(upbeat music) >> Hey everyone. Welcome to theCUBE's special program series "Women of the Cloud", brought to you by AWS. I'm your host for the program, Lisa Martin. Very pleased to welcome back one of our alumni to this special series, Dr. Tendu Yogurtcu joins us, the CTO of Precisely. >> Lisa: Tendu, it's great to see you, it's been a while, but I'm glad that you're doing so well. >> Geez, it's so great seeing you too, and thank you for having me. >> My pleasure. I want the audience to understand a little bit about you. Talk to me a little bit about you, about your role and what are some of the great things that you're doing at Precisely. >> Of course. As CTO, my current role is driving technology vision and innovation, and also coming up with expansion strategies for Precisely's future growth. Precisely is the leader in data integrity. We deliver data with trust, with maximum accuracy, consistency, and also with context. And as a CTO, keeping an eye on what's coming in the business space, what's coming up with the emerging challenges is really key for me. Prior to becoming CTO, I was General Manager for the Syncsort big data business. And previously I had several engineering and R&D leadership roles. I also have a bit of academia experience. I served as a part-time faculty in computer science department in a university. And I am a person who is very tuned to giving back to my community. So I'm currently serving as a advisory board member in the same university. And I'm also serving as a advisory board member for a venture capital firm. And I take pride in being a dedicated advocate for STEM education and STEM education for women in particular, and girls in the underserved areas. >> You have such a great background. The breadth of your background, the experience that you have in the industry as well in academia is so impressive. I've known you a long time. I'd love the audience to get some recommendations from you. For those of the audience looking to grow and expand their careers in technology, what are some of the things that you that you've experienced that you would recommend people do? >> First, stay current. What is emerging today is going to be current very quickly. Especially now we are seeing more change and change at the increased speed than ever. So keeping an eye on on what's happening in the market if you want to be marketable. Now, some of the things that I will say, we have shortage of skills with data science, data engineering with security cyber security with cloud, right? We are here talking about cloud in particular. So there is a shortage of skills in the emerging technologies, AI, ML, there's a shortage of skills also in the retiring technologies. So we are in this like spectrum of skills shortage. So stay tuned to what's coming up. That's one. And on the second piece is that the quicker you tie what you are doing to the goals of the business, whether that's revenue growth whether that's customer retention or cost optimization you are more likely to grow in your career. You have to be able to articulate what you are doing and how that brings value to business to your boss, to your customers. So that becomes an important one. And then third one is giving back. Do something for the women in technology while being a woman in technology. Give back to your community whether that's community is gender based or whether it's your alumni, whether it's your community social community in your neighborhood or in your country or ethnicity. Give back to your community. I think that's becoming really important. >> I think so too. I think that paying it forward is so critical. I'm sure that you have a a long list of mentors and sponsors that have guided you along the way. Giving back to the community paying it forward I think is so important. For others who might be a few years behind us or even maybe have been in tech for the same amount of time that are looking to grow and expand their career having those mentors and sponsors of women who've been through the trenches is inspiring. It's so helpful. And it really is something that we need to do from a diversity perspective alone, right? >> Correct. Correct. And we have seen that, we have seen, for example Covid impact in women in particular. Diverse studies done by girls who quote on Accenture that showed that actually 50% of the women above age 35 were actually dropping out of the technology. And those numbers are scary. However, on the other side we have also seen incredible amount of technology innovation during that time with cloud adoption increasing with the ability to actually work remotely if you are even living in not so secure areas, for example that created more opportunities for women to come back to workforce as well. So we can turn the challenges to opportunities and watch out for those. I would say tipping points. >> I love that you bring up such a great point. There are so, so the, the data doesn't lie, right? The data shows that there's a significant amount of churn for women in technology. But to your point, there are so many opportunities. You mentioned a minute ago the skills gap. One of the things we talk about often on theCUBE and we're talking about cybersecurity which is obviously it's a global risk for companies in every industry, is that there's massive opportunity for people of, of any type to be able to grow their skills. So knowing that there's trend, but there's also so much opportunity for women in technology to climb the ladder is kind of exciting. I think. >> It is. It is exciting. >> Talk to me a little bit about, I would love for the audience to understand some of your hands-on examples where you've really been successful helping organizations navigate digital transformation and their entry and success with cloud computing. What are some of those success stories that you're really proud of? >> Let me think about, first of all what we are seeing is with the digital transformation in general, every single business every single vertical is becoming a technology company. Telecom companies are becoming a technology company. Financial services are becoming a technology company and manufacturing is becoming a technology company. So every business is becoming technology driven. And data is the key. Data is the enabler for every single business. So when we think about the challenges, one of the examples that I give a big challenge for our customers is I can't find the critical data, I can't access it. What are my critical data elements? Because I have so high volumes growing exponentially. What are the critical data elements that I should care and how do I access that? And we work at Precisely with 99 of Fortune 100. So we have two 12,000 customers in over a hundred countries which means we have customers whose businesses are purely built on cloud, clean slate. We also have businesses who have very complex set of data platforms. They have financial services, insurance, for example. They have critical transactional workloads still running on mainframes, IBM i servers, SAP systems. So one of the challenges that we have, and I work with key customers, is on how do we make data accessible for advanced analytics in the cloud? Cloud opens up a ton of open source tools, AI, ML stack lots of tools that actually the companies can leverage for that analytics in addition to elasticity in addition to easy to set up infrastructure. So how do we make sure the data can be actually available from these transactional systems, from mainframes at the speed that the business requires. So it's not just accessing data at the speed the business requires. One of our insurance customers they actually created this data marketplace on Amazon Cloud. And the, their challenge was to make sure they can bring the fresh data on a nightly basis initially and which became actually half an hour, every half an hour. So the speed of the business requirements have changed over time. We work with them very closely and also with the Amazon teams on enabling bringing data and workloads from the mainframes and executing in the cloud. So that's one example. Another big challenge that we see is, can I trust my data? And data integrity is more critical than ever. The quality of data, actually, according to HBR Harvard Business Review survey, 47% of every new record of data has at least one critical data error, 47%. So imagine, I was talking with the manufacturing organization couple of weeks ago and they were giving me an example. They have these three letter quotes for parts and different chemicals they use in the manufacturing. And the single letter error calls a shutdown of the whole manufacturing line. >> Wow. >> So that kind of challenge, how do I ensure that I can actually have completeness of data cleanness of data and consistency in that data? Moreover, govern that on a continuous basis becomes one of the use cases that we help customers. And in that particular case actually we help them put a data governance framework and data quality in their manufacturing line. It's becoming also a critical for, for example ESG, environment, social and governance, supply chain, monitoring the supply chain, and assessing ESG metrics. We see that again. And then the third one, last one. I will give an example because I think it's important. Hybrid cloud becoming critical. Because there's a purest view for new companies. However, facilitating flexible deployment models and facilitating cloud and hybrid cloud is also where we really we can help our customers. >> You brought up some amazingly critical points where it comes to data. You talked about, you know, a minute ago, every company in every industry has to become a technology company. You could also say every company across every industry has to become a data company. They have to become a software company. But to your point, and what it sounds like precisely is really helping organizations to do is access the data access data that has high integrity data that is free of errors. Obviously that's business critical. You talked about the high percentage of errors that caused manufacturing shutdown. Businesses can't, can't have that. That could potentially be life-ending for an organization. So it sounds like what you're talking about data accessibility, data integrity data governance and having that all in real time is table stakes for businesses. Whether it's your grocery store, your local coffee shop a manufacturing company, and e-commerce company. It's table stakes globally these days. >> It is, and you made a very good point actually, Lisa when you talked about the local coffee shop or the retail. One other interesting statistic is that almost 80% of every data has a location attribute. So when we talk about data integrity we no longer talk about just, and consistency of data. We also talk about context, right? When you are going, for example, to a new town you are probably getting some reminders about where your favorite coffee shop is or what telecom company has an office in that particular town. Or if you're an insurance company and a hurricane is hitting southern Florida. Then you want to know how the path of that hurricane is going to impact your customers and predict the claims before they happen. Also understand the propensity of the potential customers that you don't yet have. So location and context, those additional attributes of demographics, visitations are creating actually more confident business insights. >> Absolutely. And and as the consumer we're becoming more and more demanding. We want to be able to transact things so easily whether it's in our personal life at the grocery store, at that cafe, or in our business life. So those demands from the customer are also really influencing the direction that companies need to go. And it's actually, I think it's quite exciting that the amount of personalization the location data that you talk about that comes in there and really helps companies in every industry deliver these the cloud can, these amazing, unique personalized experiences that really drive business forward. We could talk about that all day long. I have no problem. But I want to get in our final minutes here, Tendu. What do you see as in your crystal ball as next for the cloud? How do you see your role as CTO evolving? >> Sure. For what we are seeing in the cloud I think we will start seeing more and more focus on sustainability. Sustainable technologies and governance. Obviously cloud migrations cloud modernizations are helping with that. And we, we are seeing many of our customers they started actually assessing the ESG supply chain and reporting on metrics whether it's the percentage of face or energy consumption. Also on the social metrics on diversity age distribution and as well as compliance piece. So sustainability governance I think that will become one area. Second, security, we talked about IT security and data privacy. I think we will see more and more investments around those. Cybersecurity in particular. And ethical data access and ethics is becoming center to everything we are doing as we have those personalized experiences and have more opportunities in the cloud. And the third one is continued automation with AI, ML and more focus on automation because cloud enables that at scale. And the work that we need to do is too time-intensive and too manual with the amount of data. Data is powering every business. So automation is going to be an increased focus how my role evolves with that. So I have this unique combination. I have been open to non-linear career paths throughout my growth. So I have an understanding of how to innovate and build products that solve real business problems. I also have an understanding of how to sell them build partnerships that combined with the the scale of growth, the hyper growth that we have absorbed in precisely 10 times growth within the last 10 years through a combination of organic innovation and acquisitions really requires the speed of change. So change, implementing change at scale as well as at speed. So taking those and bringing them to the next challenge is the evolution of my role. How do I bring those and tackle keep an eye on what's coming as a challenge in the industry and how they apply those skills that I have developed throughout my career to that next challenge and evolve with it, bring the innovation to data to cloud and the next challenge that we are going to see. >> There's so much on the horizon. It's, there are certainly challenges, you know within technology, but there's so much opportunity. You've done such a great job highlighting your career path the, the big impact that you're helping organizations make leveraging cloud and the opportunity that's there for the rest of us to really get in there get our hands dirty and solve problems. Tendu, I always love our conversations. It's been such a pleasure having you back, back on theCUBE. Thank you for joining us on this special program series today. >> Thank you Lisa. And also thanks to AWS for the opportunity. >> Absolutely. This is brought, brought to us by AWS. For Dr.Tendu, you are good to go. I'm Lisa Martin. You're watching theCUBE special program series Women of the Cloud. We thank you so much for watching and we'll see you soon. (upbeat music)

(speaks in foreign language) >> Narrator: Data is at the heart of transformation and the change every company needs to succeed, but it takes more than new technology. It's about teams, talent, and cultural change. Empowering everyone on the front lines to make decisions, all at the speed of digital. The transformation starts with you. It's time to lead the way, it's time for thought leaders. >> Welcome to Thought Leaders, a digital event brought to you by ThoughtSpot. My name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not. ThoughtSpot is disrupting analytics by using search and machine intelligence to simplify data analysis, and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core, requires not only modern technology, but leadership, a mindset and a culture that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action. And today, we're going to hear from experienced leaders, who are transforming their organizations with data, insights and creating digital-first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot. First, Chief Data Strategy Officer for ThoughtSpot is Cindi Hausen. Cindi is an analytics and BI expert with 20 plus years experience and the author of Successful Business Intelligence Unlock The Value of BI and Big Data. Cindi was previously the lead analyst at Gartner for the data and analytics magic quadrant. And early last year, she joined ThoughtSpot to help CDOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi, great to see you, welcome to the show. >> Thank you, Dave. Nice to join you virtually. >> Now our second cohost and friend of theCUBE is ThoughtSpot CEO Sudheesh Nair. Hello Sudheesh, how are you doing today? >> I am well Dave, it's good to talk to you again. >> It's great to see you. Thanks so much for being here. Now Sudheesh, please share with us why this discussion is so important to your customers and of course, to our audience and what they're going to learn today? (gentle music) >> Thanks, Dave, I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Look, since we have all been cooped up in our homes, I know that the vendors like us, we have amped up our, you know, sort of effort to reach out to you with invites for events like this. So we are getting way more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time. We want to make sure that we value your time, and this is going to be useful. Number two, we want to put you in touch with industry leaders and thought leaders, and generally good people that you want to hang around with long after this event is over. And number three, as we plan through this, you know, we are living through these difficult times, we want an event to be, this event to be more of an uplifting and inspiring event too. Now, the challenge is, how do you do that with the team being change agents? Because change and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do or likes to do. The way I think of it, change is sort of like, if you've ever done bungee jumping. You know, it's like standing on the edges, waiting to make that one more step. You know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step to take. Change requires a lot of courage and when we are talking about data and analytics, which is already like such a hard topic, not necessarily an uplifting and positive conversation, in most businesses it is somewhat scary. Change becomes all the more difficult. Ultimately change requires courage. Courage to to, first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that, "You know, maybe I don't have the power to make the change that the company needs. Sometimes I feel like I don't have the skills." Sometimes they may feel that, I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations, when it comes to data and insights that you talked about. You know, there are people in the company, who are going to hog the data because they know how to manage the data, how to inquire and extract. They know how to speak data, they have the skills to do that, but they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is this silo of people with the answers and there is a silo of people with the questions, and there is gap. These sort of silos are standing in the way of making that necessary change that we all I know the business needs, and the last change to sort of bring an external force sometimes. It could be a tool, it could be a platform, it could be a person, it could be a process, but sometimes no matter how big the company is or how small the company is. You may need to bring some external stimuli to start that domino of the positive changes that are necessary. The group of people that we have brought in, the four people, including Cindi, that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to trust the rope that you will be safe and you're going to have fun. You will have that exhilarating feeling of jumping for a bungee jump. All four of them are exceptional, but my honor is to introduce Michelle and she's our first speaker. Michelle, I am very happy after watching her presentation and reading her bio, that there are no country vital worldwide competition for cool patents, because she will beat all of us because when her children were small, you know, they were probably into Harry Potter and Disney and she was managing a business and leading change there. And then as her kids grew up and got to that age, where they like football and NFL, guess what? She's the CIO of NFL. What a cool mom. I am extremely excited to see what she's going to talk about. I've seen the slides with a bunch of amazing pictures, I'm looking to see the context behind it. I'm very thrilled to make the acquaintance of Michelle. I'm looking forward to her talk next. Welcome Michelle. It's over to you. (gentle music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one. This is about as close as I'm ever going to get. So, I want to talk to you about quarterbacking our digital revolution using insights, data and of course, as you said, leadership. First, a little bit about myself, a little background. As I said, I always wanted to play football and this is something that I wanted to do since I was a child but when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines and a female official on the field. I'm a lifelong fan and student of the game of football. I grew up in the South. You can tell from the accent and in the South football is like a religion and you pick sides. I chose Auburn University working in the athletic department, so I'm testament. Till you can start, a journey can be long. It took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well not actually not so little, he played offensive line for the Alabama Crimson Tide. And for those of you who know SEC football, you know this is a really big rivalry, and when you choose sides your family is divided. So it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL, he just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands, delivering memories and amazing experiences that delight. From Universal Studios, Disney, to my current position as CIO of the NFL. In this job, I'm very privileged to have the opportunity to work with a team that gets to bring America's game to millions of people around the world. Often, I'm asked to talk about how to create amazing experiences for fans, guests or customers. But today, I really wanted to focus on something different and talk to you about being behind the scenes and backstage. Because behind every event, every game, every awesome moment, is execution. Precise, repeatable execution and most of my career has been behind the scenes doing just that. Assembling teams to execute these plans and the key way that companies operate at these exceptional levels is making good decisions, the right decisions, at the right time and based upon data. So that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves, and it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kind of world class experiences are often seeking out and leveraging next generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute. A little bit first about Disney. In '90s I was at Disney leading a project called Destination Disney, which it's a data project. It was a data project, but it was CRM before CRM was even cool and then certainly before anything like a data-driven culture was ever brought up. But way back then we were creating a digital backbone that enabled many technologies for the things that you see today. Like the MagicBand, Disney's Magical Express. My career at Disney began in finance, but Disney was very good about rotating you around. And it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team asking for data, more and more data. And I learned that all of that valuable data was locked up in our systems. All of our point of sales systems, our reservation systems, our operation systems. And so I became a shadow IT person in marketing, ultimately, leading to moving into IT and I haven't looked back since. In the early 2000s, I was at Universal Studio's theme park as their CIO preparing for and launching the Wizarding World of Harry Potter. Bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wand selects you at a wand shop. As today at the NFL, I am constantly challenged to do leading edge technologies, using things like sensors, AI, machine learning and all new communication strategies, and using data to drive everything, from player performance, contracts, to where we build new stadiums and hold events. With this year being the most challenging, yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contact tracing devices joined with testing data. Talk about data actually enabling your business. Without it we wouldn't be having a season right now. I'm also on the board of directors of two public companies, where data and collaboration are paramount. First, RingCentral, it's a cloud based unified communications platform and collaboration with video message and phone, all-in-one solution in the cloud and Quotient Technologies, whose product is actually data. The tagline at Quotient is The Result in Knowing. I think that's really important because not all of us are data companies, where your product is actually data, but we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about as thought leaders in your companies. First, just hit on it, is change. how to be a champion and a driver of change. Second, how to use data to drive performance for your company and measure performance of your company. Third, how companies now require intense collaboration to operate and finally, how much of this is accomplished through solid data-driven decisions. First, let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it. And thankfully, for the most part, knock on wood, we were prepared for it. But this year everyone's cheese was moved. All the people in the back rooms, IT, data architects and others were suddenly called to the forefront because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, The 2020 Draft. We went from planning a large event in Las Vegas under the bright lights, red carpet stage, to smaller events in club facilities. And then ultimately, to one where everyone coaches, GMs, prospects and even our commissioner were at home in their basements and we only had a few weeks to figure it out. I found myself for the first time, being in the live broadcast event space. Talking about bungee jumping, this is really what it felt like. It was one in which no one felt comfortable because it had not been done before. But leading through this, I stepped up, but it was very scary, it was certainly very risky, but it ended up being also rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at its level, highest level. As an example, the NFL has always measured performance, obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field, you can see points being scored and stats, and you immediately know that impact. Those with the best stats usually win the games. The NFL has always recorded stats. Since the beginning of time here at the NFL a little... This year is our 101st year and athlete's ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us is both how much more we can measure and the immediacy with which it can be measured and I'm sure in your business it's the same. The amount of data you must have has got to have quadrupled recently. And how fast do you need it and how quickly you need to analyze it is so important. And it's very important to break the silos between the keys to the data and the use of the data. Our next generation stats platform is taking data to the next level. It's powered by Amazon Web Services and we gather this data, real-time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast. And of course, it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize, route patterns, speed, match-ups, et cetera, so much faster than ever before. We're continuing to roll out sensors too, that will gather more and more information about a player's performance as it relates to their health and safety. The third trend is really, I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes, it's important to think about, for those of you that are IT professionals and developers, you know, more than 10 years ago agile practices began sweeping companies. Where small teams would work together rapidly in a very flexible, adaptive and innovative way and it proved to be transformational. However today, of course that is no longer just small teams, the next big wave of change and we've seen it through this pandemic, is that it's the whole enterprise that must collaborate and be agile. If I look back on my career, when I was at Disney, we owned everything 100%. We made a decision, we implemented it. We were a collaborative culture but it was much easier to push change because you own the whole decision. If there was buy-in from the top down, you got the people from the bottom up to do it and you executed. At Universal, we were a joint venture. Our attractions and entertainment was licensed. Our hotels were owned and managed by other third parties, so influence and collaboration, and how to share across companies became very important. And now here I am at the NFL an even the bigger ecosystem. We have 32 clubs that are all separate businesses, 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved, centralized control has gotten less and less and has been replaced by intense collaboration, not only within your own company but across companies. The ability to work in a collaborative way across businesses and even other companies, that has been a big key to my success in my career. I believe this whole vertical integration and big top-down decision-making is going by the wayside in favor of ecosystems that require cooperation, yet competition to co-exist. I mean, the NFL is a great example of what we call co-oppetition, which is cooperation and competition. We're in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data-driven decisions and culture. Data on its own isn't good enough. You must be able to turn it to insights. Partnerships between technology teams who usually hold the keys to the raw data and business units, who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be, data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask? It's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with, first of all, making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today, looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave and drive. Don't do the ride along program, it's very important to drive. Driving can be high risk, but it's also high reward. Embracing the uncertainty of what will happen is how you become brave. Get more and more comfortable with uncertainty, be calm and let data be your map on your journey. Thanks. >> Michelle, thank you so much. So you and I share a love of data and a love of football. You said you want to be the quarterback. I'm more an a line person. >> Well, then I can't do my job without you. >> Great and I'm getting the feeling now, you know, Sudheesh is talking about bungee jumping. My vote is when we're past this pandemic, we both take him to the Delaware Water Gap and we do the cliff jumping. >> Oh that sounds good, I'll watch your watch. >> Yeah, you'll watch, okay. So Michelle, you have so many stakeholders, when you're trying to prioritize the different voices you have the players, you have the owners, you have the league, as you mentioned, the broadcasters, your partners here and football mamas like myself. How do you prioritize when there are so many different stakeholders that you need to satisfy? >> I think balancing across stakeholders starts with aligning on a mission and if you spend a lot of time understanding where everyone's coming from, and you can find the common thread that ties them all together. You sort of do get them to naturally prioritize their work and I think that's very important. So for us at the NFL and even at Disney, it was our core values and our core purpose is so well known and when anything challenges that, we're able to sort of lay that out. But as a change agent, you have to be very empathetic, and I would say empathy is probably your strongest skill if you're a change agent and that means listening to every single stakeholder. Even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic, and having a mission, and understanding it is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling, so thank you for your leadership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. >> (gentle music) So we're going to take a hard pivot now and go from football to Chernobyl. Chernobyl, what went wrong? 1986, as the reactors were melting down, they had the data to say, "This is going to be catastrophic," and yet the culture said, "No, we're perfect, hide it. Don't dare tell anyone." Which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, additional thousands getting cancer and 20,000 years before the ground around there can even be inhabited again. This is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with and this is why I want you to focus on having, fostering a data-driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology, is it really two sides of the same coin? Real-world impacts and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, "You know, Cindi, I actually think this is two sides of the same coin, one reflects the other." What do you think? Let me walk you through this. So let's take a laggard. What does the technology look like? Is it based on 1990s BI and reporting, largely parametrized reports, on-premises data warehouses, or not even that operational reports. At best one enterprise data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change, complacency. And sometimes that complacency, it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, "No, we're measured on least to serve." So politics and distrust, whether it's between business and IT or individual stakeholders is the norm, so data is hoarded. Let's contrast that with the leader, a data and analytics leader, what does their technology look like? Augmented analytics, search and AI driven insights, not on-premises but in the cloud and maybe multiple clouds. And the data is not in one place but it's in a data lake and in a data warehouse, a logical data warehouse. The collaboration is via newer methods, whether it's Slack or Teams, allowing for that real-time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish, that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals. Whether it's the best fan experience and player safety in the NFL or best serving your customers, it's innovative and collaborative. There's none of this, "Oh, well, I didn't invent that. I'm not going to look at that." There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas, to fail fast and they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact, what we like to call the new decision-makers or really the frontline workers. So Harvard Business Review partnered with us to develop this study to say, "Just how important is this? We've been working at BI and analytics as an industry for more than 20 years, why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor." 87% said they would be more successful if frontline workers were empowered with data-driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality only 20% of organizations are actually doing this. These are the data-driven leaders. So this is the culture and technology, how did we get here? It's because state-of-the-art keeps changing. So the first generation BI and analytics platforms were deployed on-premises, on small datasets, really just taking data out of ERP systems that were also on-premises and state-of-the-art was maybe getting a management report, an operational report. Over time, visual based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data, sometimes coming from a data warehouse. The current state-of-the-art though, Gartner calls it augmented analytics. At ThoughtSpot, we call it search and AI driven analytics, and this was pioneered for large scale data sets, whether it's on-premises or leveraging the cloud data warehouses. And I think this is an important point, oftentimes you, the data and analytics leaders, will look at these two components separately. But you have to look at the BI and analytics tier in lock-step with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody hard coding a report, it's typing in search keywords and very robust keywords contains rank, top, bottom, getting to a visual visualization that then can be pinned to an existing pin board that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non-analyst to create themselves. Modernizing the data and analytics portfolio is hard because the pace of change has accelerated. You used to be able to create an investment, place a bet for maybe 10 years. A few years ago, that time horizon was five years. Now, it's maybe three years and the time to maturity has also accelerated. So you have these different components, the search and AI tier, the data science tier, data preparation and virtualization but I would also say, equally important is the cloud data warehouse. And pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI driven insights. Competitors have followed suit, but be careful, if you look at products like Power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift, or Azure Synapse, or Google BigQuery, they do not. They require you to move it into a smaller in-memory engine. So it's important how well these new products inter-operate. The pace of change, its acceleration, Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI and that is roughly three times the prediction they had just a couple of years ago. So let's talk about the real world impact of culture and if you've read any of my books or used any of the maturity models out there, whether the Gartner IT Score that I worked on or the Data Warehousing Institute also has a maturity model. We talk about these five pillars to really become data-driven. As Michelle spoke about, it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology and also the processes. And often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders. You have told me now culture is absolutely so important, and so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data-driven. It's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great data, but if you don't have the right culture, there's devastating impacts. And I will say I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data. It said, "Hey, we're not doing good cross-selling, customers do not have both a checking account and a credit card and a savings account and a mortgage." They opened fake accounts facing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture and they're trying to fix this, but even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive examples. Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant, diabetes, you know this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture. Or Verizon, a major telecom organization looking at late payments of their customers and even though the U.S. Federal Government said, "Well, you can't turn them off." They said, "We'll extend that even beyond the mandated guidelines," and facing a slow down in the business because of the tough economy, They said, "You know what? We will spend the time upskilling our people, giving them the time to learn more about the future of work, the skills and data and analytics for 20,000 of their employees rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions. Bring in a change agent, identify the relevance or I like to call it WIIFM and organize for collaboration. So the CDO, whatever your title is, Chief Analytics Officer, Chief Digital Officer, you are the most important change agent. And this is where you will hear that oftentimes a change agent has to come from outside the organization. So this is where, for example, in Europe you have the CDO of Just Eat, a takeout food delivery organization coming from the airline industry or in Australia, National Australian Bank taking a CDO within the same sector from TD Bank going to NAB. So these change agents come in, disrupt. It's a hard job. As one of you said to me, it often feels like. I make one step forward and I get knocked down again, I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIIFM What's In It For Me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So, if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor. Okay, we could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your 70s or 80s. For the teachers, teachers you ask them about data. They'll say, "We don't need that, I care about the student." So if you can use data to help a student perform better, that is WIIFM and sometimes we spend so much time talking the technology, we forget, what is the value we're trying to deliver with this? And we forget the impact on the people that it does require change. In fact, the Harvard Business Review study found that 44% said lack of change management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data-driven insights. The third point, organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI competency center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then embed these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead, an exciting time because data is helping organizations better navigate a tough economy, lock in the customer loyalty and I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at Thought Leaders. And next, I'm pleased to introduce our first change agent, Tom Mazzaferro Chief Data Officer of Western Union and before joining Western Union, Tom made his Mark at HSBC and JP Morgan Chase spearheading digital innovation in technology, operations, risk compliance and retail banking. Tom, thank you so much for joining us today. (gentle music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable different business teams and the technology teams into the future? As we look across our data ecosystems and our platforms, and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive an organization from a data standpoint, into the future. That includes being able to have the right information with the right quality of data, at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that. As part of that partnership and it's how we've looked to integrate it into our overall business as a whole. We've looked at, how do we make sure that our business and our professional lives, right? Are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go onto google.com or you go onto Bing or you go onto Yahoo and you search for what you want, search to find an answer. ThoughtSpot for us is the same thing, but in the business world. So using ThoughtSpot and other AI capability is it's allowed us to actually enable our overall business teams in our company to actually have our information at our fingertips. So rather than having to go and talk to someone, or an engineer to go pull information or pull data. We actually can have the end users or the business executives, right. Search for what they need, what they want, at the exact time that they actually need it, to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on a journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology, our... The local environments and as we move that, we've actually picked two of our cloud providers going to AWS and to GCP. We've also adopted Snowflake to really drive and to organize our information and our data, then drive these new solutions and capabilities forward. So a big portion of it though is culture. So how do we engage with the business teams and bring the IT teams together, to really help to drive these holistic end-to-end solutions and capabilities, to really support the actual business into the future. That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven. This is the key. If you can really start to provide answers to business questions before they're even being asked and to predict based upon different economic trends or different trends in your business, what decisions need to be made and actually provide those answers to the business teams before they're even asking for it. That is really becoming a data-driven organization and as part of that, it really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, based upon products, solutions or partnerships into the future. These are really some of the keys that become crucial as you move forward, right, into this new age, Especially with COVID. With COVID now taking place across the world, right? Many of these markets, many of these digital transformations are celebrating and are changing rapidly to accommodate and to support customers in these very difficult times. As part of that, you need to make sure you have the right underlying foundation, ecosystems and solutions to really drive those capabilities and those solutions forward. As we go through this journey, both in my career but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only accelerating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes, both on the platform standpoint, tools, but also what do our customers want, what do our customers need and how do we then service them with our information, with our data, with our platform, and with our products and our services to meet those needs and to really support and service those customers into the future. This is all around becoming a more data-driven organization, such as how do you use your data to support your current business lines, but how do you actually use your information and your data to actually better support your customers, better support your business, better support your employees, your operations teams and so forth. And really creating that full integration in that ecosystem is really when you start to get large dividends from these investments into the future. With that being said, I hope you enjoyed the segment on how to become and how to drive a data-driven organization, and looking forward to talking to you again soon. Thank you. >> Tom, that was great. Thanks so much and now going to have to drag on you for a second. As a change agent you've come in, disrupted and how long have you been at Western Union? >> Only nine months, so just started this year, but there have been some great opportunities to integrate changes and we have a lot more to go, but we're really driving things forward in partnership with our business teams and our colleagues to support those customers going forward. >> Tom, thank you so much. That was wonderful. And now, I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe and he is a serial change agent. Most recently with Schneider Electric but even going back to Sam's Clubs. Gustavo, welcome. (gentle music) >> So, hey everyone, my name is Gustavo Canton and thank you so much, Cindi, for the intro. As you mentioned, doing transformations is, you know, a high reward situation. I have been part of many transformations and I have led many transformations. And, what I can tell you is that it's really hard to predict the future, but if you have a North Star and you know where you're going, the one thing that I want you to take away from this discussion today is that you need to be bold to evolve. And so, in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started, barriers or opportunities as I see it, the value of AI and also, how you communicate. Especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are non-traditional sometimes. And so, how do we get started? So, I think the answer to that is you have to start for you yourself as a leader and stay tuned. And by that, I mean, you need to understand, not only what is happening in your function or your field, but you have to be very in tune what is happening in society socioeconomically speaking, wellbeing. You know, the common example is a great example and for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential for customers and communities to grow, wellbeing should be at the center of every decision. And as somebody mentioned, it's great to be, you know, stay in tune and have the skillset and the courage. But for me personally, to be honest, to have this courage is not about not being afraid. You're always afraid when you're making big changes and you're swimming upstream, but what gives me the courage is the empathy part. Like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business and what the leaders are trying to do. But I do it thinking about the mission of, how do I make change for the bigger workforce or the bigger good despite the fact that this might have perhaps implication for my own self interest in my career. Right? Because you have to have that courage sometimes to make choices that are not well seen, politically speaking, but are the right thing to do and you have to push through it. So the bottom line for me is that, I don't think we're they're transforming fast enough. And the reality is, I speak with a lot of leaders and we have seen stories in the past and what they show is that, if you look at the four main barriers that are basically keeping us behind budget, inability to act, cultural issues, politics and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, these topic about culture is actually gaining more and more traction. And in 2018, there was a story from HBR and it was about 45%. I believe today, it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand and are aware that we need to transform, commit to the transformation and set a deadline to say, "Hey, in two years we're going to make this happen. What do we need to do, to empower and enable these change agents to make it happen? You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So, I'll give you examples of some of the roadblocks that I went through as I've been doing transformations, most recently, as Cindi mentioned in Schneider. There are three main areas, legacy mindset and what that means is that, we've been doing this in a specific way for a long time and here is how we have been successful. What worked in the past is not going to work now. The opportunity there is that there is a lot of leaders, who have a digital mindset and they're up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people, you know, three to five years for them to develop because the world is going in a way that is super-fast. The second area and this is specifically to implementation of AI. It's very interesting to me because just the example that I have with ThoughtSpot, right? We went on implementation and a lot of the way the IT team functions or the leaders look at technology, they look at it from the prism of the prior or success criteria for the traditional BIs, and that's not going to work. Again, the opportunity here is that you need to redefine what success look like. In my case, I want the user experience of our workforce to be the same user experience you have at home. It's a very simple concept and so we need to think about, how do we gain that user experience with these augmented analytics tools and then work backwards to have the right talent, processes, and technology to enable that. And finally and obviously with COVID, a lot of pressure in organizations and companies to do more with less. And the solution that most leaders I see are taking is to just minimize costs sometimes and cut budget. We have to do the opposite. We have to actually invest on growth areas, but do it by business question. Don't do it by function. If you actually invest in these kind of solutions, if you actually invest on developing your talent and your leadership to see more digitally, if you actually invest on fixing your data platform, it's not just an incremental cost. It's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work and working very hard but it's not efficient and it's not working in the way that you might want to work. So there is a lot of opportunity there and just to put in terms of perspective, there have been some studies in the past about, you know, how do we kind of measure the impact of data? And obviously, this is going to vary by organization maturity, there's going to be a lot of factors. I've been in companies who have very clean, good data to work with and I've been with companies that we have to start basically from scratch. So it all depends on your maturity level. But in this study, what I think is interesting is they try to put a tagline or a tag price to what is the cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work when you have data that is flawed as opposed to having perfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something, it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be $100. But now let's say you have 80% perfect data and 20% flawed data. By using this assumption that flawed data is 10 times as costly as perfect data, your total costs now becomes $280 as opposed to $100. This just for you to really think about as a CIO, CTO, you know CHRO, CEO, "Are we really paying attention and really closing the gaps that we have on our data infrastructure?" If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact, but as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this or how do I break through some of these challenges or some of these barriers, right? I think the key is, I am in analytics, I know statistics obviously and love modeling, and, you know, data and optimization theory, and all that stuff. That's what I came to analytics, but now as a leader and as a change agent, I need to speak about value and in this case, for example, for Schneider. There was this tagline, make the most of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that, I understood what kind of language to use, how to connect it to the overall strategy and basically, how to bring in the right leaders because you need to, you know, focus on the leaders that you're going to make the most progress, you know. Again, low effort, high value. You need to make sure you centralize all the data as you can, you need to bring in some kind of augmented analytics, you know, solution. And finally, you need to make it super-simple for the, you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation, but it was two years for this component of augmented analytics. It took about two years to talk to, you know, IT, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics portal. It was actually launched in July of this year and we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many, many factors but one thing that is really important is as you bring along your audience on this, you know. You're going from Excel, you know, in some cases or Tableu to other tools like, you know, ThoughtSpot. You need to really explain them what is the difference and how this tool can truly replace some of the spreadsheets or some of the views that you might have on these other kinds of tools. Again, Tableau, I think it's a really good tool. There are other many tools that you might have in your toolkit but in my case, personally, I feel that you need to have one portal. Going back to Cindi's points, that really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory and I will tell you why, because it took a lot of effort for us to get to this stage and like I said, it's been years for us to kind of lay the foundation, get the leadership, initiating culture so people can understand, why you truly need to invest on augmented analytics. And so, what I'm showing here is an example of how do we use basically, you know, a tool to capturing video, the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics. Hours saved, user experience and adoption. So for hours saved, our ambition was to have 10 hours per week for employee to save on average. User experience, our ambition was 4.5 and adoption 80%. In just two months, two months and a half of the pilot, we were able to achieve five hours per week per employee savings, a user experience for 4.3 out of five and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications, obviously the operations things and the users. In HR safety and other areas that might be basically stakeholders in this whole process. So just to summarize, this kind of effort takes a lot of energy. You are a change agent, you need to have courage to make this decision and understand that, I feel that in this day and age with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these great resource for this organization and that give me the confident to know that the work has been done and we are now in a different stage for the organization. And so for me, it's just to say, thank you for everybody who has belief, obviously in our vision, everybody who has belief in, you know, the work that we were trying to do and to make the life of our, you know, workforce or customers and community better. As you can tell, there is a lot of effort, there is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied with the accomplishments of this transformation and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream, you know, work with mentors, work with people in the industry that can help you out and guide you on this kind of transformation. It's not easy to do, it's high effort, but it's well worth it. And with that said, I hope you are well and it's been a pleasure talking to you. Talk to you soon. Take care. >> Thank you, Gustavo. That was amazing. All right, let's go to the panel. (light music) Now I think we can all agree how valuable it is to hear from practitioners and I want to thank the panel for sharing their knowledge with the community. Now one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time and it is critical to have support from the top. Why? Because it directs the middle and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard is that you all prioritize database decision making in your organizations. And you combine two of your most valuable assets to do that and create leverage, employees on the front lines, and of course the data. Now as as you rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it, well COVID has broken everything and it's great to hear from our experts, you know, how to move forward, so let's get right into it. So Gustavo, let's start with you. If I'm an aspiring change agent and let's say I'm a budding data leader, what do I need to start doing? What habits do I need to create for long-lasting success? >> I think curiosity is very important. You need to be, like I said, in tune to what is happening, not only in your specific field, like I have a passion for analytics, I've been doing it for 50 years plus, but I think you need to understand wellbeing of the areas across not only a specific business. As you know, I come from, you know, Sam's Club, Walmart retail. I've been in energy management, technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to just continuous improvement, that's just going to take you so far. What you have to do is, and that's what I try to do, is I try to go into areas, businesses and transformations, that make me, you know, stretch and develop as a leader. That's what I'm looking to do, so I can help transform the functions, organizations, and do the change management, the essential mindset that's required for this kind of effort. >> Well, thank you for that. That is inspiring and Cindi you love data and the data is pretty clear that diversity is a good business, but I wonder if you can, you know, add your perspectives to this conversation? >> Yeah, so Michelle has a new fan here because she has found her voice. I'm still working on finding mine and it's interesting because I was raised by my dad, a single dad, so he did teach me how to work in a predominantly male environment, but why I think diversity matters more now than ever before and this is by gender, by race, by age, by just different ways of working and thinking, is because as we automate things with AI, if we do not have diverse teams looking at the data, and the models, and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority you are, finding your voice, having a seat at the table and just believing in the impact of your work has never been more important and as Michelle said, more possible. >> Great perspectives, thank you. Tom, I want to go to you. So, I mean, I feel like everybody in our businesses is in some way, shape, or form become a COVID expert, but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth, actually, in our digital business over the last 12 months really, even acceleration, right, once COVID hit. We really saw that in the 200 countries and territories that we operate in today and service our customers in today, that there's been a huge need, right, to send money to support family, to support friends, and to support loved ones across the world. And as part of that we are very honored to be able to support those customers that, across all the centers today, but as part of the acceleration, we need to make sure that we have the right architecture and the right platforms to basically scale, right? To basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did accelerate some of our plans on digital to help support that overall growth coming in and to support our customers going forward, because during these times, during this pandemic, right, this is the most important time and we need to support those that we love and those that we care about. And doing that some of those ways is actually by sending money to them, support them financially. And that's where really our products and our services come into play that, you know, and really support those families. So, it was really a great opportunity for us to really support and really bring some of our products to the next level and supporting our business going forward. >> Awesome, thank you. Now, I want to come back to Gustavo. Tom, I'd love for you to chime in too. Did you guys ever think like you were pushing the envelope too much in doing things with data or the technology that it was just maybe too bold, maybe you felt like at some point it was failing, or you're pushing your people too hard? Can you share that experience and how you got through it? >> Yeah, the way I look at it is, you know, again, whenever I go to an organization, I ask the question, "Hey, how fast you would like to conform?" And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions and I collaborate in a specific way. Now, in the case of COVID, for example, right, it forces us to remove silos and collaborate in a faster way. So to me, it was an opportunity to actually integrate with other areas and drive decisions faster, but make no mistake about it, when you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing, and you need to be okay with that. Sometimes you need to be okay with tension or you need to be okay, you know, debating points or making repetitive business cases until people connect with the decision because you understand and you are seeing that, "Hey, the CEO is making a one, two year, you know, efficiency goal. The only way for us to really do more with less is for us to continue this path. We can not just stay with the status quo, we need to find a way to accelerate the transformation." That's the way I see it. >> How about Utah, we were talking earlier with Sudheesh and Cindi about that bungee jumping moment. What can you share? >> Yeah, you know, I think you hit upon it. Right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right, this is what I tell my team, is that you need to be, you need to feel comfortable being uncomfortable. Meaning that we have to be able to basically scale, right? Expand and support the ever changing needs in the marketplace and industry and our customers today, and that pace of change that's happening, right? And what customers are asking for and the competition in the marketplace, it's only going to accelerate. So as part of that, you know, as you look at how you're operating today in your current business model, right? Things are only going to get faster. So you have to plan and to align and to drive the actual transformation, so that you can scale even faster into the future. So it's part of that, that's what we're putting in place here, right? It's how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> Yeah, we're definitely out of our comfort zones, but we're getting comfortable with it. So Cindi, last question, you've worked with hundreds of organizations and I got to believe that, you know, some of the advice you gave when you were at Gartner, which was pre-COVID, maybe sometimes clients didn't always act on it. You know, not my watch or for whatever, variety of reasons, but it's being forced on them now. But knowing what you know now that, you know, we're all in this isolation economy, how would you say that advice has changed? Has it changed? What's your number one action and recommendation today? >> Yeah, well first off, Tom, just freaked me out. What do you mean, this is the slowest ever? Even six months ago I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more very aware of the power in politics and how to bring people along in a way that they are comfortable and now I think it's, you know what, you can't get comfortable. In fact, we know that the organizations that were already in the cloud have been able to respond and pivot faster. So, if you really want to survive, as Tom and Gustavo said, get used to being uncomfortable. The power and politics are going to happen, break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy, as Michelle said and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where Sudheesh is going to go bungee jumping. (all chuckling) >> Guys, fantastic discussion, really. Thanks again to all the panelists and the guests, it was really a pleasure speaking with you today. Really, virtually all of the leaders that I've spoken to in theCUBE program recently, they tell me that the pandemic is accelerating so many things. Whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise-wide digital transformation, not just as I said before, lip service. You know, sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done right, the right culture is going to deliver tournament results. You know, what does that mean? Getting it right. Everybody's trying to get it right. My biggest takeaway today is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions, decisions that can drive new revenue, cut costs, speed access to critical care, whatever the mission is of your organization, data can create insights and informed decisions that drive value. Okay, let's bring back Sudheesh and wrap things up. Sudheesh, please bring us home. >> Thank you, thank you, Dave. Thank you, theCUBE team, and thanks goes to all of our customers and partners who joined us, and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I heard from all four of our distinguished speakers. First, Michelle, I will simply put it, she said it really well. That is be brave and drive, don't go for a drive alone. That is such an important point. Often times, you know the right thing that you have to do to make the positive change that you want to see happen, but you wait for someone else to do it, not just, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk. Cindi talked about finding, the importance of finding your voice. Taking that chair, whether it's available or not, and making sure that your ideas, your voice is heard and if it requires some force, then apply that force. Make sure your ideas are heard. Gustavo talked about the importance of building consensus, not going at things all alone sometimes. The importance of building the quorum, and that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom, instead of a single takeaway, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in and they were able to make the change that is necessary through this difficult time in a matter of months. If they could do it, anyone could. The second thing I want to do is to leave you with a takeaway, that is I would like you to go to ThoughtSpot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is, please go to ThoughtSpot.com/beyond. Our global user conference is happening in this December. We would love to have you join us, it's, again, virtual, you can join from anywhere. We are expecting anywhere from five to 10,000 people and we would love to have you join and see what we've been up to since last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing. We'll be sharing things that we have been working to release, something that will come out next year. And also some of the crazy ideas our engineers have been cooking up. All of those things will be available for you at ThoughtSpot Beyond. Thank you, thank you so much.

>> Announcer: From around the globe, it's theCUBE with digital coverage of BizOps Manifesto Unveiled, brought to you by BizOps Coalition. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE for our ongoing coverage of the big unveil. It's the BizOps Manifesto Unveil and we're going to start that again. >> From the top. >> Three. >> Crew Member: Yeah, from the top. Little bleep bleep bleep, there we go. >> Manifesto. >> Crew Member: Second time's the charm, coming to you in five, four, three, two. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE coming to you from our Palo Alto studios today for a big, big reveal. We're excited to be here. It's the BizOps Manifesto Unveiling. Things have been in the works for a while and we're excited to have our next guest, one of the really the powers behind this whole effort and he's joining us from Boston. It's Serge Lucio, the Vice President and General Manager, Enterprise Software Division at Broadcom. Serge, great to see you. >> Good to see you, Jeff, Glad to be here. >> Absolutely. So, you've been in this business for a very long time, you've seen a lot of changes in technology. What is the BizOps Manifesto? What is this coalition all about? Why do we need this today in 2020? >> Yeah, so I've been in this business for close to 25 years, right? So, about 20 years ago, the Agile Manifesto was created. And the goal of the Agile Manifesto was really to address the uncertainty around software development and the inability to predict the effort to build software. And if you roll back kind of 20 years later and if you look at the current state of the industry, the Project Management Institute estimates that we're wasting about a million dollars every 20 seconds in digital transformation initiatives that do not deliver on business results. In fact, we recently surveyed a number of executives in partnership with Harvard Business Review and 77% of those executives think that one of the key challenges that they have is really at the collaboration between business and IT. And that's been kind of the case for almost 20 years now. So, the key challenge we're faced with is really that we need a new approach. And many of the players in the industry, including ourselves, have been using different terms, right? Some are talking about value stream management, some are talking about software delivery management. If you look at the Site Reliability Engineering movement, in many ways, it embodies a lot of these kind of concepts and principles. So, we believe that it became really imperative for us to crystallize around that one concept. And so, in many ways, the BizOps concept and the BizOps Manifesto are around bringing together a number of ideas which have been emerging in the last five years or so and defining the key values and principles to finally help these organizations truly transform and become digital businesses. And so, the hope is that by joining our forces and defining the key principles and values, we can help the industry, not just by providing them with support, but also the tools and consulting that is required for them to truly achieve the kind of transformation that everybody is seeking. >> Right, right. So, COVID, now, we're six months into it approximately, seven months into it, a lot of pain, a lot of bad stuff still happening, we've got two ways to go. But one of the things that on the positive side, right, and you seen all the memes in social media is a driver of digital transformation and a driver of change 'cause we had this light switch moment in the middle of March and there was no more planning, there was no more conversation, you suddenly got remote workforces, everybody's working from home and you got to go, right? So, the reliance on these tools increases dramatically. But I'm curious kind of short of the beginnings of this effort and short of kind of COVID which came along unexpectedly, I mean, what were those inhibitors 'cause we've been making software for a very long time, right? The software development community has adopted kind of rapid change and iterative delivery and sprints, what was holding back the connection with the business side to make sure that those investments were properly aligned with outcomes? >> Well, you have to understand that IT is kind of its own silos and traditionally, IT has been treated as a cost center within large organizations and not as a value center. And so as a result, kind of the traditional dynamic between IT and the business is basically one of kind of supplier up to kind of a business. And if you go back to I think Elon Musk a few years ago basically had these concepts of the machines to build the machines and he went as far as saying that the machines or the production line is actually the product. So, meaning that the core of the innovation is really about building kind of the engine to deliver on the value. And so, in many ways, we have missed on this shift from kind of IT becoming this kind of value center within the enterprises. And it's all about culture. Now, culture is the sum total of behaviors and the reality is that if you look at IT, especially in the last decade, with Agile, with DevOps, with hybrid infrastructures, it's way more volatile today than it was 10 years ago. And so, when you start to look at the velocity of the data, the volume of data, the variety of data to analyze the system, it's very challenging for IT to actually even understand and optimize its own processes, let alone to actually include business as kind of an integral part of a delivery chain. And so, it's both kind of a combination of culture, which is required, as well as tools, right? To be able to start to bring together all these data together. And then, given the volume, variety, velocity of the data, we have to apply some core technologies, which have only really truly emerged in the last five to 10 years around machine learning and analytics. And so, it's really kind of a combination of those things, which are coming together today to really help organizations kind of get to the next level. >> Right, right. So, let's talk about the manifesto. Let's talk about the coalition, the BizOps Coalition. I just like that you put down these really simple kind of straightforward core values. You guys have four core values that you're highlighting, business outcomes over individual projects and outputs, trust and collaboration over siloed teams and organizations, data driven decisions, what you just talked about, over opinions and judgment and learn to respond and pivot. I mean, Serge, these sounds like pretty basic stuff, right? I mean, isn't everyone working to these values already? And I think you touched on it, on culture, right? Trust and collaboration, data driven decisions. I mean, these are fundamental ways that people must run their business today or the person that's across the street that's doing it is going to knock them right off their block. >> Yeah, so that's very true. So, I'll mention another survey we did I think about six months ago. It was in partnership with an industry analyst. And we surveyed, again, a number of IT executives to understand how many were tracking business outcomes, how many of these software executives, IT executives were tracking business outcomes. And there were less than 15% of these executives who were actually tracking the outcomes of the software delivery. And you see that every day, right? So, in my own teams, for instance, we've been adopting a lot of these core principles in the last year or so. And we've uncovered that 16% of our resources were basically aligned around initiatives which were not strategic for us. I take another example. For instance, one of our customers in the airline industry uncovered, for instance, that a number of... That they had software issues that led to people searching for flights and not returning any kind of availability. And yet, the IT teams, whether it's operations or software development, were completely oblivious to that because they were completely blindsided to it. And so, the connectivity between the inwards metrics that IT is using, whether it's database uptime, cycle time or whatever metric we use in IT, are typically completely divorced from the business metrics. And so, at its core, it's really about starting to align the business metrics with the software delivery chain, right? This system which is really a core differentiator for these organizations. It's about connecting those two things and starting to infuse some of the Agile culture and principles that emerge from the software side into the business side. Of course, the Lean movement and other movements have started to change some of these dynamic on the business side. And so, I think this is the moment where we are starting to see kind of the imperative to transform now, COVID obviously has been a key driver for that. The technology is right to start to be able to weave data together and really kind of also the cultural shifts through Agile, through DevOps, through the SRE movement, through Lean business transformation. All these things are coming together and are really creating kind of conditions for the BizOps Manifesto to exist. So, Clayton Christensen, great Harvard Professor, "Innovator's Dilemma", still my all-time favorite business book, talks about how difficult it is for incumbents to react to disruptive change, right? Because they're always working on incremental change 'cause that's what their customers are asking for and there's a good ROI.' When you talk about companies not measuring the right thing, I mean, clearly, IT has some portion of their budget that has to go to keeping the lights on, right? That's always the case, but hopefully, that's an ever decreasing percentage of their total activity. So, what should people be measuring? I mean, what are kind of the new metrics in BizOps that drive people to be looking at the right things, measuring the right things and subsequently making the right decisions, investment decisions, on whether they should move project A along or project B? >> So, there are really two things, right? So, I think what you were talking about is portfolio management, investment management, right? And which is a key challenge, right? In my own experience, right? Driving strategy or a large scale kind of software organization for years, it's very difficult to even get kind of a base data as to who's doing what. I mean, some of our largest customers we're engaged with right now are simply trying to get a very simple answer, which is, how many people do I have in that specific initiative at any point in time and just tracking down information is extremely difficult. And again, back to the Project Management Institute, they have estimated that on average, IT organizations have anywhere between 10 to 20% of their resources focused on initiatives which are not strategically aligned. So, that's one dimension on portfolio management. I think the key aspect though, that's we're really keen on is really around kind of the alignment of a business metrics to the IT metrics. So, I'll use kind of two simple examples, right? And my background is around quality and I've always believed that fitness for purpose is really kind of a key philosophy, if you will. And so, if you start to think about quality as fitness for purpose, you start to look at it from a customer point of view, right? And fitness for purpose for a core banking application or mobile application are different, right? So, the definition of a business value that you're trying to achieve is different. And yet, if you look at our IT operations are operating, they were using kind of a same type of inward metrics, like a database uptime or a cycle time or what is my point velocity, right? And so, the challenge really is this inward facing metrics that the IT is using which are divorced from ultimately the outcome. And so, if I'm trying to build a core banking application, my core metric is likely going to be uptime, right? If I'm trying to build a mobile application or maybe a social mobile app, it's probably going to be engagement. And so, what you want is for everybody across IT to look at these metric and what are the metrics within the software delivery chain which ultimately contribute to that business metric? In some cases, cycle time may be completely irrelevant, right? Again, my core banking app, maybe I don't care about cycle time. And so, it's really about aligning those metrics and be able to start to differentiate. The key challenge you mentioned around the disruption that we see is or the investor's dilemma is really around the fact that many IT organizations are essentially applying the same approaches for innovation, right? For basically scrap work than they would apply to kind of other more traditional projects. And so, there's been a lot of talk about two-speed IT. And yes, it exists, but in reality, are really organizations truly differentiating how they operate their projects and products based on the outcomes that they're trying to achieve? And this is really where BizOps is trying to affect. >> I love that. Again, it doesn't seem like brain surgery, but focus on the outcomes, right? And it's horses for courses, as you said. This project, what you're measuring and how you define success isn't necessarily the same as on this other project. So, let's talk about some of the principles. We talked about the values, but I think it's interesting that the BizOps coalition just basically took the time to write these things down and they don't seem all that super insightful, but I guess you just got to get them down and have them on paper and have them in front of your face. But I want to talk about one of the key ones, which you just talked about, which is changing requirements, right? And working in a dynamic situation, which is really what's driven the software to change in software development because if you're in a game app and your competitor comes out with a new blue sword, you got to come out with a new blue sword. So, whether you had that on your Kanban wall or not. So, it's really this embracing of the speed of change and making that the rule, not the exception. I think that's a phenomenal one. And the other one you talked about is data, right? And that today's organizations generate more data than humans can process. So, informed decisions must be generated by machine learning and AI. And the big data thing with Hadoop started years ago, but we are seeing more and more that people are finally figuring it out, that it's not just big data and it's not even generic machine learning or artificial intelligence, but it's applying those particular data sets and that particular types of algorithms to a specific problem to your point, to try to actually reach an objective, whether that's increasing your average ticket or increasing your checkout rate with shopping carts that don't get left behind and these types of things. So, it's a really different way to think about the world in the good old days, probably when you guys started when we had big giant MRDs and PRDS and sat down and coded for two years and came out with a product release and hopefully, not too many patches subsequently to that. >> It's interesting, right? Again, back to one of these surveys that we did with about 600 IT executives. And we purposely designed those questions to be pretty open. And one of them was really around requirements. And it was really around kind of what is the best approach? What is your preferred approach towards requirements? And if I remember correctly, over 80% of the IT executives said that the best approach, their preferred approach, is for requirements to be completely defined before software development starts. So, let me pause there. We're 20 years after the Agile Manifesto, right? And for 80% of these IT executives to basically claim that the best approach is for requirements to be fully baked before software development starts, basically shows that we still have a very major issue. And again, our hypothesis in working with many organizations is that the key challenge is really the boundary between business and IT, which is still very much contract-based. If you look at the business side, they basically are expecting for IT to deliver on time on budget, right? But what is the incentive for IT to actually deliver on the business outcomes, right? How often is IT measured on the business outcomes and not on an SLA or on a budget type criteria. And so, that's really the fundamental shift that we really need to drive out as an industry. And, we talk about kind of this imperative for organizations to operate as one. And back to the the "Innovator's Dilemma", the key difference between these larger organization is really kind of a... If you look at the amount of capital investment that they can put into pretty much anything, why are they losing compared to startups? Why is it that more than 40% of personal loans today are issued, not by your traditional brick and mortar banks, but by startups? Well, the reason, yes, it's the traditional culture of doing incremental changes and not disrupting ourselves, which Christensen covered at length, but it's also the inability to really fundamentally change kind of the dynamic between business and IT and partner, right? To deliver on a specific business outcome. >> Right, I love that. That's a great summary and in fact, getting ready for this interview, I saw you mentioning another thing where the problem with the Agile development is that you're actually now getting more silos 'cause you have all these autonomous people working kind of independently. So, it's even a harder challenge for the business leaders, as you said, to know what's actually going on. But Serge, I want to close and talk about the coalition. So clearly, these are all great concepts. These are concepts you want to apply to your business every day. Why the coalition? Why take these concepts out to a broader audience, including your competition and the broader industry to say, "Hey, we as a group need to put a stamp of approval on these concepts, these values, these principles?" >> So first, I think we want everybody to realize that we are all talking about the same things, the same concepts. I think we're all from our own different vantage point realizing that things have to change. And again, back to whether it's value stream management or Site Reliability Engineering or BizOps, we're all kind of using slightly different languages. And so, I think one of the important aspects of BizOps is for us, all of us, whether we're talking about consulting, Agile transformation experts, whether we're talking about vendors, right? To provides kind of tools and technologies or these large enterprises to transform for all of us to basically have kind of a reference that lets us speak around kind of in a much more consistent way. The second aspect, to me, is for these concepts to start to be embraced, not just by us or vendors, system integrators, consulting firms, educators, thought leaders, but also for some of our own customers to start to become evangelists of their own in the industry. So, our objective with the coalition is to be pretty, pretty broad. And our hope is by starting to basically educate our joint customers or partners, that we can start to really foster these behaviors and start to really change some of dynamics. So, we're very pleased that if you look at some of the companies which have joined the manifesto, so we have vendors, such as Tasktop, or Appvance or PagerDuty, for instance, or even Planview, one of my direct competitors, but also thought leaders like Tom Davenport or Capgemini or smaller firms like Business Agility Institute or AgilityHealth. And so, our goal really is to start to bring together thought leaders, people who've been helping large organizations do digital transformation, vendors who are providing the technologies that many of these organizations use to deliver on this digital transformation and for all of us to start to provide the kind of education, support and tools that the industry needs. >> Yeah, that's great, Serge, and congratulations to you and the team. I know this has been going on for a while, putting all this together, getting people to sign on to the manifesto, putting the coalition together and finally today, getting to unveil it to the world in a little bit more of a public opportunity. So again, really good values, really simple principles, something that shouldn't have to be written down, but it's nice 'cause it is and now you can print it out and stick it on your wall. So, thank you for sharing the story and again, congrats to you and the team. >> Thank you, thanks, Jeff, appreciate it. >> My pleasure, all righty, Serge. If you want to learn more about the BizOps Manifesto, go to bizopsmanifesto.org, read it and you can sign it and you can stay here for more coverage on theCUBE of the BizOps Manifesto Unveiled. Thanks for watching, see you next time. (upbeat music)

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Welcome back, everyone to the Cubes. Live coverage of Del Technologies World Here at the Sands Expo at the Venetian. I'm your host, Rebecca Knight, along with my co host Stew Minutemen. We have Sean Kinney joining the program. He is a senior director primary storage marketing at Delhi emcee Thank you so much. Thrilled to redirect from Boston, >> the home of the universe, >> it's indeed well, we would say so so and so lots of news coming out this morning yesterday. Talk about some of the mean. If you want to start with talking about the storage platform, the mid range storage market in general sort of lay the foundation What you're seeing, what you're hearing, and then how the new the new products fit in with what with what customers air needing. We'LL >> break that a couple pieces. I believe that the mid range of the storage market is the most competitive. They're the most players. There are different architectures and implementations, and it's the biggest part of the market. About fifty eight percent or so so that attracts a lot of investments in competition. So what we announced today, it was the deli emcee Unity X t Siri's and that built on all the momentous on the success we had with Unity, which we actually announce basically the same conference three years ago. So we've sold forty thousand systems Good nowhere market leader, and the first part is the external storage market. It's declined, continues to be exaggerated. One of the Ellis firms predicted it wasn't gonna grow it all last year. Well, crew sixteen percent actually grew three billion dollars. It's with unity. Its original design points like the sort of Day one engineering principles were really around a couple of things. One was a true, unified architecture being told to do. Block storage, file storage and VM. Where've evils that was built in, not bolted on like no gateways, no extra window licensing, no limitations on file system size. The second was around operational simplicity and making it easy for a customer to install easier for custom manage. He was a customer of use remotely manage, and then we took that forward by adding all inclusive software, making it easy to own like not him to worry about software contracts. So all of that goodness is rolling forward in the engineering challenge that we took on with E x t wass. You know, a lot of mid range systems switch of those that have an active, passive architectural design. It's hard to do everything at once. Process, application data run, data reduction, run data services like snapshots of replications, all without significantly impacting performance. And a lot of cases, our competitors and other platforms have to make compromises. They say. Okay, if you want performance turned this function off. What was that challenge that our engineers took on? And that's what we came up with. No compromise for midrange storage. That's unity. Extinct. >> Yeah, Shawn, it's it's really interesting you could I could probably do a history lesson on some of the space thing back to, you know, early days when you know we were first to DMC. It was like, Oh, the data general product line. You know, getting merged in very competitive landscape is, as you said, most companies had multiple solutions, you know, unity in the name of it was to talk about Dell and AMC coming together, but what I want you coming on is there was often, you know, okay, somebody came out with, like, a new a new idea, and they sold that as a product. And then it got baked into a feature, and we saw that happened again and again and again. And the storage market, what are some of those key drivers is toe. You know what customers look for? How you differentiate yourself. Are we past that? You know, product feature churn way in the platform phase. Now, you know, we always say it would be great if software was just independent of some of these. But there's a reason why we still have storage raise. Despite the fact that, you know, it's been, you know, it's been nibbled at by some of the other, you know, cloud and hyper converge. You know, talk applications. >> Yeah. Uh, let's say that a couple ways in that, especially in the mid range. Our customers expect the system to do everything you know. It has to do everything Well, it doesn't get to be specialized for a lot of our customers. It is thie infrastructure. It is that data capital, which is the lifeblood of their business. So the first thing is it has to do everything. The second thing I would say is that because it has to do everything and one feature isn't really gonna break through anymore. The architecture's the intelligence, the reliability, the resiliency that takes years of hardening. Okay, the new competitors has to start a ground zero all over again. So I would say that that's part of the second thing I would say is, it's about the experience inside the box from the feature function and outside the box. How do we get a better experience? And for us, that starts with Cloud I. Q. It's a storage, monitoring and analytics platform that you can really you have infrastructure insight in the palm of your hand. You're not tied to a terminal, and if you want to be, of course you can. But you can now remotely monitor your entire storage environment. Unity, Power Max SC Extreme Io. Today we announce connect trick support for sandwiches in VM support. So we're going broader and deeper, you know, as well as making its water. So it's hard to have one feature breakthrough when you need the first ten to even get in the game. >> Well, as you said, for for these customers, this infrastructure has to do it all. And and so how do you manage expectations? And how do you How do you work with your customers? Maybe who have unrealistic expectations about what it can do. >> Our customers are the best. I mean, everybody says it, but because they push us and they push the product and they want to see how far it can go and they want to test it. So I love them. I love because they push us to be better. They push us to think in new ways. Uh, but yeah, there are different architectures. Have differences. Thumbs Power Max is an enterprise. High end, resilient architecture. It's never going to hit a ten thousand dollar price point like the architecture wasn't designed. And so for our customers that wants all these high end features like an end to end envy me implementation. Well, that's actually why we have power, Max. So you don't want to build another Power Macs with unity. So while the new unit e x t, it is envy Emmy ready and that'LL give us a performance boost We're balancing the benefits of envy. Emmy with the economics, the price point that come with it. >> All right, So, Sean, talk about Get front from the user standpoint, you know, we've We've talked about simplicity for a long time. I remember used to be contest. It's like All right, well, you know, bring in the kids and has he how fast they can go through the wizard Or, you know, he had a hyper converts infrastructure. It should just be a button you press and I mean had clouded. Just kind of does it. When we look at the mid range, you know, where are we in that? You know, management. You talked about Cloud like you, you know, how do we measure and how to customers look at you know how invisible their infrastructure is? >> I think every I don't think any marketing person worth his salt would say, My product is hard to use. It's easy to use the word simplicity, but I think it's we're evolving. And again, it's that outside the box experience now, the element manager Unisphere for um, for unity is very easy to use with tons of tests and research. But it's going beyond that is how do we plug into the VM? Where tools. How do we plug? How do we support containers? How do we support playbooks with Ansel? Forget it. It's moving the storage. Management's out of storage. Still remember, twenty years ago, we helped create the concept of a storage admin. You know, things that coming full circle. And except for the biggest companies, you know that it's becoming of'em where admin that wants to manage the whole environment. >> Okay, I wonder if you could walk us up the stack a little bit. You know, when you talk about these environments at the keynote this morning, we're talking about a lot of new application. You're talking about a I and M l. What's the applications, Stace? That's the sweet spot for unity. And, you know, you know, you mentioned kind of container ization in there, you know, Cloud native. How much does that tie into the mid range today? >> Yeah, I think it goes back to that. All of the above. Its some database, some file sharing, some management and movement of work loads to the cloud. Whether be cloud tearing. What? Running disaster recovery As a service where you know you need the replication You just don't want to pay for and manage and owned that second sight in the cloud. We'Ll do that as a service. So I, uh I think it's again. It goes back to that being able to do everything and with the rise of the Internet of things with the rise of new workloads, new workload types, they're just more uses for data and data continues to be the light flooding of business. But it you need the foundation. You need the performance. And with X t now twice as fast as the previous generation, you need the data reduction with compression. Indeed, implication with extra that's now up to five to one. You need the overall system efficiency so the system doesn't have a ton of overhead, and you need multiple paths to the cloud For those customers that already ofwork loads in the cloud. No, they're going to go there in the next twelve months or know that they have to at least think about it and so that we future proof them across all boys. So you need those sort of foundational aspects and we believe we're basically best in class across all of them. But then you get more >> advanced. I want to get your thoughts on where this market is going. As you said that analysts that the news of its demise has been greatly exaggerated, analysts are just not getting it right. I mean, they said it wasn't gonna grow a gross. Sixty grew sixteen percent. Why are they getting it wrong? Are there and also do? What do you see as sort of the growth trajectory of this market? I'm not >> sure they're getting it wrong. And they may be underestimating the new use cases and the new ways customers using data What I think we should probably do a better job of as an industry is realize that there is a lot of space for both best of breed infrastructure and converged infrastructure and things like Piper converge. It's not an or conversation, it's an and conversation, and no one thinks that I love working about Del Technologies is we have the aunt, you know, for us, it's not one or the other, and that's all we could sell. We have the aunt, and that allows us to really better serve our customers because over eighty percent of our customers have both. >> So, Sean, you mentioned working for Del Technologies. There are a couple people that have been at this show for a while there. Like boy, they didn't spend a lot of time in the keynotes talking about storage. Bring us in a little bit. And inside there, you know, still a deli emcee. You got still a storage company. >> Still, you've seen the name isn't there very much. So you know that we wouldn't be spending all this time and R and D and you've heard about the investments we've made in our stores sales organization and our partner organization. You don't do those investments. If you're not committed to storage it, you know, way struggled for a while. We're losing share for awhile, but that ship has turned for the last four quarters. We've grown market share in revenue, but we're pretty good trajectory. I like our chances. >> I want to ask you about something else that was brought up in the keynote. And that is this idea of a very changing workforce. The workforce is now has five generations in it. Uh, it is a much younger workforce in a in a work first that wants to work in different ways. Collaborate in different ways. Uh, how are you personally dealing with that with your team, Maybe a dispersed team. How are you managing new forms of creativity and collaboration and innovation in the workforce? And then how are you helping your customers think about these challenges? >> You know, I, uh, maybe I can't write for the Harvard Business Review. For me personally, this is my approach that is one guy's opinion for me. It's about people like you want to manage the project, not the people I expected. I trust my staff, and they range from twenty two to sixty two to be adults in to get the job done and whether they do it in the office or at home, whether they do it Tuesday at two o'Clock or Tuesday at nine o'Clock. If it's due Wednesday, I'm gonna trust them to get it done. So it's, uh, there's a little of professionals. It does require sometimes more empathy and some understanding of flexibility. But I participate in that change to I don't want to miss my kid's game, and I wanna make sure I bring my daughter to the dentist, So I, uh, I think it's for the best, because we're blurring the lines of on and off. I could see again. I don't write for our business, really a time in the next few years where vacation time is no longer tracked. I don't think that far away >> a lot of companies don't even have it at all. I mean, it's >> just you >> get your work done, do what you need to do. >> So I love it because then we come back to being more of it. It's even more about, um, a meritocracy and performance and delivery and execution. So, uh, I think it's only the better and more productive employees, happier employees. It's actually reinforcing cycle. What I found, >> and that's good for business. That's a bottom line. >> Employees. You good >> for Harvard Business Review. >> So, Sean, last thing I wanted to get is for people that didn't make it to show. Give them a beginning of flavor about what's happening from a mid range to orange around the environment here and tell us, how much time have you been spending at the Fenway and, you know, pro Basketball Hall of Fame sex mons you know, in the Expo Hall there because I know what a big sports got. You >> are not enough is the first question, quite simply, the best mid range storage just got better now the market leader, when all the advantages, we have immunity. We just rolled them forward to a new, more efficient, better performing platform. So it's, ah, our customers are gonna love over bringing forward, and I think it's our sales. Guys will find it much easier to sell. So we're, uh, we're thrilled with today's announcements. Were thrilled with where the marketplaces were thrilled with our market position and best is yet to come. >> Well, we were thrilled to have you on the cute. So thank you so much for coming on. >> It's always a pleasure. >> I'm Rebecca Knight for Stew Minutemen. We will have much more of the cubes Live coverage from Del Technologies World coming up in just a little bit

>> Live from San Francisco, it's theCube, covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. >> Hello everyone, welcome back, this is theCUBE's live coverage, we're here in San Francisco, Moscone West for Google Cloud's big conference called Next 2018. The hashtag is GoogleNext18. I'm John Furrier, Dave Vellante, our next guest is Traci Gusher, Principal, Data and Analytics at KPMG. Great to have you on, thanks for joining us today. >> Yeah, thanks for having me. >> We love bringing on the big system, global, some integrators, you guys have great domain expertise. You also work with customers, you have all the best stories. You work with the best tech. Google Cloud is like a kid in the candy store >> It sure is. when it comes to tech, so my first question is obviously AI in super important to Google. Huge scale, they bring out all the goodies to the party. Spanner, Bigtable, BigQuery, I mean they got a lot of good stuff. TensorFlow, all this open source goodness, pretty impressive, right, >> Yeah, absolutely. the past couple years what they've done. How are you guys partnering with Google, because now that's out there, they need help, they've been acknowledging it for a couple years, they're building an ecosystem, and they want to help end user customers. >> Yeah, we've been working with Google for quite some time, but we actually just formalized our partnership with Google in May of this year. From our perspective, all of the good work that we have done, we're ready to hit the accelerator on and really move forward fast. Some of the things that were announced this week, I think, are prime examples of areas where we see opportunity for us to hit the accelerator on. Something like what was announced this week with their new contact center, API suite, launched by the Advanced Solutions Lab. We had early access to test some of that and really were able to witness just how accelerated some of these things can help us be when we're building end-to-end solutions for clients. >> There's a shortcut to the solutions because with Cloud, the time to value is so much faster, so it's almost an innovator's dilemma. The longer deployments probably meant more billings, ( laughs) right, for a lot of integrators. We've heard people saying hey we've gone, the old days were eight months to eight weeks to eight minutes on some of these techs, so the engagements have changed. At the end of the day, there's still a huge demand for architectural shift. How has the delivery piece of tech helped you guys serve your customers, because I think that's now a conversation that we're hearing is that look, I can move faster, but I don't want to break anything. The old Facebook move fast, break stuff, that doesn't fly in enterprise. >> No, it doesn't (laughs). >> I want to move fast, but I need to have some support there. What are some of the things that you're seeing that are impacting the delivery from integrators? >> Well, some of the technology that's come, that's reduced the length of time to deliver, we see and a lot of our customers see as opportunity to do the next thing, right? If you can implement a solution to a problem quicker, better, faster, than you can move on to the next problem and implement that one quicker, better, faster. I think the first impact is just being able to solve more problems, just being able to really apply some benefits in a lot more areas. The second thing is that we're looking at problems differently, the way that problems used to be solved is changing, and that's most powerfully noted, as we see, at this conference by what's happening with artificial intelligence and with all the accelerators that are being released in machine learning and the like. There's a big difference in just how we're solving the problems that impacts it. >> What are some of the problems that you guys are attacking now, obviously AI's got a lot of goodness to it. What are some of the challenges that you're attacking for customers, what are some examples? >> Our customers have varying problems as they're looking to capitalize on artificial intelligence. One of the big problems is where do I start, right? Often you'll have a big hype cycle where people are really interested, executives are really interested, and I want to use AI, I want to be an AI-enabled company. But they're not really sure where to start. One of the areas that we're really hoping a lot of our customers do is identify where the low hanging fruit is to get immediate value. And at the same time, plan for longer strategic types of opportunities. The second area is that one of the faults that we're seeing, or failure points that we're seeing in using artificial intelligence is failure to launch. What I mean by that is there's a lot of great modeling, a lot of great prototyping and experimentation happening in the lab as it relates to applying AI to different problems and opportunities, but they're staying in the lab, they're not making it in to production, they're not making it in to BAU, business as usual processes inside organizations. So a big area that we're helping our clients in is actually bridging that gap, and that's actually how I refer to it, I refer to it as mind the gap. >> That is a great example, I hear this all the time, classic. Is it, what's the reasons, just group think, I'm nervous, there's no process, what's holding that back from the failure to launch? >> There's a few things. The first is that a lot of traditional IT organizations embedded in enterprises don't necessarily have all of the skills and capabilities or the depth of skills and capabilities that they need to deploy these models in to production. There's even just basic programming types of gaps, where a lot of models are being constructed using things like Python, and a lot of traditional IT organizations are Java shops and they're saying what do I do now? Do I convert, do I learn, do I use different talent? There's technology areas that prove to be challenging. The other area is in the people, and I actually spoke with an analyst this morning about this very topic. There's a lot of organizations that have started productionalizing some of these systems and some of these applications, and they're a little bit discouraged that they're not seeing the kind of lift and the kind of benefits that they thought they would. In most cases-- >> Who, the customers or the analysts? >> The customers. >> OK, alright. >> Yeah, I was having a conversation with an analyst about it. But in most cases, it's not that the technology is falling short, it's not that the model isn't as accurate as you need it to be, it's that the workforce hasn't been transitioned to utilize it, the processes haven't been changed. >> Operationalizing it, yeah. >> The user interfaces aren't transitioning the workforce to a new type of model, they're not being retrained on how to utilize the new technology or the new insights coming from these models. >> That's a huge issue, I agree. >> Isn't there also, Traci, some complacency in certain industries? I mean you think about businesses that haven't yet totally transformed, I think of healthcare, I think of financial services, as examples that are ripe for transformation but really haven't yet. You hear a lot of people say well, it's not really urgent for us, we're doing pretty well, I'll be retired by then, there seems to be a sense of complacency in certain segments of enterprises. Do you see that? >> I do. And I'll say that we've seen a lot more movement in some of those complacent industries in the last six to 18 months than we have previously. I'll also say going back to that where do I start element, there's a lot of organizations that have pressing business challenges, those burning platforms, and that's where they're starting and I'm not advocating against it, I'm actually advocating very much for that, because that's how you can prove some real immediate value. Some organizations, particularly in life sciences or financial services, they're starting to use these technologies to solve their regulatory challenges. How do I comply faster, how do I comply better, how do I avoid any type of compliance issues in the future, how do I avoid other challenges that could come in those areas? The answer to a lot of those questions is if I use AI, I can do it quicker, more accurately, etc. >> Are you able to help them get ancillary value out of that or is it just sort of, compliance a lot of times is like insurance, if I don't do it I get in trouble or I get fined. But are you able to, this is like the holy grail of compliance and governance, are you able to get additional value out of that when you sort of apply machine intelligence to solve those problems? >> That's always the goal. Solving the regulatory problem is certainly what I would say are the table stakes, right? The must-have. But the ability to gain insight that can actually drive value in the organization, that's where your aim really is. In fact, we've worked with a lot of organizations, take life sciences, we've worked with some life sciences organizations that are trying to solve some compliance issues and what we've found is that many times in helping them solve these compliance issues, we're actually gathering insights that significantly increase the capability of their sales organization, because the insights are giving them real information about their customers, their customers' buying patterns, how they're buying, where they might be buying improperly. And it's not the table stake of what we're trying to do, the table stake was maybe contract compliance, but the value that they're actually getting out of it is not only the compliance over their distributors or their pharmacies, but it's also over the impact that they're going to have on their sales organization. For something like an internal audit department to have value to sales, that' like holy grail stuff. >> Yeah, right, yeah. >> What about the data challenges? Even in a bank, who's essentially a data company, the data tends to be very siloed, maybe tucked away in different business units. How are you seeing organizations, how are you helping organizations deal with that data silo problem, specifically as it relates to AI? >> It used to be that the devil was in the details, but now the devil's in the data, right? >> I love that. >> There was a great Harvard Business Review article that came out, and I think Diane Green actually quoted this in one of her presentations, that companies that can't do analytics well can't do AI yet. A lot of companies that can't do analytics well yet, it isn't because they don't have the analytical talent, it's not because they don't know the insights they want to drive, it's because the data isn't in the right format, isn't usable to be able to gain value from it. There's a few different ways that we're helping our clients deal with those things. Just at the very basic level is good data governance. Do you have data stewards that are owning data, that are making sure that data is being created and governed the right way? >> That's a huge deal, I imagine-- >> Inequality and. >> It's huge. >> Inequality-- >> inequality, meta data. >> Garbage in, garbage out. >> Lineage of data, how it's transformed. Being able to govern those things is just imperative. >> It could be just a database thing, could be a database thing, too, it's one of those things where there's so many areas that could be mistakes on the data side. Want to get your thoughts on the point you said earlier which I thought was about technology not coming out and getting commercialized or operationalized. For a variety of reasons, one of them being processes in place, and we hear this a lot. This is a big opportunity, because the human side of these new jobs, whether you're operating the network, really they need help, customers need help. I think you guys should do a great job there given the history. The other trend that came out of the keynote today I want to get your reaction to is there's a tweet here, I'll read it, it says "GCB Cloud will start serving "managing services, enterprise workloads, including Oracle, RAC and Oracle exit data, and SAP HANA through partners." Interesting mind shift again, talk about a mind shift, OK. Partners aren't used to dealing with multi-vendors, but now as a managed service will change the mechanism a bit on delivery because now it's like OK, hey, you want to sling some APIs around, no problem. You want to manage it, we got Kubernetes and Istio. You want a little Oracle with a little bit of HANA? It brings up a much more diverse landscape of solutions. >> It does. Which makes the partners like sous chefs. You can cut the solutions up any way you want. To your point about going faster, to the next challenge. Normal, is that going to be the new normal, this kind of managed service dashboarding? You see that as the... >> I think it is, and I'll take it a step, sir, I'll take it a step further beyond managed service and actually get a little more discreet. One of the things that we're doing increasingly more of is insights as a service, right? If you think about managed service in the traditional sense of I've got a process and you're going to manage that process end to end for me, that technology end to end for me, I do think that that's going to slowly become more and more prevalent. That has to happen with our movement to putting our applications in the cloud, and our ERPs in the cloud. I think it is going to become more of the norm than the less but I also think that it's opening the door for a lot of other things as a service, including insights as a service. Organizations can't find the data science talent that they need to do the really complex types of analysis. >> Your insights as a service comment just gave me an insightful, original idea, thank you very much. >> You're welcome. >> I'll put this in the wrap-up, Dave, when we talk about it. Think about insight as a service, to make that happen with all the underpinning tech, whether it's Oracle or whatever, the insights are an abstraction layer on top of that so if the job is to create great experiences or insights, it should be independent of that. Google Cloud is bringing out a lot more of the concept of abstractions. Kubernetes, Istio, so this notion of an abstraction layer is not just technical, there's also business logic involved. >> Yeah, absolutely. >> This is going to be a dream scenario for KPMG, >> We think so. for your customers, for other partners. Cause now you can add value in those abstraction layers. >> Absolutely. >> By reducing the complexity. Well Oracle, that's not my department, that's HANA's, that's SAP, who does that? He or she's the product lead over it, gone. Insights as a service completely horizontally flattens that. >> Yeah, and to that point, there's magic that happens when you bring different data together. Having data silos because their data's in different systems just, that's the analytics of 1990. Organizations can't operate on that anymore, and real analytics comes when you are working at a layer above the system's and working with the data that's coming from those systems and in fact even creating signals from the data. Not even using the data anymore, creating a signal from the data as an input to a model. I couldn't agree with you more. >> Whole new way of doing business. This is digital transmitting, this is the magic of Cloud. Traci, great to have you on. >> Yeah, thanks for having me. >> It's going to be a whole new landscape changeover, new way to do business. You guys are doing a great job, KPMG, Traci Gusher. Here inside theCUBE talking about analytics AI. If you can't do analytics good, why even go to AI? Love that line. theCUBE bringing you all the data here, stick with us for more after this short break. (bubbly electronic tones)

>> Announcer: From the Corinium Chief Analytics Officer Conference Spring, San Francisco It's theCUBE. >> Hey welcome back, everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring Event about a hundred CAO's as opposed to CDO's talking about big data, transformation and analytics and the role of analytics and a lot of practitioners are really excited to have our next guest. He's up from Mexico City, it's Jose Murillo. He's the chief analytics officer from Banorte. Jose, great to see you. >> Thank you for having me, Jeff. >> Absolutely, so for people that aren't familiar with Banorte give us a quick overview. >> Banorte's the second largest financial group in Mexico. We, for the last, during the last three years were able to leapfrog city bank. >> Congratulations, and as we were talking before we turned the cameras on, you and your project had a big part of that. So before we get in it, you are a chief analytics officer. How did you come in, what's the reporting structure, how do you work within the broader spectrum of the bank? >> Well I moved to Banorte like about five years ago from, I was working at the central bank where I spent about 10 years in the MPC, the Monitor Policy Committee, and I was invited by initially by the president of the board and when the new chief operating officer was named he invited me to, to lead a new analytics business unit that he wanted to create. And that's the way that I arrived there. >> Okay so you report in to the COO. >> He's the COO/CFO, so he's not only a very smart guy but a very powerful guy running the organization. >> And does the CIO also report to him? >> The CIO, the CDO, the CMO report to him. >> Okay so you have a CDO as well Chief Data Officer. >> We have a CDO who I work very close with him. >> We could go for a long time I might not let you leave for lunch. So I'm just curious on the relationship between the CDO and the CAO, the data officer and the analytics officer. We often hear one or the other, it's very seldom that I've heard both. So how do you guys divide and conquer your responsibilities? How do you parse that out? >> I guess he provides the foundation that we need to find analytics projects that are going to transform the financial group and he has been a very good partner in providing the data that we need and basically what we do as the CAO we find those opportunities to improve the efficiency, to bring the customer to the center, and be able to deliver value to our stakeholders. >> Right, so he's really kind of giving you the infrastructure if you will, of making that data available, getting it to you from all various sources, et cetera, that then you can use for your analytics magic on top. >> Exactly >> Okay, so that's very good, so when we sat down you said an exciting report has come out from, I believe it was HBR, about the tremendous ROI that you guys have realized. So you tell the story better than I, what did they find in your recent article? >> Well in the recent article from the Harvard Business Review is how Banorte has made its analytics business unit pay off. And what we have found in the past two and a half years is we've been able to deliver massive value and by now we have surpassed a billion dollars in net income creation. From analytics projects made on cost saving strategies and revenue generating projects. >> So you paid for yourself just barely >> Yeah. >> No I mean that's such a great story, just barely 'cause it's so it's so important. So as you said, that billion dollars have been realized both in cost savings but more importantly on incremental revenue and that's really the most important thing. >> Exactly >> So how are you measuring that ROI? >> So basically the way we measure it is on cost saving strategies that are related to a risk operational and financial cost. It's the contemporary news effect. And that can be audited. And on the other side, on revenue generating projects, the way we do it is we estimate the customer lifetime value, which is nothing else than the net present value of the relationship with our customers, so we need to estimate survival rates plus the depth of the relationship with our customers. >> So I just love, so you're doing all kinds of projects, you're measuring the value of the projects. What are some of the projects that had a high ROI that you would've never guessed that you guys applied some analytics to and said wow, terrific value relative to what we expected. >> Let me tell you about two types of projects. The first project that we started on was on cost of risk cutting strategies. And we delivered massive value and very quickly. So that helped us gain credibility. And the way we do it, we did it, is like to analyze a dicing of the data where we had excessive cost of risk. And in the first year, actually, that was the first quarter of Operations, we yielded about a 25% incremental value to the credit card business. And after that, we start to work with them and started the discovery data process. And from there, we were able to optimize analytically the cross cell process. And that's a project that has already a three year maturity. And by this time, we are able to sell, without having any bricks or mortars, about 25% of the credit cards sold by the financial group. If we were a territory within the financial group, we would be the largest one with 400 basis points lower on cost of risk, 30% more on activation rates. And it's no surprise that the acquisition cost is 30% less, vis-a-vis our most efficient channel. >> Right, I just want to keep digging down into this, Jose, there's a lot of this stuff to go. I mean, you've been issuing cards forever. So was it just a better way to score customers, was it a better way to avoid the big fraud customers, was it a better way to steal customers maybe from a competitor with a competitive rate that you can afford, I mean, what are some of the factors that allowed you to grow this business in such a big way? >> I guess it's something that has been improving during the first three years. The first thing is that we made like, a very simple cascade on seeing why we were not that efficient cross cell process. And we kind of fixed every part of it. Like on the income estimation models that we had, and we partner with the risk department to improve them. Up to the information that we had on our customers to contact them, and we partner with data governance to improve those. And finally, on the delivery process and all the engaging process with the customers. And it seemed that we were going to find something that was going to be more costly, but it was something that we had at the center of the customers so that it was more likely for them to go and pick up the card and we deliver it to their homes. And finally, that process was much more efficient and the gains that we had, we shared them with our customers. And after three years, we've done things with artificial intelligence to have much better scripts so that we are better able to serve our customers. We do a lot of experimentation, experimentation that we didn't do before. And we use some concepts from behavioral economics to try to explain much better the value proposition to our customers. >> So I just, I love this point, is that it was a bunch of small, it was optimizing lots of little steps and little pieces of the pie that added up to such a significant thing, it wasn't like this magic AI pixie dust. >> Initially, it as a big bang, and then it has been something incremental that has since, it's a project that at the end of the day, we own, and it's something that we are tracking. We are willing to put all the effort to have all the incremental efficiency within the process. >> So people, process, and technology, we talk about, those are the three pieces always to drive organizational change. And usually, the technology is the easy part, the hard part is the people and the process. So as you and your team have started to work with the various lines of businesses for all these different pieces. Promotional piece, customary attention piece, risk and governance piece, cross sale pice, how has their attitude towards your group changed over time as you've started to deliver insight and all this incremental deltas into their business. >> I guess you are hitting just on the spot. Building the models is the easy part. The hard part is to build the consensus around, to change a process that has run for 20 years, there's a lot of inertia. >> Right, right. >> And there are a lot of silos within organizations. So initially, I guess, the credibility that we gained initially helped us move faster. And at the end of the day, I think what happens is the way that we are set up is that the incentives are very well aligned within the different units that need to interact in the sense that we are a unit that is sponsored by the, corporately sponsored, and we make it easier for our partners to attain their goals. So that's, and they don't share the cost of us, so that helps. >> And those are the goals they already had. So you're basically helping them achieve their objectives that they already had better and more efficiently. >> Yeah, and you are pointing out correctly, it's the people, and besides the math, it's a highly, you could say diplomatic or political position in the sense that you need to have all the different partners and stakeholders aligned to change something that has been running for 20 years. >> Right, right. And i just love it, it's a ton of little marginal improvements across a wide variety of tough points, it's so impactful. So as you look forward now, is there another big bang out there, or do you just see kind of this constant march of incremental improvement, and, or are you just going to start getting into more different businesses or kind of different areas in the bank to apply the same process, where do you go next? >> Well, we started with the credit card business, but we moved toward the verticals within the financial group. From mortgages, auto loans, payroll loans, to we are working with the insurance company, the long term savings company. So we've increased the scope of the group. And we moved not only from cost to revenue generating projects. And so far, it has been, we have been on an exponential increase of our impact, I guess that's the big question. The first, we were able to do 46 times our cost. The second year, we made 106 times our cost, the third year, we are close to 200 times our cost with an incremental base. And so far, we've been on this increasing slide. At some point, it's, I guess, we are going to decelerate, but so far, we haven't hit the point. >> Right, the law of big numbers, eventually, you got to, eventually, you'll slow down a little bit. All right, well Jose, I'll give you the last word before we sign off here. Kind of tips and tricks that you would share with a peer if we're sitting around on a Friday afternoon on a back porch. You know, as you've gone through this journey, three and a half years and really sold you and your vision into the company, what would you share with a peer that's kind of starting this journey or starting to run into some of the early hurdles to get past. >> I guess there are two things that I could share. And once you have built a group like this and you have already, the incentives aligned and you have support from the top in the sense that they know that there's no other way they want really to compete and be successful, and suppose that you have all these preconditions set up and suddenly, you have a bunch of really smart people that are coming to a company, so you need to focus on ROI, high ROI projects. I;s very easy to get distracted on non-impactful projects. And I guess, the most important thing is that you have to learn to say no to a lot of things. >> Speaking my language, I love it. Learn to say no, it's the most important thing you'll ever, all right, well Jose, thanks for spending a few minutes and congratulations on all your success, what a great story. >> Thank you for having me, Jeff. >> Absolutely, he's Jose, I'm Jeff, you're watching theCUBE from the Corinium Chief Analytics Officer Summit in downtown San Francisco. (electronic music)

(lively music) >> Hello, and welcome to a special CUBE Conversation here in Palo Alto studios of theCUBE, I am John Furrier, the co-founder of SiliconANGLE Media and also the co-host of theCUBE. We are here with Blake Morgan, who is the futurist, author, speaker, around the concept of customer experience, and has a great new book out called, More is More. Blake, Welcome to theCUBE Conversation. >> Thank you John. >> Thanks for coming in. So I love that it is a hard cover book, the book is great, it feels good, the pages, it's a really good read, but it's got a lot of meaty topics in there. So let's just jump in, what's the motivation for the book? Why the book? Why More is More? >> So I have been in the contact center space for over 10 years and basically everyone under the sun is a customer and we all know what it feels like to have a bad customer experience. Have you had a bad customer experience ever? >> John: Oh yes. >> Yeah, right. >> So there is no shortage of work to be done in this space. I think now it's a great time to be in customer experience because there is more awareness about what it actually means. So, I wrote the book to basically provide some kind of definition and to really help people understand, What is customer experience?. Is it customer service? No, it's not. So what does it mean? How can businesses improve customer experience and what do they need to know to get started? >> How about the evolution? Because you know digital has really changed the game. You are seeing cloud computing, machine learning, AI techniques, bots certainly. I mean Twitter came out over ten years ago. I remember when Comcast Cares came out, you know that was a revolution. It was this one guy who decided to be on Twitter. We saw that beginning of that, that trend, where you can now serve and touch folks with customer service and experience, but then again, the blinds between customer experience and customer experience is blurring. Now those multiple channels, do you send them a Snapchat? Do you Instagram? All kinds of new things are emerging, so how do you define, as a frame, the customer experience in this new context? >> Yeah, you're right, there are so many channels. It's really overwhelming for a lot of businesses. So I think it is important to really cut out the noise to think about, Who are you as a business?, and Who is your customer?. What does your customer need? And I really encourage businesses to make their life harder to make it easier on the customer, because in so many situations, companies make it easier on themselves and make it harder on their customers. For example, say you do tweet a company, they might tell you, Hey, now you need to call us and repeat yourself or Now you need to send us an email. Well that's not easy for me as the customer. So it's really all about making customers' lives easier and better. That's the name of the game. >> So what was the findings in the book, when you did the research for the book, what was the core problem that companies are facing? Was it understanding customer experience? Was it the re imagining of customer experience? Was it just a strategic imperative? What was the problem that you uncovered that was the core to this new customer experience equation? >> So a lot of people equate customer experience with customer service and that's a big problem because for most companies, customer service is a cost center. It's not a revenue generating arm of the business. It's not exciting, it's not a money maker, it's not marketing or sales, and so that is really what people think of, when they think of customer experience. But the book is based on this DO MORE framework and DO MORE is basically represents as an acronym. Each piece of the six piece framework represents a different piece of where customer experience lives. So the first D is design something special. The second, I'm not going to read you every, I'm not going to bore you every single word, but the second is about loving your employees, so that is a part of it too. So culture, modernizing with technology, obsessing over your customers, having a culture of customer centricity and embracing innovation and disruption. So these are all varying pieces of DO MORE, which really helps companies understand, it's not simply something that sits in the contact center. For example, let's say you've got your laptop here, and you love your laptop, but your experience of the laptop is not only shaped by, say you have to contact the call center, it is also shaped by how that laptop was built and how about those people who built the laptop. Were they fighting at work with each other? Did they like their jobs? Did they like their boss? Honestly, that's going to impact your experience. >> Yeah, was it a sweat shop. >> Was it a sweat shop? There you go. >> I mean there's all kind of issues about social good too kind of comes into it with that. >> It actually does, I write a lot about social good in my book and some really great CEOs today get that social good is important, like the CEO of Patagonia or Marc Benioff. I mean you can just rattle off so many examples of stuff that he's doing, whether it is equal pay for woman, or his huge house in Hawaii where he's housed monks, to help them when one of the monks had cancer actually. Salesforce is constantly doing good for it's employees and for the community at large. >> Take me through your view on how executives should think about customer experience with all the digital transformation, because a lot of business models are shifting, you are seeing mobile apps, changing the financial services market, because now the app is the teller. So you have three kinds of companies out there, you've got the customer service oriented company, like a Zappos, or you've got a tech company like Google, but they are all about product innovation. Then you've got companies like Apple and others, that are like the big brand and culture personalities, so you've got these three different kind of companies as an example, each one might have a different view on customer experience. How do you tie, how does an executive figure out how to match the more into their DNA? >> That's a fantastic question. I think it's important to have somebody accountable to it, whether it's a Chief Customer Officer or your CMO, because the CEO is ultimately responsible, however, the CEO has their hand in so many things, it's not scalable for them to be so involved on a granular level, on customer centric metrics and so on and so forth throughout the organization. So I would encourage a company to actually hire somebody who is accountable, who creates even tiger teams across the organization with these customer centric metrics in mind, so everybody is working together and they know their job, no matter if they are HR or finance or marketing or customer service, that their metrics, their performance metrics, are tied back to the customer satisfaction. >> I know you do a lot of talks and you do a lot of speeches out there and events, what's the common question that you get? I mean what are people really struggling with or what are they interested in, what are some of the things that you are hearing when you are out on the road giving talks? >> I think it's hard to actually put some of these practices, I think it's actually hard to put some of these ideas into practice. For example, I recently gave a talk at a large technology company down here in San Jose and I presented some pretty wild ideas about actually the energy for influencing change. So how do we keep that high level of stamina with our employees when it's just quite hard to sometimes even keep up. I remember I gave this speech, I talked about a lot of very eccentric ideas about self-management, like when you are a worker you need to take care of yourself because the corporation is never going to give you a pass to let's say, rest, or do what you need to do to feel good, to be good at work. I noticed some of the people in the audience were all texting each other and afterwards someone came up to me and said, you know we are all texting each other because you say these things and the speech was purchased by the leader of the company, however, when it comes to actually working here, that is not really the vibe here, that's not the culture. So I think that a lot of, even the best companies today, still struggle every single day with some of these ideas, because when you DO MORE, when you work harder than others, it's tiring, it can take it's toll on employees. So how do you keep people fresh? >> So fatigue is a huge issue. >> Fatigue, yes. It is an issue. >> So how do they solve that? Because again, that is an experience and the employees itself represent brands. >> Yeah. >> So what are some of the solutions for that? >> Yeah so it's normal that people in these big companies feel fatigued when they are working harder for the customer, but it is really important for people to just manage themselves because no one is going to give you permission to take ten minutes to go for a walk, take ten minutes to go meditate, so it's really about management providing the room for employees to breathe and also modeling it as an example, if leaders just worked 24/7, it's all about the grind, the grind, the grind, that's not a healthy culture, so they need to push their people, but also give them some kind of safety that they can take care of themselves as well. >> So talk about the book target. Who is the ideal candidate for the book? Who are you writing the book for? What do you hope to accomplish for the reader and the outcome? >> So I write for Forbes and Harvard Business Review and Hemispheres Magazine, I have a lot of different types of readers because customer experience really affects everybody in business. So it could be the CMO, it could be the Chief Customer Officer, it could be the CEO, in fact the CEO of 1-800-Flowers wrote the foreword for my book, Chris McCann. So this book is really relevant for a wide variety of people who are interested in making their company more competitive. >> That's a great point, so let's trill down on that, customer experience just doesn't end in a department, we've seen this in IT, information technology, it's a department that becomes now pervasive with cloud computing, you see social media out there, so customer experience has multiple touch points, hence the broad appeal, how should someone think about being the customer experience champion? Because you always have the champions that kind of drives the change, so you've got change agents and you have kind of to me, the pre-existing management in place, what's the human role in this? Because remember, you have machines out there, you have bots, and all those machine learning technology out there, it's important that the human piece is integral to this, right? I mean what's your view on the role of the person? >> Yeah I'm not anti-technology, I'm not anti-bot, I am excited about the Amazon Go cashier-less stores, Amazon Go stores, but I do feel that technology can help us without totally replacing us. I think that we need thoughtful people in charge of these technologies to lead us, to make smart decisions, but you can't just let the technology go. I think that can be really scary. We've definitely seen so many TV shows about this, you can't blink without seeing another TV show about robots taking over the world. >> So it's a concern. What's the biggest thing you've learned from the book? What was the key learnings for you, personally, when you wrote this book? >> Well, writing a book, there is a lot of learning. I actually had my daughter, I was pregnant while I wrote this book and so I think for me to be totally candid, it was a lesson in patience and working through that period for me being pregnant. So I was like giving birth to the book and an actual baby. To be totally truthful, that was my learning. >> You got a lot more than the book. >> Blake: Laughing >> Well, congratulations, how old is the baby? >> She's sixteen months. >> Congratulations, awesome. >> Thank you. >> Well thanks for coming in and sharing about More is More, Blake Morgan, futurist author on the customer experience, More is More, it's theCUBE Conversation and really an impactful thought because customer experience transcends not just a department, it really is a mindset, it's about culture, it's about a lot of things, and it's certainly in the digital revolution, it's really going to be fundamental. Thanks for sharing your thoughts. >> Blake: Thanks so much. >> Appreciate it. I am John Furrier here in the Palo Alto studios for CUBE Conversation, thanks for watching. (lively music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> Announcer: Live, from Orlando, Florida, it's TheCUBE, covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to sunny Orlando everybody. This is TheCUBE, the leader in live tech coverage. My name is Dave Vellante and this is PentahoWorld, #PWorld17. Don DeLoach here, he's the co-chair of the midwest IoT council. Thanks so much for coming on TheCUBE. >> Good to be here. >> So you've just written a new book. I got it right in my hot off the presses in my hands. The Future of IoT, leveraging the shift to a data-centric world. Can you see that okay? Alright, great, how's that, you got that? Well congratulations on getting the book done. >> Thanks. >> It's like, the closest a male can come to having a baby, I guess. But, so, it's fantastic. Let's start with sort of the premise of the book. What, why'd you write it? >> Sure, I'll give you the short version, 'cause that in and of itself could go on forever. I'm a data guy by background. And for the last five or six years, I've really been passionate about IoT. And the two converged with a focus on data, but it was kind of ahead of where most people in IoT were, because they were mostly focused on sensor technology and communications, and to a limited extent, the workflow. So I kind of developed this thesis around where I thought the market was going to go. And I would have this conversation over and over and over, but it wasn't really sticking and so I decided maybe I should write a book to talk about it and it took me forever to write the book 'cause fundamentally I didn't know what I was doing. Fortunately, I was able to eventually bring on a couple of co-authors and collectively we were able to get the book written and we published it in May of this year. >> And give us the premise, how would you summarize? >> So the central thesis of the book is that the market is going to shift from a focus on IoT enabled products like a smart refrigerator or a low-fat fryer or a turbine in a factory or a power plant or whatever. It's going to shift from the IoT enabled products to the IoT enabled enterprise. If you look at the Harvard Business Review article that Jim Heppelmann and Michael Porter did in 2014, they talked about the progression from products to smart products to smart, connected products, to product systems, to system of systems. We've largely been focused on smart, connected products, or as I would call IoT enabled products. And most of the technology vendors have focused their efforts on helping the lighting vendor or the refrigerator vendor or whatever IoT enable their product. But when that moves to mass adoption of IoT, if you're the CIO or the CEO of SeaLand or Disney or Walmart or whatever, you're not going to want to be a company that has 100,000 IoT enabled products. You're going to want to be an IoT enabled company. And the difference is really all around data primacy and how that data is treated. So, right now, most of the data goes from the IoT enabled product to the product provider. And they tell you what data you can get. But that, if you look at the progression, it's almost mathematically impossible that that is sustainable because company, organizations are going to want to take my, like let's just say we're talking about a fast food restaurant. They're going to want to take the data from the low-fat fryer and the data from the refrigerator or the shake machine or the lighting system or whatever, and they're going to want to look at it in the context of the other data. And they're going to also want to combine it with their point-of-sale or crew scheduling, or inventory and then if they're smart, they'll start to even pull in external data, like pedestrian traffic or street traffic or microweather or whatever, and they'll create a much richer signature. And then, it comes down to governance, where I want to create this enriched data set, and then propagate it to the right constituent in the right time in the right way. So you still give the product provider back the data that they want, and there's nothing that precludes you from doing that. And you give the low-fat fryer provider the data that they want, but you give your regional and corporate offices a different view of the same data, and you give the FDA or your supply chain partner, it's still the same atomic data, but what you're doing is you're separating the creation of the data from the consumption of the data, and that's where you gain maximum leverage, and that's really the thesis of the book. >> It's data, great summary by the way, so it's data in context, and the context of the low-fat fryer is going to be different than the workflow within that retail operation. >> Yeah, that's right and again, this is where, the product providers have initially kind of pushed back because they feel like they have stickiness and loyalty that's bred out of that link. But, first of all, that's going to change. So if you're Walmart or a major concern and you say, "I'm going to do a lighting RFP," and there's 10 vendors that say, "Hey, we want to compete for this," and six of 'em will allow Walmart to control the data, and four say, "No, we have to control the data," their list just went to six. They're just not going to put up with that. >> Dave: Period, the end, absolutely. >> That's right. So if the product providers are smart, they're going to get ahead of this and say, "Look, I get where the market's going. "We're going to need to give you control of the data, "but I'm going to ask for a contract that says "I'm going to get the data I'm already getting, "'cause I need to get that, and you want me to get that. "But number two, I'm going to recognize that "they can give, Walmart can give me my data back, "but enrich it and contextualize it "so I get better data back." So everybody can win, but it's all about the right architecture. >> Well and the product guys going to have the Trojan horse strategy of getting in when nobody was really looking. >> Don: That's right. >> And okay, so they've got there. Do you envision, Don, a point at which the Walmart might say, "No, that's our data "and you don't get it." >> Um, not really- >> or is there going to be a quid pro quo? >> and here's why. The argument that the product providers have made all along is, almost in a condescending way sometimes, although not intentionally condescending, it's been, look, we're selling you this low-fat fryer for your fast food restaurant. And you say you want the data, but you know, we had a team of people who are experts in this. Leave that to us, we'll analyze the data and we'll give you back what you need. Now, there's some truth to the fact that they should know their products better than anybody, and if I'm the fast food chain, I want them to get that data so that they can continually analyze and help me do my job better. They just don't have to get that data at my expense. There are ways to cooperatively work this, but again, it comes back to just the right architecture. So what we call the first receiver is in essence, setting up an abstraction close to the point of the ingestion of all this data. Upon which it's cleansed, enriched, and then propagated again to the right constituent in the right time in the right way. And by the way, I would add, with the right security considerations, and with the right data privacy considerations, 'cause like, if you look around the market now, things like GEP are in Europe and what we've seen in the US just in the wake of the elections and everything around how data is treated, privacy concerns are going to be huge. So if you don't know how to treat the data in the context of how it needs to be leveraged, you're going to lose that leverage of the data. >> Well, plus the widget guys are going to say "Look, we have to do predictive maintenance "on those devices and you want us to do that." You know, they say follow the money. Let's follow the data. So, what's the data flow look like in your mind? You got these edge devices. >> Yep, physical or virtual. Doesn't have to be a physical edge. Although, in a lot of cases, there are good reasons why you'd want a physical edge, but there's nothing technologically that says you have to have a physical edge. >> Elaborate on that, would you? What do you mean by virtual? >> Sure, so let's say I have a server inside a retail outfit. And it's collecting all of my IoT data and consolidating it and persisting it into a data store and then propagating it to a variety of constituents. That would be creating the first receiver in the physical edge. There's nothing that says that that edge device can't grab that data, but then persist it in a distributed Amazon cloud instance, or a Rackspace instance or whatever. It doesn't actually need to be persisted physically on the edge, but there's no reason it can't either. >> Okay, now I understand that now. So the guys at Wikibon, which is a sort of sister company to TheCUBE, have envisioned this three tiered data model where you've got the devices at the edge where real-time activity's going on, real-time analytics, and then you've got this sort of aggregation point, I guess call it a gateway. And then you've got, and that's as I say, aggregation of all these edge devices. And then you've got the cloud where the heavy modeling is done. It could be your private cloud or your public cloud. So does that three tier model make sense to you? >> Yeah, so what you're describing as the first tier is actually the sensor layer. The gateway layer that you're describing, in the book would be characterized as the first receiver. It's basically an edge tier that is augmented to persist and enrich the data and then apply the proper governance to it. But what I would argue is, in reality, I mean, your reference architecture is spot-on. But if you actually take that one step further, it's actually an n-tier architecture. Because there's no reason why the data doesn't go from the ten franchise stores, to the regional headquarters, to the country headquarters, to the corporate headquarters, and every step along the way, including the edge, you're going to see certain types of analytics and computational work done. I'll put a plug for my friends at Hitachi Lumada in on this, you know, there's like 700 horizontal IoT platforms out there. There aren't going to be 700 winners. There's going to be probably eight to 10, and that's only because the different specific verticals will provide for more winners than it would be if it was just one like a search engine. But, the winners are going to have to have an extensible architecture that is, will ultimately allow enterprises to do the very things I'm talking about doing. And so there are a number out there, but one of the things, and Rob Tiffany, who's the CTO of Lumada, I think has a really good handle on his team on an architecture that is really plausible for accomplishing this as the market migrates into the future. >> And that architecture's got to be very flexible, not just elastic, but sometimes we use the word plastic, plasticity, being able to go in any direction. >> Well, sure, up to and including the use of digital twins and avatars and the logic that goes along with that and the ability to spin something up and spin something down gives you that flexibility that you as an enterprise, especially the larger the enterprise, the more important that becomes, need. >> How much of the data, Don, at that edge do you think will be persisted, two part question? It's not all going to be persisted, is it? Isn't that too expensive? Is it necessary to persist all of that data? >> Well, no. So this is where, you'll hear the notion of data exhaust. What that really means is, let's just say I'm instrumenting every room in this hotel and each room has six different sensors in it and I'm taking a reading once a second. The ratio of inconsequential to consequential data is probably going to be over 99 to one. So it doesn't really make sense to persist that data and it sure as hell doesn't make sense to take that data and push it into a cloud where I spend more to reduce the value of the payload. That's just dumb. But what will happen is that, there are two things, one, I think people will see the value in locally persisting the data that has value, the consequential data, and doing that in a way that's stored at least for some period of time so you can run the type of edge analytics that might benefit from having that persisted store. The other thing that I think will happen, and this is, I don't talk much, I talk a little bit about it in the book, but there's this whole notion where when we get to the volumes of data that we really talk about where IoT will go by like 2025, it's going to push the physical limitations of how we can accommodate that. So people will begin to use techniques like developing statistical metadata models that are a highly accurate metadata representation of the entirety of the data set, but probably in about one percent of the space that's queryable and suitable for machine learning where it's going to enable you to do what you just physically couldn't do before. So that's a little bit into the future, but there are people doing some fabulous work on that right now and that'll creep into the overall lexicon over time. >> Is that a lightweight digital twin that gives you substantially the same insight? >> It could augment the digital twin in ways that allow you to stand up digital twins where you might not be able to before. The thing that, the example that most people would know about are, like in the Apache ecosystem, there are toolsets like SnappyData that are basically doing approximation, but they're doing it via sampling. And that is a step in that direction, but what you're looking for is very high value approximation that doesn't lose the outlier. So like in IoT, one of the things you normally are looking for is where am I going to pick up on anomalous behavior? Well if I'm using a sample set, and I'm only taking 15%, I by definition am going to lose a lot of that anomalous behavior. So it has to be a holistic representation of the data, but what happens is that that data is transformed into statistics that can be queryable as if it was the atomic data set, but what you're getting is a very high value approximation in a fraction of the space and time and resources. >> Ok, but that's not sampling. >> No, it's statistical metadata. There are, there's a, my last company had developed a thing that we called approximate query, and it was based on that exact set of patents around the formation of a statistical metadata model. It just so happens it's absolutely suited for where IoT is going. It's kind of, IoT isn't really there yet. People are still trying to figure out the edge in its most basic forms, but the sheer weight of the data and the progression of the market is going to force people to be innovative in how they look at some of these things. Just like, if you look at things like privacy, right now, people think in terms of anonymization. And that's, basically, I'm going to de-link data contextually where I'm going to effectively lose the linkages to the context in order to conform with data privacy. But there are techniques, like if you look at GDCAR, their techniques, within certain safe harbors, that allow you to pseudonymize the data where you can actually relink it under certain conditions. And there are some smart people out there solving these problems. That's where the market's going to go, it's just going to get there over time. And what I would also add to this equation is, at the end of the day, right now, the concepts that are in the book about the first receiver and the create, the abstraction of the creation of the data from the consumption of the data, look, it's a pretty basic thing, but it's the type of shift that is going to be required for enterprises to truly leverage the data. The things about statistical metadata and pseudonymization, pseudonymization will come before the statistical metadata. But the market forces are going to drive more and more into those areas, but you got to walk before you run. Right now, most people still have silos, which is interesting, because when you think about the whole notion of the internet of things, it infers that it's this exploitation of understanding the state of physical assets in a very broad based environment. And yet, the funny thing is, most IoT devices are silos that emulate M2M, sort of peer to peer networks just using the internet as a communication vehicle. But that'll change. >> Right, and that's really again, back to the premise of the book. We're going from these individual products, where all the data is locked into the product silo, to this digital fabric, that is an enterprise context, not a product context. >> That's right and if you go to the toolsets that Pentaho offers, the analytic toolsets. Let's just say, now that I've got this rich data set, assuming I'm following basic architectural principles so that I can leverage the maximum amount of data, that now gives me the ability to use these type of toolsets to do far better operational analytics to know what's going on, far better forensic analysis and investigative analytics to mine through the date and do root cause analysis, far better predictive analytics and prescriptive analytics to figure out what will go on, and ultimately feed the machine learning algorithms ultimately to get to in essence, the living organism, the adaptive systems that are continuously changing and adapting to circumstances. That's kind of the Holy Grail. >> You mentioned Hitachi Vantara before. I'm curious what your thoughts are on the Hitachi, you know, two years ago, we saw the acquisition, said, okay, now what? And you know, on paper it sounded good, and now it starts to come together, it starts to make more sense. You know, storage is going to the cloud. HDS says, alright, well we got this Hitachi relationship. But what do you make of that? How do you assess it, and where do you see it going? >> First of all, I actually think the moves that they've done are good. And I would not say that if I didn't think it. I'd just find a politically correct way not to say that. But I do think it's good. So they created the Hitachi Insight Group about a year and a half ago, and now that's been folded into Hitachin Vantara, alongside HDS and Pentaho and I think that it's a fairly logical set of elements coming together. I think they're going down the right path. In full disclosure, I worked for Hitachi Data Systems from '91 til '94, so it's not like I'm a recent employee of them, it's 25 years ago, but my experience with Hitachi corporate and the way they approach things has been unlike a lot of really super large companies, who may be super large, but may not be the best engineers, or may not always get everything done so well, Hitachi's a really formidable organization. And I think what they're doing with Pentaho and HDS and the Insight Group and specifically Lumada, is well thought out and I'm optimistic about where they're going. And by the way, they won't be the only winner in the equation. There's going to be eight or nine different key players, but they'll, I would not short them whatsoever. I have high hopes for them. >> The TAM is enormous. Normally, Hitachi eventually gets to where it wants to go. It's a very thoughtful company. I've been watching them for 30 years. But to a lot of people, the Pentaho and the Insight's play make a lot of sense, and then HDS, you used to work for HDS, lot of infrastructure still, lot of hardware, but a relationship with Hitachi Limited, that is quite strong, where do you see that fit, that third piece of the stool? >> So, this is where there's a few companies that have unique advantages, with Hitachi being one of them. Because if you think about IoT, IoT is the intersection of information technology and operational technology. So it's one thing to say, "I know how to build a database." or "I can build machine learning algorithms," or whatever. It's another thing to say, "I know how to build trains "or CAT scans or smart city lighting systems." And the domain expertise married with the technology delivers a set of capabilities that you can't match without that domain expertise. And, I mean, if you even just reduce it down to artificial intelligence and machine learning, you get an expert ML or AI guy, and they're only as good as the limits of their domain expertise. So that's why, and again, that's why I go back to the comparison to search engines, where there's going to be like, there's Google and maybe Yahoo. There's probably going to be more platform winners because the vertical expertise is going to be very, very important, but there's not going to be 700 of 'em. But Hitachi has an advantage that they bring to the table, 'cause they have very deep roots in energy, in medical equipment, in transportation. All of that will manifest itself in what they're doing in a big way, I think. >> Okay, so, but a lot of the things that you described, and help me understand this, are Hitachi Limited. Now of course, Hitachi Data Systems started as, National Advance Systems was a distribution arm for Hitachi IT products. >> Don: Right, good for you, not many people remember. >> I'm old. So, like I said, I had a 30 year history with this company. Do you foresee that that, and by the way, interestingly, was often criticized back when you were working for HDS, it was like, it's still a distribution hub, but in the last decade, HDS has become much more of a contributor to the innovation and the product strategy and so forth. Having said that, it seems to me advantageous if some of those things you discussed, the trains, the medical equipment, can start flowing back through HDS. I'm not sure if that's explicitly the plan. I didn't necessarily hear that, but it sort of has to, right? >> Well, I'm not privy to those discussions, so it would be conjecture on my part. >> Let's opine, but right, doesn't that make sense? >> Don: It makes perfect sense. >> Because, I mean HDS for years was just this storage silo. And then storage became a very uninteresting business, and credit to Hitachi for pivoting. But it seems to me that they could really, and they probably have a, I had Brian Householder on earlier I wish I had explored this more with him. But it just seems, the question for them is, okay, how are you going to tap those really diverse businesses. I mean, it's a business like a GE or a Siemens. I mean, it's very broad based. >> Well, again, conjecture on my part, but one way I would do it would be to start using Lumada in the various operations, the domain-specific operations right now with Hitachi. Whether they plan to do that or not, I'm not sure of. I've heard that they probably will. >> That's a data play, obviously, right? >> Well it's a platform play. And it's enabling technology that should augment what's already going on in the various elements of Hitachi. Again, I'm, this is conjecture on my part. But you asked, let's just go with this. I would say that makes a lot of sense. I'd be surprised if they don't do that. And I think in the process of doing that, you start to crosspollinate that expertise that gives you a unique advantage. It goes back to if you have unique advantages, you can choose to exploit them or not. Very few companies have the set of unique advantages that somebody like Hitachi has in terms of their engineering and massive reach into so many, you know, Hitachi, GE, Siemens, these are companies that have big reach to the extent that they exploit them or not. One of the things about Hitachi that's different than almost anybody though is they have all this domain expertise, but they've been in the technology-specific business for a long time as well, making computers. And so, they actually already have the internal expertise to crosspollinate, but you know, whether they do it or not, time will tell. >> Well, but it's interesting to watch the big whales, the horses in the track, if you will. Certainly GE has made a lot of noise, like, okay, we're a software company. And now you're seeing, wow, that's not so easy, and then again, I'm sanguine about GE. I think eventually they'll get there. And then you see IBM's got their sort of IoT division. They're bringing in people. Another company with a lot of IT expertise. Not a lot of OT expertise. And then you see Hitachi, who's actually got both. Siemens I don't know as well, but presumably, they're more OT than IT and so you would think that if you had to evaluate the companies' positions, that Hitachi's in a unique position. Certainly have a lot of software. We'll see if they can leverage that in the data play, obviously Pentaho is a key piece of that. >> One would assume, yeah for sure. No, I mean, I again, I think, I'm very optimistic about their future. I think very highly of the people I know inside that I think are playing a role here. You know, it's not like there aren't people at GE that I think highly of, but listen, you know, San Ramon was something that was spun up recently. Hitachi's been doing this for years and years and years. You know, so different players have different capabilities, but Hitachi seems to have sort of a holistic set of capabilities that they can bring together and to date, I've been very impressed with how they've been going about it. And especially with the architecture that they're bringing to bear with Lumada. >> Okay, the book is The Future of IoT, leveraging the shift to a data-centric world. Don DeLoach, and you had a co-author here as well. >> I had two co-authors. One is Wael Elrifai from Pentaho, Hitachi Vantara and the other is Emil Berthelsen, a Gartner analyst who was with Machina Research and then Gartner acquired them and Emil has stayed on with them. Both of them great guys and we wouldn't have this book if it weren't for the three of us together. I never would have pulled this off on my own, so it's a collective work. >> Don DeLoach, great having you on TheCUBE. Thanks very much for coming on. Alright, keep it right there buddy. We'll be back. This is PentahoWorld 2017, and this is TheCUBE. Be right back.

>> Narrator: Live from Santa Clara, California, it's The Cube. Covering Open Networking Summit 2017. Brought you to by the Linux Foundation. >> Hey, welcome back everybody. Jeff Frick here with The Cube, along with Scott Raynovich. We're at the Open Networking Summit 2017. Linux Foundation has taken over this show a couple years ago, it's a lot of excitement. A lot of people would say that the networking was kind of the last piece of the puzzle to get software defined, to get open. We're really excited to kick off the show with a really great representative of SDN and everything that it represents. Martin Casado, now with Andreessen Horowitz, Martin, great to see you. >> Hey, I'm super happy to be here. >> So, coming off your keynote, you said it was ten years ago almost to the day that you guys started the adventure called Nicira, which kind of put us where we are now. >> You know, you and I are growing old together here. It has been a decade. I've actually been on The Cube throughout, so I'm very happy to be here. Thanks so much for the intro. >> Absolutely. So, what were your takeaways, Scott, on that keynote? >> It was great, we had some great stuff this morning. Not only was Martin giving the history of Nicira and the origins of SDN and talking about how you made it successful after all these challenges but we also had AT&T unveiling a new incredible white box program, where they're running open networking on their entire network now, so, it was kind of a, I thought, a big day in general to show how far we've gone, right? And you talked a little about that. >> Yeah, listen having come over here since the inception of ONS, what strikes me is, it originally, it was so speculative, it was kind of like wouldn't it be nice and you had all these dreamers. It was largely academics or people from the CTO's office and if you compare those first meetings to now, we're in the industry proper now, right? If you come and you look around, there's huge representation from Telcos, from vendors, from customers, and academics. So, I think we've seen a massive maturation in general. >> I just think I could make a mash-up of all the times we've had you on the Cube table where it's coming! We're almost here! >> Martin: And we're like it's here! >> It's here! But now John Donovan said that their goal, I don't know if it's in the short term or the very near term, is to be over 50 percent software defined, so I guess that's a pretty good definition of being here. >> Yeah, I think so. I think that we're seeing, and I think that the AT&T talk was fantastic, but I think you're seeing this across the industry, which is large customers that have been traditionally conservative, have these targets, and they're actually implementing. I mean, it's one thing to have something on the roadmap. And it's one thing to have something planned. It's another thing to actually start seeing it roll out. >> Jeff: Right. >> Again, this is a process. A lot of my talk was like, how long does it take for an industry to mature? But now, there's many things you can point to that are very real, and I think that was one great example of it. >> Well, the other thing I thought was great in your talk is you mapped out the 10 year journey and you said it so discounts often the hardest part which is changing behavior of the market. That is much harder than the technology and some of the other pieces. >> Right, exactly. So, take this from a technologist standpoint. I basically made a career on making fun of hardware. I'm like, software is so much faster than hardware, and hardware is so slow. But now if I stand back and take a long view, yeah, fine hardware's slower than software, but it's nothing compared to changing organizational behavior or consumer behavior and so, for me it was actually pretty humbling going through this last decade, because you realize that even if you have product market fit, and even if you have a good technical solution, there is a natural law of market physics that you have to overcome a moment of inertia that takes probably a decade, certainly five or six years. >> And that's before things like vendor viability, when you're trying to enter the enterprise space, or legacy infrastructure which is just not getting ripped out, you know? So many hurdles. >> Strictly consumer behavior, right? Consumers are used to doing one thing. I always talk to new entrepreneurs and I say the following: You have two jobs as an entrepreneur. Job number one is you identify a constituency. That constituency wakes up, they think about everything in the world, but they don't think about your thing, so job number one is to get them to think about your thing. That's difficult. It's like Inception. It's like Leonardo DiCaprio Inception. You're putting an idea in somebody's head and then the second thing that you have to do is you have to attach a value to that. So, just because they have the idea doesn't mean that they actually value it. So, you actually have to say, listen, this is worth X amount of dollars. And it turns out that this takes a long time and that's why market category creation is such an effort. That's why it's so neat, we're standing here and we're seeing that this has actually happened, which is fantastic. >> You talked about Nicira, which today, correct me if I'm wrong, it's still the biggest success story in SDN in terms of a startup, you know, 1.3 billion. You talked about different iterations, I think you said, six or seven product iterations and being frustrated at many levels. Did you ever sit there one day and think, "uh, we're going to fail." >> Martin: (laughs) >> Was failure a common- >> Oh man, I don't think there wasn't a quarter when we're like "we're dead." (laughs) By the way, that's every startup. I mean, I'm on- >> Scott: That's just normal, right? >> There's six or seven boards right now, I mean every startup has this oscillator. When we started at Nicira, it was in 2007. And in 2008, the nuclear winter set in, if you remember. The whole economy collapsed, and I think that alone could've killed us. So absolutely, and all startups who do that. But one thing that I never lost faith in was that the problem was real. I wasn't sure we had the right solution or the right approach, and we iterated on that, but I knew there was a real problem here. And when that is kind of a guiding star and a guiding light, we just kept going towards that. I think that's why ultimately we ended up solving the problem we set out to, it was just we took a very crooked path to get there. >> What was the feedback mechanism? Was it like just talking to as many customers as possible or? You talked about the market fit versus the industry fit, how did you gather that information? >> I think in core technical infrastructure, the strategic leaders of a startup have to be piped into the nervous system of both the technology trends and the product market fit. Technology trends because, technology trends provide the momentum for what's going to get adopted and what it looks like. And the product market fit is what is the customer problems that need to be solved. And so I think it's really critical to be deeply into both of those things, which is why things like ONS are so important, because they do kind of find a convergence of both of that. What do customers need but also where's the technology going. >> And it's really neat, that's kind of like the platform versus the application. You're going down the new platform strategy, right? Which is the software-defined networking, but at the end of the day, people buy solutions to their problems that they need to get fixed today. No one's buying a new platform today. >> Yeah, so there's two issues, you're right. There's the technical directions and then the specific applications for that, and one thing I talked about and I really believe is we focus a little bit too much on the technology platform, how those are shifting, early on and less on what the customers need. I don't think you want to 100% flip that, you need to focus on both, but I think that they both should be even-handed. What do customers need and then what is the right technical approach to get there. >> And you also stuck on a couple of really interesting points about decisions. You're going to make a lot mistakes going down the road. But you said, you got to make two or three really good ones and that will make up for a whole lot of little missteps along the path. >> So in retrospect, and this was actually a big a-ha! for me and maybe it's obvious to other people, but this was a big a-ha! to me, even as I was putting together this talk. So, the way venture capital works is you make a lot of bets, but only one in ten will actually produce returns, so you're kind of swinging for the fences and almost all the returns comes from the Googles and the Facebooks and the Ubers and so forth. That's just how it is. Now, as a venture capitalist, you can have a portfolio, you can place ten of those bets in parallel. Going back through all of the slides and everything we've done, I hadn't realized before how similar doing a startup is, which is you make a lot of mistakes in startups, but a few key decisions really drive the strategy. Does that make sense? I always thought maybe you need to do 50/50, or maybe even 80/20, 80% correct and 20 wrong, but it's not that. There's a few key decisions that make it correct, and so the key is you're straddling these two pieces of human nature. On one side, you want to stick with something, you want to make sure that you're not sticking too long with something that isn't going to work, and then the other side you don't want to get rid of something before it's going to work. You want to be both honest with yourself when it's not working and you want to be patient. And if you do that long enough I think that you will find one of the critical decisions to drive the startup forward. >> Yeah, one interesting thing you said, you arrived at a conclusion that the products and individual applications were more important than the platform, and that kind of runs contrary to the meme that you have now where the Harvard Business Review is saying "build a platform, build the next Airbnb." And what you're saying is kind of contrary to that. >> Right, so I went into this with a path from Mindframe, if you look at our original slide deck, which I showed, it was a platform. Now, I think that there's two aspects for this, I think in SDN specifically, there is a reason technically why a platform doesn't work, and the reason for that is networking is about distributed state management, which is very specific to applications. So it's hard for a platform to register that, so technically, I think there's reason for that. From a startup perspective, customers don't buy platforms, customers buy products. I think if you focus on the product, you build a viable business, and then for stickiness you turn that into a platform. But most customers don't know what to do with a platform because that's still a value-add. Products before platforms, I think, is a pretty good adage to live by. >> But design your product with a platform point of view. That way so you can make that switch when that day comes and now you're just adding applications, applications. So, I want to shift gears a little bit just kind of about open source and ONS specifically. We hear time and time again about how open source is such an unbelievable driver of innovation. Think of how your story might have changed if there wasn't, and maybe there was, I wasn't there, something here and how does an open source foundation help drive the faster growth of this space? >> So, I actually think, and I'm probably in the minority of this, but I've always thought that open source does not tend to innovation. That's not like the value of open source is innovation. If you look at most successful open source projects, traditionally they've actually entered mature markets. Linux entered Unix, which is, so I'd say the innovation was Unix not Linux. I would say, Android went into Palm, and Blackberry, and iPhone. I would say MySQL went into Oracle. And so, I think the power and beauty of open source is more on the proliferation of technology and more on the customer adoption, and less on the innovation. But what it's doing is it's driving probably the biggest shift in buying that we've ever seen in IT. So, IT is a 4 trillion dollar market that's this massive market, and right now, in order to sell something, you pretty much have to make it open source or offer it as a service. And the people that buy open source, they do it very different than you traditionally do it. It allows them to get educated on it, it allows them to use it, they get a community as part of it. And that shift from a traditional direct vendor model to that model means a lot of new entrants can come in and offer new things. And so, I think it's very important to have open source, I think it's changing the way people buy things, I think building communities like this is a very critical thing to do, but I do think it's more about go-to-market and actually less about innovation. >> So what does it mean for all these proprietary networking vendors? I mean, are they dead now? >> No, here's actually another really interesting thing, which is I think customers these days like to buy things open source or as a service. Those are the two consumption models. Now, for shipping software, I think shipping closed source software, I think those days are over or they're coming to the end. Like, that's done. But, customers will view, whether it's on-prem or off-prem, an appliance as a service. So, let's say I create MartinHub. So, it's my online service, MartinHub, people like MartinHub. I can sell them that on-premise. Now, MartinHub could be totally closed source, right? Like, Amazon is totally closed source, right? But people still consume it. Because it's a service, they think it's open. And if they want something on-prem, I can deploy that and they still consume it as a service. So, I think the proprietary vendors need to move from shipping closed source software to offering a service, but I think that service can just be on-prem. And I think prem senior shift happens, so I don't think there's going to be like a massive changing of the guard. I do think we're going to see new entrants. I think we're going to see a shift in the market share, but this isn't like a thermonuclear detonation that's going to kill the dinosaurs. (laughs) >> I want to get your take, Martin, on the next big wave that we're seeing which is 5G, and really 5G as an enabler for IoT. So, you've been playing in this space for a while. As you see this next thing getting ready to crest, what are some of your thoughts, also sitting in a VC chair, you probably see all kinds of people looking to take advantage of this thing. >> That's funny. I'm actually going to answer a different question. (laughs) Which is, I-- >> Scott: That's cause 5G doesn't exist yet, right? >> No, I love the question, but it's like, this is really a space that's really near and dear to my heart, which is cellular. And I've actually started looking at it personally, and even in the United States alone, there are something like 20 million people that are under-connected. And I think the only practical way to connect them is to use cellular. And so I've been looking at this problem for about a year, I've actually created a non-profit in it that brings cellular connectivity to indigenous communities. Like, Native American tribes, and so forth. >> Jeff: As the ultimate last mile. >> As the ultimate last mile. Which is interesting, like 5G is fantastic, but if you look at the devices available to these people that have coverage, I think LTE is actually sufficient. So what I'm excited about, and I'm sorry about answering a different question, but it's such a critical point, what I'm excited about is, it used to be 150 thousand dollars to set up a cell tower. Using SDN, I can set up an LTE cell tower for about five thousand dollars and I can use existing fiber at schools as backhaul, so I think now we have these viable deployment models that are relatively cheap that we can actually connect the underprivileged with. And I don't think it's about the next new cellular technology, I think it's actually SDN's impact on the existing one. And that's an area of course that's very personal to me. >> All right, love it. It is as you said, it's repackaging stuff in a slightly different way leveraging the technology to do a new solution. >> And it's truly SDN. If you look at this, there's an LTE stack all in software running on proprietary hardware. I'm sorry, on general purpose hardware that's actually being controlled from Amazon. And again, a factor of ten reduction in the price to set up a cell tower. >> Jeff: Awesome. >> What about the opportunity with Internet of Things and connecting the things with networks' artificial intelligence? >> So, as a venture capitalist, when it comes to networking I'm interested in two areas. One area is networking moving from the machine connecting machines to connecting APIs. So, we're moving up a layer. So we've got microservices, now we need a network to connect those and there're different types of end points, and they require different types of connectivity. But I'm also interested in networks moving out. So, it used to be connecting a bunch of machines but now there's all these new problem domains, the Internet is moving out to interact with the physical world. It's driving cars. It's doing manufacturing, it's doing mining, it's doing forestry. As we reach out to these more mature industries, and different deployment environments, we have to rethink the type of networks to build. So, that's definitely an area that I'm looking at from the startup space. >> What kind of activity's there? I mean, you have guys coming in every day pitching new automated connect-the-car software. >> I think for me it's the most exciting time in IT, right? It's like, the last, say ten fifteen years of the Internet has been the World Wide Web. Which is kind of information processing, it's information in, information out. But because of recent advances in sensors due to the cellphone, the ubiquity of cellphones, the recent advances in AI, the recent advances in robotics, that Internet is now growing hands and eyes and ears. And it's manipulating the physical world. Any industry that's out there, whether it's driving, whether it's farming, is now being automated, so we see all the above. People are coming in, they're changing the way we eat food, they're changing the way we drive cars, they're changing the way we fly airplanes. So, it's almost like IT is the new control layer for the world. >> All right, Martin, thanks again for stopping by. Unfortunately we got to leave it there, we could go all day I'm sure. I'll come up with more good questions for you. >> All right, I really appreciate you taking the time. It's good to see both of you. Thanks very much. >> Absolutely, all right, he's Martin Casado from Andreessen Horowitz. I'm Jeff Frick, along with Scott Raynovich. You're watching The Cube from Open Networking Summit 2017. We'll be back after this short break. Thanks for watching. (mellow music) >> Announcer: Robert Herjavec. >> Man: People obviously know you from Shark Tank, but the Herjavec group has been really laser fo--

>> Announcer: Live from Las Vegas, it's theCUBE covering InterConnect 2017 brought to you by IBM. >> Hey, welcome back everyone. We're live here in Las Vegas for IBM InterConnect 2017. This is theCUBE coverage of their cloud and big data event Watson Analytics, and IoT Cloud. It's theCUBE coverage for three days. A lot of great interviews. I'm John Furrier, my co-host Dave Vellante. Our next guest is Scott Francis, an entrepreneur, CEO, co-founder of BP3. Welcome to The Cube. >> Thank you, glad to be here. >> Great to have an entrepreneur on because you've been, in your business, you co-founded it, built it form the ground up, >> Scott: Right. >> Hundreds of employees. Now, over 100 employees. >> Scott: Right. >> IBM partner, great story. >> Yeah, we started with just two of us 10 years ago. And, we'll have our 10th anniversary in May this year. >> John: Congratulations. So take us through the, you know, state of the art. I mean, go back 10 years ago. You've actually provisioned your own servers. You actually had to load routers and networking gear. That's like, I'd say a tax of at least 100K in just gear. And then you've got the ISP chart, all that stuff. >> Right, well the economics have totally changed, right? For us and for our customers, and I think the main benefit is you can get to business value so much faster now and spend less money that's sort of wasted spend, right? >> So take a minute and talk about what you guys do and what your role is here. And then I want to get into some of the things that are changing the market place, that people are seizing opportunities around, certainly around processing and new innovations. So, give us a quick update on who you guys are, and your role here today. >> Yeah, so our focus is on business process and decision management. And, you know, our experience is that it is foundational technology and foundational aspect to almost everything you're hearing going on, right? Whether it's block chain or cognitive, or moving to the cloud. What are their key considerations? How does it impact my business process? How does it impact my operations? How does it impact my decisions? So we feel like in our space, we're right at the sweet spot of what all our customers are worried about. And when we hear them talk about block chain, we know we've got a process problem we've got to address. And when we hear about moving to the cloud, we better address all the Halo applications around that, application that's moving to the cloud and make sure they're all addressed and part of the new business process. >> It's interesting, the whole decoupling of existing systems models >> Right. >> Is really kind of what I see as the micro trend over the past six years, and like you mentioned, foundational building blocks is key, right? >> Scott: Right. So that's key. And, so let's take this to the next level. I want to ask you a question because I think this is something we see all the time on theCUBE when we do interviews, is that technology now is so much different. In the old days it was, we knew the process. >> Scott: Right. >> And we don't really know the technology. Let's go automate that accounting, blah, blah, blah. You know we saw that, ERPs, CRM, all those vendors. Now it's, I have technology, I don't know what the process is going to be because some new, big data analytics people changed the insight, and changed the value chain, or changed the business model, one tweak radically will disrupt proven, process which no one wants to change. Whoa, you know, so there's now a real factor. Give us some insight and color around how that goes down, because someone has an insight, they want to roll it in and implement it. It changes the entire process flow. >> Right, well the key thing is, having an insight as a single person in a process is one issue, but rolling it out across a Fortune 500 company is a whole other proposition, right? You've got regulatory issues and compliance issues, and customer experience issues that you've got to work through. And all those accommodations may be there. The value prop may be there, but you've got to work through it. You can't, you know, at a billion dollar organization, you can't just change it for that, you have to work all that out. >> John: So what's the playbook? >> Yeah, so the playbook is when we have an insight, what we talk to customers about is you've got all these tools now to arrive at insights you couldn't get to before, or by the time you got to them, you're doing your analytics over data that's six months old. Okay, now I have an insight about what would've worked six months ago. The difference is with cognitive and machine learning algorithms, and the analytics you have available today, and the access to the data, those insights are available now. We have to re-architect the processes to reflect that and to let me make new decisions within that operational context. >> Go ahead. >> Operationalizing those insights. Go ahead, finish your thought. >> Well the data first thing that you talked about is key. We just had our big data event. It's look in value in conjunction with strata hadoop was data in motion and badge are working together now to your point, the times series of data is relevant in the time you need it, right? >> Scott: Right. >> Not yesterday. So this brings up the question of, Okay, you've got some spark thing going on. I see IBM has got spark, that's cool. But now, how do you get into the app, right? To developers? I'm a developer. I'm a coder. Do I need to be a wrangler, data wrangler, or data scientist, to make that happen? So this is the conversation people are trying to figure out. What's your perspective on that? >> I think a lot of the tools that are, that are available now, basically made a common coder, right? Has a decent chance OF that competing with their data scientist friends. There's a different level of expertise, obviously, for the data scientist. But much like in business process, you know years ago, you had to get your lean six black belt, and you really had to study it to get good at it, and really master statistics, and I've got tools that will run the statistics for you, right? So you don't have to master the statistics but you've got to collect the right data, you have to engage in the business. So I think you see a sort of, democratization of data science, right? With the tools that are available now. >> Talk a little bit more about decision management. Go back to the mid-2000s and the Harvard Business Review is writing articles that gut feel trumps, you know, paralysis, analysis, paralysis by analysis every time. That's seemingly changed but what specifically has changed in regards to operationalizing those insights? >> Well I think they're a couple of things that are interesting. If you look at how processes were traditionally designed, you know, before BPM came along, BPM and decision management tools came along, just write the code. Build your application. And when you wanted to change the decision, well you had to find where that was modeled in the code, and edit the code, right? And that was a challenging proposition. The guys that wrote it might have moved to other projects. So how do you figure it out? >> So gut feel was faster. >> Yeah, and BPM, and OEM, you know, gave us tools for managing those things. BPM in terms of process, having a diagram that a mere mortal can understand and find the right context for whenever that decision gets made. And decision management to mange rule sets and the interactions between these rules in a more codified way that again, mere mortals can understand, right? So you don't have to go hunting through code. We're looking at a model, a representative model. I think the change now with machine learning, with cognitive computing, the real time access to data is that you have to really rethink your processes and allow those decisions to be altered in real time, not later, six months later, when I'm doing a revamp of the process as a separate, sort of institutional operation but actually as I'm running my process. We design it to accommodate the idea that as we're collecting data we're going to learn and get better, and actually affect those decisions, or recommend a different decision to the person whose Johnny-on-the-spot. >> Are you finding that the business impact is that your customers, the consumers of this sort of new way of doing decision management are seeing things that they wouldn't have seen before, or is it more greater conviction and faster time to everybody pulling the same direction? >> Well, I think for sure they're seeing things they haven't seen before. We're surfacing data that they just didn't have access to before in a timely fashion. And in the context of their process which was always a difficult thing to do in traditional systems, right? For any of your traditional ERP, or CRM system, the notion of where you are in your cross functional process may not be present. Today you have that context. You have the real time access to it. That really changes the nature of what you're seeing. I think the other bit is, yeah, the action ability, right? How easy it is to turn that insight into an action. >> And have you seen any effect on the politics of decision making, because we all know the P and L manager whose the strong voice in the organization, he or she is going to pull data that supports their business case. Have you been able to, sort of, neutralize that sometimes damaging effect in organizations? >> Yeah, well, I think in the cycle of the economic cycle, you know, if we rewind five or six years ago, almost every project we engage with with a customer is about operational controls, reducing costs, trying to produce the same result with fewer resources, right? And that has shifted dramatically over the last few years. The last two years it's been almost entirely about capturing revenue. >> Dave: Opportunistic, yeah. >> Serving new revenue streams without having to hire as much to support it. It's much more about revenue capture and customer experience. And I think that reflects the stage we're in in the cycle. >> Dave: Is that a bubbling cater? I hope it reflects a good long term view. >> Dave: I hope so too. >> You know, but it's interesting. There's a customer speaking here at InterConnect today, StubHub, about their customer experience. And they BPM to manage their customer experience, and back in 2009, 2010, when everybody was pulling back, and they were all focused on cost containment. You know, I recall StubHub was working on how to make their customer experience better. It's kind of interesting, right? And they've done very well over the years, right? So I think that value system in that culture really pays off over time, but you have to really mean it. If you're just swinging back and forth with the ebb and flow of the economy, then I think it's very difficult. >> Well, if you're doubling down when everybody else is sitting on their hands, you're going to get a competitive. >> It's a great opportunity, right? >> So, talk a little bit more about the IBM connection. What's going on in InterConnect, and what's the relationship there? >> Well, IBM is our best partner. You know, we've been partnered very closely with IBM ever since they acquired Lombardi which was our company that we came out of back in 2007. And that has become, you know, the heart of the IBM, BPM portfolio. And we work with their business process products, decision management, as well as cognitive and blue mix. So we're in the mix with IBM in a big way, and I think this conference is a great opportunity for us to not only reconnect with folks from IBM, but also with our customers who tend to come to this conference as well. So it's a great opportunity for us. >> So specifically you're leveraging IBM tooling, sort of. >> That's right. >> Repackaging that in your solutions for your clients. >> Right. So we are a reseller. We're also OEM IBM software, and we do delivery work for IBM customers. So, it's kind of a trifecta. >> You started this company 10 years ago. We love this start up story. Tell us, you and your colleagues started. Tell us your start up story and how you go to where you are now. >> Well we were, you know, we would meet up at a coffee shop, right? And get together and kind of talk about, you know, the fact that it felt like there was a big opportunity out there. >> Dave: This is in Austin. >> Yeah in Austin. My co-founder and I, you know, we were working at Lombardi but we felt like there was an opportunity to build a great services firm in our space, right? In this business process space, that there was a lot of untapped potential. And as we met and talked about it, we just got the bug that we needed to go out and do it. And when we started the company, you know. It was just the two of us initially. We bootstrapped the firm. Last summer, for the first time, we actually raised money, outside capital, to help fund the growth. >> Dave: 10 years then. >> Yeah, yeah. But all that time we self funded which was a great experience. A great learning experience. Certainly lost some sleep over the years. But, you know, there is an aspect of kind of putting the band back together. You know, hiring people we really enjoyed working with in previous lives, previous jobs, and putting together a killer team to go after it. >> So the decision to take outside capital, maybe talk a little bit about that because that's probably wasn't an easy one, or maybe it was, I don't know. >> No, I think, you know, what we've been fortunate to do is we've taken some calculated risks over time, right? We used to only operate in the United States. We acquired a business in London to expand to Europe. And now a third of our business is in Europe. But those risks, you can put the whole company at risk taking a chance like that. And so it occurred to us, after taking a few of those calculated risks and winning that maybe we should hedge our bets a little bit and have some more capital to work with, and have a good financial partner that if we were engaged in that kind of discussion, someone who could help, both advise and also possibly fund if we got into that situation. And so, we took an investment from Petra Capital based out of Nashville. They're a great growth equity firm, and they invest in healthcare and tech start ups, like ourselves. And so we got some great people on the board as a result. Mike Simmons from T2 Systems, and Jeff Rich from another capital investment firm. These guys have been operators. They've run companies much bigger than ours but they've also been in the mix at our size. So we've got some great outcomes out of taking that investment. >> So you've been cashflow positive since the early days. You had to be. Is it the plan to continue to do that, or do you make gasoline in the fire type investments? >> You know, I think it's cultural, right? I know there's a lot of business models where there's actually some good since in the running and not worrying about profit for awhile, but I also think you need to develop habits and our business serving enterprise customers, I think they deserve to know that we're being responsible with our money, with how we spend, with how we grow, and that we have a responsible level of growth. We could spend more and grow faster at the same type of process. >> John: At the risk of service. >> But at the risk of service quality for our customers and that's not worth it for us because ultimately, it's the repeat business with customers that really drives our growth long term. >> We feel the same way, obviously self funded. You know I'd say Silicon Valley is a story like that. Heirarchy of entrepreneurs and it's well known that the number one position is self funded growth without outside capital. It's a lot harder. No offense to my VC funded friends. It's a lot harder to do it from the ground up than just get other people's money. So tier one is do it yourself, which you guys are in. Get some capital, grow that and have an exit. Three, try and fail, or four, work for a company. (laughs) >> I think the key thing is it takes patience. If you're going to do it yourself and self fund it, you know, let the business fund itself, not just throw in your own personal money, but actually make the business fund itself. You have to have a lot of patience to stick with it. And I think whether by hook or crook, we picked a space that afforded us some of that patience, right? >> Yeah, you get rewarded for innovation. You get awarded for good service delivery. >> We feel like business is a human endeavor, right? So a good business process and good decisions are going to be problems that our children will face, not just us. >> And they're going to get more exciting for you as processes get automated with machine learning and AI right here on the doorstep, and Devops exploding with IoT coming on full line. It's going to change the game big time. >> Yeah, and I can't remember who said it but someone just yesterday was saying, you know, "It's not so much about automation "as it is about augmentation." And I really think that's true. I think if you automate out all the mundane, what's left is the stuff that's really interesting, right? And that's kind of how we view our job is to automate all the stuff that's getting in the way of highly skilled people doing their job taking care of their customers. >> I always love the story when IBM super computer beat Garry Kasparov at chess. You've heard this a million times. Kasparov didn't just say, "All right we're done." He created a competition, and he beat the computer, and now the greatest chess player in the world is a combination of human and machine. So it's that creativity, that common atoria factor that's drives the machine. >> It's actually better than the machine only, right? >> The creativity is going to change the game. Scott Francis, entrepreneur, founder, co-founder and CEO of BP3 in Austin. Thanks for joining us, appreciate it. More live coverage here. Stay with us, theCube is at IBM Interconnect here in Las Vegas. More great interviews after this short break. (upbeat techno music)

>> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Data Privacy Day at Twitter's World Headquarters in downtown San Francisco. Full-day event, a lot of seminars and sessions talking about the issue of privacy. Even though Scott McNealy in 1999 said, "Privacy's dead, get over it," everyone here would beg to differ; and it's a really important topic. We're excited to have Michelle Dennedy. She's the Chief Privacy Officer from Cisco. Welcome, Michelle. >> Indeed, thank you. And when Scott said that, I was his Chief Privacy Officer. >> Oh you were? >> I'm well acquainted with my young friend Scott's feelings on the subject. >> It's pretty interesting, 'cause that was eight years before the iPhone, so a completely different world than actually one of the prior guests we were talking about privacy is an issue in the Harvard Business Review from 125 years ago. So this is not new. >> Absolutely. >> So how have things changed? I mean that's a great perspective that you were there. What was he kind of thinking about and really what are the privacy challenges now compared to 1999? >> So different. Such a different world. I mean fascinating that when that statement was made the discussion was a press conference where we were introducing Connectivity. It was an offshoot of Java, and it basically allowed you to send from your personal computer a wireless message to your printer so that a document could come out (gasp). >> That's what it was? >> Yeah. >> Wireless printing? >> Wireless printing. And really it was gyro technology, so anything wirelessly could start talking to each other in an internet of things world. >> Right. >> So, good news bad news. The world has exploded from there, obviously; but the base premise of, can I be mobile, can I live in a world of connectivity, and still have control over my story, who I am, where I am, what I'm doing? And it was really a reframing moment of when you say privacy is dead, if what you mean by that is secrecy and hiding away and not being connected to the world around you, I may agree with you. However, privacy as a functional definition of how we define ourselves, how we live in a culture, what we can expect in terms of morality, ethics, respect, and security, alive and well, baby. Alive and well. >> (laughs) No shortage of opportunity to keep you busy. We talked to a lot of people who go to a lot of tech conferences. I have to say I don't know that we've ever talked to a Chief Privacy Officer. >> You're missing out. >> I know, so not you get to define the role, I love it. So what are your priorities as Chief Priority Officer? What are you keeping an eye on day to day as well as what are your more strategic objectives? >> It's a great question. So the rise of the Chief Privacy Officer, actually Scott was a big help in that and gave me exactly the right amount of rope to hang myself with. The way I look at it is, probably the simplest analogy is, should you have a Chief Financial Officer? >> Yeah. >> I would guess yeah, right? That didn't exist about 100 years ago. We just kind of loped along, and whoever had the biggest bag of money at the end was deemed to be successful. Where if somebody else who had no money left at the end but bought another store, you would have no way of measuring that. So the Chief Privacy Officer is that person for your digital currency. I look at the pros and the cons, the profit and the loss, of data and the data footprint for our company and for all the people to whom we sell. We think about, what are those control mechanisms for data? So think of me as your data financial officer. >> Right, right. But the data in and of itself is just stagnant, right? It's really just the data in the context of all these other applications. How it's used, where it's used, when it's used, what it's combined with, that really starts to trip into areas of value as well as potential problems. >> I feel like we scripted this before, but we didn't. >> Jeff: We did not script it, we don't script the-- >> So if I took out a rectangle out of my wallet, and it had a number on it, and it was green, what would you say that thing probably is? >> Probably Andrew Jackson on the front. >> Yeah, probably Andrew Jackson. What is that? >> A 20 dollar bill. >> Why is that a 20 dollar bill? >> Because we agree that you're going to give it to me and it has that much value, and thankfully the guy at Starbucks will give me 20 bucks worth of coffee for it. >> (laughs) Exactly. Well which could be a cup the way we're going. >> Which could be a cup. >> But that's exactly right. So is that 20 dollar bill stagnant? Yes. That 20 dollar bill just sitting on the table between us is nothing. I could burn it up, I could put it in my pocket and lose it and never see it again. I could flush it down the toilet. That's how we used to treat our data. If you recognize instead the story that we share about that piece of currency, we happen to be in a place where it's really easy to alienate that currency. I could go downstairs here and spend it. If I was in Beijing I probably would have to go and convert it into a different currency, and we'd tell a story about that conversion because our standards interface is different. Data is exactly the same way. The story that we share together today is a valuable story because we're communicating out, we're here for a purpose. >> Right. >> We're making friends. I'm liking you because you're asking me all these great questions that I would have fed you had I been able to feed you questions. >> Jeff: (laughs) But it's only that context, it's only that communicability that brings it value. We now assume as a populous that paper currency is valuable. It's just paper. It's only as good as the story that enlivens it. So now we're looking at smaller, smaller Microdata transactions of how am I tweeting out information to people who follow me? >> Jeff: Right, right. >> How do I share that with your following public, and does that give me a greater opportunity to educate people about security and privacy? Does that allow my company to sell more of my goods and services because we're building ethics and privacy into the fabric of our networks? I would say that's as valuable or more valuable than that Andrew Jackson. >> So it's interesting 'cause you talk about building privacy into the products. We often hear about building security into the products, right? Because the old way of security of building a bigger wall doesn't work any more and you really have to bake it in at all steps of the application: development, the data layer, the database, et cetera, et cetera. When you look at privacy versus security, and especially 'cause Cisco's sitting on, I mean you guys are sitting on the pipes, everything is running through your machines. >> That's right. >> How do you separate the two, how do you prioritize, and how do you make sure the privacy discussion is certainly part of that gets the right amount of relevance within the context of the security conversation? >> It's a glib answer that's much more complicated, but the security is really in many instances the what. I can really secure almost any batch of data. It can be complete gobbley gook zeroes and ones. It could be something really critical. It could be my medical records. The privacy and the data about what that context is, that's the why. I don't see them as one or the other at all. I see security and security not as not a technology but a series of verb things that you actually physically, people process technologies. That enactment should be addressed to a why. So it's kind of Peter Drucker's management of you manage what you measure. That was like incendiary advice when it first came out. Well I wanted to say that you secure what you treasure. So if you treasure a digital interaction with your employees, your customers, and your community, you should probably secure that. >> Right. But it seems like there's a little bit of a disconnect about maybe what should be treasured and what is the value with folks that have grown up. Let's pick on the young kids, not really thinking through or having the time or knowing an impact of a negative event in terms of just clicking and accepting the EULA and using that application on their phone. They just look at in a different way. Is that valid? How do they change that behavior? How do you look at this new generation, and there's this sea of data which is far larger than it used to be coming off all these devices, internet of things, obviously. People are things too. The mobile devices with all that geolocation data, and the sensor data, and then oh by the way it's all going to be in our cars and everything else shortly. How's that landscape changing and challenging you in new ways, and what are you doing about it? >> The speed and dynamics are astronomical. How do you count the stars, right? >> Jeff: (laughs) >> And should you? Isn't that kind of a waste of time? >> Jeff: Right, right. >> It used to be that knowledge, when I was a kid, was knowing what was in A to Z of the Encyclopedia Britannica. Now facts are cheap. Facts used to be expensive. You had to take time and commit to them, and physically find them, and be smart enough to read, and on, and on, and on. The dumbest kid is smarter than I was with my Encyclopedia Britannica because we have search engines. Now their commodity is how do I critically think? How do I make my brand and make my way? How do I ride and surf on a wave of untold quantities of information to create a quality brand for myself? So the young people are actually in a much better position than, I'll still count us as young. >> Jeff: Yeah, Uh huh. >> But maybe less young. >> Less young, less young than we were yesterday. >> We are digital natives, but I think I am hugely optimistic that the kids coming up are really starting to understand the power of brand: personal brand, family brand, cultural brand. And they're feeling very activist about the whole thing. >> Yeah, which is interesting 'cause that was never a factor when there was no personal brand, right? You were part of-- >> No way. >> whatever entity that you were in. >> Well, you were in a clique. >> Right. >> Right? You identified as when I was home I was the third out of four kids. I was a Roman Catholic girl in the Midwest. I was a total dork with a bowl haircut. Now kids can curate who and what and how they are over the network. Young professionals can connect with people with experience. Or they can decide, I get this all the time on Twitter actually. How did you become a Chief Privacy Officer? I'm really interested in taking a pivot in my career. And I love talking to those people 'cause they always educate me, and I hope that I give them a little bit of value too. >> Right, right. Michelle, we could go on for on and on and on. But, unfortunately, I think you got to go cover a session. So we're going to let you go. >> Thank you. >> Michelle Dennedy, thanks for taking a few minutes of your time. >> Thank you, and don't miss another Data Privacy Day. >> I will not. We'll be back next year as well. I'm Jeff Frick. You're watching theCUBE. See you next time.

>> It's the Cube covering IBM cheap Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts Day villain day and still minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. We here at the Chief Data Officers Summit that IBM is hosting in Boston. I'm joined by Courtney Abercrombie. According your your title's too long. I'm just gonna call you a cognitive rockstar on >> Alec Crane is >> here from Yusa. System by President, Vice President at that firm. Welcome to the Cube. Great to see you guys. Thank you. So this event I love it. I mean, we first met at the, uh, the mighty chief data officer conference. You were all over that networking with the CEO's helping him out and just really, I think identified early on the importance of this constituency. Why? How did you sort of realize and where have you taken it? >> It's more important than it's ever been. And we're so grateful every time that we see a new chief data officer coming in because you just can't govern and do data by committee. Um, if you really hope to be transformational in your company. All these huge, different technologies that are out there, All this amazing, rich data like weather data and the ability to leverage, you know, social media information, bringing that all together and really establishing an innovation platform for your company. You can't do that by committee. You really have to have a leader in charge of it. and that’s what chief data officers are here to do. And so every time we see one, we're so grateful >> that just so >> that we just heard from Inderpal Bhandari on his recommendation for how you get started. It was pretty precise and prescriptive. But I wonder, Alan. So tell us about the chief data officer role at USAA. Hasn't been around for a while. Of course, it's a regulated business. So probably Maur, data oriented are cognizant than most businesses. But tell us about your journey. >> We started probably about 4 or 5 years ago, and it was a combination of trying to consolidate data and analytics operations and then decentralized them, and we found that there was advantages and pros and cons of doing both. You'd get the efficiencies, but once you got the efficiencies, you'd lose the business expertise, and then we'd have to tow decentralize. So we ended up landing a couple of years ago. What we call a hub and spoke system where we have centralized governance and management of key data assets, uh, data modelling data science type work. And then we still allow the, uh, various lines of business to have their own data offices. And the one I run for USAA is our distribution channels office for all of the data and analytics. And we take about 100,000,000 phone calls a year. About 2,000,000,000 webb interactions. Mobile interactions. We take about 18,000 hours. That's really roughly two years of phone conversation data in per day. Uh, we take about 50,000,000 lines of, uh, Web analytic traffic per day as well. So trying to make sense of that to nurture remember, relationships, reinforce trust and remove obstacles >> for your supporting the agent systems. Is that right? >> I support the agent systems as well as the, um, digital >> systems. Okay. And so the objective is obviously toe to grow the business, keep it running, keep the customers happy. Very operate, agent Just efficient. Okay. Um and so when you that's really interesting. This sort of hub and spoke of decentralization gets you speed and closer to the business. Centralization get you that that efficiency. Do you feel like you found that right balance? I mean, if you think so. I >> think you know, early on, we it was mme or we had more cerebral alignment, you know, meaning that it seemed logical to us. But actually, once the last couple of years, we've had some growing pains with roles, responsibilities, overlaps, some redundancy, those types of things. But I think we've landed in a good place. And that's that's what I'm pretty proud of because we've been able to balance the agility with the governance necessary toe, have good governance and put in place, but then also be able to move at the speed the businessmen. >> So Courtney, one of things we heard one of the themes this morning within IBM it's of the role of the chief Data officer's office is to really empower the lines of business with data so that you can empower your customers is what Bob Tatiana was telling us, right? With data. So how are you doing? That is you have new services. You have processes or how is that all working >> right? We dio We have a lot of things, actually, because we've been working so much with people like Allen's group who have been leaders at, quite frankly, in establishing best practices on even how to set up these husbands votes. A lot of people are, you know, want to talk, Teo, um, the CDO and they've spun off even a lot of CEOs into other organizations, in fact, but I mean, they're really a leader in this area. So one of the things that we've noticed is you know, the thing that gives everybody the biggest grief is trying to figure out how to work with unstructured data. Um, and all this volume of data, it's just insane. And just like I was saying in the panel earlier, only about 5% of your actual internal data is enough to actually create a context around your customers. You really have to be able to go with all this exogenous data to understand what were the bigger ramifications that were going on in any customer event, whether it's a call in or whether it's, uh, you know, I'm not happy today with something that you tried to sell me or something that you didn't respond too fast enough, which I'm sure Alan could, you know, equate to. But so we have this new data as a service that we've put together based on the way the weather data has, the weather company has put their platform together. We're using a lot of the same kind of like micro services that you saw Bob put on the screen. You know, everything from, I mean, open source. As much open sources we can get, get it. And it's all cloud based. So and it's it's ways to digest and mix up both that internal data with all of that big, voluminous external data. >> So I'm interested in. So you get the organizational part down. Least you've settled on approach. What are some of the other big challenges that you face in terms of analytics and cognitive projects? Your organization? How are you dealing with those? >> Well, uh, >> to take a step back, use a We're, uh, financial services company that supports the military and their families. We now have 12 million members, and we're known for our service. And most of the time, those moments of truth, if you will, where our service really shines has been when someone talks to you, us on the phone when those member service reps are giving that incredible service that they're known for on the reason being is that the MSR is the aggregator of all that data. When you call in, it's all about you. There's two screens full of your information and the MSR is not interested in anything else but just serving you, our digital experiences more transactional in orientation. And it was It's more utilitarian, and we're trying to make it more personal, trying to make it more How do we know about you? And so one of the cues that were that were taking from the MSR community through cognitive learning is we like to say the only way to get into the call is to get into the call, and that is to truly get into the speech to text, Then do the text mining on that to see what are the other topics that are coming out that could surface that we're not actually capturing. And then how do we use those topics at a member level two then help inform the digital experience to make it more personal. How do I detect life events? Our MSR's are actually trained to listen for things like words like fiance, marriage moving, maybe even a baby crying in the background. How do we take that knowledge and turn that into something that machine learning can give us insights that can feedback into our digital transact actions. So >> this's what our group. >> It's a big task. So So how are >> you doing that? I mean, it's obviously we always talk about people processing technology. Yeah, break that down for us. I mean, how are you approaching that massive opportunity? >> Part of it is is, uh, you know, I look at it. It is like a set of those, you know, Russian nesting dolls. You know, every time you solve one problem, there's another problem inside of it. The first problem is getting access to the data. You know, where and where do you store? We're taking in two years of data per day of phone call data into a system where you put all that right and then you're where you put a week's worth a month's worth a quarter's worth of data like that. Then once you solve that problem, how do you read Act all that personal information So that that private information that you really don't need that data exhaust that would actually create a liability for you in our in our world so that you can really stay focused on what of the key themes that the member needs? And then the third thing is now had. Now that you've got access to the data, it's transcribed for you. It's been redacted from its P I I type work well, now you need the horse power and of analysts on, we're exploring partnerships with IBM, both locally and in in the States as well as internationally to look at data science as a service and try to understand How can we tap into this huge volume of data that we've got to explore those types of themes that are coming up The biggest challenges in typical transaction logging systems. You have to know what your logging You have to know what you're looking for before you know what to put the date, where to put the data. And so it's almost like you kind of have to already know that it's there to know how much you're acquiring for it and what we need to do more as we pivot more towards machine learning is that we need the data to tell us what's important to look at. And that's really the vat on the value of working with these folks. >> So obviously, date is increasingly on structure we heard this morning and whatever, 80 90% is structured. So here you're no whatever. You're putting it into whatever data fake swamp, ocean, everything center everywhere, and you're using sort of machine learning toe both find signal, but also protected yourself from risk. Right. So you've got a T said you gotta redact private information. So much of that information could be and not not no schema? Absolutely. Okay, So you're where are you in terms of solving that problem in the first inning or you deeper than that, >> we're probably would say beyond the first inning, but we so we've kind of figured out what that process is to get the data and all the piece parts working together. We've made some incredible insights already. Things that people, you know, I had no idea that was there. Um, but, uh, I'd say we still have a long way to go. Is particularly terms of scaling scaling the process, scaling the thie analytics, scaling the partnerships, figuring out how do we get the most throughput? I would say it's It's one of those things. We're measuring it on, maybe having a couple of good wins this year. A couple of really good projects that have come across. We want to kind of take that tube out 10 projects next year in this space. And that's how we're kind of measuring the velocity and the success >> data divas. I walked away and >> there was one of them Was breakfast this morning. Data divas. You hold this every year. >> D'oh! It's growing. Now we got data, >> dudes. So I was one of the few data dudes way walked in >> one of the women chief date officers. I got no problem with people calling me a P. >> I No. Yeah, I just sell. Sit down. Really? Bath s o. But also, >> what's the intent of that? What learning is that you take out of those? >> I think it's >> more. It's You know, you could honestly say this isn't just a data Debo problem. This is also, you know, anybody who feels like they're not being heard. Um, it's really easy to get drowned out in a lot of voices when it comes to data and analytics. Um, everybody has an opinion. I think. Remember, Ursula is always saying, Ah, all's fair in love, war and data. Um and it feels like, you know, sometimes you go, I'll come to the table and whoever has the loudest voice and whoever bangs their test the loudest, um, kind of wins the game. But I think in this case, you know, a lot of women are taking these roles. In fact, we saw, you know, a while back from Gardner that number about 25% of chief data officers are actually women because the role is evolving out of the business lines as opposed Thio more lines. And so I mean, it makes sense that, you know, were natural collaborators. I mean, like the biggest struggle and data governance isn't setting up frameworks. It's getting people to actually cooperate and bring data to the table and talk about their business processes that support that. And that's something that women do really well. But we've got to find our voice and our strength and our resolve. And we've got to support each other in trying to bring more diverse thinking to the table, you know? So it's it's all those kinds of issues and how do you balance family? I mean, >> we're seeing >> more and more. You know, I don't know if you know this, but there's actual statistics around millennials and that males are actually starting to take on more more role of being the the caregiver in the family. So I mean as we see that it's an interesting turnabout because now all the sudden, it's no longer, you know, women having that traditional role of, you know, I gotta always be home. Now we're actually starting to see a flip of that, which is which is, >> You know, I think it's kind of welcome. My husband's definitely >> I say he's a better parent than me. >> Friday. It's >> honest he'll watch this and he >> can thank me later that it was >> a great discussion this morning. Alan, I want to get your feedback on this event and also you participate in a couple of sessions yesterday. Maybe you could share with our audience Some of the key takeaways in the event of general and specific ones that you worked on yesterday. >> Well, I've been fortunate to come to the event for a couple of years now. And when we were just what 50 or so of us that were showing up? So, you know, I see that the evolution just in a couple of years time conversations have really changed. First meeting that we had people were saying, Where do you report in the organization? Um, how many people do you have? What do you do for your job? They were very different answers to any of that everywhere. From I'm an independent contributor that's a data evangelist to I run legions of data analysts and reporting shops, you know, and so forth and everything in between. And so what I see what it's offers in first year was really kind of a coalescing of what it really means to be a data officer in the company that actually happened pretty quickly in my mind, Um, when by seeing it through through the lens of my peers here, the other thing was when you when you think about the topics the topics are getting a lot more pointed. They're getting more pointed around the monetization of data communicating data through visualization, storytelling, key insights that you, you know, using different technologies. And we talked a lot yesterday about storytelling and storytelling is not through visual days in storytelling is not just about like who has the most, you know, colors on on a slide or or ah you know, animation of your bubble charts and things like that. But sometimes the best stories are told with the most simple charts because they resonate with your customers. And so what I think is it's almost like kind of getting a back to the basics when it comes to taking data and making it meaningful. We're only going to grow our organizations and data and data scientists and analysts. If we can communicate to the rest of the organization, our value and the key to creating that value is they can see themselves in our data. >> Yeah, the visit is we like to call it sometimes is critical to that to that storytelling. Sometimes I worry and we go onto these conferences and you go into a booth and look what we can do with machine learning, and we would just be looking at just this data. So what do I do? What >> I do with all this? Yeah. >> I don't know how it would make sense of it. So So is there a special storyteller role within your organization or you all storytellers? Do you cross train on that? Or >> it's funny you'd ask that one of the gentlemen of my team. He actually came to me about six months ago, and he says I'm really good at at the analysis part, but I really have a passion for things like Photoshopped things like, uh uh, uh the various, uh, video and video editing type software. He says I want to be your storyteller. I want to be creating a team of data and analytics storytellers for the rest of the organization. So we pitched the idea to our central hub and spoke leadership group. They loved it. They loved the idea. And he is now, um, oversubscribed. You would say in terms of demand for how do you tell the data? How do you tell the data story and how it's moving the business forward? And that takes the form kind of everything from infographics tell you also about how do you make it personal when, when? Now 7,000 m s. Ours have access to their own data. You know, really telling that at a at a very personal level, almost like a vignette of animus are who's now able to manage themselves using the data that they were not able able tto have before we're in the past, only managers had access to their performance results. This video, actually, you know, pulls on the heartstrings. But it it not only does that, but it really tells the story of how doing these types of things and creating these different data assets for the rest of your organization can actually have a very meaningful benefit to how they view work and how they view autonomy and how they view their own personal growth. >> That's critical, especially in a decentralized organization. Leased a quasi decentralized organization, getting everybody on the same page and understand You know what the vision is and what the direction is. It s so often if you don't have that storytelling capability, you have thousands of stories, and a lot of times there's dissonance. I mean, I'm not saying there's not in your in your organization, but have you seen the organization because of that storytelling capability become Mohr? Yeah, Joe. At least Mohr sort of effective and efficient, moving forward to the objectives. Well, >> you know, as a as a data person, I'm always biased thatyou know data, you know, can win an argument if presented the right way. It's the The challenge is when you're trying to overcome or go into a direction. And in this case, it was. We wanted to give more autonomy. Toothy MSR community. Well, the management of that call center were 94 year old company. And so the management of that of that call center has been doing things a certain way for many, many, many, many years. And the manager's having access to the data. The reps not That was how we did things, you know. And so when you make a change like that, there's a lot of hesitation of what is this going to do to us? How is this going to change? And what we're able to show with data and with through these visualizations is you really don't have anything to worry about? You're only gonna have upside, you know, in this conversation because at the end of the day, what's going to empower people this having access and power of >> their own destiny? Yeah, access is really the key isn't because we've all been in the meetings where somebody stands up and they've got some data point in there pounding the table, >> right? Oftentimes it's a man, all right. It >> is a powerful pl leader on jamming data down your throats, and you don't necessarily know the poor sap that he's, you know, beating up. Doesn't think Target doesn't have access to the data. This concept of citizen data scientists begins to a level that playing field doesn't want you seeing that >> it does. And I want to actually >> come back to what you're saying because there's a larger thought there, which is that we don't often address, and that's this change banishment concept. I mean, we we look at all these. I mean, everybody looks at all these technologies and all this information, and how much data can you possibly get your >> hands on? But at the end of >> the day, it's all about trying to create an outcome. A some joint outcome for the business and it could be threatening. It could be threatening to the C suite people who are actually deploying the use of these data driven tools because >> it may go >> against their gut. And, you >> know, oftentimes the poor messenger of that, >> When when you have to be the one that stands up and go against that, that senior vice presidents got it, the one who's pounding and saying No, but I know better >> That could be a >> tough position to be in without having some sort of change management philosophy going on with the introduction of data and analytics and with the introduction of tools, because there's a whole reframing that, Hey, my gut instinct that got me here all the way to the top doesn't necessarily mean that it's going to continue to scale in this new world with all of all of our competitors and all these, you know, massive changes going on in the market place right now. My guts not going to get me there anymore. So it's hard, it's hard, and I think a lot of executives don't really know to invest in that change management, if you know that goes with it that you need to change philosophies and mindsets and slowly introduced visualizations and things that get people slowly onboard, as opposed to just throwing it at him and saying here, believe it. >> Think I mean, it wasn't that >> long ago. Certainly this this millennium, where you know, publications like Harvard Business Review had, uh, cover stories on why gut feel, you know, beats, you know, analysis by paralysis. >> That seems to be changing. And >> the data purists would say the data doesn't lie. It was long as you could interpret it correctly. Let the data tell us what to do, as opposed to trying to push an agenda. But they're still politics. >> There's just things out >> there that you can't even perceive of that air coming your way. I mean, like, Blockbuster Netflix, Alibaba versus standard retailers. I mean, >> there's just things out >> there that without the use of things like machine learning and being comfortable with the use, the things like mission learning a lot of people think of that kind of stuff is >> Well, don't get your >> hoodoo voodoo into my business. You know, I don't know what that algorithm stuff does. It's >> going Yeah, I mean, e. I mean to say, What the hell is this? And now, yeah, it's coming and >> you need to get ready. >> There's an >> important role, though I think instinct, you know, you don't want to dismiss a 20 year leader in a particular operations because they've they've they've getting themselves where they're at because in large part, maybe they didn't have all the data. But they learned through a lot of those things, and I think it's when you marry those things up. And if you kenbrell in a kind of humble way to that kind of leader and win them over and show how it may be validating some of their, um uh yeah, that some of their points Or maybe how it explains it in a different way. Maybe it's not exactly what they want to see, but it's helping to inform their business, and you come into him as a partner, as opposed to gotcha, you know. Then then you know you can really change the business that way. And >> what is it? Was Linda Limbic brain is it just doesn't feel right. Is that the part of the brain that informs you that? And so It's hard to sometimes put, but you're right. Uh, there there is a component of this which is gut feel instinct and probably relates to to experience. So it's It's like, uh, when, when, uh, Deep blue beat Garry Kasparov. We talk about this all the time. It turns out that the best chess player in the world isn't a machine. It's a It's a human in the machine. >> That's right. That's exactly right. It's always the training that people training these things, that's where it gets its information. So at the end of the day, you're right. It's always still instinct to some >> level. I could We gotta go. All right. Last word on the event. You know what's next? >> Don't love my team. Data officer. Miss, you guys. It is good >> to be here. We appreciate it. All right, We'll leave it there. Thank you, guys. Thank you. All right, keep right. Everybody, this is Cuba. Live from IBM Chief Data Officer, Summit in Boston Right back. My name is Dave Volante.