essentially, you know, the the ability to have access to people around the world to build your team, um, and to have infrastructure like Amazon, it just enables us to achieve great things. It enables us to give back more than we ever thought possible. So I think it's being able to know where you need to play the gap and then plugging that with infrastructure, which is strong and enables you to continue to grow and can really help you go forward. >>So talk to me about your current situation as a leader, as a woman in tech. Now, you have a company you're giving back, fulfilling your dream. You have a life, you gotta live your life and your life, and you're doing it all. What's it like being a leader and being a high-performance entrepreneur? >>Yeah, I love being able to give back and give back and industry, um, where it's just growing every day. The the environment is changing. We have to keep up to the play with all the new technologies that are coming through all the new capability. So that we don't get left behind. Technology enables you to become more efficient and effective and what we're working on three years ago, that's now changed significantly in terms of what it looks like now, how fast you can go, how much reach we can achieve when we're going out to our other customers and, uh, from across the globe. Also, I think that, um when you look at a woman in both of professional and a personal standpoint, I'm also a mother of four Children, and I'm also a wife. And so what I have to do is be able to balance running a typical organization as well as running the house. Unfortunately, even though I'm a C e o of a technology company, it's certainly doesn't enabled me to turn off the the mother light at the end of the night or at the beginning of the morning, when the kids at school I might be sitting in a meeting and doing a full negotiation for a for a high-value contract and in the back of my head, I'm thinking I have to take out the months later or I have to make sure that my daughter and members to take. It talks to school tomorrow. So we're quite lucky. Woman. We essentially running two parts of our brains, one of those being able to continue to nurture and and be the supporter of their husbands and our families and our Children at home as well as run these tech companies. So we're we're very lucky. I also think it's interesting that the majority of funding that that's made available by J Visas is not to women. I don't know why that is. But if you imagine having a woman who can literally, what run two worlds at the same time and be successful at both, then I think that that's high productivity that you want to be a part of. >>Yeah, that's that's injectable and more women leaders again having role models like you out there. And the story is really compelling and super inspirational. I love the 22 worlds just having to start at the same time. Yeah, talented, Um, but I love your comment also about the underdog, and I know a lot of entrepreneurs and being one myself and even people who are ultra successful, they still have the chip on the shoulder they still have the underdog mindset. So, um, is that true for you? Do you still feel like you're underdog? You always kind of. Is that something you'll never give up even when you're super successful? >>Yeah. Thanks. So, um and it's not an underdog from a really vicious, uncomfortable standpoint where I'm trying to, um, where I'm trying to get back at anybody. What it does do is as an indigenous person coming from low poverty, um, you know, the expectation of where I would end up was really low. If I if I wasn't pregnant or I wasn't in jail by 16, I was successful, and I had one. And so the bar has always been set really low for me. Even when I went and did a degree, Um, the first one was, Well, you should go and do Maori or a bachelor of arts at at University. And I said, Well, why can I go and do that thing over there? There's no Maoris or there's not a lot of women sitting in the finance, um, elections. Why don't I don't go and do a degree in finance. And so, as I've worked through my education and also my career. The expectation that achieved great things just wasn't there. And so that that drive does have to come from you internally. Um, sometimes you're not always surrounded by people who understand your value and what you can contribute to the world. And so what you do have to do is you have to have a personal belief system that enables you to actually leverage that underdog position. And so rather than letting that get you down like oh, they don't believe in me or they don't think I can do this so I can achieve that. Basically, what you do is you use it is like a little stepping stone. You're like, Thanks for that. I'll just put that over here and all it does is just enables you to prepare yourself forward. >>It's motivational. It's also curiosity. So, Steve Steve Jobs once said, Stay curious, you know, and, uh, stay foolish, actually. Say foolish, Amazon says. Be curious. That's the kind of slogan, >>but they >>will be foolish and stay curious. Whatever it is. That's kind of the mindset. And again what I love about the story, and I think this is a trend that we're seeing is that if you are underrepresented or you are the underdog now more than ever, the ability to level up is better than ever before. Anyone can start a company, you can get a cloud computing, and Amazon gives the education for free. If everyone someone stuck, you can just go online courses. So there's now plate paths to go from here to here quickly. Um, this is amazing. >>Yeah, but it is hard work, so right, so it doesn't come easy. Um And so that is one thing I think that people underestimate about the ability to stand up for business. And then it becomes this, you know, apple or Amazon or Google. And so, yes, my vision is that we're on the road trip back. We're focussed on being able to list in the last five years time with a billion dollar valuation and use that as a vision. But being able to be open-minded about what it's going to take to actually get there is really important, and so you can have conviction, but you need to follow through and have action. Um, you need to be open-minded about changing the way you thought it was going to look. I mean, every day, I probably three or four times since we've gone live last year. Um, and that was because she wasn't where she needed to be. We needed to private her so that we can continue to ensure that we ended up with the product market fit that enabled us to meet our vision, but also to achieved financial and strategic >>goals. That's a great point. You've got to do the work. You've got to grind it out. Sometimes you gotta be sensitive to the customers and the market. This is the secret final question for you. What a great conversation. Um, as an entrepreneur, we all know it's the trials. Tribulated the roller coaster. A lot of emotion. Like raising a family. You don't know what you're gonna get. You know, anything is possible. How do you maintain the balance? Emotionally as you go in and continue to build out your business, you gotta take the highs and the lows. >>Oh, look, in the early days of standing out today, I was very naive. Not because I was a woman just because I was new to the game. Um, I had always worked for global organizations that already established that had big bits of money that had resources that I could call on. And so I'd say that first 6 to 12 months was really hard. There was a time there where I had to rebuild i-i. They changed the back end infrastructure. Um, I've spoken to zero and Amazon. Alexa and I had to achieve a certain I had to go through a number of different gates. And what that means is that I had to rebuild build here. Um, I think I cried initially for the first couple of days, but then it was actually, it took me about a month to get over myself. And what I mean by that is I had this vision and this dream about how it was going to be. I was going to do this and then all these steps we're going to follow, and everything was going to turn out how I expected. Um, and then it hurt me within the first three months of trying to get accreditation That it wasn't It wasn't going to turn out how I wanted. I didn't have the resources or the money to execute it. How I wanted. And therefore what I had to do was understand why. Why? Because what happened was I was able to use my why It is the basis for why I was making decisions going forward. So rather than it being just this vision about where I was going to land, it ended up being It doesn't matter the how the pathway we get there. Obviously, we want to do it with integrity, but I don't necessarily know all the steps of how that's going to happen. But I need to be open to the fact that it won't. Now when I get disappointed and things don't happen, how I expect them now, I basically just perfect. Initially I cried and I sit there and complain to my husband, and I feel like, Oh, my God, let me do this. So it was like, I've turned me down and I'm not gonna do it this way. And, you know, I just complain and wind, Um, but three years on, basically, whenever I had a wall or I had a roadblock, I'm just I just step back and go right. I can't go that way. Let's find another way. And so I think you have to be really resilient around accepting that things won't always go away. But there is always another way. >>Don't worry. Great conversation. Building a business and text from your dreams. Getting educated, going out in the arena, being successful again. Once you're successful, you can write your original story The victory. The victor writes the narrative, as they say, so is it can be disappointing. Sometimes when you're learning to grow like that, businesses like that's a great story. And congratulations. And thank you so much for taking the time to to share on the Cube as part of our celebration of International Women's Day. Thank you so much. >>Okay, thanks so much. >>Okay, that's the presentation of women in Tech Global Event celebrating International Women's Day. I'm John for most of the Cube. Thanks for watching. Yeah, Yeah, yeah. Hm. Yeah, yeah,

>> - From around the globe, it's the cube covering data citizens, 21 brought to you by Collibra. >> Hello, everyone, John Walls here, As we continue our cube conversations here as part of data citizens, 21, the conference ongoing. Collibra at the heart of that, really at the heart of data these days and helping companies and corporations make sense. Although this data chaos that they're dealing with, trying to provide new insights, new analysis being a lot more efficient and effective with your data. That's what Collibra is all about. And their founder and their chief data citizen, if you will, Stan Christiaens joins us today. And Stan, I love that title, chief data citizen. What does that all about? What does that mean? >> Hey John, thanks for having me over. And hopefully we'll get to a point where the chief data citizen Titelist cleaves to you. Thanks by the way, for giving us the opportunity to speak a little bit about what we're doing with our chief data citizen. We started the company about 13 years ago, 2008. And over those years, as a founder I've worn many different hats from product to pre-sales to partnerships and a bunch of obvious things. But ultimately the company reaches a certain point a certain size where systems and processes become absolutely necessary if you want to scale further. And for us, this is the moment in time where we said, okay we probably need a data office right now ourselves, something that we've seen with many of our customers. So we said, okay, let me figure out how to lead our own data office and figured out how we can get value out of data using our own software at Collibra itself. And that's where the chief data citizen role comes in. On Friday evening, we like to call that drinking our own champagne moment morning, either eating our own dog food but, essentially this is what we help our customers do, build out the data offices. So we're doing this ourselves now, when we're very hands-on. So there's a lot of things that we're learning, again just like our customers do. And for me, at Collibra, this means that I'm responsible as a chief data citizen for our overall data strategy, which talks a lot about data products, as well as our data infrastructure, which is needed to power data products. Now, because we're doing this in the company and also doing this in a way that is helpful to our customers. We're also figuring out how do we translate the learnings that we have ourselves and give them back to our customers, to our partners, to the broader ecosystem as a whole. And that's why if you summarize the strategy, I like to sometimes refer to it as data office 2025, it's 2025. What is the data office look like by then? And we recommend to our customers to also have that forward looking view just as well. So if I summarize the, the answer a little bit and it's fairly similar to achieve that officer role but, because it has the external evangelization component, helping other data leaders, we like to refer to it as the chief data citizens. >> Yeah, and that, that kind of, you talked about evangelizing, obviously with that, that you're talking about certain kinds of responsibilities and obligations. And I, when I think of citizenship in general I think about privileges and rights and you know, about national citizenship. You're talking about data citizenship, So I assume that with that you're talking about appropriate behaviors and the most well-defined behaviors, and kind of keeping it between the lanes basically. Is that, is that how you look at being a data citizen or, and if not, how would you describe that to a client about being a data citizen? >> It's a very good point, as a citizen you have rights and responsibilities, and the same is exactly true for a data citizen. For us, starting with what it is, right for us, A data citizen is somebody who uses data to do their job. And we've purposely made that definition very broad because today we believe that everyone in some way uses data to do their job. You know, data is universal. It's critical to business processes and it's importance is only increasing. And we want all the data citizens to have appropriate access to data and the ability to do stuff with data but, also to do that in the right way. And if you think about it this is not just something that applies to you in your job but, also extends beyond the workplace because as a data citizen, you're also a human being, of course. So, the way you do data at home with your friends and family, all of this becomes important as well. And we like to think about it as informed privacy aware, data citizens should think about trust in data all the time, because ultimately everybody's talking today about data as an asset, and data is the new gold, and the new oil, and the new soil, and there is a ton of value in data but, as much as organizations themselves to see this, it's also the bad actors out there. We're reading a lot more about data breaches, for example. So, ultimately there's no value without risk. So, as a data citizen, you can achieve a value but, you also have to think about, how do I avoid these risks, and as an organization, if you manage to combine both of those, that's when you can get the maximum value out of data in a trusted manner. >> Yeah, I think this is pretty, an interesting approach that you've taken here because obviously there there are processes with regard to data, right? I mean, the, you know, that that's pretty clear but, there are also, there's a culture that you're talking about here that, that not only are we going to have an operational plan for how we do this certain activity and how we're going to analyze here, input here, action, or perform action on that, whatever but we're going to have a mindset or an approach mentally that we want our company to embrace. So, if you would walk me through that process a little bit in terms of creating that kind of culture, which is very different than kind of the X's and O's and the technical side of things. >> Yeah. That's I think when organizations face the biggest challenge, because, you know maybe they're hiding the best most unique data scientists in the world but, it's not about what that individual can do, right? It's about what the combination of data citizens across the organization can do. And I think it starts first by thinking as an individual about universal goal, golden rule, treat others as you would want to be treated yourself, right? The way you would ethically use data at your job. Think about that, There's other people at other companies, who you would want to do the same thing. Now, from our experience, in our own data office at Collibra, as well as what we see with our customers. A lot of that personal responsibility which is where culture starts, starts with data literacy. And, you know, we talked a little bit about Plymouth rock and the small statues in Brussels Belgium, where I'm from but, essentially here we speak a couple of languages in Belgium. And for organizations, for individuals data literacy is very similar. You know, you're able to read and write which are pretty essential for any job today. And so we want all data citizens to also be able to speak and read and write data fluently. If I, if I can express it this way. And one of the key ways of getting that done and establishing that culture around data, lies with the one who leads data in the organization, the chief data officer, or however the role is called. They play a very important role in this. In comparison, maybe that I always make there is think about other assets in your organization. You know, you're organized for the money assets, for the talent assets, with HR and a bunch of other assets. So let's talk about the, the money assets for a little bit, right? You have a finance department, you have a chief financial officer, and obviously their responsibility is around managing that money asset. But it's also around making others in the organization think about that money. And they do that through established processes and responsibilities like budgeting and planning but, also ultimately to the individual where, you know, through expense sheets that we all love so much, they make you think about money. So, if the CFO makes everyone in the company thinks about think about money, that data officer, or the data lead, has to think, has to make everyone think in the company about data assets, asset, just as well. And those rights, those responsibilities in that culture, they also change, right? Today, they're set this and this way because of privacy and policy X and Y and Z. But tomorrow, for example, as, as with the European union's new regulation around BI, there's a bunch of new responsibilities you'll have to think about. >> You mentioned security and about value and risk, which is certainly, they are part and parcel, right? If I have something important I've got to protect it because somebody else might want to, to create some damage, some harm and and steal my value, basically when that's, what's happening as you point out in the data world these days. So, so what kind of work are you doing in that regard in terms of reinforcing the importance of security culture, privacy culture, you know, this kind of protective culture within an organization so that everybody fully understands, you know, the risks but, also the huge upsides. If you do enforce this responsibility and these good behaviors that that obviously the company can gain from, and then provide value to their client base. So how do you reinforce that within your clients to spread that culture, if you will, within their organizations? >> Spreading a culture is not always an easy thing, And especially a lot of organizations think about the value around data, but to your point, not always about the risks that come associated with it. Sometimes just because they don't know about it yet, right, there's new architectures that come into play, like the clouds and that comes with a whole bunch of new risks. That, that's why one of the things that we recommend always to our customers and to data officers in our customer's organizations, is that next to establishing that, that data literacy, for example, and working on data products is that they also partner strongly with other leaders in their organization. On the one hand, for example, the legal folks, where typically you find the the aspects around privacy and on the other hand, the information security folks, because if you're building up sort of map of your data, look at it like a castle, right, that you're trying to protect. If you don't have a map of your castle, with the strong points and the weak points, and you know where people can build, dig a hole under your wall or what have you, then it's very hard to defend. So, you have to be able to get a map of your data, a data map if you will, know what data is out there. Who its being used by, and why and how, and then you want to prioritize that data, which is the most important what are the most important uses and put the appropriate protections and controls in place. And it's fundamental that you do that together with your legal and information security partners because you may have as a data lead that you may have the data knowledge, the data expertise but, there's a bunch of other things that come into play when you're trying to protect, not just the data but, really your company on its data as a whole. >> No, you Were talking about 2025 a little bit ago, and I thought good for you, that's quite a crystal ball that you have it, you know looking to, you know, with the headlights that far down the road, but I know you have to be, you know that kind of progressive thinking is very important. What do you see in, in the long-term for number one, your kind of position as a chief data citizen, if you will, and then the role of the chief data officer, which you think is kind of migrating toward that citizenship, if you will. So, maybe put on those long-term vision goggles of yours again, and tell me, what do you see as far as these evolving roles and, and these new responsibilities for people who are CEOs these days? >> Well, 2025 is closer than we think right? Then obviously, my crystal ball is as fuzzy as everyone else's but, there's a few things, that trends that you can easily identify and that we've seen by doing this for so long at Collibra. And one is the, the push around data. I think last year, the years, 2020,` where sort of COVID became the executive director of digitalization. Forced everyone to think more about digital, and I expect that to continue. So, that's an important aspect. The second important aspect that I expect to continue for the next couple of years, easily in 2025 is the whole movement to the cloud. So these cloud native architectures become important, as well as the, you know, preparing your data around it, preparing your policies around it, etc.. I also expect that privacy regulations will continue to increase as well as the needs to protect your data assets. And I expected a lot of key data officers will also be very busy building out those data products. So if you, if you take that that trend then, okay data products are getting more important for key data officer's, then data quality is something that's increasingly important today to get right, otherwise, becomes a garbage in garbage out kind of situation, where your data products are being fed bad foods and ultimately their outcomes aren't very clear. So for us, for the chief data officers, I think it was about one of them in 2002, and then 2019 ish, let's say there were 10,000. So there's plenty of upsides for the chief data officer there's plenty of roles like that needed across the world. And they've also evolved in, in responsibility. And I expect that their position, you know, as it it is really a C-level position today in most organizations. Expect that, that trend will also continue to grow. But ultimately those chief data officers have to think about the business, right? Not just the defensive and offensive positions around data, like almost policies and regulations but, also the support for businesses who are today, shifting very fast and will continue to, to digital. So, those key data officers will be seen as key notes. Especially when they can build out the factory of data products that really supports the business. But at the same time, they have to figure out how to reaching all of the branch to their technical counterparts, because you cannot build a factory of data products in my mind at least, without the proper infrastructure. And that's where your technical teams come in. And then obviously the partnerships with your video and information security folks, of course. >> Well heroes, everybody wants to be the hero. And I know that's a, you painted a pretty clear path right now, as far as the chief data officer's concerned and their importance and the value to companies down the road. Stan, we thank you very much for the time today and for the insight, and wish you continued success at the conference. Thank you very much. >> Thank you very much. Have a nice day. Stay healthy. >> Thank you very much Stan Christiaen's joining us, talking about chief data citizenship, if you will, as part of data citizens, 21 the conference being put on by Collibra. I'm John Walls. Thanks for joining us here on the cube. (upbeat music)

(bright music) >> Announcer: From around the globe, it's "theCUBE" with digital coverage of IBM Think 2021 brought to you by IBM. >> Welcome back to "theCUBE" coverage of IBM Think 2021. I'm John Furrier, host of "theCUBE". We're here with Ed Lynch, vice president of IBM Business Automation. Topic here is AI Powered Business Automation as he leads the team, the Business Automation offering management team driving the automation platform altering multicloud and built in AI and low code tools. Ed, thanks for joining me on "theCUBE" today. >> Thank you John. Thanks for having me. >> So, automation is really the focus of this event. If you peel back all the announcements and automation which is data process, transformation, innovation scale, all kind of points to eigth automation. How has the past year changed the automation market? >> It's been a fascinating ride. Fascinating ride more than just the COVID part, but some interesting, interesting observations as we look back over the year. I called this the AD for BC before COVID and AD, the Anno, not the Anno Domini, but Anno Damum meaning year of the house, living in the house. The thing that we really learned is that clients are engaging differently with their, let's say the companies that they work with. They're engaging digitally. Not a big surprise. You look at all of the big digital brands. You look at the way that we engage. We buy things from home. We don't go to the store anymore. We get delivery at home. Work from home completely different. If you think about what happened to the business on the business side, work from home changed everything. And the real bottom line is companies that invested ahead of time in automation technology, they've flourished. The companies that didn't, they're not so flourishing. So, we're seeing, right now we're seeing skyrocketing demand. That's bonus for us. Skyrocketing demand and also that this demand on the supply side we're seeing competition. More competition in the automation space. And I believe any company that's got more than two guys in a go in the back in a basement are entering the automation space. So, it's a fun time. It's a really fun time to be in this space. >> Great validation on the market. Great call out there on the whole competition thing. Cause you really look at this competition from you know, two guys in the garage or you know, early stage startup but the valuations are an indicator. It's a hot market. Most of those startups have massive valuations. Even the pre IPO ones are just like enormous valuations. This is a tell sign. That process automation and digital supply chains, value chains, business is being rewritten with software right? So, you know, there's an underlying hybrid cloud kind of model that's been standardized. Now you have all these things now on top thousand flowers, blooming or apps, if you will more apps and more apps, more apps, less of the kind of like CRM, like the... you're going to have sub systems large subsystems, but you're going to have apps everywhere. Everything's an app now. So this means things have to be re-automated. >> Yeah. >> What's your advice for companies trying to figure this out? >> So my advice is start small. Like one of the big temptations is that you can jump in and say, God mighty we've got this perfect opportunity for rejiggering, rebuilding the entire company from scratch. That's a definition of insanity. Like you don't want to do that. What you want to do is you want to start small and then you want to prove. Second big thing is you want to make sure that you start with the data. Just like any, any good management system you have to start with the facts. You have to discover what's going on. You have to decide which piece you're going to focus on. And then you have to act. And then act leads to optimization. Optimization allows them to say, I'm looking at a dashboard I'm making progress or I'm heading in the wrong direction. Stop. Those kinds of things. So start small, start with the data and make sure that you line up your allies. You have to have, this is a culture change that you have to have your CEO lined up from the top and you have to have buy-in from the bottom. If any of those pieces are missing you're asking for trouble. >> Can you share an example of a customer of yours that's using intelligent automation. Take me through that process. And what's the drivers behind. >> Yeah, sure. A good example. There's a, there's a client of ours in Morocco and it's not a big country but it's a very interesting story. They, the company is called CDG Prevoyance. CDG Prevoyance, this is a, it's a French company, obviously. That was my French accent. But there they are a company that does pension benefits. So think of this as you're putting money away, you're in in the US you have, 401ks. In Canada we have RSPs. You're putting money away for the future. And the company that you're putting money into has to manage your account along with millions of other accounts. And this is where CDG started. It was a very paper-based business. Extremely paper-based. Like the forms that you had to fill out. The way that you engage with, with CDG was was a very form-based thing. Like document based thing. They, the onboarding time to actually enter a new account for a new employee, looking to get their pension plan done was weeks. With automation they changed from being a paper-based thing to being an electronic based thing. They changed the workflow associated with gathering information, getting on onboarded. They onboard now in minutes, as opposed to weeks. This is an example of the kind of thing. Now, if you go back to the first question that you asked, Old companies change. The companies that you engage with digitally are the ones that give you that kind of experience where it doesn't, you know you don't have to crawl through broken glass in order to engage with them. That's what CDG did. And they managed to really ring out some of the human labor out of that onboarding process. >> Great, great stuff. You know, this Mayflower is an exciting story. I've been checking out the, using this decisioning together with you guys with automation. Can you tell me about that? >> Mayflower is really exciting. This is one of those things that just jazzes me. It jazzes me because I think to myself how the heck did they do that? So the Mayflower is a boat. It's like a sailing vessel, like any other sailing vessel. It's 15 meters long. It's powered entirely by solar. It's making a voyage from England to Plymouth. The landing place, you know, where the pilgrims landed, and this, this, this whole voyage is going to be done without human interaction. It's all going to be powered by the machine. So you think about autonomous vehicles. You think about this whole story of autonomous vehicles piloting across the ocean is way different than piloting the car down a highway. >> So this is an autonomous ship, then. >> This is an autonomous ship. Exactly. So think of this as there is there's nobody piloting this thing. It's all piloted by software. The software is, is my business software, interestingly. It has all these sensors that allow you to say, Oh there's a boat over there, steer clear of the boat. But more importantly, when you come to the Harbor you have to negotiate the marks. You have to, you know, steer in the lanes. Different from steering a car you steer a car between the two white lines. You know, you might have a dashed line here and a white line here. You steer the car to come in the middle. Very easy. Steering a boat, that's really hard. Steering a boat in the middle of the ocean when you've got monstrous waves and you've got, you know, potential this, potential that. Like this, this thing is really exciting. I find this whole data, AI decisioning, fascinating. >> Dave, Dave Alonzo is going to love this next question I'm going to ask you. He's my co-host of theCUBE. You always talk about data lakes. How about data ocean? Now we have a data ocean out here which I've always used the metaphor ocean so much more dynamic, but here literally the data is the ocean. You got to factor in conditions that are going to be completely dynamic, wave height, countermeasures on, on navigation. All this is being done. Is that, how does it all work? I mean, has it all been driven by data scenarios? I mean... >> No, it's so it's all driven so it starts with the sensors, the sensor, you have a vision sensor that tells you what it sees. So it sees boats and it sees marks. It sees big waves coming. It's all powered by weather data. So there is a weather feed, but more importantly like the sailing across the ocean part you don't have to worry other than when you know a boat comes or a whale comes. You steer clear of it, fine. That part's relatively easy. When you come close to the shore then you have to make decisions about where to go. And the decisions are all informed by data. So you gather all this data you run machine learning algorithms against the data. You run a decision priorities mechanism. And then you have to, you have to confer with the rules. Like, what are the rules of navigation? I don't know if you're a sailor, but the rules of navigation on the open sea are actually really simple to understand because it's, you know the person on the left has the, has the priority. If you're overtaking, you have to steer clear. All those kind of things. In a Harbor it's way different. And so you have to be able to demonstrate to the government that you have open decisions an open decision-making mechanism to steer around the marks. The government wants to know that you can do that. Otherwise they say, stay out of my Harbor. Very interesting. >> It actually is. It actually encapsulates a lot of business challenges too. You have a lot of data mashing up going on. I mean, you've got navigation, what's under the water. What's on top of the water. You got weather data over the top. It's good to own the weather company for IBM. That helps probably a lot. Then you've got policies, you know? And policy based decision-making. It sounds like a data center and multicloud opportunity. >> It is exactly. That's why I love this opportunity because it's, it's it's almost the, the complete stall from being a business problem to being an experiment problem. Because the way that these, these guys, these engineers built this thing, they're, they're looking for research. They're looking for the ability to really press that edge of where AI and uh you know, machine learning and decisioning come together with ocean research, because what they're doing is social research. They're looking for water temperature and whales and that kind of stuff. >> Unmanned vehicles, unmanned drones is another another big thing we're seeing that with, with, from from managing this. This brings up the point I see about leaders in the industry, and I know we don't have a lot of time. I want to get back to the the announcement that you guys made a while back but I want to stay on this point real quick. If you can just comment. Business leaders that are curious around automation, really the ones that have to invent this. Think about the autonomous ship. On top of the autonomous business I mean, here at theCUBE, we have a studio. What about autonomous studio work? So the notion of automation if you're not thinking about it, you can't do it. What's your advice to people? >> So, so I think the, the advice is that you look for areas of opportunity, like be, be discreet and be like just choose the thing that you want to go after. In the, in the Mayflower case what they were doing was they were looking for a way to navigate in the Harbor. Opens, you've got this big wide ocean. You can go wherever you want to. Navigating in the Harbor is much trickier. And so what they did was they applied technology very specific pieces of technology to that specific problem. That's the advice that I would give to a business. Don't look to turn everything upside down. That's craziness. Like, you're in business for a reason. What you want to do is you want to pick a specific thing to go after and go and fix that. Then pick adjacent things, go fix that. And eventually it gets to the point where you have straight through processing, which is where everybody wants to get. >> I can imagine great opportunities for you guys and your team. Congratulations on all that work. 'Cause there's certainly more to do. I can see so much happening as you guys are building out the stack and acquiring companies. You know, last month you guys had announced to acquire process mining company, myInvenio. what does that announcement mean for IBM and the AI powered automation? Because you guys also have business deals with others in the industry. Take, take us through the, the what this acquisition means for IBM. >> Sure. So myInvenio is a, is a business. First, just get the facts. myInvenio is a business and it's a it's a company that's based in Italy. They do what's called process mining. Process mining is a tool that does what I was just talking about. It allows you to identify places where you have weakness in your workflows. Workflows, like big macro workflows like procure to pay the ability to go all the way from buying something to paying for. Companies spend noodles of money on procure to pay as an example. But inevitably there are humans in that, in that process humans means that there are ways to become more efficient. You could change a person's job. You can change a person's profile. All of that is what this tool is about. This, this tool gives us an excellent addition to our portfolio, our automation portfolio which allows clients to understand where the weaknesses are. And then we can apply specific automations to fix those weaknesses. That's what myInvenio means to us. It puts us in a position of having a complete set of technologies that match up with Gartner's hyper automation market texture. That gives us a very powerful advantage in the marketplace. So I'm very, very happy about this acquisition. >> Yeah. Ed, thanks for coming on theCUBE. Really appreciate it. Final word. I'd love to get you spend the last minute just talking about IBM's commitment to open and also integration um, integrating with other companies. Take a minute to explain that. >> Yeah, sure. So the, the, the open part is something that we've understood for very, very long time. One of the jobs that I had a long time ago was open source and bringing open source into IBM. I'm a very strong proponent of open source. Open means no barriers to entry no barriers to substitution. And what it means is you have a fair fight. You have, we all have proprietary technology. We all have intellectual property. Sure. But if you have an open base then what that gives you is the ability to inter-operate with other people, other, you know other competitors, frankly, that to me is goodness for the client, because at the end of the day, the client doesn't get locked in. That's the thing that they are really looking for. They want to have the flexibility to move. They want to have the flexibility to put the best, you know best technology in place. So we are strong proponents of open. >> All right. Ed Lynch, vice president of IBM Business Automation. AI powered business automation is coming. Autonomous vehicles, autonomous ships, autonomous business. Everything's going automation soon. We're going to have the autonomous cube. And so, Ed, thanks for coming on theCUBE. I really appreciate it. >> Okay, John. Thank you. >> Okay. Cube coverage of IBM Think 2021, virtual launch. I'm John Furrier, your host of theCUBE. Thanks for watching. (bright music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Welcome back to "theCUBE" coverage of IBM Think 2021. I'm John Furrier, host of "theCUBE". We're here with Ed Lynch, vice president of IBM Business Automation. Topic here is AI Powered Business Automation as he leads the team, the Business Automation offering management team driving the automation platform altering multicloud and built in AI and low code tools. Ed, thanks for joining me on "theCUBE" today. >> Thank you John. Thanks for having me. >> So, automation is really the focus of this event. If you peel back all the announcements and automation which is data process, transformation, innovation scale, all kind of points to automation. How has the past year changed the automation market? >> It's been a fascinating ride. Fascinating ride more than just the COVID part, but some interesting, interesting observations as we look back over the year. I called this the AD for BC before COVID and AD, the Anno, not the Anno Domini, but Anno Domuo meaning year of the house, living in the house. The thing that we really learned is that clients are engaging differently with their, let's say the companies that they work with. They're engaging digitally. Not a big surprise. You look at all of the big digital brands. You look at the way that we engage. We buy things from home. We don't go to the store anymore. We get delivery at home. Work from home completely different. If you think about what happened to the business on the business side, work from home changed everything. And the real bottom line is companies that invested ahead of time in automation technology, they've flourished. The companies that didn't, they're not so flourishing. So, we're seeing, right now we're seeing skyrocketing demand. That's bonus for us. Skyrocketing demand and also that this demand on the supply side we're seeing competition. More competition in the automation space. And I believe any company that's got more than two guys in a go in the back in a basement are entering the automation space. So, it's a fun time. It's a really fun time to be in this space. >> Great validation on the market. Great call out there on the whole competition thing. Cause you really look at this competition from you know, two guys in the garage or you know, early stage startup but the valuations are an indicator. It's a hot market. Most of those startups have massive valuations. Even the pre IPO ones are just like enormous valuations. This is a tell sign. That process automation and digital supply chains, value chains, business is being rewritten with software right? So, you know, there's an underlying hybrid cloud kind of model that's been standardized. Now you have all these things now on top thousand flowers, blooming or apps, if you will more apps and more apps, more apps, less of the kind of like CRM, like the... you're going to have sub systems large subsystems, but you're going to have apps everywhere. Everything's an app now. So this means things have to be re-automated. >> Yeah. >> What's your advice for companies trying to figure this out? >> So my advice is start small. Like one of the big temptations is that you can jump in and say, God almighty we've got this perfect opportunity for rejiggering, rebuilding the entire company from scratch. That's a definition of insanity. Like you don't want to do that. What you want to do is you want to start small and then you want to prove. Second big thing is you want to make sure that you start with the data. Just like any, any good management system you have to start with the facts. You have to discover what's going on. You have to decide which piece you're going to focus on. And then you have to act. And then act leads to optimization. Optimization allows them to say, I'm looking at a dashboard I'm making progress or I'm heading in the wrong direction. Stop. Those kinds of things. So start small, start with the data and make sure that you line up your allies. You have to have, this is a culture change that you have to have your CEO lined up from the top and you have to have buy-in from the bottom. If any of those pieces are missing you're asking for trouble. >> Can you share an example of a customer of yours that's using intelligent automation. Take me through that process. And what's the drivers behind. >> Yeah, sure. A good example. There's a, there's a client of ours in Morocco and it's not a big country but it's a very interesting story. They, the company is called CDG Prevoyance. CDG Prevoyance, this is a, it's a French company, obviously. That was my French accent. But there they are a company that does pension benefits. So think of this as you're putting money away, you're in in the US you have, 401ks. In Canada we have RSPs. You're putting money away for the future. And the company that you're putting money into has to manage your account along with millions of other accounts. And this is where CDG started. It was a very paper-based business. Extremely paper-based. Like the forms that you had to fill out. The way that you engage with, with CDG was was a very form-based thing. Like document based thing. They, the onboarding time to actually enter a new account for a new employee, looking to get their pension plan done was weeks. With automation they changed from being a paper-based thing to being an electronic based thing. They changed the workflow associated with gathering information, getting on onboarded. They onboard now in minutes, as opposed to weeks. This is an example of the kind of thing. Now, if you go back to the first question that you asked, Old companies change. The companies that you engage with digitally are the ones that give you that kind of experience where it doesn't, you know you don't have to crawl through broken glass in order to engage with them. That's what CDG did. And they managed to really ring out some of the human labor out of that onboarding process. >> Great, great stuff. You know, this Mayflower is an exciting story. I've been checking out the, using this decisioning together with you guys with automation. Can you tell me about that? >> Mayflower is really exciting. This is one of those things that just jazzes me. It jazzes me because I think to myself how the heck did they do that? So the Mayflower is a boat. It's like a sailing vessel, like any other sailing vessel. It's 15 meters long. It's powered entirely by solar. It's making a voyage from England to Plymouth. The landing place, you know, where the pilgrims landed, and this, this, this whole voyage is going to be done without human interaction. It's all going to be powered by the machine. So you think about autonomous vehicles. You think about this whole story of autonomous vehicles piloting across the ocean is way different than piloting the car down a highway. >> So this is an autonomous ship, then. >> This is an autonomous ship. Exactly. So think of this as there is there's nobody piloting this thing. It's all piloted by software. The software is, is my business software, interestingly. It has all these sensors that allow you to say, Oh there's a boat over there, steer clear of the boat. But more importantly, when you come to the Harbor you have to negotiate the marks. You have to, you know, steer in the lanes. Different from steering a car you steer a car between the two white lines. You know, you might have a dashed line here and a white line here. You steer the car to come in the middle. Very easy. Steering a boat, that's really hard. Steering a boat in the middle of the ocean when you've got monstrous waves and you've got, you know, potential this, potential that. Like this, this thing is really exciting. I find this whole data, AI decisioning, fascinating. >> Dave, Dave Alonzo is going to love this next question I'm going to ask you. He's my co-host of theCUBE. You always talk about data lakes. How about data ocean? Now we have a data ocean out here which I've always used the metaphor ocean so much more dynamic, but here literally the data is the ocean. You got to factor in conditions that are going to be completely dynamic, wave height, countermeasures on, on navigation. All this is being done. Is that, how does it all work? I mean, has it all been driven by data scenarios? I mean... >> No, it's so it's all driven so it starts with the sensors, the sensor, you have a vision sensor that tells you what it sees. So it sees boats and it sees marks. It sees big waves coming. It's all powered by weather data. So there is a weather feed, but more importantly like the sailing across the ocean part you don't have to worry other than when you know a boat comes or a whale comes. You steer clear of it, fine. That part's relatively easy. When you come close to the shore then you have to make decisions about where to go. And the decisions are all informed by data. So you gather all this data you run machine learning algorithms against the data. You run a decision priorities mechanism. And then you have to, you have to confer with the rules. Like, what are the rules of navigation? I don't know if you're a sailor, but the rules of navigation on the open sea are actually really simple to understand because it's, you know the person on the left has the, has the priority. If you're overtaking, you have to steer clear. All those kind of things. In a Harbor it's way different. And so you have to be able to demonstrate to the government that you have open decisions an open decision-making mechanism to steer around the marks. The government wants to know that you can do that. Otherwise they say, stay out of my Harbor. Very interesting. >> It actually is. It actually encapsulates a lot of business challenges too. You have a lot of data mashing up going on. I mean, you've got navigation, what's under the water. What's on top of the water. You got weather data over the top. It's good to own the weather company for IBM. That helps probably a lot. Then you've got policies, you know? And policy based decision-making. It sounds like a data center and multicloud opportunity. >> It is exactly. That's why I love this opportunity because it's, it's it's almost the, the complete stall from being a business problem to being an experiment problem. Because the way that these, these guys, these engineers built this thing, they're, they're looking for research. They're looking for the ability to really press that edge of where AI and uh you know, machine learning and decisioning come together with ocean research, because what they're doing is social research. They're looking for water temperature and whales and that kind of stuff. >> Unmanned vehicles, unmanned drones is another another big thing we're seeing that with, with, from from managing this. This brings up the point I see about leaders in the industry, and I know we don't have a lot of time. I want to get back to the the announcement that you guys made a while back but I want to stay on this point real quick. If you can just comment. Business leaders that are curious around automation, really the ones that have to invent this. Think about the autonomous ship. On top of the autonomous business I mean, here at theCUBE, we have a studio. What about autonomous studio work? So the notion of automation if you're not thinking about it, you can't do it. What's your advice to people? >> So, so I think the, the advice is that you look for areas of opportunity, like be, be discreet and be like just choose the thing that you want to go after. In the, in the Mayflower case what they were doing was they were looking for a way to navigate in the Harbor. Opens, you've got this big wide ocean. You can go wherever you want to. Navigating in the Harbor is much trickier. And so what they did was they applied technology very specific pieces of technology to that specific problem. That's the advice that I would give to a business. Don't look to turn everything upside down. That's craziness. Like, you're in business for a reason. What you want to do is you want to pick a specific thing to go after and go and fix that. Then pick adjacent things, go fix that. And eventually it gets to the point where you have straight through processing, which is where everybody wants to get. >> I can imagine great opportunities for you guys and your team. Congratulations on all that work. 'Cause there's certainly more to do. I can see so much happening as you guys are building out the stack and acquiring companies. You know, last month you guys had announced to acquire process mining company, myInvenio. what does that announcement mean for IBM and the AI powered automation? Because you guys also have business deals with others in the industry. Take, take us through the, the what this acquisition means for IBM. >> Sure. So myInvenio is a, is a business. First, just get the facts. myInvenio is a business and it's a it's a company that's based in Italy. They do what's called process mining. Process mining is a tool that does what I was just talking about. It allows you to identify places where you have weakness in your workflows. Workflows, like big macro workflows like procure to pay the ability to go all the way from buying something to paying for. Companies spend noodles of money on procure to pay as an example. But inevitably there are humans in that, in that process humans means that there are ways to become more efficient. You could change a person's job. You can change a person's profile. All of that is what this tool is about. This, this tool gives us an excellent addition to our portfolio, our automation portfolio which allows clients to understand where the weaknesses are. And then we can apply specific automations to fix those weaknesses. That's what myInvenio means to us. It puts us in a position of having a complete set of technologies that match up with Gartner's hyper automation market texture. That gives us a very powerful advantage in the marketplace. So I'm very, very happy about this acquisition. >> Yeah. Ed, thanks for coming on theCUBE. Really appreciate it. Final word. I'd love to get you spend the last minute just talking about IBM's commitment to open and also integration um, integrating with other companies. Take a minute to explain that. >> Yeah, sure. So the, the, the open part is something that we've understood for very, very long time. One of the jobs that I had a long time ago was open source and bringing open source into IBM. I'm a very strong proponent of open source. Open means no barriers to entry no barriers to substitution. And what it means is you have a fair fight. You have, we all have proprietary technology. We all have intellectual property. Sure. But if you have an open base then what that gives you is the ability to inter-operate with other people, other, you know other competitors, frankly, that to me is goodness for the client, because at the end of the day, the client doesn't get locked in. That's the thing that they are really looking for. They want to have the flexibility to move. They want to have the flexibility to put the best, you know best technology in place. So we are strong proponents of open. >> All right. Ed Lynch, vice president of IBM Business Automation. AI powered business automation is coming. Autonomous vehicles, autonomous ships, autonomous business. Everything's going automation soon. We're going to have the autonomous cube. And so, Ed, thanks for coming on theCUBE. I really appreciate it. >> Okay, John. Thank you. >> Okay. Cube coverage of IBM Think 2021, virtual launch. I'm John Furrier, your host of theCUBE. Thanks for watching. (bright music)