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

>>Yeah, yeah. >>Welcome back. I'm so excited to introduce our next session empowerment through inclusion, reimagining society and technology. This is a topic that's personally very near and dear to my heart. Did you know that there's only 2% of Latinas in technology as a Latina? I know that there's so much more we could do collectively to improve these gaps and diversity. I thought spot diversity is considered a critical element across all levels of the organization. The data shows countless times. A diverse and inclusive workforce ultimately drives innovation better performance and keeps your employees happier. That's why we're passionate about contributing to this conversation and also partnering with organizations that share our mission of improving diversity across our communities. Last beyond, we hosted the session during a breakfast and we packed the whole room. This year, we're bringing the conversation to the forefront to emphasize the importance of diversity and data and share the positive ramifications that it has for your organization. Joining us for this session are thought spots Chief Data Strategy Officer Cindy Housing and Ruhollah Benjamin, associate professor of African American Studies at Princeton University. Thank you, Paola. So many >>of you have journeyed with me for years now on our efforts to improve diversity and inclusion in the data and analytic space. And >>I would say >>over time we cautiously started commiserating, eventually sharing best practices to make ourselves and our companies better. And I do consider it a milestone. Last year, as Paola mentioned that half the room was filled with our male allies. But I remember one of our Panelists, Natalie Longhurst from Vodafone, suggesting that we move it from a side hallway conversation, early morning breakfast to the main stage. And I >>think it was >>Bill Zang from a I G in Japan. Who said Yes, please. Everyone else agreed, but more than a main stage topic, I want to ask you to think about inclusion beyond your role beyond your company toe. How Data and analytics can be used to impact inclusion and equity for the society as a whole. Are we using data to reveal patterns or to perpetuate problems leading Tobias at scale? You are the experts, the change agents, the leaders that can prevent this. I am thrilled to introduce you to the leading authority on this topic, Rou Ha Benjamin, associate professor of African studies at Princeton University and author of Multiple Books. The Latest Race After Technology. Rou ha Welcome. >>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today, and I thought I would just kick things off with some opening reflections on this really important session theme. And then we could jump into discussion. So I'd like us to as a starting point, um, wrestle with these buzzwords, empowerment and inclusion so that we can have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design in the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism, and I'll explain what that means in just a minute. Techno determinism comes in two forms. The first, on your left is the idea that technology automation, um, all of these emerging trends are going to harm us, are going to necessarily harm humanity. They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story and this is what Hollywood loves to sell us in the form of movies like The Matrix or Terminator. The other version on your right is the techno utopian story that technologies automation. The robots as a shorthand, are going to save humanity. They're gonna make everything more efficient, more equitable. And in this case, on the surface, he seemed like opposing narratives right there, telling us different stories. At least they have different endpoints. But when you pull back the screen and look a little bit more closely, you see that they share an underlying logic that technology is in the driver's seat and that human beings that social society can just respond to what's happening. But we don't really have a say in what technologies air designed and so to move beyond techno determinism the notion that technology is in the driver's seat. We have to put the human agents and agencies back into the story, the protagonists, and think carefully about what the human desires worldviews, values, assumptions are that animate the production of technology. And so we have to put the humans behind the screen back into view. And so that's a very first step and when we do that, we see, as was already mentioned, that it's a very homogeneous group right now in terms of who gets the power and the resource is to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to create more participation of those who are working behind the scenes to design technology now to dig a little more a deeper into this, I want to offer a kind of low tech example before we get to the more hi tech ones. So what you see in front of you here is a simple park bench public bench. It's located in Berkeley, California, which is where I went to graduate school and on this particular visit I was living in Boston, and so I was back in California. It was February. It was freezing where I was coming from, and so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D, and I quickly realized, actually, I couldn't lay down on this bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought. Okay, the the armrest have, ah functional reason why they're there. I mean, you could literally rest your elbows there or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. When I was nine months pregnant, it could help me get up and down or for the elderly, the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are homeless from sleeping on the bench. And this is the Bay area that we were talking about where, in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown within equity because we haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, Okay, this crisis is growing in this area, and so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the where they're implemented and So this is what we might call structural inequity. By the way something is designed. It has certain effects that exclude or harm different people. And so it may not necessarily be the intense, but that's the effect. And I did a little digging, and I found, in fact, it's a global phenomenon, this thing that architects called hostile architecture. Er, I found single occupancy benches in Helsinki, so only one booty at a time no laying down there. I found caged benches in France. And in this particular town. What's interesting here is that the mayor put these benches out in this little shopping plaza, and within 24 hours the people in the town rallied together and had them removed. So we see here that just because we have, uh, discriminatory design in our public space doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the meter bench. In this case, this bench is designed with spikes in them, and to get the spikes to retreat into the bench, you have to feed the meter you have to put some coins in, and I think it buys you about 15 or 20 minutes. Then the spikes come back up. And so you'll be happy to know that in this case, this was designed by a German artists to get people to think critically about issues of design, not just the design of physical space but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harm, whether we're talking about education or health care. And the meter bench also presents something interesting. For those of us who care about technology, it creates a technical fix for a social problem. In fact, it started out his art. But some municipalities in different parts of the world have actually adopted this in their public spaces in their parks in order to deter so called lawyers from using that space. And so, by a technical fix, we mean something that creates a short term effect, right. It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside in the first place. And so, in addition to techno determinism, we have to think critically about technical fixes that don't address the underlying issues that technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with or that we create. And the question we really have to continuously ask ourselves is, What values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something. This is the way things have always been done. So we inherit the spike bench. What is our responsibility when we noticed that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem? What is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for Apple, the Apple card and, in one case, a husband and wife applied and the husband, the husband received a much higher limit almost 20 times the limit as his wife, even though they shared bank accounts, they lived in Common Law State. And so the question. There was not only the fact that the husband was receiving a much better interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know what the factors were that they were being judged that was creating this form of discrimination. So in terms of financial technologies, it's not simply the outcome that's the issue. Or that could be discriminatory, but the process that black boxes, all of the decision making that makes it so that consumers and the general public have no way to question it. No way to understand how they're being judged adversely, and so it's the process not only the product that we have to care a lot about. And so the case of the apple cart is part of a much broader phenomenon of, um, racist and sexist robots. This is how the headlines framed it a few years ago, and I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creator's biases. And now I think we've entered a phase of attempts to override and address the default settings of so called racist and sexist robots, for better or worse. And here robots is just a kind of shorthand, that the way people are talking about automation and emerging technologies more broadly. And so as I was encountering these headlines, I was thinking about how these air, not problems simply brought on by machine learning or AI. They're not all brand new, and so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the New Jim Code, which plays on the phrase Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era, and I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim Code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those were the kinds of inequalities tech mediated inequalities that we can generally see coming. They're kind of obvious. But then we go down the line and we see it becomes harder to detect. It's happening in our own backyards. It's happening around us, and we don't really have a view into the black box, and so it becomes more insidious. And so in the remaining couple minutes, I'm just just going to give you a taste of the last three of these, and then a move towards conclusion that we can start chatting. So when it comes to default discrimination. This is the way that social inequalities become embedded in emerging technologies because designers of these technologies aren't thinking carefully about history and sociology. Ah, great example of this came Thio headlines last fall when it was found that widely used healthcare algorithm affecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm healthcare algorithm does not explicitly take note of race. That is to say, it is race neutral by using cost to predict healthcare needs. This digital triaging system unwittingly reproduces health disparities because, on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect, and so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code coded exposure, there's attention because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera or on the computer. And so coated exposure names this tension between wanting to be seen and included and recognized, whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over included. When you're surveiled when you're to centered. And so we should note that it's not simply in being left out, that's the problem. But it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point. It's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim Code. That means the the most insidious, let's say, is technologies that are touted as helping US address bias, so they're not simply including people, but they're actively working to address bias. And so in this case, There are a lot of different companies that are using AI to hire, create hiring software and hiring algorithms, including this one higher view. And the idea is that there there's a lot that AI can keep track of that human beings might miss. And so so the software can make data driven talent decisions. After all, the problem of employment discrimination is widespread and well documented. So the logic goes, Wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the look of the idea of techno benevolence trying to do good without fully reckoning with what? How technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. E. And what they found was that in fact, the algorithm associating black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003, before all of the emerging technologies were on the scene where two University of Chicago economists sent out thousands of resumes to employers in Boston and Chicago, and all they did was change the names on those resumes. All of the other work history education were the same, and then they waited to see who would get called back. And the applicants, the fictional applicants with white sounding names received 50% more callbacks than the black applicants. So if you're presented with that study, you might be tempted to say, Well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So, too, with gender coded words and names Amazon learned a couple years ago when its own hiring algorithm was found discriminating against women. Nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If Onley there was a way to slay centuries of racist and sexist demons with a social justice box beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment but a curse from any jobseekers, as this headline puts it, your next interview could be with a racist spot, bringing us back to that problem space we started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers test and creating fake applications as informal audits of their own. In terms of a more collective response, there's a federation of European Trade unions call you and I Global that's developed a charter of digital rights for work, others that touches on automated and a I based decisions to be included in bargaining agreements. And so this is one of many efforts to change their ecosystem to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the US There's the algorithmic accountability bill that's been presented, and it's one effort to create some more protections around this ubiquity of automated decisions, and I think we should all be calling from more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag Tech won't build it. Among other statements that they have made and walking out and petitioning their companies. Who one group said, as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can we can grow a more historically and socially literate approach to tech design. And this is just one, um, resource that you all can download, Um, by developed by some wonderful colleagues at the Data and Society Research Institute in New York and the goal of this interventionist threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies, not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations, and a commitment to take action to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer a couple last provocations. The first is for us to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to a I systems without historical or social depth, is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. The last provocation is this. If, as I suggested at the start, inequity is woven into the very fabric of our society, it's built into the design of our. Our policies are physical infrastructures and now even our digital infrastructures. That means that each twist, coil and code is a chance for us toe. We've new patterns, practices and politics. The vastness of the problems that we're up against will be their undoing. Once we accept that we're pattern makers. So what does that look like? It looks like refusing color blindness as an anecdote to tech media discrimination rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting. And how do we create a more participatory rather than predatory form of inclusion? And ultimately, it also means owning our own power in these systems so that we can change the patterns of the past. If we're if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more just and equitable technologies. So with that, I look forward to our conversation. >>Thank you, Ruth. Ha. That was I expected it to be amazing, as I have been devouring your book in the last few weeks. So I knew that would be impactful. I know we will never think about park benches again. How it's art. And you laid down the gauntlet. Oh, my goodness. That tech won't build it. Well, I would say if the thoughts about team has any saying that we absolutely will build it and will continue toe educate ourselves. So you made a few points that it doesn't matter if it was intentional or not. So unintentional has as big an impact. Um, how do we address that does it just start with awareness building or how do we address that? >>Yeah, so it's important. I mean, it's important. I have good intentions. And so, by saying that intentions are not the end, all be all. It doesn't mean that we're throwing intentions out. But it is saying that there's so many things that happened in the world, happened unwittingly without someone sitting down to to make it good or bad. And so this goes on both ends. The analogy that I often use is if I'm parked outside and I see someone, you know breaking into my car, I don't run out there and say Now, do you feel Do you feel in your heart that you're a thief? Do you intend to be a thief? I don't go and grill their identity or their intention. Thio harm me, but I look at the effect of their actions, and so in terms of art, the teams that we work on, I think one of the things that we can do again is to have a range of perspectives around the table that can think ahead like chess, about how things might play out, but also once we've sort of created something and it's, you know, it's entered into, you know, the world. We need to have, ah, regular audits and check ins to see when it's going off track just because we intended to do good and set it out when it goes sideways, we need mechanisms, formal mechanisms that actually are built into the process that can get it back on track or even remove it entirely if we find And we see that with different products, right that get re called. And so we need that to be formalized rather than putting the burden on the people that are using these things toe have to raise the awareness or have to come to us like with the apple card, Right? To say this thing is not fair. Why don't we have that built into the process to begin with? >>Yeah, so a couple things. So my dad used to say the road to hell is paved with good intentions, so that's >>yes on. In fact, in the book, I say the road to hell is paved with technical fixes. So they're me and your dad are on the same page, >>and I I love your point about bringing different perspectives. And I often say this is why diversity is not just about business benefits. It's your best recipe for for identifying the early biases in the data sets in the way we build things. And yet it's such a thorny problem to address bringing new people in from tech. So in the absence of that, what do we do? Is it the outside review boards? Or do you think regulation is the best bet as you mentioned a >>few? Yeah, yeah, we need really need a combination of things. I mean, we need So on the one hand, we need something like a do no harm, um, ethos. So with that we see in medicine so that it becomes part of the fabric and the culture of organizations that that those values, the social values, have equal or more weight than the other kinds of economic imperatives. Right. So we have toe have a reckoning in house, but we can't leave it to people who are designing and have a vested interest in getting things to market to regulate themselves. We also need independent accountability. So we need a combination of this and going back just to your point about just thinking about like, the diversity on teams. One really cautionary example comes to mind from last fall, when Google's New Pixel four phone was about to come out and it had a kind of facial recognition component to it that you could open the phone and they had been following this research that shows that facial recognition systems don't work as well on darker skin individuals, right? And so they wanted Thio get a head start. They wanted to prevent that, right? So they had good intentions. They didn't want their phone toe block out darker skin, you know, users from from using it. And so what they did was they were trying to diversify their training data so that the system would work better and they hired contract workers, and they told these contract workers to engage black people, tell them to use the phone play with, you know, some kind of app, take a selfie so that their faces would populate that the training set, But they didn't. They did not tell the people what their faces were gonna be used for, so they withheld some information. They didn't tell them. It was being used for the spatial recognition system, and the contract workers went to the media and said Something's not right. Why are we being told? Withhold information? And in fact, they told them, going back to the park bench example. To give people who are homeless $5 gift cards to play with the phone and get their images in this. And so this all came to light and Google withdrew this research and this process because it was so in line with a long history of using marginalized, most vulnerable people and populations to make technologies better when those technologies are likely going toe, harm them in terms of surveillance and other things. And so I think I bring this up here to go back to our question of how the composition of teams might help address this. I think often about who is in that room making that decision about sending, creating this process of the contract workers and who the selfies and so on. Perhaps it was a racially homogeneous group where people didn't want really sensitive to how this could be experienced or seen, but maybe it was a diverse, racially diverse group and perhaps the history of harm when it comes to science and technology. Maybe they didn't have that disciplinary knowledge. And so it could also be a function of what people knew in the room, how they could do that chest in their head and think how this is gonna play out. It's not gonna play out very well. And the last thing is that maybe there was disciplinary diversity. Maybe there was racial ethnic diversity, but maybe the workplace culture made it to those people. Didn't feel like they could speak up right so you could have all the diversity in the world. But if you don't create a context in which people who have those insights feel like they can speak up and be respected and heard, then you're basically sitting on a reservoir of resource is and you're not tapping into it to ensure T to do right by your company. And so it's one of those cautionary tales I think that we can all learn from to try to create an environment where we can elicit those insights from our team and our and our coworkers, >>your point about the culture. This is really inclusion very different from just diversity and thought. Eso I like to end on a hopeful note. A prescriptive note. You have some of the most influential data and analytics leaders and experts attending virtually here. So if you imagine the way we use data and housing is a great example, mortgage lending has not been equitable for African Americans in particular. But if you imagine the right way to use data, what is the future hold when we've gotten better at this? More aware >>of this? Thank you for that question on DSO. You know, there's a few things that come to mind for me one. And I think mortgage environment is really the perfect sort of context in which to think through the the both. The problem where the solutions may lie. One of the most powerful ways I see data being used by different organizations and groups is to shine a light on the past and ongoing inequities. And so oftentimes, when people see the bias, let's say when it came to like the the hiring algorithm or the language out, they see the names associated with negative or positive words that tends toe have, ah, bigger impact because they think well, Wow, The technology is reflecting these biases. It really must be true. Never mind that people might have been raising the issues in other ways before. But I think one of the most powerful ways we can use data and technology is as a mirror onto existing forms of inequality That then can motivate us to try to address those things. The caution is that we cannot just address those once we come to grips with the problem, the solution is not simply going to be a technical solution. And so we have to understand both the promise of data and the limits of data. So when it comes to, let's say, a software program, let's say Ah, hiring algorithm that now is trained toe look for diversity as opposed to homogeneity and say I get hired through one of those algorithms in a new workplace. I can get through the door and be hired. But if nothing else about that workplace has changed and on a day to day basis I'm still experiencing microaggressions. I'm still experiencing all kinds of issues. Then that technology just gave me access to ah harmful environment, you see, and so this is the idea that we can't simply expect the technology to solve all of our problems. We have to do the hard work. And so I would encourage everyone listening to both except the promise of these tools, but really crucially, um, Thio, understand that the rial kinds of changes that we need to make are gonna be messy. They're not gonna be quick fixes. If you think about how long it took our society to create the kinds of inequities that that we now it lived with, we should expect to do our part, do the work and pass the baton. We're not going to magically like Fairy does create a wonderful algorithm that's gonna help us bypass these issues. It can expose them. But then it's up to us to actually do the hard work of changing our social relations are changing the culture of not just our workplaces but our schools. Our healthcare systems are neighborhoods so that they reflect our better values. >>Yeah. Ha. So beautifully said I think all of us are willing to do the hard work. And I like your point about using it is a mirror and thought spot. We like to say a fact driven world is a better world. It can give us that transparency. So on behalf of everyone, thank you so much for your passion for your hard work and for talking to us. >>Thank you, Cindy. Thank you so much for inviting me. Hey, I live back to you. >>Thank you, Cindy and rou ha. For this fascinating exploration of our society and technology, we're just about ready to move on to our final session of the day. So make sure to tune in for this customer case study session with executives from Sienna and Accenture on driving digital transformation with certain AI.

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

>>Yeah, >>yeah. Hello, everyone. And welcome to the cultivating a data slowing culture. Jack, my name is Paula Johnson. I'm thought Spots head of community, and I am so excited to be your host heared at beyond. One of my favorite things about beyond is connecting with everyone and just feeling that buzz and energy from you all. So please don't be shy and engage in the chat. I'll be there shortly. We all know that when it comes to being fluent in a language, it's all about how do you take data in the sense and turn it into action? We've seen that in the hands of employees. Once they have access to this information, they are more engaged in their role. They're more productive, and most importantly, they're making better decisions. I think all of us want a little bit more of that, don't we? In today's track, you'll hear from expert partners and our customers and best practices that you could start applying to build that data. Fluent culture in your organization that we're seeing is powering the digital transformation across all industries will also discuss the role that the analysts of the future plays when it comes to this cultural shift and how important it is for diversity in data that helps us prevent bias at scale. To start us off our first session of the day is cultivating a data fluent culture, the essence and essentials. Our first speaker, CEO and founder of the Data Lodge, Valerie Logan. Valerie, Thank you for joining us today of passings over to you Now. >>Excellent. Thank you so much while it's so great to be here with the thought spot family. And there is nothing I would love to talk more about than data literacy and data fluency. And I >>just want to take a >>second and acknowledge I love how thought spot refers to this as data fluency and because I really see data literacy and fluency at, you know, either end of the same spectrum. And to mark that to commemorate that I have decorated the Scrabble board for today's occasion with fluency and literacy intersecting right at the center of the board. So with that, let's go ahead and get started and talking about how do you cultivate a data fluent culture? So in today's session, I am thrilled to be able to talk through Ah, few dynamics around what's >>going >>on in the market around this area. Who are the pioneers and what are they doing to drive data fluent culture? And what can you do about it? What are the best practices that you can apply to start this? This momentum and it's really a movement. So how do you want to play a part in this movement? So the market in the myths, um you know, it's 2020. We have had what I would call an unexpected awakening for the topic of data literacy and fluency. So let's just take a little trip down memory lane. So the last few years, data literacy and data fluency have been emerging as part of the chief data officer Agenda Analytics leaders have been looking at data culture, um, and the up skilling of the workforce as a key cornerstone to how do you create Ah, modern data and analytic strategy. But often this has been viewed as kind of just training or visualization or, um, a lot of focus on the upscaling side of data literacy. So there's >>been >>some great developments over the past few years with I was leading research at Gartner on this topic. There's other work around assessments and training Resource is. But if I'm if I'm really honest, they a lot of this has been somewhat viewed as academic and maybe a bit abstract. Enter the year 2020 where data literacy just got riel and it really can no longer be ignored. And the co vid pandemic has made this personal for all of us, not only in our work roles but in our personal lives, with our friends and families trying to make critical life decisions. So what I'd ask you to do is just to appreciate that this topic is no longer just a work thing. It is personal, and I think that's one of the ways you start to really crack. The culture code is how do you make this relevant to everyone in their personal lives? And unfortunately, cove it did that, and it has brought it to the forefront. But the challenge is how do you balance how do analytics leaders balance the need to up skill the workforce in the culture, with all of these competing needs around modernizing the platform and, um, driving trusted data and data governance? So that's what we'll be exploring is how to do this in parallel. So the very first thing that we need to do is start with the definition and I'd like to share with you how I framed data literacy for any industry across the globe. Which is first of all to appreciate that data literacy as a foundation capability has really been elevated now as >>an >>equivalent to people process and technology. And, you know, if you've been around a while, you know that classic trinity of people process and technology, It's the way that we have thought about how do you change an organization but with the digitization of our work, our lives, our society, you know anything from how do we consume information? How do we serve customers? Um, you know, we're walking sensors with our smartphones are worlds are digital now, and so data has been elevated as an equivalent Vector two people process and technology. And this is really why the role of the chief data officer in the analytics leader has been elevated to a C suite role. And it's also why data literacy and fluency is a workforce competency, not just for the specialist eso You know, I'm an old math major quant. So I've always kind of appreciated the role of data, but now it's prevalent to all right in work in life. So this >>is a >>mindset shift. And in addition to the mindset shift, let's look at what really makes up the elements of what does it mean to be data literate. So I like to call it the ability to read, write and communicate with data in context in both work in life and that it has two pieces. It has a vocabulary, so the vocabulary includes three basic sets of terms. So it includes data terms, obviously, so data sources, data attributes, data quality. There are analysis methods and concepts and terms. You know, it could be anything from, ah, bar Chart Thio, an advanced machine learning algorithm to the value drivers, right? The business acumen. What problems are resolving. So if you really break it down, it's those three sets of terms that make up the vocabulary. But it's not just the terms. It's also what we do with those terms and the skills and the skills. I like to refer to those as the acronym T T E a How do you think? How do you engage with others and how do you act or apply with data constructively? So hopefully that gives you a good basis for how we think about data literacy. And of course, the stronger you get in data literacy drives you towards higher degrees of data fluency. So I like to say we need to make this personal. And when we think about the different roles that we have in life and the different backgrounds that we bring, we think about the diversity and the inclusion of all people and all backgrounds. Diversity, to me is in addition to diversity of our gender identification, diversity of our racial backgrounds and histories. Diversity is also what is what is our work experience in our life experience. So one of the things I really like to do is to use this quote when talking about data literacy, which is we don't see things as they are. We see them as we are. So what we do is we create permission to say, you know what? It's okay that maybe you have some fear about this topic, or you may have some vulnerability around using, um you know, interactive dashboards. Um, you know, it's all about how we each come to this topic and how we support each other. So what I'd like to dio is just describe how we do that and the way that I like to teach that is this idea that we we foster data literacy by acknowledging that really, you learn this language, you learn this through embracing it, like learning a second language. So just take a second and think about you know what languages you speak right? And maybe maybe it's one. Maybe it's too often there's, you know, multiple. But you can embrace data literacy and fluency like it's a language, and somehow that creates permission for people to just say, you know, it's OK that I don't necessarily speak this language, but but I can try. So the way that we like to break this down and I call this SL information as a second language built off of the SL construct of English as a second language and it starts with that basic vocabulary, right? Every language has a vocabulary, and what I mentioned earlier in the definition is this idea that there are three basic sets of terms, value information and analysis. And everybody, when they're learning things like Stow have like a little pneumonic, right? So this is called the V A model, and you can take this and you can apply it to any use case. And you can welcome others into the conversation and say, You know, I really understand the V and the I, but I'm not a Kwan. I don't understand the A. So even just having this basic little triangle called the Via Model starts to create a frame for a shared conversation. But it's not just the vocabulary. It's also about the die elects. So if you are in a hospital, you talk about patient outcomes. If you are in insurance, you talk about underwriting and claims related outcomes. So the beauty of this language is there is a core construct for a vocabulary. But then it gets contextualized, and the beauty of that is, even if you're a classic business person that don't you don't think you're a data and analytics person. You bring something to the party. You bring something to this language, which is you understand the value drivers, so hopefully that's a good basis for you. But it's not just the language. It's also the constructs. How do you think? How do you interact and how do you add value? So here's a little double click of the T E. A acronym to show you it's Are you aware of context? So when you're watching the news, which could be interesting these days, are you actually stepping back and taking pause and saying E wonder what the source of that ISS? I wonder what the assumptions are or when you're in interacting with others. What is your degree of the ability? Thio? Tele Data story, Right? Do you have comfort and confidence interacting with others and then on the applying? This is at the end of the day, this is all about helping people make decisions. So when you're making a decision, are you being conscientious of the ethics right, the ethics or the potential bias in what you're looking at and what you're potentially doing? So I hope this provides you a nice frame. Just if you take nothing else away, take away the V A model as a way to think about a use case and application of data that there's different dialects. So when you're interacting with somebody, think of what dialect are they speaking? And then these three basic skill sets that were helping the workforce to up skill on. But the last thing is, um, you know, there's there's different levels of proficiency, and this is the point of literacy versus fluency. Depending on your role. Not everyone needs to speak data at the same level. So what we're trying to do is get everyone, at least to a shared level of conversational data, right? A basic level of foundation literacy. But based on your role, you will develop different degrees of fluency. The last point of treating this as a language is the idea that we don't just learn language through training. We learn language through interaction and experience. So I would encourage you. Just think about all what are all the different ways you can learn language and apply those to your relationship with data. Hopefully, that makes sense. Um, >>there's a >>few myths out there around this topic of data literacy, and I just want to do a little myth busting real quickly just so you can be on the lookout for these. So first of all data literacy is not about just about training. Training and assessments are certainly a cornerstone, however, when you think about developing a language, yeah, you can use a Rosetta Stone or one of those techniques, but that only gets you. So far. It's conversations you have. It's immersion. Eso keep in mind. It's not just about training. There are many ways to develop language. Secondly, data literacy is not just about internal structure, data and statistics. There are so many different types of data sets, audio, video, text, um, and so many different methods for synthesizing that content. So keep in mind, this isn't just about kind of classic data and methods. The third is visualization and storytelling are such a beautiful way to bring data literacy toe life. But it's not on Lee about visualization and storytelling, right? So there are different techniques. There are different methods on. We'll talk in a minute about health. Top Spot is embedding a lot of the data literacy capabilities into the environment. So it's not just about visualization and storytelling, and it's certainly not about making everybody a junior data scientist. The key is to identify, you know, if you are a call center representative. If you are a Knop orations manager, if you are the CEO, what is the appropriate profile of literacy and fluency for you? The last point and hopefully you get this by now is thistle is not just a work skill. And I think this is one of the best, um, services that we can provide to our employees is when you train an employee and help them up. Skill their data fluency. You're actually up Skilling, the household and their friends and their family because you're teaching them and then they can continue to teach. So at the >>end of >>the day, when we talk about what are the needs and drivers like, where's the return and what are the main objectives of, you know, having a C suite embrace state illiteracy as, ah program? There are primarily four key themes that come up that I hear all the time that I work with clients on Number one is This is how you help accelerate the shift to a data informed, insight driven culture. Or I actually like how thought spot refers to signals, right? So it's not even just insights. It's How do you distill all this noise right and and respond to the signals. But to do that collectively and culturally. Secondly, this is about unlocking what I call radical collaboration so well, while these terms often, sometimes they're viewed as, oh, we need to up skill the full population. This is as much about unlocking how data scientists, data engineers and business analysts collaborate. Right there is there is work to be done there, an opportunity there. The third is yes, we need to do this in the context of up Skilling for digital dexterity. So what I mean by that is data literacy and fluency is in the context of whole Siris of other up Skilling objectives. So becoming more agile understanding, process, automation, understanding, um, the broader ability, you know, ai and in Internet of things sensors, right? So this is part of a portfolio of up skilling. But at the end of the day, it comes down to comfort and confidence. If people are not comfortable with decision making in their role at their level in their those moments that matter, you won't get the kind of engagement. So this is also about fostering comfort and confidence. The last thing is, you know, you have so much data and analytics talent in your organization, and what we want to do is we want to maximize that talent. We really want to reduce dependency on reports and hey, can you can you put that together for me and really enable not just self service but democratizing that access and creating that freedom of access, but also freed up capacity. So if you're looking to build the case for a program, these air the primary four drivers that you can identify clear r A y and I call r o, I, I refer to are oh, I two ways return on investment and also risk of ignoring eso. You gotta be careful. You ignore these. They're going to come back to haunt you later. Eso Hopefully this helps you build the case. So let's take a look at what is a data literacy program. So it's one thing to say, Yeah, that sounds good, but how do you collectively and systemically start to enable this culture change? So, in pioneering data literacy programs, I like to call a data literacy program a commitment. Okay, this is an intentional commitment to up skill, the workforce in the culture, and there's really three pieces to that. The first is it has to be scoped to say we are about enabling the full potential of all associates. And sometimes some of my clients are extending that beyond the virtual walls of their organization to say S I'm working with a U. S. Federal agency. They're talking about data literacy for citizens, right, extending it outside the wall. So it's really about all your constituents on day and associates. Secondly, it is about fostering shared language and the modern data literacy abilities. The third is putting a real focus on what are the moments that matter. So with any kind of heavy change program, there's always a risk that it can. It can get very vague. So here's some examples of the moments that you're really trying to identify in the moments that matter. We do that through three things. I'll just paint those real quick. One is engagement. How do you engage with the leaders? How do you develop community and how do you drive communications? Secondly, we do that through development. We do that through language development, explicitly self paced learning and then of course, broader professional development and training. The third area enablement. This one is often overlooked in any kind of data literacy program. And this is where Thought spot is driving innovation left and right. This is about augmentation of the experience. So if we expect data literacy and data fluency to be developed Onley through training and not augmenting the experience in the environment, we will miss a huge opportunity. So thought spot one. The announcement yesterday with search assist. This is a beautiful example of how we are augmenting guided data literacy, right to support unending user in asking data rich questions and to not expect them to have to know all the forms and features is no different than how a GPS does not tell you. Latitude, longitude, a GPS tells you, Turn left, turn right. So the ability to augment that the way that thought spot does is so powerful. And one of my clients calls it data literacy by design. So how are we in designing that into the environment? And at the end of the day, the last and fourth lever of how you drive a program is you've gotta have someone orchestrating this change. So there is a is an art and a science to data literacy program development. So a couple of examples of pioneers So one pioneer nationwide building society, um, incredible work on how they are leveraging thought spot In particular, Thio have conversations with data. They are creating frictionless voyages with data, and they're using the spot I Q tool to recommend personalized insight. Right? This is an example of that enablement that I was just explaining. Second example, Red hat red hat. They like to describe this as going farther faster than with a small group of experts. They also refer to it as supporting data conversations again with that idea of language. So what's the difference between pioneers and procrastinators? Because what I'm seeing in the market right now is we've got these frontline pioneers who are driving these programs. But then there's kind of a d i Y do it yourself mentality going on. So I just wanted to share what I'm observing as this contrast. So procrastinators are kind of thinking I have no idea where they even start with us, whereas pioneers air saying, you know what, this is absolutely central. Let's figure it out procrastinators are saying. You know what? This probably isn't the right time for this program. Other things are more important and pioneers air like you know what? We don't have an option fast forward a year from now. Do we really think this is gonna organically change? This is pervasive to everything we dio procrastinators. They're saying I don't even know who to put in charge for this. And pioneers there saying this needs a lead. This needs someone focusing on it and a network of influencers. And then finally, procrastinators, They're generally going, you know, we're just gonna wing this and we'll just we'll stand up in academy. We'll put some courses together and pioneers air saying, You know what? We need to work smart. We need a launch, We need a leverage and we need to scale. So I hope that this has inspired you that, you know, there really are many ways to go forward, as FDR said, and only one way of standing still. So not taking an action is a choice. And there were, you know, it does have impact. So a couple of just quick things to wrap up one is how do you get started with the data literacy program, so I recommend seven steps. Who's your sponsor and who is the lead craft? Your case for change. Make it explicit. Developed that narrative craft a blueprint that's scalable but that has an initial plan where data literacy is part of not separate. Run some pilot workshops. These can be so fun and you can tackle the fear and vulnerability concern with really going after, Like how? How do we speak data across different diverse parts of the team. Thes are so fun. And what I find is when I teach people how to run a workshop like this, they absolutely want to repeat it and they get demand for more and more workshops launch pragmatically, right? We don't have any time or energy for big, expansive programs. Identify some quick winds, ignite the grassroots movement, low cost. There are many ways to do that. Engage the influencers right, ignite this bottom up movement and find ways to welcome all to the party. And then finally, you gotta think about scale right over time. This is a partnership with learning and development partnership with HR. This becomes the fabric of how do you onboard people. How do you sustain people? How do you develop? So the last thing I wanted to just caution you on is there's a few kind of big mistakes in this area. One is you have to be clear on what you're solving for, right? What does this really mean? What does it look like? What are the needs and drivers? Where is this being done? Well, today, to be very clear on what you're solving for secondly, language matters, right? If if that has not been clear, language is the common thread and it is the basis for literacy and fluency. Third, going it alone. If you try to tackle this and try to wing it. Google searching data literacy You will spend your time and energy, which is as precious of a currency as your money on efforts that, um, take more time. And there is a lot to be leveraged through through various partnerships and leverage of your vendor providers like thought spot. Last thing. A quick story. Um, over 100 years ago, Ford Motor Company think about think about who the worker population was in the plants. They were immigrants coming from all different countries having different native languages. What was happening in the environment in the plants is they were experiencing significant safety issues and efficiency issues. The root issue was lack of a shared language. I truly believe that we're at the same moment where we're lacking a shared language around data. So what Ford did was they created the Ford English school and they started to nurture that shared language. And I believe that that's exactly what we're doing now, right? So I couldn't I couldn't leave this picture, though, and not acknowledge. Not a lot of diversity in that room. So I know we would have more diversity now if we brought everyone together. But I just hope that this story resonates with you as the power of language as a foundation for growing literacy and fluency >>for joining us. We're actually gonna be jumping into the next section, so grab a quick water break, but don't wander too far. You definitely do not want to miss the second session of today. We're going to be exploring how to scale the impact and how to become a change agent in your organization and become that analysts of the future. So season

>>Yeah, yeah. >>Welcome back, everyone. And let's get ready for session number two, which is all around picking the right use cases. We're going to take a look at how to make the most of your data driven journey through the lens of some instructive customer examples. So today we're joined by thought squads David Copay, who is a director of business value consulting like Daniel, who's a customer success manager and then engagement manager. Andrea Frisk, who not so long ago was actually a product manager. Canadian Tire, who are one of our customers. And she was responsible for the thoughts. What implementation? So we figured Who better to get involved? But yeah, let's Let's take it away, David. >>Thanks, Gina. Welcome, everybody. And Andrea Blake looking forward to this session with you. A zoo. We all know preparation early is key to success on Duin. Any project having the right team on sponsorship Thio, build and deploy. Ah, use case is critical being focused on three outcome that you have in mind both the business deliverables and then also the success criteria of how you're going to manage, uh, manage and define success. When you get there, Eyes really critical to to set you up in the right direction initially. So, Andrea, as as we mentioned, uh, you came from an organization that quite several use cases on thoughts about. So maybe you can talk us through some of those preparation steps that, yeah, that you went through and and share some insights on how folks can come prepare appropriately. >>Eso having the right team members makes such a difference. Executive support really helped the Canadian tire adoption spread. It gave the project presence and clout in leadership meetings and helped to drive change from the top down. We had clear goals and success criteria from our executive that we used to shape the go forward plan with training and frame the initial use case roadmap. One of the other key benefits over executive sponsor was that the reporting team for our initial use case rolled up by underhand. So there was a very clear directive for a rapid phase out of the old tools once thought Spot supported the same data story. And this is key because as you start to roll through use cases, you wanna realize the value. And if you're still executing the old the same time as the new. That's not gonna happen. As we expanded into areas where we were unfamiliar with the data in business utilization, we relied on the data experts and and users to inform what success would look like in the new use cases. We learned early on that those who got volunteer old and helping didn't always become the champions. That would help you drive value from the use case. Using the thoughts about it meant tables. We started to seek out users who are consistently logging in after an initial training, indicating their curiosity and appetite to learn more. We also looked for activities outside of just pin board views toe identify users that had the potential to build and guide new users as subject matter experts, not just in a data but in thought spot. This helps us find the right people to cultivate who were already excited about the potential of thought spot and could help us champion a use case. >>That's really helpful, great, great insight for someone who's been there and done that. Blake is as a customer success manager. Obviously, you approach many of the same situations, anything you'd like to add that >>I still along with the right team. My first question with any use cases. Why Why are we doing this? You've gathered all this data and now we want to use it. But But what for? When you get that initial response on Why this use case? Don't stop there. Keep asking Why keep digging? Keep digging. Keep digging. So what you're essentially trying to get at is what does the decision is that we will be made or potentially be made because of this use case. For example, let's say that we're looking at an expenses use case. What will be done with the insides gathered with this use case? Are those insights going? Thio change the expense approval process Now, Once you have that, why defined now it becomes a lot easier to define the success criteria. Success criteria they use. Face can sometimes be difficult to truly defined. But when you understand why it becomes much easier, so now you can document that success criteria. And the hard part at that point is to actually track that success over time, track the success of the use case, which is something that is easily miss but It's something that is incredibly useful to the overall initiative. >>Right measure. Measure the outcomes. You can't manage what you what? You can't what you don't measure right? As the old adage goes, and you know it's part of the business consulting team. That's really where we come in. Is helping customers really fundamentally define? How are we going to measure a success? Aziz. We move forward. Andi, I think you know, I think we've alluded to this a little bit in terms of that sort of ongoing nature of This is, you know, after the title of the session, eyes choosing the right news cases in the plural right? So it's very important to remember that this is not a single point in time event that happens once. This is a constant framework or process, because most organizations will find that there's many use cases, potentially dozens of use cases that thoughts what could be used for, and clearly you can't move forward with all of them. At the same time, eso. Another thing that our team helps customers walk through is what's the impact, the potential value, other particular use case. You know, you, Blake, you mentioned some of those outcomes, is it? Changing the expense processes it around? Reducing customer churn is an increasing speed toe insight and speak the market on defining those measurable outcomes that define the vertical axis here. The strategic importance off that use case. Um, but that's not the only dimension that you're gonna look at the East to deploy factors into that you could have the most valuable use case ever. But if it's going to take you to three years to get it implemented for various reasons, you're not really gonna start with that one, right? So the combination of east to deploy, aligned with the strategic importance or business value really gives you that road map of where to focus to prioritize on use cases. Eso again, Andrea, you've been through this, um, in your prior time at Canadian time. Maybe you can share some thoughts on how you approach that. >>Yeah. So our initial use case was a great launching platform because the merchandizing team had a huge amount across full engagement. So once we had the merchants on board, we started to plan or use case roadmap looking for other areas, and departments were thought spot had already started to spread by word of mouth and we where we felt there was a high strategic importance. As we started to scope these areas, the ease of deployment started to get more complicated. We struggled to get the right people engaged and didn't always have the top down support for resources in the new use case area. We wanted to maintain momentum with the adoption, but it was starting to feel like we were stalling out on the freeway. Then the strategic marketing team reached out and was really excited about getting into thought spot. This was an underserved team where when it came to data, they always had someone else running it for them, and they'd have to request reports and get the information in. Um, and our initial roadmap focused on the biggest impact areas where we could get the most users, and this team was not on the radar. But when we started to engage with them, we realized that this was gonna be an easy deployment. We already had the data and thought spot to support their needs, and it turned into such a great win because as a marketing team, they were so thrilled to have thought spot and to get the data when they needed it and wanted it. They continued to spread the word and let everyone know. But it also gave the project team a quick win to put some gas in the tank and keep us moving. So you want to plan your use case trajectory, but you also need to be willing to adapt to keep the momentum going. >>Yeah, no, that's a That's a really great point. So So Blake is a customer success manager. I'm sure you lived through some integration of this all the time. So any anything you wanted to add that >>Yes. So to Andrew's point, continuous delivery is key for technical folks out there were talking and agile methodology mindset versus a waterfall. So to show value, there's many different factors that air at play. You need to look at the overall business initiatives. We need to look at financial considerations. We need to look at different career objectives and also resource limitations. So when you start thinking about all those different factors, this becomes a mixture of art and science. So, for example, at the beginning of a project when thought spot is has just been purchased or whatever tool has just been purchased. You want to show immediate value to justify that purchase. So in order to show immediate value, you might want to look at a project or a use case that is tightly aligned to a business objective. Therefore, it shows value, and it has data that is ready to go without many different transformations. But as you move forward, you have to come up with a plan that is going to mix together these difficult use cases with the easier use cases and high business values cases versus the lower. So in order to do that, my most successful customers are evaluating those different business factors and putting those into place with an overall use case development plan. >>Really good feedback. That's great. Thank you. Thanks, Blake. Um, I think s a little bit of a reality check here. Right. So I think we all recognize that any technology implementation, um, is gonna have her bumps in the road. It's not gonna be smooth sailing all along the way. You know, we talk about people, process and technology. The technology wrote wrote roadblocks can be infrastructure related there could be some of the data quality issues that you're alluding to there. Like Onda, people in process fall into the sort of the cultural, uh, cultural cultural side of it. Blake, maybe you can spend a couple minutes going through. What? What if some of those bigger roadblocks that people may face on that, um, technical side on how they could both prepare for them and then address them as they come along? >>Yeah. So the most intimidating part of any business intelligence or analytics initiative is that it's going to put the data directly into the hands of the business users. And this is especially true with ocelot. So why this is intimidating is because it's going toe, lay bare and expose any data issues that exist. So this is going to lead to the most common objective that I hear to starting. Any new use case or any FBI initiative overall, which is our data isn't ready. And essentially that is fear of failure. So when data isn't ready and companies aren't ready to start these projects, what happens is to get around those data issues. There's a lot of patchwork that's happening, you know, this patchwork is necessary just to keep the wheels in motion just to keep things going. So what I mean by the patchwork is extracting the data from a source doing some manual manipulation, doing some manipulation directly within the within the database in order to satisfy those business users request. So this keeps things going, but it's not addressing the key issues that are in place now. While it's intimidating to start these initiatives, the beauty of starting these B I initiatives is it's going to force your company to address and fix these issues. And this, to me, is somewhere where thoughts what is a gigantic benefit? It's not something that we talk about necessarily or market, but thought Spot is really good at helping fix these data issues. And I say this for two reasons. One his data quality. So, with thoughts about you can run, searches directly against your most granular level data and find where those data issues exist, and now, especially with embrace, you're running it directly against the source. So thats what is going to really help you figure out those data quality issues. So as you develop a use case, we can uncover those data quality issues and address them accordingly. And second is data governance. So especially again with embrace and our cloud, our cloud structure is you are going to be bringing Companies are going to be bringing data sources from all over the place all into one source and into one logical view. And so traditionally, the problem with that is that your data and source a might be the theoretically the same data and source B. But the numbers are different. And so you have different versions of the truth. So what thoughts about helps you do is when you bring those sources together. Now you're gonna identify those issues, and now you're gonna be forced to address them. You're gonna be forced to address naming convention issues, business logic issues, which business logic translates to the technical logic toe transform that data and then also security and access. Who was actually able to see this data across these different data sources. So overall, the biggest objective eye here is our data isn't ready. But I challenge that. And I say that by taking on this initiative with thought spot, you were going to be directly addressing that issue and thoughts. What's going to help you fix it? >>Yeah, that's Ah, I'd love that observation that, you know, data quality issues. They're not gonna go away by themselves. And if thoughts, thoughts what could be part of the solution, then even better. So that's a That's a really great observation. Eso Andrea, looking at the sort of the cultural side of things the people in process, Um, what are some of the challenges that you've seen there that folks in the audience could that could learn from? >>Yeah. So think about the last time you learned a new system or tool. How long did it take you to get adjusted and get the performance you wanted from it? Maybe you hit the ground running, but maybe you still feel like you're not quite getting the most out of it. Everyone deals with change differently, and sometimes we get stuck in the change curve and never fully adapt. Companies air no different. Ah, lot of the roadblocks you may face are not only from individual struggling to get on board, but can be the result of an organizational culture that may not be used to change or managing it. Their external impacts on how we accept change such as Was there a clear message about the upcoming changes and impacts? Was there a communication channel for questions and concerns? Did individuals feel like their input was sought after and valued? Where there are multiple mediums, toe learn from was their time to learn? Organizational change is hard. And if there isn't a culture that allocates time and resources to training, then realizing success is gonna be an uphill battle. It will be harder to move people forward if they don't have the time to get comfortable and feel acclimated to the new way of doing things. Without the training and change support from the organization, you'll end up running the old and the new simultaneously, which we talked about not in our live supporting users, in both eyes going to negate that value. There were times at Canadian Tire where we really struggled to get key stakeholders engaged or to get leadership by it on the time of the resources that we're gonna be needed and committed Thio to make a use case successful. So gauging where people and the organization are in the change curve is the first step in moving them along the path towards acceptance and integration. So you'll wanna have an action plan to address the concerns and resistance and a way to solicit and channel feedback. >>Yeah, that's Zo great feedback. And I particularly like what you talked about sort of the old and the new because, you know, we've talked about success and measurement on value quite a bit in this session, and ultimately that's that's the goal, right? Is to live a Value s o. This is a framework that we found really helpful visit. Value Team is defining those success criteria really actually falls into two categories on the right hand side. Better decisions. Um, that's ultimately what you're looking to drive with thoughts about right. You're looking to get newer inside faster to be able to drive action and outcomes based on decisions that do. Maybe we're using your gut for previously on the words under that heading. They're going to change by organizations. So you know, those don't get too caught up on those, but it's really around defining, you know, one. Are those better decisions that you're looking to drive, Who what's the persona is gonna be making them one of their actually looking to accomplish when inside. So they're looking to get one of what are the actions they're going to take on those insights? And then how do we measure Thean pact of those actions that then provides us with the the foundation of a business case in our I, um, in parallel to that, it's important to remember that this use case is not just operating in a vacuum, right? Every organization has a Siri's off strategic transformational initiatives move to the cloud democratized data, etcetera. And to the extent that you can tie particular use cases into those key strategic initiatives, really elevates the importance off that use case outside of its own unique business case. In our calculation on Bazzaz several purposes, right, it raises the visibility project. It raises the visibility of the person championing project on. Do you know reality here is that every idea organization has tons of projects have taken invest in, but the ones they're gonna be more likely to invest in other ones that are tied to those strategic initiatives. So it increases the likelihood of getting the support and funding that you need to drive this forward um, that's really around defining the success success criteria upfront. Um, and >>what >>we find is a lot of organizations do that pretty well, and they've got a solid, really solid business case to move forward. But then over time, they kind of forget about that on. Do you know, a year down the line two years down the line, Maybe even, you know, three months, six months down the line. Maybe people have rotated through the business. People have come and gone, and you almost forget the benefit that you're driving, right? And so it's really important to not do that and keep an eye on and track Onda, look back and analyze and realize the value that use cases have driven on. Obviously, the structure of that and what you measure is gonna very significantly by escape. But it's really important there Thio to make sure that you're counting your success and measuring your success. Um, Andrea, I don't any any thoughts on that from from your past experience. >>Yeah, um, success will be different For each use case, 1 may be focused on reducing the time to insights in a fast competitive market, while another may be driven by a need to increase data fluency to reduce risk. The weighting of each of these criterias will shift and and the value perception should as well. Um, but one thing that we don't want to forget is to share your personal successes. So be proud of the work that you've done in the value it's created. Um, if you're a user who has taken advantage of thought spot and managed to grab a competitive edge by having faster in depth access to data, share that in your business reviews. If you're managing the adoption at your company, share your use case winds and user adoption stories. Your customer success team is here to help you articulate the value and leverage the great work being done in and because of thought spot. >>Yeah, long story short here. This benefits everybody. This is something that's easily overlooked and something that it ZZ not to do this to track adoption to define the r o I, but it benefits those benefits. Start spot benefits of customers. Everybody wins. When we do this, >>that's Ah, that's a great point. So, um, so if we talk about you know, as we wrap the session up. You know what can what can folks in the audience dio right now to start making some of this stuff happened? You know, you're Blake again, coming back to you in customer success. How have you and your role help customers take that next step and start executing on some of the things that we've talked about? >>Yeah. So to start off with, I would just say for each use case as much as possible, define the why and to find the success criteria. Just start off with those two, those two elements and over time that that process we'll get more and more refined and our goal within the CSCE or within within thoughts. But overall, not just the C s order is to enable all of our all of our customers to be able to do all these things on their own. And to be a successful, it's possible to be able to pick the right use cases to be able to execute those right use cases as effectively as possible. So we are here to help with that. CS is here to help with that. Your account executives here to help with that, we have use case workshops. We have our professional services team that can get in and help develop use cases. So lots of options available in goal. We all mutually benefit when we try to track towards thes best possible use cases. >>All right, that we're here to help. That's Ah, that's a great way. Thio, wrap up the session there. Thanks, Blake. For all of your thoughts and Andrea to hope everyone in the audience got some valuable insights here on how to choose the right news case and be successful with thoughts about, um, with that being, I'll hand it back over to you. >>Amazing. That was an awesome session. Thank you so much, guys. So our third session is up next, and we're going to be going Global s. Oh, hang on tight as we explore best practices from the extended ecosystem of cloud based analytics. >>Yeah,

>>Hello, everyone, and welcome to track three off beyond. My name is being in Yemen and I am an account executive here at Thought spot based out of our London office. If the accents throwing you off I don't quite sound is British is you're expecting it because the backgrounds Australian so you can look forward to seeing my face. As we go through these next few sessions, I'm gonna be introducing the guests as well as facilitating some of the Q and A. So make sure you come and say hi in the chat with any comments, questions, thoughts that you have eso with that I mean, this whole track, as the title somewhat gives away, is really about everything that you need to know and all the tips and tricks when it comes to adoption and making sure that your thoughts what deployment is really, really successful. We're gonna be taking off everything from user training on boarding new use cases and picking the right use cases, as well as hearing from our customers who have been really successful in during this before. So with that, though, I'm really excited to introduce our first guest, Kathleen Maley. She is a senior analytics executive with over 15 years of experience in the space. And she's going to be talking to us about all her tips and tricks when it comes to making the most out of your center of excellence from obviously an analytics perspective. So with that, I'm going to pass the mic to her. But look forward to continuing the chat with you all in the chat. Come say hi. >>Thank you so much, Bina. And it is really exciting to be here today, thanks to everyone for joining. Um, I'll jump right into it. The topic of evolving your analytics center of excellence is a particular passion of mine on I'm looking forward to sharing some of my best practices with you. I started my career, is a member of an analytic sioe at Bank of America was actually ah, model developer. Um, in my most recent role at a regional bank in the Midwest, I ran an entire analytics center of excellence. Um, but I've also been on the business side running my own P and l. So I think through this combination of experiences, I really developed a unique perspective on how to most effectively establish and work with an analytic CEO. Um, this thing opportunity is really a two sided opportunity creating value from analytics. Uh, and it really requires the analytics group and the line of business Thio come together. Each has a very specific role to play in making that happen. So that's a lot of what I'll talk about today. Um, I started out just like most analysts do formally trained in statistics eso whether your data analyst or a business leader who taps into analytical talent. I want you to leave this talk today, knowing the modern definition of analytics, the purpose of a modern sioe, some best practices for a modern sioe and and then the role that each of you plays in bringing this Kuito life. So with that said, let me start by level, setting on the definition of analytics that aligns with where the discipline is headed. Um, versus where it's been historically, analytics is the discovery, interpretation and communication of meaningful patterns in data, the connective tissue between data and effective decision making within an organization. And this is a definition that I've been working under for the last, you know, 7 to 10 years of my career notice there is nothing in there about getting the data. We're at this amazing intersection of statistics and technology that effectively eliminates getting the data as a competitive advantage on this is just It's true for analysts who are thinking in terms of career progression as it is for business leaders who have to deliver results for clients and shareholders. So the definition is action oriented. It's purposeful. It's not about getting the data. It's about influencing and enabling effective decision making. Now, if you're an analyst, this can be scary because it's likely what you spend a huge amount of your time doing, so much so that it probably feels like getting the data is your job. If that's the case, then the emergence of these new automated tools might feel like your job is at risk of becoming obsolete. If you're a business leader, this should be scary because it means that other companies air shooting out in front of you not because they have better ideas, necessarily, but because they can move so much faster. According to new research from Harvard Business Review, nearly 90% of businesses say the more successful when they equipped those at the front lines with the ability to make decisions in the moment and organizations who are leading their industries and embracing these decision makers are delivering substantial business value nearly 50% reporting increased customer satisfaction, employee engagement, improve product and service quality. So, you know, there there is no doubt that speed matters on it matters more and more. Um, but if you're feeling a little bit nervous, I want you to think of it. I want you think of it a little differently. Um, you think about the movie Hidden figures. The job of the women in hidden figures was to calculate orbital trajectories, uh, to get men into space and then get them home again. And at the start of the movie, they did all the required mathematical calculations by hand. At the end of the movie, when technology eliminated the need to do those calculations by hand, the hidden figures faced essentially the same decision many of you are facing now. Do I become obsolete, or do I develop a new set of, in their case, computer science skills required to keep doing the job of getting them into space and getting them home again. The hidden figures embraced the latter. They stayed relevant on They increase their value because they were able to doom or of what really mattered. So what we're talking about here is how do we embrace the new technology that UN burdens us? And how do we up skill and change our ways of working to create a step function increase in data enabled value and the first step, really In evolving your analytics? Dewey is redefining the role of analytics from getting the data to influencing and enabling effective decision making. So if this is the role of the modern analyst, a strategic thought partner who harnesses the power of data and directs it toward achieving specific business outcomes, then let's talk about how the series in which they operate needs change to support this new purpose. Um, first, historical CEOs have primarily been about fulfilling data requests. In this scenario, C always were often formed primarily as an efficiency measure. This efficiency might have come in the form of consistency funds, ability of resource is breaking down silos, creating and building multipurpose data assets. Um, and under the getting the data scenario that's actually made a lot of sense for modern Sealy's, however, the objective is to create an organization that supports strategic business decision ing for individuals and for the enterprises the whole. So let's talk about how we do that while maintaining the progress made by historical seaweeds. It's about really extending its extending what, what we've already done the progress we've already made. So here I'll cover six primary best practices. None is a silver bullet. Each needs to fit within your own company culture. But these air major areas to consider as you evolve your analytics capabilities first and foremost always agree on the purpose and approach of your Coe. Successfully evolving yourself starts with developing strategic partnerships with the business leaders that your organization will support that the analytics see we will support. Both parties need to explicitly blocked by in to the objective and agree on a set of operating principles on bond. I think the only way to do that is just bringing people to the table, having an open and honest conversation about where you are today, where you wanna be and then agree on how you will move forward together. It's not about your organization or my organization. How do we help the business solve problems that, you know, go beyond what what we've been able to do today? So moving on While there's no single organizational model that works for everyone, I generally favor a hybrid model that includes some level of fully dedicated support. This is where I distinguish between to whom the analyst reports and for whom the analyst works. It's another concept that is important to embrace in spirit because all of the work the analyst does actually comes from the business partner. Not from at least it shouldn't come from the head of the analytic Center of excellence. Andan analysts who are fully dedicated to a line of business, have the time in the practice to develop stronger partnerships to develop domain knowledge and history on those air key ingredients to effectively solving business problems. You, you know, how can you solve a problem when you don't really understand what it is? So is the head of an analytic sioe. I'm responsible for making sure that I hire the right mix of skills that I can effectively manage the quality of my team's work product. I've got a specialized skill set that allows me to do that, Um, that there's career path that matters to analysts on all of the other things that go along with Tele management. But when it comes to doing the work, three analysts who report to me actually work for the business and creating some consistency and stability there will make them much more productive. Um, okay, so getting a bit more, more tactical, um, engagement model answers the question. Who do I go to When? And this is often a question that business partners ask of a centralized analytics function or even the hybrid model. Who do I go to win? Um, my recommendation. Make it easy for them. Create a single primary point of contact whose job is to build relationships with a specific partner set of partners to become deeply embedded in their business and strategies. So they know why the businesses solving the problems they need to solve manage the portfolio of analytical work that's being done on behalf of the partner, Onda Geun. Make it make it easy for the partner to access the entire analytics ecosystem. Think about the growing complexity of of the current analytics ecosystem. We've got automated insights Business Analytics, Predictive modeling machine learning. Um, you Sometimes the AI is emerging. Um, you also then have the functional business questions to contend with. Eso This was a big one for me and my experience in retail banking. Uh, you know, if if I'm if I'm a deposits pricing executive, which was the line of business role that I ran on, I had a question about acquisitions through the digital channel. Do I talk Thio the checking analyst, Or do I talk to the digital analyst? Um, who owns that question? Who do I go to? Eso having dedicated POC s on the flip side also helps the head of the center of excellence actually manage. The team holistically reduces the number of entry points in the complexity coming in so that there is some efficiency. So it really is a It's a win win. It helps on both sides. Significantly. Um, there are several specific operating rhythms. I recommend each acting as a as a different gear in an integrated system, and this is important. It's an integrated decision system. All of these for operating rhythms, serves a specific purpose and work together. So I recommend a business strategy session. First, UM, a portfolio management routine, an internal portfolio review and periodic leadership updates, and I'll say a little bit more about each of those. So the business strategy session is used to set top level priorities on an annual or semiannual basis. I've typically done this by running half day sessions that would include a business led deep dive on their strategy and current priorities. Again, always remembering that if I'm going to try and solve all the business problem, I need to know what the business is trying to achieve. Sometimes new requester added through this process often time, uh, previous requests or de prioritized or dropped from the list entirely. Um, one thing I wanna point out, however, is that it's the partner who decides priorities. The analyst or I can guide and make recommendations, but at the end of the day, it's up to the business leader to decide what his or her short term and long term needs and priorities are. The portfolio management routine Eyes is run by the POC, generally on a biweekly or possibly monthly basis. This is where new requests or prioritize, So it's great if we come together. It's critical if we come together once or twice a year to really think about the big rocks. But then we all go back to work, and every day a new requests are coming up. That pipeline has to be managed in an intelligent way. So this is where the key people, both the analyst and the business partners come together. Thio sort of manage what's coming in, decking it against top priorities, our priorities changing. Um, it's important, uh, Thio recognize that this routine is not a report out. This routine is really for the POC who uses it to clarify questions. Raised risks facilitate decisions, um, from his partners with his or her partner so that the work continues. So, um, it should be exactly as long as it needs to be on. Do you know it's as soon as the POC has the information he or she needs to get back to work? That's what happens. An internal portfolio review Eyes is a little bit different. This this review is internal to the analytics team and has two main functions. First, it's where the analytics team can continue to break down silos for themselves and for their partners by talking to each other about the questions they're getting in the work that they're doing. But it's also the form in which I start to challenge my team to develop a new approach of asking why the request was made. So we're evolving. We're evolving from getting the data thio enabling effective business decision ing. Um, and that's new. That's new for a lot of analysts. So, um, the internal portfolio review is a safe space toe asks toe. Ask the people who work for May who report to May why the partner made this request. What is the partner trying to solve? Okay, senior leadership updates the last of these four routines, um, less important for the day to day, but significantly important for maintaining the overall health of the SIOE. I've usually done this through some combination of email summaries, but also standing agenda items on a leadership routine. Um, for for me, it is always a shared update that my partner and I present together. We both have our names on it. I typically talk about what we learned in the data. Briefly, my partner will talk about what she is going to do with it, and very, very importantly, what it is worth. Okay, a couple more here. Prioritization happens at several levels on Dive. Alluded to this. It happens within a business unit in the Internal Portfolio review. It has to happen at times across business units. It also can and should happen enterprise wide on some frequency. So within business units, that is the easiest. Happens most frequently across business units usually comes up as a need when one leader business leader has a significant opportunity but no available baseline analytical support. For whatever reason. In that case, we might jointly approach another business leader, Havenaar Oi, based discussion about maybe borrowing a resource for some period of time. Again, It's not my decision. I don't in isolation say, Oh, good project is worth more than project. Be so owner of Project Be sorry you lose. I'm taking those. Resource is that's It's not good practice. It's not a good way of building partnerships. Um, you know that that collaboration, what is really best for the business? What is best for the enterprise, um, is an enterprise decision. It's not a me decision. Lastly, enterprise level part ization is the probably the least frequent is aided significantly by the semi annual business strategy sessions. Uh, this is the time to look enterprise wide. It all of the business opportunities that play potential R a y of each and jointly decide where to align. Resource is on a more, uh, permanent basis, if you will, to make sure that the most important, um, initiatives are properly staffed with analytical support. Oxygen funding briefly, Um, I favor a hybrid model, which I don't hear talked about in a lot of other places. So first, I think it's really critical to provide each business unit with some baseline level of analytical support that is centrally funded as part of a shared service center of excellence. And if a business leader needs additional support that can't otherwise be provided, that leader can absolutely choose to fund an incremental resource from her own budget that is fully dedicated to the initiative that is important to her business. Um, there are times when that privatization happens at an enterprise level, and the collective decision is we are not going to staff this potentially worthwhile initiative. Um, even though we know it's worthwhile and a business leader might say, You know what? I get it. I want to do it anyway. And I'm gonna find budget to make that happen, and we create that position, uh, still reporting to the center of excellence for all of the other reasons. The right higher managing the work product. But that resource is, as all resource is, works for the business leader. Um, so, uh, it is very common thinking about again. What's the value of having these resource is reports centrally but work for the business leader. It's very common Thio here. I can't get from a business leader. I can't get what I need from the analytics team. They're too busy. My work falls by the wayside. So I have to hire my own people on. My first response is have we tried putting some of these routines into place on my second is you might be right. So fund a resource that's 100% dedicated to you. But let me use my expertise to help you find the right person and manage that person successfully. Um, so at this point, I I hope you see or starting to see how these routines really work together and how these principles work together to create a higher level of operational partnership. We collectively know the purpose of a centralized Chloe. Everyone knows his or her role in doing the work, managing the work, prioritizing the use of this very valuable analytical talent. And we know where higher ordered trade offs need to be made across the enterprise, and we make sure that those decisions have and those decision makers have the information and connectivity to the work and to each other to make those trade offs. All right, now that we've established the purpose of the modern analyst and the functional framework in which they operate, I want to talk a little bit about the hard part of getting from where many individual analysts and business leaders are today, uh, to where we have the opportunity to grow in order to maintain pain and or regain that competitive advantage. There's no judgment here. It's simply an artifact. How we operate today is simply an artifact of our historical training, the technology constraints we've been under and the overall newness of Applied analytics as a distinct discipline. But now is the time to start breaking away from some of that and and really upping our game. It is hard not because any of these new skills is particularly difficult in and of themselves. But because any time you do something, um, for the first time, it's uncomfortable, and you're probably not gonna be great at it the first time or the second time you try. Keep practicing on again. This is for the analyst and for the business leader to think differently. Um, it gets easier, you know. So as a business leader when you're tempted to say, Hey, so and so I just need this data real quick and you shoot off that email pause. You know it's going to help them, and I'll get the answer quicker if I give him a little context and we have a 10 minute conversation. So if you start practicing these things, I promise you will not look back. It makes a huge difference. Um, for the analyst, become a consultant. This is the new set of skills. Uh, it isn't as simple as using layman's terms. You have to have a different conversation. You have to be willing to meet your business partner as an equal at the table. So when they say, Hey, so and so can you get me this data You're not allowed to say yes. You're definitely not is not to say no. Your reply has to be helped me understand what you're trying to achieve, so I can better meet your needs. Andi, if you don't know what the business is trying to achieve, you will never be able to help them get there. This is a must have developed project management skills. All of a sudden, you're a POC. You're in charge of keeping track of everything that's coming in. You're in charge of understanding why it's happening. You're responsible for making sure that your partner is connected across the rest of the analytics. Um, team and ecosystem that takes some project management skills. Um, be business focused, not data focused. Nobody cares what your algorithm is. I hate to break it to you. We love that stuff on. We love talking about Oh, my gosh. Look, I did this analysis, and I didn't think this is the way I was gonna approach it, and I did. I found this thing. Isn't it amazing? Those are the things you talk about internally with your team because when you're doing that, what you're doing is justifying and sort of proving the the rightness of your answer. It's not valuable to your business partner. They're not going to know what you're talking about anyway. Your job is to tell them what you found. Drawing conclusions. Historically, Analyst spent so much of their time just getting data into a power 0.50 pages of summarized data. Now the job is to study that summarized data and draw a conclusion. Summarized data doesn't explain what's happening. They're just clues to what's happening. And it's your job as the analyst to puzzle out that mystery. If a partner asked you a question stated in words, your answer should be stated in words, not summarized data. That is a new skill for some again takes practice, but it changes your ability to create value. So think about that. Your job is to put the answer on page with supporting evidence. Everything else falls in the cutting room floor, everything. Everything. Everything has to be tied to our oi. Um, you're a cost center and you know, once you become integrated with your business partner, once you're working on business initiatives, all of a sudden, this actually becomes very easy to do because you will know, uh, the business case that was put forth for that business initiative. You're part of that business case. So it becomes actually again with these routines in place with this new way of working with this new way of thinking, it's actually pretty easy to justify and to demonstrate the value that analytic springs to an organization. Andi, I think that's important. Whether or not the organization is is asking for it through formalized reporting routine Now for the business partner, understand that this is a transformation and be prepared to support it. It's ultimately about providing a higher level of support to you, but the analysts can't do it unless you agree to this new way of working. So include your partner as a member of your team. Talk to them about the problems you're trying to sell to solve. Go beyond asking for the data. Be willing and able to tie every request to an overarching business initiative on be poised for action before solution is commissioned. This is about preserving. The precious resource is you have at your disposal and you know often an extra exploratory and let it rip. Often, an exploratory analysis is required to determine the value of a solution, but the solution itself should only be built if there's a plan, staffing and funding in place to implement it. So in closing, transformation is hard. It requires learning new things. It also requires overriding deeply embedded muscle memory. The more you can approach these changes is a team knowing you won't always get it right and that you'll have to hold each other accountable for growth, the better off you'll be and the faster you will make progress together. Thanks. >>Thank you so much, Kathleen, for that great content and thank you all for joining us. Let's take a quick stretch on. Get ready for the next session. Starting in a few minutes, you'll be hearing from thought spots. David Coby, director of Business Value Consulting, and Blake Daniel, customer success manager. As they discuss putting use cases toe work for your business

>>Yeah, yeah, >>Hello. We're back with Today's the last session in the creating engaging analytics experiences for all track breaking down data silos. A conversation with Snowflake on Western Union Earlier today, we did a few deep dives into the thought spot product with sessions on thoughts about one. Thoughts were everywhere on spot. Take you to close out this track. We're joined by industry leading experts Christian Kleinerman s VP of product at Snowflake and Tom Matzzie, Pharaoh, chief data officer at Western Union, for a thought provoking conversation on data transformation on how to avoid the pitfalls of traditional analytics. They'll be discussing in key challenges faced by organizations, why user engagement matters and looking towards the future of the industry. No Joining Thomas and Christian in conversation is Angela Cooper, vice president of customer success at Thought spot. Thank you all for being here today. We're so excited for what is what this conversation has in store. Handing it over now to Christian to kick things off. >>Hi. So, a few years ago, when when someone asked about Snowflake, the most common answer, it was like, what is snowflake and what do you do? Hopefully in the last couple off months, things have changed and and here I am showing a couple of momentum data points on, uh, where we have accomplished here it Snowflake. So we we have received Ah, a lot of attention and buzz. Recently, we were listed in the New York Stock Exchange And we even though we still think of ourselves as a small start up company, we have crossed the 2000 employees mark. More important, we count with 3 3000 plus amazing customers. And something that we obsess about is the a satisfaction of our customers. We really are working hard. The laboring technology that having a platform for better decisions, better analytics and then the promoters course off 71 depicted here is a testament of that. And last, but certainly not least about snowflake. It's very important that we know that we succeed with our partners. We know that we don't go to market by ourselves. We actually have Ah, fantastic set of partners and of course, thoughts. But it is one of our most important partners. >>Good morning. Good afternoon. Eso Amman Thomas affair on the chief kid officer here at Western Union. It's gonna be a background of a Western union and what we, uh, what we do and how we service our customers. So today we are in over 200 countries and territories worldwide. We have a 550,000 retail Asian network to service all of our customers, uh, needs from what he transfer and picking up in a depositing cash. We also have our digital transformation underway, where we now have educate abilities up and running and over 35 countries with paled options to accounts in over 120 countries. We think about our overall business and how support are over our customers and our services. It really has transformed over the past 12 months with Cove it and it's part of that We have to be able to really accelerate our transformation on a digital front to help to enable in the super those customers going forward. Eso as part of that, You know, a big, big help in a big supporter of that transformation has been snowflake and has been thought spot as part of that transformation. If you go the next to the next slide are our current, uh B I in our illegal tools right to date, uh, have been very useful up until the last one or two years. As data explodes and as as our customer needs transform and as our solutions and our time to act in our time to react in the overall market becomes faster and faster, we need to be able to basically look across our entire company, our entire organization and cross functionally to visit to leverage data leverage our insights to really basically pivot our overall business and our overall model to support our customers and our and to enable those services and products going forward. So as part of that, snowflakes been a huge part of that journey, right, allowing us to consolidate over our 30 plus data stores across the company on able to really leverage that overall data and insights to drive, uh, quick reaction right with the pivot, our business offered to enable new services and improve customer experiences going forward and then being able to use a snowflake and then being put the applications on top of that like thought spot, which allows, uh, users that are both technical and nontechnical to the go in and just, um, ask the question as if the searching on Google or Yahoo or being they can just ask any question they want and then get the results back in real time, made that business call and then really go forward through these is this larger ecosystem as a whole. It's really enabled us to really transform our business and supporter customers going forward. >>Wonderful. Thank you, Tom. Thank you, Christian, for the overview of both snowflake and Western Union. Both have big presence in Denver, which is where Tom and I are tonight. Um, I'm here. I'm the vice president of customer success for Thought spot, and I wanted to ask both of you some questions about the industry and specific things that you're facing within Western Union. So first I was hoping Christian that you could talk to me a little bit about Snowflake has thousands of customers at this point, servicing essentially located data sets. But what are you seeing? Has been the top challenges that businesses air facing and how it snowflake uniquely positioned to help. Yeah, >>so certainly the think the challenges air made. I would say that the macro challenge above everything is how to turn data into a competitive differentiator, their study after study that says companies that embrace data and insights and analytics they are outperforming their competitors. So that would be my macro challenge. Once you go into the next level, maybe I can think of three elements. The first one Tom already perfectly teed up the topic of of silence and the reality For most organizations, data is fragmented across different database systems. Even filed systems in some instances transactional databases, analytical data bases and what customers expect is to have, ah, unified experience like I am dealing with company extra company. Why? And I really don't care if behind the scenes there's 10 different teams or 100 different systems. I just want a unified experience. And the Congress is true. The opportunity to deliver personalized custom experiences is reliant on a single view of the day. The other topic that comes to mind this is the one of data governance, Um, as data becomes more important than a reorganization, understanding the constraints and security and privacy also become critical to not only advanced data capability but do it doing so responsibly and within the norms off regulation and the last one which is something court to tow our vision. We are pioneering the concept of the data cloud and the challenge that that we're addressing there is the problem around access to data, right. You can no longer as an organization think of making decisions just on your own data. But there's lots of data collaboration, data enrichment. Maybe I wanna put my data in context. And that's what we're trying to simplify and democratize access and simplify connecting to the data that improves decisions on all three fronts. Obviously, we're obsessed. That's no bling on on tearing down the silos on delivering a solution that is very focused on data governance. And for sure, the data cloud simplifies access to data. >>Wonderful. Now, I know we we really focused on those data silos is a business challenge. But Tom, going through your digital transformation journey are there specific challenges that you faced with Western Union That thought spot and snowflake have helped you overcome? >>Yeah. So? So first off fully agree what Christian just said, right? Those are absolutely, you know, problems that we faced. And we've had overcome, um, service, any company right being able to the transforming to modernize the cloud. Um, for us, one of the biggest things is being able to not just access our information, but have it in a way that it can be consumed, right? Have it in a way that it could be understood, right? Have it in a way that we can then drive business business decision points and and be able to use that information to either fix a problem that we see or better service our customers or offer a product that we're seeing right now is a miss in the marketplace to service in a underserved community or underserved, um, customer base. Also, from our standpoint, being able toe look, um um, uh and predict in forecast what's going to happen and be able to use that information and use our insights to then be proactive and thio in either, You know, be thoughtful about how do we shift our focus, or how do we then change our strategy to take advantage of that for that forecast in that position that we're seeing into the future? >>Wonderful. I've heard from many customers you could not have predicted what was going to happen to our businesses in the year 2020 with the traditional models and especially with what did you say? 30 plus different data silos. Being able to do that type of prediction across those systems must have been very, very difficult. You also mentioned going through a digital transformation at Western Union. So can you talk to me, Tom? A little bit about kind of present day? And why? Why is it important to enable your frontline knowledge workers with the right data at the right time with the right technology? >>Yeah, so? So you're spot on, by the way. But, uh, no one predicted that that we would have a pandemic that would literally consume the entire globe right And change how consumers, um uh, use and buy services and products, or how economies would either shut down or at the reopening shut down again. And then how different interests to be impacted by this? Right. So, uh, what we learned and what we were able to pivot was being able to do exactly what you just said, right. Being able to understand what's happening the date of the right time, right then being able to with the right technology with the right capabilities, understand? what's happening. I understand. Then what should our pivot be? And how should we then go focus on that pivot to go into go and transform? I think it's e. It's more than just just the front lines. Also, our executives. It's also are back office operations, right, because as you think through this, right as customers were having issues right, go into retail locations that were closed. It end of Q one Earlier, Q two. We obviously had a a large surplus right of phone calls coming into our call centers, asking for help, asking for How can we transact better? Where can we go? Right? How do we handle the operationally? Right? As we had a massive surge onto our digital platform where we were, we had 100% increase year over year in Q one and Q two. How do we make sure that our platform the technology can scale right and still provide the right S L A's and and and and the right, um uh, support to our internal customers as well as our extra customers in the future? Eso so really interesting, though, you know, on on on the front line side, our sales staff, right? And even our front line associates with our agent locations A to retail side, you know, for us, is really around. How do we best support them? So how do we partner with them to understand? You know, when a certain certain governments or certain, uh, regions were going toe lock down, how do we support them to keep them open, right. How do we make them a essential service going forward? How do we enable them? Right, the Wright systems or technology to do things a bit differently than they have in the past to adopt right with the changing times. But, you know, I'll tell you the amount of transformation in the basement we've done this year, I think you know, has a massive and actually on Lee, you know, created a larger wave for us to actually ride into the future as we can, to base to innovate, you know, in partnership with both thought spot and with the snowflake into the future. >>Absolutely. I've seen many, many a industry analyst reports talking about how companies now in 2020 have accelerated that digital transformation movement because of current day. In current time, Christian What are you seeing with the rest of the industry and other global companies about enabling data across the globe at the right time? >>Yeah, so I can't agree more with with with with what? Tom said. And he gave some very, um, compelling and very riel use cases where the timeliness of data and and and and and at the right time concept make a big difference. Right? They aske part of our data marketplace with snowflake with deliver, for example, um, up to date low ladies information on, uh, covert 19 data sets where we're infection spiking. And what were the trends? And the use case was very, very riel. Every single company was trying to make sense of the numbers. Uh, all machine learning models were sort of like, out of whack, because no trends and no patterns may make sense anymore. And it was They need to be able to join my data and my activity with this health data set and make decisions at the right time. Imagine if if the cycle to makes all these decisions waas Ah, monthlong. You would never catch up, right? And he speaks to tow a concept that it that is, um, dear, it wasa snowflake and is the lifetime value data right? The notion of ableto act on a piece of data on an event at the right time and obviously with the slow laden see it's possible, makes a big difference. And and there is no end of example. Stomach gives her all again very compelling ones. Um, there's many others, but if you're running a marketing campaign and would you want to know five minutes later that it's not working out, you're burning your daughters? Or would you want to know the next day? Or if someone is going to give you you have a subscription based business and you're going toe, for example, have a model that predicts the turn of your customer? How useful is if you find out Hey, your customer is gonna turn, but you found out two months later. Once probably you are really toe action and change the outcome. Eyes different and and and this order to manage that I'm talking about days or months are not uncommon. Many organizations today, and that's where the topic of right technology matters. Um, I love asking questions about Do you know, an organization and customers. Do you run data, transformations and ingests at two and three in the morning? And the most common answer is yes. And then you start asking why. And usually the answer is some flavor off technology made me do it and a big part of what we're trying to do, like what we're pioneering is. How about ingesting data, transforming data enriching data when the business needs it at the right time with the right timeliness? Not when the technology had cycles. So they were Scipio available, so the importance can't be overstated. There is value in in in analyzing understanding data on time, and we provide technology and platform to any of this. >>That's such a good point. Christian. We ended up on Lee doing processes and loading in the middle of the night because that's what the technology at that time would allow. You couldn't have the concurrency. You couldn't have, um, data happening all at the same time. And so wonderful point that stuff like enables. I think another piece that's interesting that you guys a hit on is that it's important to have the same user experiencing user interface at the right time. And so what I found talking to customers. And Tom what? You and I have discussed this. When you have 30 different data sets and you have a interface that's different, you have a legacy reports system. Maybe you have excel on top of another. You have thought spot on one. You have your dashboard of choice on another, those different sources in different ways. To view that data, it can all be so disjointed. And the combination of thought spot with snowflake and all the data in one place with a centralized, unified user experience just helps users take advantage off the insights that they need right at that right moment. So kind of finishing up for our last question for today I'm interested to hear about Christian will go back to you quickly about what do you see from snowflakes? Perspective is ahead. Future facing for data and analytics. >>One of the topics you just alluded toe Angela, which is the fact that many data sets are gonna be part of the processes by which we make decisions and that that's where were the experience with thoughts but a single unified search experience for a single unified. Um automatic insects, which is what's para que does That is the future, right? I I don't think that x many years from now on, and I think that that X is a small number. Organizations are going to say I had some business activity. I collected some data. I did some analysis and I have conclusions because it always has to be okay, put it in context or look at industry trends and look at other activity that can help him make more sense about my data. The example of tracking they covert are breaking is ah, timely one. But you can always say go on, put it in context with, I don't know, maybe the GDP of the country or the adoption of a platform and things like that. So I think that's ah big trend on having multiple data sets. Contributing towards better decisions towards better product experience is for better services. And, of course, Snowflake is trying to do its part, is doing its part with vision and simplify answers today and the answer on hot spot simplifying blending the interface so that would be super useful. The other big piece, of course, is, um, Predictive Analytics people Talk machine Learning and AI, which is a little bit to buzz worthy. But it is true that we have the technology to drive predictions and and do a better job of understanding behaviors off what's supposed to happen based on understanding the best and the last one. If if if I'm allowed one. Exco What's ahead for data industry, which sounds obvious, but But we're not all the way. There is both cloud the adoption and moving to the cloud as well as the topic of multi Cloud. Increasingly, I think we we finally shifted conversations from Should I go to the cloud or not? Now it's How fast do I do it? And increasingly what we hear is I may want to take the best of the different clouds and how doe I go in and and and embrace a multi cloud reality without having to learn 100 plus different services and nuances of services on on every car and this work technologies like snowflake and thoughts about that can can support a different multiple deployment are being well received by different customs, nerve fault, >>Tom industry trends, or one thing I know. Western Union is really leading in the digital transformation and in your space, What's next for Western Union? >>Yeah, so just add on Requip Thio Christian before I dive into a Western Union use case just to your point. Christian, I really see a convergence happening between how people today work or or manage their personal life, where the applications, the user experiences and the responses are at your fingertips. Easy to use don't need to learn different tools. It's just all there, right, whether you're an android user or an apple user rights, although your fingertips I ask you the same innovation and transmission happening now on the work side, where I see to your point right a convergence happening where not just that the technology teams but even the business teams. They wanna have that same feature, that same functionality, where all their insights their entire way to interact with the business with the business teams with their data with their systems with their products for their services are at their fingertips right where they can go and they can make a change on an iPad or an iPhone and instant effect. They can go change a rule. They could go and modify Uh uh, an algorithm. They can go and look at expanding their product base, and it's just there. It's instant now. This would take time, right? Because this is going to be a transformational journey right across many different industries, but it's part of that. I really see that type of instant gratification, uh, satisfaction, that type of being able to instantly get those insights. Be able thio to really, you know, do what you do on your personal life in your work life every single day. That trend is absolutely it's actually happening. And it's kind of like tag team that into what we're doing at Western Union is exactly that we are actually transforming how our business teams, uh, in our technology teams are able to interact with our customers, interact with our products, interact with our services, interact with our data and our systems instantly. Right? Perfect example that it's that spot where they could go on typing any question they want. And they instigate an answer like that that that was unheard of a year ago, at least for our business. Right being able to to to go and put in in a new rule and and have it flow through the rules engine and have an instant customer impact that's coming right. Being able to instantly change or configure a new product or service with new fee structure and launch in 15 minutes. That's coming, right? All these new transformations about how do we actually better, uh, leverage our capabilities, our products and our services to meet those customer demands instantly. That's where I see the industry going the next couple of years. >>Wonderful. Um, excited to have both of you on the panel this afternoon. So thank you so much for joining us, Christian and Tom as just a quick wrap up. I, you know, learned quite a bit about industry trends and the problems facing companies today. And from the macro view with snowflake and thousands of customers and thought spots, customers and Western Union. The underlying theme is data unity, right? No more fragmented silos, no more fragmented user experiences, but truly bringing everything together in a governed safe way for users. Toe have trust in the data to have trust in what to answer and what insight is being put in front of them. And all of this pulled together so that businesses can make those better decisions more informed and more personalized. Consumer like experiences for your customers in modern technology stacks. So again, thank you both today for joining us, and we look forward to many more conversations in the future. Thank you >>for having me very happy to be here. >>Thank you so much. >>Thanks. >>Thank you, Angela. And thank you, Tom and Christian for sharing your stories. It was really interesting to hear how the events of this year have prompted Western Union to accelerate their digital transformation with snowflake and thought spot and just reflecting on alot sessions in this track, I love seeing how we're making the search experience even easier and even more consumer like in that first session and then moving on to the second session with our customer Hayes. It was really impressive to see how quickly they'd embedded thought spot into their own MD audit product. And then, of course, we heard about Spot Ike, which is making it easier for everybody to get to the Y faster with automated insights. So I'm afraid that wraps up the sessions in this track. We've come to an end, But remember to join us for the exciting product roadmap session coming right up. And then after that, put your questions to the speakers that you've heard in Track two in I'll meet the Experts Roundtable, creating engaging analytics experiences for all. Now all that remains is for me to say thank you for joining us. We really appreciate you taking the time. I hope it's been interesting and valuable. And if it has, we'd love to pick up with you for a 1 to 1 conversation Bye for now.

>>Yeah, yeah. >>Hello and welcome back. You're just in time for our third session spot. I Q amplify your insights with AI in this session will explore how AI gets you to the why of your data capturing changes and trends in the moment they happen. >>You'll >>start to understand how you can transform your data culture by making it easier for analysts to enable business users to consume insights in real time. >>You >>might think this all sounds too good to be true. Well, since seeing is believing, we're joined by thought spots. Vika Scrotum, senior product manager. Anak Shaped Mirror, principal product manager to walk you through all of this on MAWR. Over to you actually, >>Thank you. Wanna Hello, everyone. Welcome to the session. I am Action Hera, together with my colleague because today we will talk to you about how spot I Q uses a. I to generate meaningful insights for the users Before we dwell into that. Let's see why this is becoming so important. Your business and your data is growing and moving faster than ever. Data is considered the new oil Howard. Only those will benefit who can extract value of it. The data used in most of your organization's is just the tip of the iceberg beneath the tip of the iceberg. What you don't see or what you don't know to ask. That makes the difference in this data driven world. Let's learn how one can extract maximum value of the data to make smarter business decisions. We believe that analytics should require less input while producing more output with higher quality in a traditional approach. To be honest, users generally depend on somebody else to create data models, complex data queries to get answers to their pre anticipated questions. But solution like hot spot business users already have a Google like experience where they can just go and get answers to their questions. Now, if you look at other consumer applications, there are multiple of recommendation engines which are out there, which keep recommending. Which article should I read next? Which product should I buy? Which movie should I watch in a way, helping me optimized? Where should I focus my time on in a Similarly in analytics, as your data is growing, solutions must help users uncovered insights to questions which they may not ask, we believe, and a I automated insights will help users unleash the full potential off their data Across the spectrum, we see a potential in a smart, AI driven solution toe autonomously. Monitor your data and feed in relevant insights when you need them, much like a self driving car navigates our users safely to their desired destination. With this, yeah, I'm happy to introduce you to spot like you are a driven insights engine at scale, which will help you get full potential off your data like you automatically discovers, personalize and drive insights hidden in your data. So whenever you search to create answers, spot that you continues to ask a lot more questions on your behalf as it keeps drilling and related date dimensions and measures employed insights which may be of interest to you. Now you as a user can continue to ask your questions or can dig deeper into the inside, provided by spotted you Spartak. You also provides a comprehensive set of insights, which helps user get answers to their advance business questions. In a few clicks, so spotted it. You can help you detect any outlier, for example, spot that you can not only tell you which seller has the highest returns than others, but also which product that sellers selling has higher returns than other products. Or, like you can quickly detect any trends in your data and help us answer questions like how my account sign ups are trending after my targeted campaign is over. I can quickly use for, like, toe get unanswered how my open pipeline is related to my bookings amount and what's the like there. What it means is that how much time a lead will take to convert into a deal I can use partake. You, too, create multiple clusters off my all my customer base and then get answers to questions that which customer segment is buying which particular brand and what are the attributes last and the most used feature Key drivers of change spotted you helps you get answer to a question. What factors lead to the change in sales off a store in 2020 as compared to 2019? We can do all this and simple fix. That's barbecue. What is so unique about Spartak? You how it works hand in hand with our search experience, the more you search, the smarter. The spot that you get as it keeps learning from your usage behavior on generates relevant insights for you for your users. Spartak. You ensures that users can trust every insights. A generator. It broadly does this and broadly, two ways. It keeps their insights relevant by learning the underlying data model on. By incorporating the users feedback that is, users can provide feedback to the spot I Q similar to any social media back from, they can like watching sites they find useful on dislike. What insights Do not find it useful based on users. Feedback Spot like you can downgrade any insight if the users have not find it useful. In addition to that, users can dig deep into any Spartak you insight on all calculations behind it are available for a user to look and understand. The transparency in these calculations not only increases the analytical trust among the users, but also help them learn how they can use the search bar to do much more. I'm super excited to announce Partake you is now available on embrace so our automated A insights engine can run queries life and in database on these datasets so you do not need to bring your data to thoughts about as you connect your data sources. Touch Part performs full indexing value to the data you have selected, not just the headers in the material and as you run sport in Q, it optimizes and run efficient queries on your data warehouse on. I am super pleased to introduce you. This new spot like you monitor the spot that you monitor will enable all your users to keep track of their key metrics. Spartak, you monitor will not only provide them regular updates off their key metrics, but we also analyze all the underlying data on related dimensions to help them explain. What is leading to the change of a particular metric monitor will also be available on your mobile app so that you can keep track of your metrics whenever and wherever you go, because will talk for further detail about this during the demo. So now let's see Spartak in action. But before we go there, let's meet any. Amy is an analyst at a global retail about form. Amy is preparing for her quarterly sales review meeting with the management, so Amy has to report how the sales has meat performing how, what, what factors lead to the change in the sales? And if there are any other impressing insights, which everyone should off tell to the management? So but this Let's see how immigrant use part like you to prepare for the meeting. So Amy goes to that spot, chooses the sales data set for her company. But before we see how many users what I Q to prepare for the meeting. I just wanted to highlight that all this data which we're going to talk about is residing in Snowflake. >>So >>Touch Part is going to do a life query on the snowflake database on even spot. A Q analysis will run on the Snowflake databases, so we'll go back and see how you can use it. So Amy is preparing for the sales meeting for 2019. We just ended. So images right Sales 2019 on here. She has the graph of the Continent tickets, >>so >>what she does is immediately pence it >>for >>the report. She's creating Andi now. This graph is available >>there now. >>Any Monnet observed >>that >>the Q four sales is significantly higher than Q >>three, so >>you she wants to deep dive into this. So she just select these two data points and does the right click and runs particularities. So now, as we talked earlier, Spartak, you recommends which columns Spartak Things Will best explains this change >>on. >>Not only that, you can look that Spartacus automatically understood that Amy is trying toe identify what led to this change. So the change analysis we selected So now with this, >>Amy >>has a bit more business context when he realizes that she doesn't want to add these columns. So she's been using because she thinks this is too granular for the management right now. >>If >>she wants, she can add even more columns. All columns are available for her, and she can reduce columns. So now she runs 42 analysis. So while this product Unisys is running, what the system will do with the background, this part I Q will drill across all the dimensions, which any is selected and try to explain the difference, which is approximately $10 million in sales. So let's see if Amy's report is ready. Yeah, so with this, what's product you has done is protect you has drilled across all dimensions. Amy has selected and presented how the different values in these dimensions have changed. So it's product. You will not only tell you which values in these dimensions have changed the most, but also does an attribution that how much of this change has led to the overall change scenes. So here in the first inside sport accuse telling that 10 products have the largest change out of the 3 45 values and the account for 39% increase. Overall, there has been look by the prototype category. It's saying that five product types of the largest change out of the 15 values, and they account for 98.6% of total increase. And they're not saying the sailors increased their also demonstrating that in some categories the sales has actually decreased to ensure the sales has decreased. Amy finds this inside should be super useful so immediately pins this on the same pain, but she was preparing for and she's getting ready with that. Amy also wants to dig deeper into this inside. My name goes here. She sees that spot. I Q has not only calculated the change across these product types, but has also calculated person did change. So Amy immediately sorts this by wasn't did change. And then she notices that even though Sweater as a category as a prototype, was not appearing in the change analysis but has the most significant change in terms of percentage in comparison to Q two vs Q four. So she also wants to do this so she can just quickly change the title. And she can pin this insight as well under spin board for the management to look at with this done. Now, Amy, just want to go back to this sales and see if she can find anything else interesting. So now Amy has already figured out the possible causes. What led to the increase in sales? So now, for the whole of 2019, as this is also your closing, Amy looks, uh, the monthly figures for 2019, and she gets this craft now. If Amy has to understand, if there is an interesting insight, she can dig into different dimensions and figure out on her own or immigrant, just click on this product analysis. That's product immediately suggest all the dimensions and measures immigrant analyze sales by Andi many. We will run this What will happen is this barbecue system will try to identify outliers. The different trend analysis Onda cross correlation across different measures. So Amy again realizes that this is a bit too much for her. So she reduces some of these insights, which she thinks are not required for the management right now from the business context and the business meeting. And then she just immediately runs this analysis. So now, with this, Amy is hoping to get some interesting insights from Spartak, which immigrant present to her management meeting. Let's see what sport gets for her. So now the Alice is run within 10 seconds, so spot taken started analyzing. So these are the six anomaly sport like you found across different products, where their total sales are higher than the rest. He also founded Spot. I just found eight insights off different product types which has tired total sales and look across these enemy sees that oh jackets have against the highest sales across all the categories in December as well. Amy wants toe been this to the PIN board on M. It moves further now. Amy's is that it has also shown Total Country purchased their product a me thinks this is not a useful insights. Amy can get this feedback. The system and system asked, Why are you saying you don't find this useful so the system can remember? So you can also say that anomalies are obvious right now and give this feedback and the system will remember. In addition, Amy finds that the system has automatically correlated the total sales in total contrary purchase. Amy Pence this as well to the pin board. Andi. She loves this inside where she she is that not only the total sales have increased, but total quantity purchases have increased a lot more on their training, opposed as well. So she also opens this now anything. She is ready for her meeting with the management. So she just goes and shares the PIN board, which she just created with the management. And you know what happens immediately? The jacket sales category Manager Mr Tom replies back to Amy and says in the request, Any d really like this? So now we will see how Spartak you can help any educators as request doesn't mean really need to create these kind of reports every month to cater toe Tom's request. So with this, I will handle it because to take us walk us through How spot that you can cater this request. Hi, >>everyone. So analysts like Amy are always flooded with such requests from the business users and with Spot and you monitor. Amy can set up everyone who needs updates on a on a metric in just a few simple steps and enable them to drag these metrics whenever and wherever they want. And north of the metrics, they also get the corresponding change analysis on the device off their choice with hot Spot. What I give money being available on both Web and the mobile labs. So let's get started with the demo will be set up a meet and go to the search tab and creator times we start for the metrics you want to monitor, right? And please know if the charges already created is already created. All is available is, um, usually a section in a PIN board. Also dancer. Then there's no need to create a new child. She can simply then uh, right click on the chart and select moisture from the menu, which then shows, which then shows the breakdown off the metric he's going to monitor, including the measure. What it's been grouped by on what it is filtered on. Okay, and also as this is a weekly metric, all the subscribers are going to get a weekly notification for this metric had been a monthly metric. Then the notifications would have been delivered on a monthly cadence. Next she can click on, continue and go to the configure dimensions called on Page. Here A is recommending what all dimensions could best being the change in this metric, she can go ahead with default recommendation, or she can change the columns as she seems very she can click, she conflict, continue and go to the next page, which is the subscriber stage. It is added by default to the subscriber, but she can search everyone who needs update on this metric and add them on this metric by clicking confirmed, she'll see a toast message on the bottom of the page, taking on which will take a me to this page, which is a metric detail page On the top of this page, we can see the movement of the metric and how it is changing over time, 92 you can see that the Mets jacket, since number has increased by 2.5% in the week off 23rd of December has compared toa the week off 16th of December and just below e a has invaded the man is generated in sites which are readily available for consumption. Okay to discharge. Right here says that pain products have the largest change out of all the 28 values and contributes to the 88% of the total increase in the same. And this one right here is that Midwest is the larger Midwest has the largest change and accounts for 55.66% off the total increase. Now, all this goodness is also available on the mobile lab. Right? So let me just show you how business users are going to get notified on the based. On this metric, all the business users who are subscribed to this metric are going to get a regular email as well as push notifications on the mobile lab. And when the click on this, they line on a metric detail page which has all the starts, which I just showed you on the on the bed version, okay. And one cyclic on back burden. They land on this page, which is a monitor tab, and it summarizes all the metrics Which opportunity monitoring and gives them a whole gave you to stay all I want to stay on top of their businesses. Okay. Eso that folks was monitor. Now I'll search back to slaves and cover. Summarize the key takeaways. From what? That she and I just don't know. So it's part of you wanted, uh, Summit Spartak you. It automatically discovers insights and helps you unless the full potential of your data and that's what I do is comprehensive set off analysis. You can answer your advanced business question in just a few simple steps and the end speed of your time. Bring state. And with a new support for embrace, you can run sport like you on your data in your data warehouse and with spotted you monitor, you can monitor all the business metrics and not just died. We can also understand that teaching teaching drivers on those metrics on the platform of your choice. So with that, I'll hand over toe, you know. >>Thank you so much. Both of you That was fantastic. Um, I just love spot like, because it makes me look like much more of a rock star with data than I really am. So thank you guys for that fantastic presentation. Um, so we've got a couple of minutes for a couple of questions for you. The first one is for action. Um, once spot I Q generates a number of insights. Can you run spot I Q again on one of those insights? >>Yeah, As a philosophy off Spiric, you sport like you never takes the user to the dead end Spartak. You also transparently shares the calculation. So user can not only the keeper that on edit Understand how this product you inside has been calculated, but user can also run us for like you analysts is honest for data analysis as well. Which music? And continue to do not on the first level. Second level in the third level as well. >>That's cool. Thank you. Actually on then The next one is for because for spot ik monitor is it possible to edit the dimensions used for explaining the factors to change that was detected? >>Yes. It's an owner of the metric you can change the dimensions whenever you want and save them for everyone else. >>Okay, well, I think that's about all we've got time for in this session. So all that remains is for me to say a huge thank you to Because an Akshay Andi, we've got the last session of this track coming up in a few minutes. So grab a snack. Come right back and listen to an amazing customer story with Snowflake on Western Union, they're up next.

>>Yeah, yeah. >>Welcome back to session, too. Thoughts about everywhere. Unlock new revenue streams with embedded search and I Today we're joined by our senior director of Global Oh am Rick Dimel, along with speakers from our thoughts about customer Hayes to discuss how thought spot is open for everyone by unlocking unprecedented value through data search in A I, you'll see how thoughts about compound analytics in your applications and hear how industry leaders are creating new revenue streams with embedded search and a I. You'll also learn how to increase app stickiness on how to create an autonomous this experience for your end users. I'm delighted to introduce our senior director of Global OPM from Phillips Spot, Rick DeMARE on then British Ramesh, chief technology officer, and Leon Roof, director of product management, both from Hayes over to you. Rick, >>Thank you so much. I appreciate it. Hi, everybody. We're here to talk to you about Fox Spot everywhere are branded version of our embedded analytics application. It really our analytics application is all about user experience. And in today's world, user experience could mean a lot of things in ux design methodologies. We want to talk about the things that make our product different from an embedded perspective. If you take a look at what product managers and product design people and engineers are doing in this space, they're looking at a couple of key themes when they design applications for us to consume. One of the key things in the marketplace today is about product led growth, where the product is actually the best marketing tool for the business, not even the sales portion or the marketing department. The product, by the word of mouth, is expanding and getting more people onto the system. Why is that important? It's important because within the first few days of any application, regardless of what it is being used binding users, 70% of those users will lose. Interest will stop coming back. Why do they stop coming back? Because there's no ah ha moment through them. To get engaged within the technology, today's technologies need to create a direct relationship with the user. There can't be a gatekeeper between the user and the products, such as marketing or sales or information. In our case. Week to to make this work, we have toe leverage learning models in leverage learning as it's called Thio. Get the user is engaged, and what that means is we have to give them capabilities they already know how to use and understand. There are too many applications on the marketplace today for for users to figure out. So if we can leverage the best of what other APS have, we can increase the usage of our systems. Because in today's world, what we don't want to do from a product perspective is lead the user to a dead end or from a product methodology. Our perspective. It's called an empty state, and in our world we do that all the time. In the embedded market place. If you look at at the embedded marketplace, it's all visualizations and dashboards, or what I call check engine lights in your application's Well, guess what happens when you hit a check engine life. You've got to call the dealer to get more information about what just took place. The same thing happens in the analytic space where we provide visualizations to users. They get an indicator, but they have to go through your gatekeepers to get access to the real value of that data. What am I looking at? Why is it important the best user experiences out on the marketplace today? They are autonomous. If we wanna leverage the true value of digital transformation, we have to allow our developers to develop, not have them, the gatekeepers to the rial, content to users want. And in today's world, with data growing at much larger and faster levels than we've ever seen. And with that shelf life or value of that data being much shorter and that data itself being much more fragmented, there's no developer or analysts that can create enough visualizations or dashboards in the world to keep the consumption or desire for these users to get access to information up to speed. Clients today require the ability to sift through this information on their own to customize their own content. And if we don't support this methodology, our users are gonna end up feeling powerless and frustrated and coming back to us. The gatekeepers of that information for more information. Loyalty, conversely, can be created when we give the users the ability toe access this information on their own. That is what product like growth is all about in thought spot, as you know we're all about search. It's simple. It's guided as we type. It gives a super fast responses, but it's also smart on the back end handling complexities, and it's really safe from a governance and as well as who gets access to what perspective it's unknown learned environment. Equally important in that learned environment is this expectation that it's not just search on music. It's actually gonna recommend content to me on the fly instantly as I try content I might not even thought of before. Just the way Spotify recommends music to us or Netflix recommends a movie. This is a expected learned behavior, and we don't want to support that so that they can get benefit and get to the ah ha moments much quicker. In the end, which consumption layer do you want to use, the one that leads you to the Dead End Street or the one that gets you to the ah ha moment quickly and easily and does it in an autonomous fashion. Needless to say, the benefits of autonomous user access are well documented today. Natural language search is the wave of the future. It is today. By 2004 75% of organizations are going to be using it. The dashboard is dead. It's no longer going to be utilized through search today, I if we can improve customer satisfaction and customer productivity, we're going to increase pretensions of our retention of our applications. And if we do that just a little bit, it's gonna have a tremendous impact to our bottom line. The way we deploy hotspots. As you know, from today's conversations in the cloud, it could be a manage class, not offering or could be software that runs in your own VPC. We've talked about that at length at this conference. We've also talked about the transformation of application delivery from a Cloud Analytics perspective at length here it beyond. But we apply those same principles to your product development. The benefits are astronomical because not only do you get architectural flexibility to scale up and scale down and right size, but your engineers will increase their productivity because their offerings, because their time and effort is not going to be spent on delivering analytics but delivering their offerings. The speed of innovation isn't gonna be released twice a year or four times a year. It's gonna It can happen on a weekly basis, so your time to market in your margins should increase significantly. At this point, I want a hand. The microphone over to Revert. Tesche was going to tell you a little bit about what they're doing. It hes for cash. >>Thanks, Rick. I just want to introduce myself to the audience. My name is Rotational. Mention the CTO Europe ace. I'm joined my today by my colleague Gillian Ruffles or doctor of product management will be demoing what we have built with thoughts about, >>um but >>just to my introduction, I'm going to talk about five key things. Talk about what we do. What hes, uh we have Really, um what we went through the select that spot with other competitors What we have built with that spot very quickly and last but not least, some lessons learned during the implementation. So just to start with what we do, uh, we're age. We are health care compliance and revenue integrity platform were a saas platform voter on AWS were very short of l A. That's it. Use it on these around 1 50 customers across the U. S. On these include large academic Medical Insight on. We have been in the compliant space for the last 30 plus years, and we were traditionally consulting company. But very recently we have people did more towards software platform model, uh, in terms off why we chose that spot. There were three business problems that I faced when I took this job last year. At age number one is, uh, should be really rapidly deliver new functionality, nor platform, and he agile because some of our product development cycles are in weeks and not months. Hey had a lot of data, which we collected traditionally from the SAS platform, and all should be really create inside stretch experience for our customers. And then the third Big one is what we saw Waas large for customers but really demanding self service capabilities. But they were really not going for the static dash boats and and curated content, but instead they wanted to really use the cell service capabilities. Thio mind the data and get some interesting answers during their questions. So they elevated around three products around these problems statements, and there were 14 reasons why we just start spot number one wars off course. The performance and speed to insights. Uh, we had around 800 to a billion robot of data and we wanted to really kind of mind the data and set up the data in seconds on not minutes and hours. We had a lot of out of the box capabilities with that spot, be it natural language search, predictive algorithms. And also the interactive visualization, which, which was which, Which gave us the agility Thio deliver these products very quickly. And then, uh, the end user experience. We just wanted to make sure that I would users can use this interface s so that they can very quickly, um, do some discovery of data and get some insights very quickly. On last but not least, talksport add a lot of robust AP ice around the platform which helped us embed tot spot into are offering. But those are the four key reasons which we went for thoughts part which we thought was, uh, missing in in the other products we evaluated performance and search, uh, the interactive visualization, the end user experience, and last but not least flexible AP ice, which we could customize into our platform in terms of what we built. We were trying to solve to $50 billion problem in health care, which is around denials. Um so every year, around 2, 50 to $300 billion are denied by players thes air claims which are submitted by providers. And we built offering, which we called it US revenue optimizer. But in plain English, what revenue optimizer does is it gives the capability tow our customers to mind that denials data s so that they can really understand why the claims were being denied. And under what category? Recent reasons. We're all the providers and quarters who are responsible for these claims, Um, that were dryland denials, how they could really do some, uh, prediction off. It is trending based on their historical denial reasons. And then last but not least, we also build some functionality in the platform where we could close the loop between insights, action and outcome that Leon will be showing where we could detect some compliance and revenue risks in the platform. On more importantly, we could, uh, take those risks, put it in a I would say, shopping card and and push it to the stakeholders to take corrective action so the revenue optimizer is something which we built in three months from concept to lunch and and that that pretty much prove the value proposition of thoughts. But while we could kind of take it the market within a short period of time Next leopard >>in terms >>off lessons learned during the implementation thes air, some of the things that came to my mind asses, we're going through this journey. The first one is, uh, focus on the use case formulation, outcomes and wishful story boarding. And that is something that hot spot that's really balance. Now you can you can focus on your business problem formulation and not really focus on your custom dash boarding and technology track, etcetera. So I think it really helped our team to focus on the versus problem, to focus on the outcomes from the problem and more importantly, really spend some time on visualizing What story are we say? Are we trying to say to our customers through revenue optimizer The second lesson learned first When we started this implementation, we did not dualistic data volume and capacity planning exercise and we learned it our way. When we are we loaded a lot of our data sets into that spot. And then Aziz were doing performance optimization. XYZ. We figured out that we had to go back and shot the infrastructure because the data volumes are growing exponentially and we did not account for it. So the biggest lesson learned This is part of your architectural er planning, exercise, always future proof your infrastructure and make sure that you work very closely with the transport engineering team. Um, to make sure that the platform can scale. Uh, the last two points are passport as a robust set of AP Ice and we were able to plug into those AP ice to seamlessly ended the top spot software into a platform. And last but not least, one thing I would like to closest as we start these projects, it's very common that the solution design we run into a lot of surprises. The one thing I should say is, along those 12 weeks, we very closely work with the thoughts, part architecture and accounting, and they were a great partner to work with us to really understand our business problem, and they were along the way to kind of government suggested, recommends and workarounds and more importantly, also, helpers put some other features and functionality which you requested in their engineering roadmap. So it's been a very successful partnership. Um, So I think the biggest take of it is please make sure that you set up your project and operating model value ember thoughts what resources and your team to make sure that they can help you as you. It's some obstacles in the projects so that you can meet your time ones. Uh, those are the key lessons learned from the implementation. And with that, I would pass this to my colleague Leon Rough was going to show you a demo off what we go. >>Thanks for Tesh. So when we were looking Thio provide this to our customer base, we knew that not everyone needed do you access or have available to them the same types of information or at the same particular level of information. And we do have different roles within RMD auto Enterprise platform. So we did, uh, minimize some roles to certain information. We drew upon a persona centric approach because we knew that those different personas had different goals and different reasons for wanting to drive into these insights, and those different personas were on three different levels. So we're looking at the executive level, which is more on the C suite. Chief Compliance Officer. We have a denial trending analyses pin board, which is more for the upper, uh, managers and also exact relatives if they're interested. And then really, um, the targeted denial analysis is more for the day to day analysts, um, the usage so that they could go in and they can really see where the trends are going and how they need to take action and launch into the auditing workflow so within the executive or review, Um, and not to mention that we were integrating and implementing this when everyone was we were focused on co vid. So as you can imagine, just without covert in the picture, our customers are concentrated on denials, and that's why they utilize our platform so they could minimize those risks and then throw in the covert factor. Um, you know, those denial dollars increase substantially over the course of spring and the summer, and we wanted to be able to give them ah, good view of the denials in aggregate as well as's we focus some curated pin boards specific to those areas that were accounting for those high developed denials. So on the Executive Overview Board, we created some banner tiles. The banner tiles are pretty much a blast of information for executives thes air, particular areas where there concentrating and their look looking at those numbers consistently so it provides them away to take a good look at that and have that quick snapshot. Um, more importantly, we did offer as I mentioned some curated pin boards so that it would give customers this turnkey access. They wouldn't necessarily have to wonder, You know, what should I be doing now on Day one, but the day one that we're providing to them these curated insights leads the curiosity and increases that curiosity so that they can go in and start creating their own. But the base curated set is a good overview of their denial dollars and those risks, and we used, um, a subject matter expert within our organization who worked in the field. So it's important to know you know what you're targeting and why you're targeting it and what's important to these personas. Um, not everyone is necessarily interests in all the same information, and you want to really hit on those critical key point to draw them and, um, and allowed them that quick access and answer those questions they may have. So in this particular example, the curated insight that we created was a monthly denial amount by functional area. And as I was mentioning being uber focused on co vid, you know, a lot of scrutiny goes back to those organizations, especially those coding and H i M departments, um, to ensure that their coding correctly, making sure that players aren't sitting on, um, those payments or denying those payments. So if I were in executive and I came in here and this was interesting to me and I want to drill down a little bit, I might say, You know, let me focus more on the functional area than I know probably is our main concern. And that's coating and h i M. And because of it hit in about the early winter. I know that those claims came in and they weren't getting paid until springtime. So that's where I start to see a spike. And what's nice is that the executive can drill down, they may have a hunch, or they can utilize any of the data attributes we made available to them from the Remittance file. So all of these data, um, attributes are related to what's being sent on the 8 35 fear familiar with the anti 8 35 file. So in particular, if I was curious and had a suspicion that these were co vid related or just want to concentrate in that area, um, we have particular flag set up. So the confirmed and suspected cases are pulling in certain diagnosis and procedure codes. And I might say 1.27 million is pretty high. Um, toe look at for that particular month, and then they have the ability to drill down even further. Maybe they want to look at a facility level or where that where that's coming from. Furthermore, on the executive level, we did take advantage of Let me stop here where, um also provided some lagged a so leg. This is important to organizations in this area because they wanna know how long does it take before they re submit a claim that was originally denied before they get paid industry benchmark is about 10 days of 10 days is a fairly good, good, um, basis to look at. And then, obviously anything over that they're going to take a little bit more scrutiny on and want to drill in and understand why that is. And again, they have that capabilities in order to drill down and really get it. Those answers that they're looking for, we also for this particular pin board. And these users thought it would be helpful to utilize the time Siri's forecasting that's made available. So again, thes executives need thio need to keep track and forecast where they're trends were going or what those numbers may look like in the future. And we thought by providing the prediction pins and we have a few prediction pins, um would give them that capability to take a look at that and be able to drill down and use that within, um, certain reporting and such for their organization. Another person, a level that I will go to is, um, Mawr on the analyst side, where those folks are utilizing, um, are auditing workflow and being in our platform, creating audits, completing audits, we have it segregated by two different areas. And this is by claim types so professional or institutional, I'm going to jump in here. And then I am going to go to present mode. So in this particular, um, in this particular view or insight, we're providing that analysts view with something that's really key and critical in their organization is denials related Thio HCC s andi. That's a condition category that kind of forecast, the risk of treatment. And, you know, if that particular patient is probably going to be seen again and have more conditions and higher costs, higher health care spending. So in this example, we're looking at the top 15 attending providers that had those HCC denials. And this is, um, critical because at this point, it really peaks in analyst curiosity. Especially, You know, they'll see providers here and then see the top 15 on the top is generating Ah, hide denial rate. Hi, denial. The dollars for those HCC's and that's a that's a real risk to the organization, because if that behavior continues, um, then those those dollars won't go down. That number won't go down so that analysts then can go in and they can drill down um, I'm going to drill down on diagnosis and then look at the diagnosis name because I have a suspicion, but I'm not exactly sure. And what's great is that they can easily do this. Change the view. Um, you know, it's showing a lot of diagnoses, but what's important is the first one is sepsis and substance is a big one. Substances something that those organizations see a lot of. And if they hover, they can see that 49.57 million, um, is attributed to that. So they may want to look further into that. They'd probably be interested in closing that loop and creating an audit. And so what allowed us to be able to do that for them is we're launching directly into our auditing workflow. So they noticed something in the carried insight. It sparked some investigation, and then they don't have to leave that insight to be able to jump into the auditing workflow and complete that. Answer that question. Okay, so now they're at the point where we've pulled back all the cases that attributed to that dollar amount that we saw on the Insight and the users launching into their auditing workflow. They have the ability Thio select be selective about what cases they wanna pull into the audit or if they were looking, um, as we saw with sepsis, they could pull in their 1600 rose, but they could take a sampling size, which is primarily what they would do. They went audit all 1600 cases, and then from this point in they're into, they're auditing workflow and they'd continue down the path. Looking at those cases they just pulled in and being able Thio finalized the audit and determine, you know, if further, um, education with that provider is needed. So that concludes the demo of how we integrated thought spot into our platform. >>Thank you, LeAnn. And thank you. Re test for taking the time to walk us through. Not only your company, but how Thought spot is helping you Power analytics for your clients. At this point, we want to open this up for a little Q and A, but we want to leave you with the fact that thought spot everywhere. Specifically, it cannot only do this for Hayes, but could do it for any company anywhere they need. Analytical applications providing these applications for their customers, their partners, providers or anybody within their network for more about this, you can see that the website attached below >>Thanks, Rick and thanks for tests and Leon that I find it just fascinating hearing what our customers are doing with our technology. And I certainly have learned 100% more about sepsis than I ever knew before this session. So thank you so much for sharing that it's really is great to see how you're taking our software and putting it into your application. So that's it for this session. But do stay tuned for the next session, which is all about getting the most out of your data and amplifying your insights. With the help of A, I will be joined by two thought spot leaders who will share their first hand experiences. So take a quick breather and come right back

>>Yeah, >>yeah. Hello and welcome to this track to creating engaging analytics experiences for all. I'm Hannah Sinden Thought spots Omiya director of marketing on. I'm delighted to have you here today. A boy Have we got to show for you now? I might be a little bit biased as the host of this track, but in my humble opinion, you've come to a great place to start because this track is all about everything. Thought spot. We'll be talking about embedded search in a I thought spot one spot I. Q. We've got great speakers from both thoughts about andare customers as well as some cool product demos. But it's not all product talk. We'll be looking at how to leverage the tech to give your users a great experience. So first up is our thoughts about one deep dive. This session will be showing you how we've built on our already superb search experience to make it even easier for users across your company to get insight. We've got some great speakers who are going to be telling you about the cool stuff they've been working on to make it really fantastic and easy for non technical people to get the answers they need. So I'm really delighted to introduce Bob Baxley s VP of design and experience That thought spot on Vishal Kyocera Thought spots director of product management. So without further ado, I'll hand it over to Bob. Thanks, >>Hannah. It's great to be here with everybody today and really excited to be able to present to you thought spot one. We've been working on this for months and months and are super excited to share it before we get to the demo with Shawl, though, I just want to set things up a little bit to help people understand how we think about design here. A thought spot. The first thing is that we really try to think in terms of thought. Spot is a consumer grade product, terms what we wanted. Consumer grade you x for an analytics. And that means that for reference points rather than looking at other enterprise software companies, we tend to look at well known consumer brands like Google, YouTube and WhatsApp. We firmly believe that people are people, and it doesn't matter if they're using software for their own usage or thought are they're using software at work We wanted to have a great experience. The second piece that we were considering with thoughts about one is really what we call the desegregation of bundles. So instead of having all of your insights wraps strictly into dashboards, we want to allow users to get directly to individual answers. This is similar to what we saw in music. Were instead of you having to buy the entire album, of course, you could just buy individual songs. You see this in iTunes, Spotify and others course. Another key idea was really getting rid of gate keepers and curators and kind of changing people from owning the information, helping enable users to gather together the most important and interesting insights So you can follow curator rather than feeling like you're limited in the types of information you can get. And finally, we wanted to make search the primary way, for people are thinking about thought spot. As you'll see, we've extended search from beyond simply searching for your data toe, also searching to be able to find pin boards and answers that have been created by other people. So with that, I'll turn it over to my good friend Rachel Thio introduce more of thought, spot one and to show you a demo of the product. >>Thank you, Bob. It's a pleasure to be here to Hello, everyone. My name is Michelle and Andy, product management for Search. And I'm really, really excited to be here talking about thoughts about one our Consumer analytics experience in the Cloud. Now, for my part of the talk, we're gonna first to a high level overview of thoughts about one. Then we're going to dive into a demo, and then we're gonna close with just a few thoughts about what's coming next. So, without any today, let's get started now at thought spot. Our mission is to empower every user regardless of their expertise, to easily engage with data on make better data driven decisions. We want every user, the nurse, the neighborhood barista, the teacher, the sales person, everyone to be able to do their jobs better by using data now with thoughts about one. We've made it even more intuitive for all these business users to easily connect with the insights that are most relevant for them, and we've made it even easier for analysts to do their jobs more effectively and more efficiently. So what does thoughts about one have? There's a lot off cool new features, but they all fall into three main categories. The first main category is enhanced search capabilities. The second is a brand new homepage that's built entirely for you, and the third is powerful tools for the analysts that make them completely self service and boost their productivity. So let's see how these work Thought Spot is the pioneer for search driven analytics. We invented search so that business users can ask questions of data and create new insights. But over the years we realized that there was one key piece off functionality that was missing from our search, and that was the ability to discover insights and content that had already been created. So to clarify, our search did allow users to create new content, but we until now did not have the ability to search existing content. Now, why does that matter? Let's take an example. I am a product manager and I am always in thought spot, asking questions to better understand how are users are using the product so we can improve it now. Like me, A lot of my colleagues are doing the same thing. Ah, lot of questions that I asked have already been answered either completely are almost completely by many of my colleagues, but until now there's been no easy way for me to benefit from their work. And so I end up recreating insights that already exists, leading to redundant work that is not good for the productivity off the organization. In addition, even though our search technology is really intuitive, it does require a little bit of familiarity with the underlying data. You do need to know what metric you care about and what grouping you care about so that you can articulate your questions and create new insights. Now, if I consider in New employees product manager who joins Hotspot today and wants to ask questions, then the first time they use thought spot, they may not have that data familiarity. So we went back to the drawing board and asked ourselves, Well, how can we augment our search so that we get rid off or reduced the redundant work that I described? And in addition, empower users, even new users with very little expertise, maybe with no data familiarity, to succeed in getting answers to their questions the first time they used Hot Spot, and we're really proud and excited to announce search answers. Search answers allows users to search across existing content to get answers to their questions, and its a great compliment to search data, which allows them to search the underlying data directly to create new content. Now, with search answers were shipping in number of cool features like Answer Explainer, Personalized search Results, Answer Explorer, etcetera that make it really intuitive and powerful. And we'll see how all of these work in action in the demo. Our brand new homepage makes it easier than ever for all these business users to connect with the insights that are most relevant to them. These insights could be insights that these users already know about and want to track regularly. For example, as you can see, the monitor section at the top center of the screen thes air, the KP eyes that I may care most about, and I may want to look at them every day, and I can see them every day right here on my home page. By the way, there's a monitoring these metrics in the bankrupt these insights that I want to connect with could also be insights that I want to know more about the search experience that I just spoke about ISS seamlessly integrated into the home page. So right here from the home page, I can fire my searchers and ask whatever questions I want. Finally, and most interestingly, the homepage also allows me to connect with insights that I should know about, even if I didn't explicitly ask for them. So what's an example? If you look at the panel on the right, I can discover insights that are trending in my organization. If I look at the panel on the left, I can discover insights based on my social graph based on the people that I'm following. Now you might wonder, How do we create this personalized home page? Well, our brand new, personalized on boarding experience makes it a piece of cake as a new business user. The very first time I log into thought spot, I pay three people I want to follow and three metrics that I want to follow, and I picked these from a pool of suggestions that Ai has generated. And just like that, the new home page gets created. And let's not forget about analysts. We have a personalized on boarding experience specifically for analysts that's optimized for their needs. Now, speaking of analysts, I do want to talk about the tools that I spoke off earlier that made the analysts completely self service and greatly boost their productivity's. We want analysts to go from zero to search in less than 30 minutes, and with our with our new augmented data modeling features and thoughts about one, they can do just that. They get a guided experience where they can connect, model and visualize their data. With just a few clicks, our AI engine takes care off a number of tasks, including figuring out joints and, you know, cleaning up column names. In fact, our AI engine also helps them create a number of answers to get started quickly so that these analysts can spend their time and energy on what matters most answering the most complicated and challenging and impactful questions for the business. So I spoke about a number of different capabilities off thoughts about one, but let's not forget that they are all packaged in a delightful user experience designed by Bob and his team, and it powers really, really intuitive and powerful user flows, from personalized on boarding to searching to discover insights that already exist on that are ranked based on personalized algorithms to making refinements to these insights with a assistance to searching, to create brand new insights from scratch. And finally sharing all the insights that you find interesting with your colleagues so that it drives conversations, decisions and, most importantly, actions so that your business can improve. With that said, let's drive right into the demo for this demo. We're going to use sales data set for a company that runs a chain off retail stores selling apparel. Our user is a business user. Her name is Charlotte. She's a merchandiser, She's new to this company, and she is going to be leading the genes broader category. She's really excited about job. She wants to use data to make better decisions, so she comes to thought spot, and this is what she sees. There are three main sections on the home page that she comes to. The central section allows you to browse through items that she has access to and filter them in various ways. Based for example, on author or on tags or based on what she has favorited. The second section is this panel on the right hand side, which allows her to discover insights that are trending within her company. This is based on what other people within her company are viewing and also personalized to her. Finally, there's this search box that seamlessly integrated into the home page. Now Charlotte is really curious to learn how the business is doing. She wants to learn more about sales for the business, so she goes to the search box and searches for sales, and you can see that she's taken to a page with search results. Charlotte start scanning the search results, and she sees the first result is very relevant. It shows her what the quarterly results were for the last year, but the result that really catches her attention is regional sales. She'd love to better understand how sales are broken down by regions. Now she's interested in the search result, but she doesn't yet want to commit to clicking on it and going to that result. She wants to learn more about this result before she does that, and she could do that very easily simply by clicking anywhere on the search result card. Doing that reveals our answer. Explain our technology and you can see this information panel on the right side. It shows more details about the search results that she selected, and it also gives her an easy to understand explanation off the data that it contains. You can see that it tells her that the metrics sales it's grouped by region and splitter on last year. She can also click on this preview button to see a preview off the chart that she would see if she went to that result. It shows her that region is going to be on the X axis and sales on the Y axis. All of this seems interesting to her, and she wants to learn more. So she clicks on this result, and she's brought to this chart now. This contains the most up to date data, and she can interact with this data. Now, as she's looking at this data, she learns that Midwest is the region with the highest sales, and it has a little over $23 million in sales, and South is the region with the lowest sales, and it has about $4.24 million in sales. Now, as Charlotte is looking at this chart, she's reminded off a conversation she had with Suresh, another new hire at the company who she met at orientation just that morning. Suresh is responsible for leading a few different product categories for the Western region off the business, and she thinks that he would find this chart really useful Now she can share this chart with Suresh really easily from right here by clicking the share button. As Charlotte continues to look at this chart and understand the data, she thinks, uh, that would be great for her to understand. How do these sales numbers across regions look for just the genes product category, since that's the product category that she is going to be leading? And she can easily narrow this data to just the genes category by using her answer Explorer technology. This panel on the right hand side allows her to make the necessary refinements. Now she can do that simply by typing in the search box, or she can pick from one off the AI generated suggestions that are personalized for her now. In this case, the AI has already suggested genes as a prototype for her. So with just a single click, she can narrow the data to show sales data for just jeans broken down by region. And she can see that Midwest is still the region with the highest sales for jeans, with $1.35 million in sales. Now let's spend a minute thinking about what we just saw. This is the first time that Charlotte is using Thought spot. She does not know anything about the data sources. She doesn't know anything about measures or attributes. She doesn't know the names of the columns. And yet she could get to insights that are relevant for her really easily using a search interface that's very much like Google. And as she started interacting with search results, she started building a slightly better understanding off the underlying data. When she found an insight that she thought would be useful to a colleague offers, it was really seamless for her to share it with that colleague from where she Waas. Also, even though she's searching over content that has already been created by her colleagues in search answers. She was in no way restricted to exactly that data as we just saw. She could refine the data in an insight that she found by narrowing it. And there's other things you can do so she could interact with the data for the inside that she finds using search answers. Let's take a slightly more complex question that Charlotte may have. Let's assume she wanted to learn about sales broken down by, um, by category so that she can compare her vertical, which is jeans toe other verticals within the company. Again, she can see that the very first result that she gets is very relevant. It shows her search Sorry, sales by category for last year. But what really catches her attention about this result is the name of the author. She's thrilled to note that John, who is the author of this result, was also an instructor for one off for orientation sessions and clearly someone who has a lot of insight into the sales data at this company. Now she would love to see mawr results by John, and to do that, all she has to do is to click on his name now all of the search results are only those that have been authored by John. In fact, this whole panel at the top of the results allow her to filter her search results or sort them in different ways. By clicking on these authors filter, she can discover other authors who are reputed for the topic that she's searching for. She can also filter by tags, and she can sort these results in different ways. This whole experience off doing a search and then filtering search results easily is similar to how we use e commerce search engines in the consumer world. For example, Amazon, where you may search for a product and then filter by price range or filter by brand. For example, Let's also spend a minute talking about how do we determine relevance for these results and how they're ranked. Um, when considering relevance for these results, we consider three main categories of things. We want to first make sure that the result is in fact relevant to the question that the user is asking, and for that we look at various fields within the result. We look at the title, the author, the description, but also the technical query underpinning that result. We also want to make sure that the results are trustworthy, because we want users to be able to make business decisions based on the results that they find. And for that we look at a number of signals as well. For example, how popular that result is is one of those signals. And finally, we want to make sure the results are relevant to the users themselves. So we look at signals to personalize the result for that user. So those are all the different categories of signals that we used to determine overall ranking for a search result. You may be wondering what happens if if Charlotte asks a question for which nobody has created any answer, so no answers exist. Let's say she wants to know what the total sales of genes for last year and no one's created that well. It's really easy for her to switch from searching for answers, which is searching for content that has already been created to searching the data directly so she can create a new insight from scratch. Let's see how that works. She could just click here, and now she's in the search data in her face and for the question that I just talked about. She can just type genes sales last year. And just like that, she could get an answer to her question. The total sales for jeans last year were almost $4.6 million. As you can see, the two modes off search searching for answers and searching, the data are complementary, and it's really easy to switch from one to the other. Now we understand that some business users may not be motivated to create their own insights from scratch. Or sometimes some of these business users may have questions that are too complicated, and so they may struggle to create their own inside from scratch. Now what happens usually in these circumstances is that these users will open a ticket, which would go to the analyst team. The analyst team is usually overrun with these tickets and have trouble prioritizing them. And so we started thinking, How can we make that entire feedback loop really efficient so that analysts can have a massive impact with as little work as possible? Let me show you what we came up with. Search answers comes with this system generated dashboard that analysts can see to see analytics on the queries that business users are asking in search answers so it contains high level K P. I is like, You know how many searches there are and how many users there are. It also contains one of the most popular queries that users are asking. But most importantly, it contains information about what are popular queries where users are failing. So the number on the top right tells you that about 10% off queries in this case ended with no results. So the user clearly failed because there were no results on the table. Right below it shows you here are the top search queries for original results exist. So, for example, the highlighted row there says jean sales with the number three, which tells the analysts that last week there were three searches for the query jean sales and the resulted in no results on search answers. Now, when an analyst sees a report like this, they can use it to prioritize what kind of content they could be creating or optimizing. Now, in addition to giving them inside into queries which led to no results or zero results. This dashboard also contains reports on creatives that lead to poor results because the user did get some results but didn't click on anything, meaning that they didn't get the answer that they were looking for. Taking all these insights, analysts can better prioritize and either create or optimize their content to have maximum impact for their business users with the least amount of for. So that was the demo. As you can see with search answers, we've created a very consumer search interface that any business user can use to get the answers to their questions by leveraging data or answers that have already been created in the system by other users in their organization. In addition, we're creating tools that allow analysts toe create or optimized content that can have the highest impact for these business users. All right, so that was the demo or thoughts about one and hope you guys liked it. We're really excited about it. Now Let me just spend a minute talking about what's coming next. As I've mentioned before, we want to connect every business user with the insights that are most relevant for them, and for that we will continue to invest in Advanced AI and personalization, and some of the ways you will see it is improved relevance in ranking in recommendations in how we understand your questions across the product within search within the home page everywhere. The second team that will continue to invest in is powerful analyst tools. We talked about tools and, I assure you, tools that make the analysts more self service. We are committed to improving the analyst experience so that they can make the most off their time. An example of a tool that we're really excited about is one that allows them to bridge the vocabulary difference that this even business user asks questions. A user asked a question like revenue, but the column name for the metric in the data set its sales. Now analysts can get insights into what are the words that users air using in their questions that aren't matching anything in the data set and easily create synonyms so that that vocabulary difference gets breached. But that's just one example of how we're thinking about empowering the analysts so that with minimal work, they can amplify their impact and help their business users succeed. So there's a lot coming, and we're really excited about how we're planning to evolve thoughts about one. With all that said, Um, there's just, well, one more thing that my friend Bob wants to talk to you guys about. So back to you, Bob. >>Thanks, Michelle. It's such a great demo and so fun to see all the new work that's going on with thought. Spot one. All the happenings for the new features coming out that will be under the hood. But of course, on the design side, we're going to continue to evolve the front end as well, and this is what we're hoping to move towards. So here you'll see a new log in screen and then the new homepage. So compared to the material that you saw just a few minutes ago, you'll notice this look is much lighter. A little bit nicer use of color up in the top bar with search the features over here to allow you to switch between searching against answers at versus creating new answers, the settings and user profile controls down here and then on the search results page itself also lighter look and feel again. Mork color up in the search bar up the top. A little bit nicer treatments here. We'll continue to evolve the look and feel the product in coming months and quarters and look forward to continue to constantly improving thoughts about one Hannah back to you. >>Thanks, Bob, and thank you both for showing us the next generation of thought spot. I'd love to go a bit deeper on some of the points you touched on there. I've got a couple of questions here. Bob, how do you think about designing for consumer experience versus designing for enterprise solutions? >>Yes, I mentioned Hannah. We don't >>really try to distinguish so much between enterprise users and consumer users. It's really kind of two different context of use. But we still always think that users want some product and feature and experience that's easy to use and makes sense to them. So instead of trying to think about those is two completely different design processes I think about it may be the way Frank Lloyd Wright would approached architecture. >>Er I >>mean, in his career, he fluidly moved between residential architecture like falling water and the Robie House. But he also designed marquis buildings like the Johnson wax building. In each case, he simply looked at the requirements, thought about what was necessary for those users and designed accordingly. And that's really what we do. A thought spot. We spend time talking to customers. We spend time talking to users, and we spent a lot of time thinking through the problem and trying to solve it holistically. And it's simply a possible >>thanks, Bob. That's a beautiful analogy on one last question for you. Bischel. How frequently will you be adding features to this new experience, >>But I'm glad you asked that, Hannah, because this is something that we are really really excited about with thoughts about one being in the cloud. We want to go really, really fast. So we expect to eventually get to releasing new innovations every day. We expect that in the near future, we'll get to, you know, every month and every week, and we hope to get to everyday eventually fingers crossed on housing. That can happen. Great. Thanks, >>Michelle. And thank you, Bob. I'm so glad you could all join us this morning to hear more about thoughts about one. Stay close and get ready for the next session. which will be beginning in a few minutes. In it will be introduced to thoughts for >>everywhere are >>embedded analytics product on. We'll be hearing directly from our customers at Hayes about how they're using embedded analytics to help healthcare providers across billing compliance on revenue integrity functions. To make more informed decisions on make effective actions to avoid risk and maximize revenue. See you there.

>>Yeah, yeah, hello again and welcome to our last session of the day before we head to the meat. The experts roundtables how T Mobile is building a data driven organization with thought spot and whip prone. Today we'll hear how T Mobile is leaving Excel hell by enabling all employees with self service analytics so they can get instant answers on curated data. We're lucky to be closing off the day with these two speakers. Evo Benzema, manager of business intelligence services at T Mobile Netherlands, and Sanjeev Chowed Hurry, lead architect AT T Mobile, Netherlands, from Whip Chrome. Thank you both very much for being with us today, for today's session will cover how mobile telco markets have specific dynamics and what it waas that T Mobile was facing. We'll also go over the Fox spot and whip pro solution and how they address T mobile challenges. Lastly, but not least, of course, we'll cover Team Mobil's experience and learnings and takeaways that you can use in your business without further ado Evo, take us away. >>Thank you very much. Well, let's first talk a little bit about T Mobile, Netherlands. We are part off the larger deutsche Telekom Group that ISS operating in Europe and the US We are the second largest mobile phone company in the Netherlands, and we offer the full suite awful services that you expect mobile landline in A in an interactive TV. And of course, Broadbent. Um so this is what the Mobile is appreciation at at the moment, a little bit about myself. I'm already 11 years at T Mobile, which is we part being part of the furniture. In the meantime, I started out at the front line service desk employee, and that's essentially first time I came into a touch with data, and what I found is that I did not have any possibility of myself to track my performance. Eso I build something myself and here I saw that this need was there because really quickly, roughly 2020 off my employer colleagues were using us as well. This was a little bit where my efficient came from that people need to have access to data across the organization. Um, currently, after 11 years running the BR Services Department on, I'm driving this transformation now to create a data driven organization with a heavy customer focus. Our big goal. Our vision is that within two years, 8% of all our employees use data on a day to day basis to make their decisions and to improve their decision. So over, tuition Chief. Now, thank >>you. Uh, something about the proof. So we prize a global I T and business process consulting and delivery company. Uh, we have a comprehensive portfolio of services with presents, but in 61 countries and maybe 1000 plus customers. As we're speaking with Donald, keep customers Region Point of view. We primary look to help our customers in reinventing the business models with digital first approach. That's how we look at our our customers toe move to digitalization as much as possible as early as possible. Talking about myself. Oh, I have little over two decades of experience in the intelligence and tell cope landscape. Calico Industries. I have worked with most of the telcos totally of in us in India and in Europe is well now I have well known cream feed on brownfield implementation off their house on big it up platforms. At present, I'm actively working with seminal data transform initiative mentioned by evil, and we are actively participating in defining the logical and physical footprint for future architectures for criminal. I understand we are also, in addition, taking care off and two and ownership off off projects, deliveries on operations, back to you >>so a little bit over about the general telco market dynamics. It's very saturated market. Everybody has mobile phones already. It's the growth is mostly gone, and what you see is that we have a lot of trouble around customer brand loyalty. People switch around from provider to provider quite easily, and new customers are quite expensive. So our focus is always to make customer loyal and to keep them in the company. And this is where the opportunities are as well. If we increase the retention of customers or reduce what we say turned. This is where the big potential is for around to use of data, and we should not do this by only offering this to the C suite or the directors or the mark managers data. But this needs to be happening toe all employees so that they can use this to really help these customers and and services customers is situated. This that we can create his loyalty and then This is where data comes in as a big opportunity going forward. Yeah. So what are these challenges, though? What we're facing two uses the data. And this is, uh, these air massive over our big. At least let's put it like that is we have a lot of data. We create around four billion new record today in our current platforms. The problem is not everybody can use or access this data. You need quite some technical expertise to add it, or they are pre calculated into mawr aggregated dashboard. So if you have a specific question, uh, somebody on the it side on the buy side should have already prepared something so that you can get this answer. So we have a huge back lock off questions and data answers that currently we cannot answer on. People are limited because they need technical expertise to use this data. These are the challenges we're trying to solve going forward. >>Uh, so the challenge we see in the current landscape is T mobile as a civil mentioned number two telco in Europe and then actually in Netherlands. And then we have a lot of acquisitions coming in tow of the landscape. So overall complexity off technical stack increases year by year and acquisition by acquisition it put this way. So we at this time we're talking about Claudia Irureta in for Matic Uh, aws and many other a complex silo systems. We actually are integrated where we see multiple. In some cases, the data silos are also duplicated. So the challenge here is how do we look into this data? How do we present this data to business and still ensure that Ah, mhm Kelsey of the data is reliable. So in this project, what we looked at is we curated that around 10% off the data of us and made it ready for business to look at too hot spot. And this also basically help us not looking at the A larger part of the data all together in one shot. What's is going to step by step with manageable set of data, obviously manages the time also and get control on cost has. >>So what did we actually do and how we did? Did we do it? And what are we going to do going forward? Why did we chose to spot and what are we measuring to see if we're successful is is very simply, Some stuff I already alluded to is usual adoption. This needs to be a tool that is useable by everybody. Eso This is adoption. The user experience is a major key to to focus on at the beginning. Uh, but lastly, and this is just also cold hard. Fact is, it needs to save time. It needs to be faster. It needs to be smarter than the way we used to do it. So we focused first on setting up the environment with our most used and known data set within the company. The data set that is used already on the daily basis by a large group. We know what it's how it works. We know how it acts on this is what we decided to make available fire talksport this cut down the time around, uh, data modeling a lot because we had this already done so we could go right away into training users to start using this data, and this is already going on very successfully. We have now 40 heavily engaged users. We go went life less than a month ago, and we see very successful feedback on user experience. We had either yesterday, even a beautiful example off loading a new data set and and giving access to user that did not have a training for talk sport or did not know what thoughts, what Waas. And we didn't in our he was actively using this data set by building its own pin boards and asking questions already. And this shows a little bit the speed off delivery we can have with this without, um, much investments on data modeling, because that's part was already done. So our second stage is a little bit more ambitious, and this is making sure that all this information, all our information, is available for frontline uh, employees. So a customer service but also chills employees that they can have data specifically for them that make them their life easier. So this is performance KP ice. But it could also be the beautiful word that everybody always uses customer Terry, 60 fuse. But this is giving the power off, asking questions and getting answers quickly to everybody in the company. That's the big stage two after that, and this is going forward a little bit further in the future and we are not completely there yet, is we also want Thio. Really? After we set up the government's properly give the power to add your own data to our curated data sets that that's when you've talked about. And then with that, we really hope that Oh, our ambition and our plan is to bring this really to more than 800 users on a daily basis to for uses on a daily basis across our company. So this is not for only marketing or only technology or only one segment. This is really an application that we want to set in our into system that works for everybody. And this is our ambition that we will work through in these three, uh, steps. So what did we learn so far? And and Sanjeev, please out here as well, But one I already said, this is no which, which data set you start. This is something. Start with something. You know, start with something that has a wide appeal to more than one use case and make sure that you make this decision. Don't ask somebody else. You know what your company needs? The best you should be in the driver seat off this decision. And this is I would be saying really the big one because this will enable you to kickstart this really quickly going forward. Um, second, wellness and this is why we introduce are also here together is don't do this alone. Do this together with, uh I t do this together with security. Do this together with business to tackle all these little things that you don't think about yourself. Maybe security, governance, network connections and stuff like that. Make sure that you do this as a company and don't try to do this on your own, because there's also again it's removes. Is so much obstacles going forward? Um, lastly, I want to mention is make sure that you measure your success and this is people in the data domain sometimes forget to measure themselves. Way can make sure everybody else, but we forget ourselves. But really try to figure out what makes its successful for you. And we use adoption percentages, usual experience, surveys and and really calculations about time saved. We have some rough calculations that we can calculate changes thio monetary value, and this will save us millions in years. by just automating time that is now used on, uh, now to taken by people on manual work. So, do you have any to adhere? A swell You, Susan, You? >>Yeah. So I'll just pick on what you want to mention about. Partner goes live with I t and other functions. But that is a very keating, because from my point of view, you see if you can see that the data very nice and data quality is also very clear. If we have data preparing at the right level, ready to be consumed, and data quality is taken, care off this feel 30 less challenges. Uh, when the user comes and questioned the gator, those are the things which has traded Quiz it we should be sure about before we expose the data to the Children. When you're confident about your data, you are confident that the user will also get the right numbers they're looking for and the number they have. Their mind matches with what they see on the screen. And that's where you see there. >>Yeah, and that that that again helps that adoption, and that makes it so powerful. So I fully agree. >>Thank you. Eva and Sanjeev. This is the picture perfect example of how a thought spot can get up and running, even in a large, complex organization like T Mobile and Sanjay. Thank you for sharing your experience on how whip rose system integration expertise paved the way for Evo and team to realize value quickly. Alright, everyone's favorite part. Let's get to some questions. Evil will start with you. How have your skill? Data experts reacted to thought spot Is it Onley non technical people that seem to be using the tool or is it broader than that? You may be on. >>Yes, of course, that happens in the digital environment. Now this. This is an interesting question because I was a little bit afraid off the direction off our data experts and are technically skilled people that know how to work in our fight and sequel on all these things. But here I saw a lot of enthusiasm for the tool itself and and from two sides, either to use it themselves because they see it's a very easy way Thio get to data themselves, but also especially that they see this as a benefit, that it frees them up from? Well, let's say mundane questions they get every day. And and this is especially I got pleasantly surprised with their reaction on that. And I think maybe you can also say something. How? That on the i t site that was experienced. >>Well, uh, yeah, from park department of you, As you mentioned, it is changing the way business is looking at. The data, if you ask me, have taken out talkto data rather than looking at it. Uh, it is making the interactivity that that's a keyword. But I see that the gap between the technical and function folks is also diminishing, if I may say so over a period of time, because the technical folks now would be able to work with functional teams on the depth and coverage of the data, rather than making it available and looking at the technical side off it. So now they can have a a fair discussion with the functional teams on. Okay, these are refute. Other things you can look at because I know this data is available can make it usable for you, especially the time it takes for the I t. G. When graduate dashboard, Uh, that time can we utilize toe improve the quality and reliability of the data? That's yeah. See the value coming. So if you ask me to me, I see the technical people moving towards more of a technical functional role. Tools such as >>That's great. I love that saying now we can talk to data instead of just looking at it. Um Alright, Evo, I think that will finish up with one last question for you that I think you probably could speak. Thio. Given your experience, we've seen that some organizations worry about providing access to data for everyone. How do you make sure that everyone gets the same answer? >>Yes. The big data Girlfriends question thesis What I like so much about that the platform is completely online. Everything it happens online and everything is terrible. Which means, uh, in the good old days, people will do something on their laptop. Beirut at a logic to it, they were aggregated and then they put it in a power point and they will share it. But nobody knew how this happened because it all happened offline. With this approach, everything is transparent. I'm a big I love the word transparency in this. Everything is available for everybody. So you will not have a discussion anymore. About how did you get to this number or how did you get to this? So the question off getting two different answers to the same question is removed because everything happens. Transparency, online, transparent, online. And this is what I think, actually, make that question moot. Asl Long as you don't start exporting this to an offline environment to do your own thing, you are completely controlling, complete transparent. And this is why I love to share options, for example and on this is something I would really keep focusing on. Keep it online, keep it visible, keep it traceable. And there, actually, this problem then stops existing. >>Thank you, Evelyn. Cindy, That was awesome. And thank you to >>all of our presenters. I appreciate your time so much. I hope all of you at home enjoyed that as much as I did. I know a lot of you did. I was watching the chat. You know who you are. I don't think that I'm just a little bit in awe and completely inspired by where we are from a technological perspective, even outside of thoughts about it feels like we're finally at a time where we can capitalize on the promise that cloud and big data made to us so long ago. I loved getting to see Anna and James describe how you can maximize the investment both in time and money that you've already made by moving your data into a performance cloud data warehouse. It was cool to see that doubled down on with the session, with AWS seeing a direct query on Red Shift. And even with something that's has so much scale like TV shows and genres combining all of that being able to search right there Evo in Sanjiv Wow. I mean being able to combine all of those different analytics tools being able to free up these analysts who could do much more important and impactful work than just making dashboards and giving self service analytics to so many different employees. That's incredible. And then, of course, from our experts on the panel, I just think it's so fascinating to see how experts that came from industries like finance or consulting, where they saw the imperative that you needed to move to thes third party data sets enriching and organizations data. So thank you to everyone. It was fascinating. I appreciate everybody at home joining us to We're not quite done yet. Though. I'm happy to say that we after this have the product roadmap session and that we are also then going to move into hearing and being able to ask directly our speakers today and meet the expert session. So please join us for that. We'll see you there. Thank you so much again. It was really a pleasure having you.

>>Yeah, yeah. Welcome back to the third session in our building, A vibrant data ecosystem track. This session is unleash the power of your cloud data warehouse. So what comes after you've moved your data to the cloud in this session will explore White Enterprise Analytics is finally ready for the cloud, and we'll discuss how you can consume Enterprise Analytics in the very same way he would cloud services. We'll also explore where analytics meets cloud and see firsthand how thought spot is open for everyone. Let's get going. I'm happy to say we'll be hearing from two folks from thought spot today, Michael said Cassie, VP of strategic partnerships, and Vika Valentina, senior product marketing manager. And I'm very excited to welcome from our partner at AWS Gal Bar MIA, product engineering manager with Red Shift. We'll also be sharing a live demo of thought spot for BTC Marketing Analytics directly on Red Shift data. Gal, please kick us off. >>Thank you, Military. And thanks. The talks about team and everyone attending today for joining us. When we talk about data driven organizations, we hear that 85% of businesses want to be data driven. However, on Lee. 37% have been successful in We ask ourselves, Why is that and believe it or not, Ah, lot of customers tell us that they struggled with live in defining what being data driven it even means, and in particular aligning that definition between the business and the technology stakeholders. Let's talk a little bit. Let's look at our own definition. A data driven organization is an organization that harnesses data is an asset. The drive sustained innovation and create actionable insights. The super charge, the experience of their customers so they demand more. Let's focus on a few things here. One is data is an asset. Data is very much like a product needs to evolve sustained innovation. It's not just innovation innovation, it's sustained. We need to continuously innovate when it comes to data actionable insights. It's not just interesting insights these air actionable that the business can take and act upon, and obviously the actual experience we. Whether whether the customers are internal or external, we want them to request Mawr insights and as such, drive mawr innovation, and we call this the for the flywheel. We use the flywheel metaphor here where we created that data set. Okay, Our first product. Any focused on a specific use case? We build an initial NDP around that we provided with that with our customers, internal or external. They provide feedback, the request, more features. They want mawr insights that enables us to learn bringing more data and reach that actual data. And again we create MAWR insights. And as the flywheel spins faster, we improve on operational efficiencies, supporting greater data richness, and we reduce the cost of experimentation and legacy environments were never built for this kind of agility. In many cases, customers have struggled to keep momentum in their fleet, flywheel in particular around operational efficiency and experimentation. This is where Richie fits in and helps customer make the transition to a true data driven organization. Red Shift is the most widely used data warehouse with tens of thousands of customers. It allows you to analyze all your data. It is the only cloud data warehouse that sits, allows you to analyze data that sits in your data lake on Amazon, a street with no loading duplication or CTL required. It is also allows you to scale with the business with its hybrid architectures it also accelerates performance. It's a shared storage that provides the ability to scale toe unlimited concurrency. While the UN instant storage provides low late and say access to data it also provides three. Key asks that customers consistently tell us that matter the most when it comes to cost. One is usage based pricing Instead of license based pricing. Great value as you scale your data warehouse using, for example, reserved instances they can save up to 75% compared to on the mind demand prices. And as your data grows, infrequently accessed data can be stored. Cost effectively in S three encouraged through Amazon spectrum, and the third aspect is predictable. Month to month spend with no hitting charges and surprises. Unlike and unlike other cloud data warehouses, where you need premium versions for additional enterprise capabilities. Wretched spicing include building security compression and data transfer. >>Great Thanks. Scout um, eso. As you can see, everybody wins with the cloud data warehouses. Um, there's this evolution of movement of users and data and organizations to get value with these cloud data warehouses. And the key is the data has to be accessible by the users, and this data and the ability to make business decisions on the data. It ranges from users on the front line all the way up to the boardroom. So while we've seen this evolution to the Cloud Data Warehouse, as you can see from the statistic from Forrester, we're still struggling with how much of that data actually gets used for analytics. And so what is holding us back? One of the main reasons is old technology really trying to work with today's modern cloud data warehouses? They weren't built for it. So you run into issues of trying to do data replication, getting the data out of the cloud data warehouse. You can do analysis and then maintaining these middle layers of data so that you can access it quickly and get the answers you need. Another issue that's holding us back is this idea that you have to have your data in perfect shape with the perfect pipeline based on the exact dashboard unique. Um, this isn't true. Now, with Cloud data warehouse and the speed of important business data getting into those cloud data warehouses, you need a solution that allows you to access it right away without having everything to be perfect from the start, and I think this is a great opportunity for GAL and I have a little further discussion on what we're seeing in the marketplace. Um, one of the primary ones is like, What are the limiting factors, your Siegel of legacy technologies in the market when it comes to this cloud transformation we're talking about >>here? It's a great question, Michael and the variety of aspect when it comes to legacy, the other warehouses that are slowing down innovation for companies and businesses. I'll focus on 21 is performance right? We want faster insights. Companies want the ability to analyze MAWR data faster. And when it comes to on prem or legacy data warehouses, that's hard to achieve because the second aspect comes into display, which is the lack of flexibility, right. If you want to increase your capacity of your warehouse, you need to ensure request someone needs to go and bring an actual machine and install it and expand your data warehouse. When it comes to the cloud, it's literally a click of a button, which allows you to increase the capacity of your data warehouse and enable your internal and external users to perform analytics at scale and much faster. >>It falls right into the explanation you provided there, right as the speed of the data warehouses and the data gets faster and faster as it scales, older solutions aren't built toe leverage that, um, you know, they're either they're having to make technical, you know, technical cuts there, either looking at smaller amounts of data so that they can get to the data quicker. Um, or it's taking longer to get to the data when the data warehouse is ready, when it could just be live career to get the answers you need. And that's definitely an issue that we're seeing in the marketplace. I think the other one that you're looking at is things like governance, lineage, regulatory requirements. How is the cloud you know, making it easier? >>That's That's again an area where I think the cloud shines. Because AWS AWS scale allows significantly more investment in securing security policies and compliance, it allows customers. So, for example, Amazon redshift comes by default with suck 1 to 3 p. C. I. Aiso fared rampant HIPPA compliance, all of them out of the box and at our scale. We have the capacity to implement those by default for all of our customers and allow them to focus. Their very expensive, valuable ICTY resource is on actual applications that differentiate their business and transform the customer experience. >>That's a great point, gal. So we've talked about the, you know, limiting factors. Technology wise, we've mentioned things like governance. But what about the cultural aspect? Right? So what do you see? What do you see in team struggling in meeting? You know, their cloud data warehouse strategy today. >>And and that's true. One of the biggest challenges for large large organizations when they moved to the cloud is not about the technology. It's about people, process and culture, and we see differences between organizations that talk about moving to the cloud and ones that actually do it. And first of all, you wanna have senior leadership, drive and be aligned and committed to making the move to the cloud. But it's not just that you want. We see organizations sometimes Carol get paralyzed. If they can't figure out how to move each and every last work clothes, there's no need to boil the ocean, so we often work with organizations to find that iterative motion that relative process off identifying the use cases are date identifying workloads in migrating them one at a time and and through that allowed organization to grow its knowledge from a cloud perspective as well as adopt its tooling and learn about the new capabilities. >>And from an analytics perspective, we see the same right. You don't need a pixel perfect dashboard every single time to get value from your data. You don't need to wait until the data warehouse is perfect or the pipeline to the data warehouse is perfect. With today's technology, you should be able to look at the data in your cloud data warehouse immediately and get value from it. And that's the you know, that's that change that we're pushing and starting to see today. Thanks. God, that was That was really interesting. Um, you know, as we look through that, you know, this transformation we're seeing in analytics, um, isn't really that old? 20 years ago, data warehouses were primarily on Prem and the applications the B I tools used for analytics around them were on premise well, and so you saw things like applications like Salesforce. That live in the cloud. You start having to pull data from the cloud on Prem in order to do analytics with it. Um, you know, then we saw the shift about 10 years ago in the explosion of Cloud Data Warehouse Because of their scale, cost reduced, reduce shin reduction and speed. You know, we're seeing cloud data. Warehouses like Amazon Red Shift really take place, take hold of the marketplace and are the predominant ways of storing data moving forward. What we haven't seen is the B I tools catch up. And so when you have this new cloud data warehouse technology, you really need tools that were custom built for it to take advantage of it, to be able to query the cloud data warehouse directly and get results very quickly without having to worry about creating, you know, a middle layer of data or pipelines in order to manage it. And, you know, one company captures that really Well, um, chick fil A. I'm sure everybody has heard of is one of the largest food chains in America. And, you know, they made a huge investment in red shift and one of the purposes of that investment is they wanted to get access to the data mawr quickly, and they really wanted to give their business users, um, the ability to do some ad hoc analysis on the data that they were capturing. They found that with their older tools, the problems that they were finding was that all the data when they're trying to do this analysis was staying at the analyst level. So somebody needed to create a dashboard in order to share that data with a user. And if the user's requirements changed, the analysts were starting to become burdened with requests for changes and the time it took to reflect those changes. So they wanted to move to fought spot with embrace to connect to Red Shift so they could start giving business users that capability. Query the database right away. And with this, um, they were able to find, you know, very common things in in the supply chain analysis around the ability to figure out what store should get, what product that was selling better. The other part was they didn't have to wait for the data to get settled into some sort of repository or second level database. They were able to query it quickly. And then with that, they're able to make changes right in the red shift database that were then reflected to customers and the business users right away. So what they found from this is by adopting thought spot, they were actually able to arm business users with the ability to make decisions very quickly. And they cleared up the backlog that they were having and the delay with their analysts. And they're also putting their analysts toe work on different projects where they could get better value from. So when you look at the way we work with a cloud data warehouse, um, you have to think of thoughts about embrace as the tool that access that layer. The perfect analytic partner for the Cloud Data Warehouse. We will do the live query for the business user. You don't need to know how to script and sequel, um Thio access, you know, red shift. You can type the question that you want the answer to and thought spot will take care of that query. We will do the indexing so that the results come back faster for you and we will also do the analysis on. This is one of the things I wanted to cover, which is our spot i. Q. This is new for our ability to use this with embrace and our partners at Red Shift is now. We can give you the ability to do auto analysis to look at things like leading indicators, trends and anomalies. So to put this in perspective amount imagine somebody was doing forecasting for you know Q three in the western region. And they looked at how their stores were doing. And they saw that, you know, one store was performing well, Spot like, you might be able to look at that analysis and see if there's a leading product that is underperforming based on perhaps the last few quarters of data. And bring that up to the business user for analysis right away. They don't need to have to figure that out. And, um, you know, slice and dice to find that issue on their own. And then finally, all the work you do in data management and governance in your cloud data warehouse gets reflected in the results in embrace right away. So I've done a lot of talking about embrace, and I could do more, but I think it would be far better toe. Have Vika actually show you how the product works, Vika. >>Thanks, Michael. We learned a lot today about the power of leveraging your red shift data and thought spot. But now let me show you how it works. The coronavirus pandemic has presented extraordinary challenges for many businesses, and some industries have fared better than others. One industry that seems to weather the storm pretty well actually is streaming media. So companies like Netflix and who Lou. And in this demo, we're going to be looking at data from B to C marketing efforts. First streaming media company in 2020 lately, we've been running campaigns for comedy, drama, kids and family and reality content. Each of our campaigns last four weeks, and they're staggered on a weekly basis. Therefore, we always have four campaigns running, and we can focus on one campaign launch per >>week, >>and today we'll be digging into how our campaigns are performing. We'll be looking at things like impressions, conversions and users demographic data. So let's go ahead and look at that data. We'll see what we can learn from what's happened this year so far, and how we can apply those learnings to future decision making. As you can already see on the thoughts about homepage, I've created a few pin boards that I use for reporting purposes. The homepage also includes what others on my team and I have been looking at most recently. Now, before we dive into a search, will first take a look at how to make a direct connection to the customer database and red shift to save time. I've already pre built the connection Red Shift, but I'll show you how easy it is to make that connection in just three steps. So first we give the connection name and we select our connection type and was on red Shift. Then we enter our red shift credentials, and finally, we select the tables that we want to use Great now ready to start searching. So let's start in this data to get a better idea of how our marketing efforts have been affected either positively or negatively by this really challenging situation. When we think of ad based online marketing campaigns, we think of impressions, clicks and conversions. Let's >>look at those >>on a daily basis for our purposes. So all this data is available to us in Thought spot, and we can easily you search to create a nice line chart like this that shows US trends over the last few months and based on experience. We understand that we're going to have more clicks than impressions and more impressions and conversions. If we started the chart for a minute, we could see that while impressions appear to be pretty steady over the course of the year, clicks and especially conversions both get a nice boost in mid to late March, right around the time that pandemic related policies were being implemented. So right off the bat, we found something interesting, and we can come back to this now. There are few metrics that we're gonna focus on as we analyze our marketing data. Our overall goal is obviously to drive conversions, meaning that we bring new users into our streaming service. And in order to get a visitor to sign up in the first place, we need them to get into our sign up page. A compelling campaign is going to generate clicks, so if someone is interested in our ad, they're more likely to click on it, so we'll search for Click through Rape 5% and we'll look this up by campaign name. Now even compare all the campaigns that we've launched this year to see which have been most effective and bring visitors star site. And I mentioned earlier that we have four different types of campaign content, each one aligned with one of our most popular genres. So by adding campaign content, yeah, >>and I >>just want to see the top 10. I could limit my church. Just these top 10 campaigns automatically sorted by click through rate and assigned a color for each category so we could see right away that comedy and drama each of three of the top 10 campaigns by click through rate reality is, too, including the top spot and kids and family makes one appearance as well. Without spot. We know that any non technical user can ask a question and get an answer. They can explore the answer and ask another question. When you get an answer that you want to share, keep an eye on moving forward, you pin the answer to pin board. So the BBC Marketing Campaign Statistics PIN board gives us a solid overview of our campaign related activities and metrics throughout 2020. The visuals here keep us up to date on click through rate and cost per click, but also another really important metrics that conversions or cost proposition. Now it's important to our business that we evaluate the effectiveness of our spending. Let's do another search. We're going to look at how many new customers were getting so conversions and the price cost per acquisition that we're spending to get each of these by the campaign contact category. So >>this is a >>really telling chart. We can basically see how much each new users costing us, based on the content that they see prior to signing up to the service. Drama and reality users are actually relatively expensive compared to those who joined based on comedy and kids and family content that they saw. And if all the genres kids and family is actually giving us the best bang for our marketing >>buck. >>And that's good news because the genres providing the best value are also providing the most customers. We mentioned earlier that we actually saw a sizable uptick in conversions as stay at home policies were implemented across much of the country. So we're gonna remove cost per acquisition, and we're gonna take a daily look how our campaign content has trended over the years so far. Eso By doing this now, we can see a comparison of the different genres daily. Some campaigns have been more successful than others. Obviously, for example, kids and family contact has always fared pretty well Azaz comedy. But as we moved into the stay at home area of the line chart, we really saw these two genres begin to separate from the rest. And even here in June, as some states started to reopen, we're seeing that they're still trending up, and we're also seeing reality start to catch up around that time. And while the first pin board that we looked at included all sorts of campaign metrics, this is another PIN board that we've created so solely to focus on conversions. So not only can we see which campaigns drug significant conversions, we could also dig into the demographics of new users, like which campaigns and what content brought users from different parts of the country or from different age groups. And all this is just a quick search away without spot search directly on a red shift. Data Mhm. All right, Thank you. And back to you, Michael. >>Great. Thanks, Vika. That was excellent. Um, so as you can see, you can very quickly go from zero to search with thought Spot, um, connected to any cloud data warehouse. And I think it's important to understand that we mentioned it before. Not everything has to be perfect. In your doubt, in your cloud data warehouse, um, you can use thought spot as your initial for your initial tool. It's for investigatory purposes, A Z you can see here with star, Gento, imax and anthem. And a lot of these cases we were looking at billions of rows of data within minutes. And as you as your data warehouse maturity grows, you can start to add more and more thoughts about users to leverage the data and get better analysis from it. So we hope that you've enjoyed what you see today and take the step to either do one of two things. We have a free trial of thoughts about cloud. If you go to the website that you see below and register, we can get you access the thought spots so you can start searching today. Another option, by contacting our team, is to do a zero to search workshop where 90 minutes will work with you to connect your data source and start to build some insights and exactly what you're trying to find for your business. Um thanks, everybody. I would especially like to thank golf from AWS for joining us on this today. We appreciate your participation, and I hope everybody enjoyed what they saw. I think we have a few questions now. >>Thank you, Vika, Gal and Michael. It's always exciting to see a live demo. I know that I'm one of those comedy numbers. We have just a few minutes left, but I would love to ask a couple of last questions Before we go. Michael will give you the first question. Do I need to have all of my data cleaned and ready in my cloud data warehouse before I begin with thought spot? >>That's a great question, Mallory. No, you don't. You can really start using thought spot for search right away and start getting analysis and start understanding the data through the automatic search analysis and the way that we query the data and we've seen customers do that. Chick fil a example that we talked about earlier is where they were able to use thoughts bought to notice an anomaly in the Cloud Data Warehouse linking between product and store. They were able to fix that very quickly. Then that gets reflected across all of the users because our product queries the Cloud Data Warehouse directly so you can get started right away without it having to be perfect. And >>that's awesome. And gal will leave a fun one for you. What can we look forward to from Amazon Red Shift next year? >>That's a great question. And you know, the team has been innovating extremely fast. We released more than 200 features in the last year and a half, and we continue innovating. Um, one thing that stands out is aqua, which is a innovative new technology. Um, in fact, lovely stands for Advanced Square Accelerator, and it allows customers to achieve performance that up to 10 times faster, uh, than what they've seen really outstanding and and the way we've achieved that is through a shift in paradigm in the actual technological implementation section. Uh, aqua is a new distributed and hardware accelerated processing layer, which effectively allows us to push down operations analytics operations like compression, encryption, filtering and aggregations to the storage there layer and allow the aqua nodes that are built with custom. AWS designed analytics processors to perform these operations faster than traditional soup use. And we no longer need to bring, you know, scan the data and bring it all the way to the computational notes were able to apply these these predicates filtering and encourage encryption and compression and aggregations at the storage level. And likewise is going to be available for every are a three, um, customer out of the box with no changes to come. So I apologize for being getting out a little bit, but this is really exciting. >>No, that's why we invited you. Call. Thank you on. Thank you. Also to Michael and Vika. That was excellent. We really appreciate it. For all of you tuning in at home. The final session of this track is coming up shortly. You aren't gonna want to miss it. We're gonna end strong, come back and hear directly from our customer a T mobile on how T Mobile is building a data driven organization with thought spot in which >>pro, It's >>up next, see you then.

>>welcome back. And thanks for joining us for our second session. External data, your new leading indicators. We'll be hearing from industry leaders as they share best practices and challenges in leveraging external data. This panel will be a true conversation on the part of the possible. All right, let's get to >>it >>today. We're excited to be joined by thought spots. Chief Data Strategy Officer Cindy Housing Deloitte's chief data officer Manteo, the founder and CEO of Eagle Alfa. And it Kilduff and Snowflakes, VP of data marketplace and customer product strategy. Matt Glickman. Cindy. Without further ado, the floor is yours. >>Thank you, Mallory. And I am thrilled to have this brilliant team joining us from around the world. And they really bring each a very unique perspective. So I'm going to start from further away. Emmett, Welcome. Where you joining us from? >>Thanks for having us, Cindy. I'm joining from Dublin, Ireland, >>great. And and tell us a little bit about Eagle Alfa. What do you dio >>from a company's perspective? Think of Eagle Alfa as an aggregator off all the external data sets on a word I'll use a few times. Today is a big advantage we could bring companies is we have a data concierge service. There's so much data we can help identify the right data sets depending on the specific needs of the company. >>Yeah. And so, Emma, you know, people think I was a little I kind of shocked the industry. Going from gardener to a tech startup. Um, you have had a brave journey as well, Going from financial services to starting this company, really pioneering it with I think the most data sets of any of thes is that right? >>Yes, it was. It was a big jump to go from Morgan Stanley. Uh, leave the comforts of that environment Thio, PowerPoint deck and myself raising funding eight years ago s So it was a big jump on. We were very early in our market. It's in the last few years where there's been real momentum and adoption by various types of verticals. The hedge funds were first, maybe then private equity, but corporate sar are following quite quickly from behind. That will be the biggest users, in our view, by by a significant distance. >>Yeah, great. Thank um, it So we're going to go a little farther a field now, but back to the U. S. So, Juan, where you joining us from? >>Hey, Cindy. Thanks for having me. I'm joining you from Houston, Texas. >>Great. Used to be my home. Yeah, probably see Rice University back there. And you have a distinct perspective serving both Deloitte customers externally, but also internally. Can you tell us about that? >>Yeah, absolutely. So I serve as the Lord consultants, chief data officer, and as a professional service firm, I have the responsibility for overseeing our overall data agenda, which includes both the way we use data and insights to run and operate our own business, but also in how we develop data and insights services that we then take to market and how we serve our dealers and clients. >>Great. Thank you, Juan. And last but not least, Matt Glickman. Kind of in my own backyard in New York. Right, Matt? >>Correct. Joining I haven't been into the city and many months, but yes, um, based in New York. >>Okay. Great. And so, Matt, you and Emmett also, you know, brave pioneers in this space, and I'm remembering a conversation you and I shared when you were still a J. P. Morgan, I believe. And you're Goldman Sachs. Sorry. Sorry. Goldman. Can you Can you share that with us? >>Sure. I made the move back in 2015. Um, when everyone thought, you know, my wife, my wife included that I was crazy. I don't know if I would call it Comfortable was emitted, but particularly had been there for a long time on git suffered in some ways. A lot of the pains we're talking about today, given the number of data, says that the amount of of new data sets that are always demand for having run analytics teams at Goldman, seeing the pain and realizing that this pain was not unique to Goldman Sachs, it was being replicated everywhere across the industry, um, in a mind boggling way and and the fortuitous, um, luck to have one of snowflakes. Founders come to pitch snowflake to Goldman a little bit early. Um, they became a customer later, but a little bit early in 2014. And, you know, I realized that this was clearly, you know, the answer from first principles on bond. If I ever was going to leave, this was a problem. I was acutely aware of. And I also was aware of how much the man that was in financial services for a better solution and how the cloud could really solve this problem in particular the ability to not have to move data in and out of these organizations. And this was something that I saw the future of. Thank you, Andi, that this was, you know, sort of the pain that people just expected to pay. Um, this price if you need a data, there was method you had thio. You had to use you either ftp data in and out. You had data that was being, you know, dropped off and, you know, maybe in in in a new ways and cloud buckets or a P i s You have to suck all this data down and reconstruct it. And God forbid the formats change. It was, you know, a nightmare. And then having issues with data, you had a what you were seeing internally. You look nothing like what the data vendors were seeing because they want a completely different system, maybe model completely differently. Um, but this was just the way things were. Everyone had firewalls. Everyone had their own data centers. There was no other way on git was super costly. And you know this. I won't even share the the details of you know, the errors that would occur in the pain that would come from that, Um what I realized it was confirmed. What I saw it snowflake at the time was once everyone moves to run their actual workloads in this in the cloud right where you're now beyond your firewall, you'll have all this scale. But on top of that, you'll be able to point at data from these vendors were not there the traditional data vendors. Or, you know, this new wave of alternative data vendors, for example, like the ones that eagle out for brings together And bring these all these data sets together with your own internal data without moving it. Yeah, this was a fundamental shift of what you know, it's in some ways, it was a side effect of everyone moving to the cloud for costs and scale and elasticity. But as a side effect of that is what we talked about, You know it snowflake summit, you know, yesterday was this notion of a data cloud that would connect data between regions between cloud vendors between customers in a way where you could now reference data. Just like your reference websites today, I don't download CNN dot com. I point at it, and it points me to something else. I'm always seeing the latest version, obviously, and we can, you know, all collaborate on what I'm seeing on that website. That's the same thing that now can happen with data. So And I saw this as what was possible, and I distinctly asked the question, you know, the CEO of the time Is this possible? And not only was it possible it was a fundamental construct that was built into the way that snowflake was delivered. And then, lastly, this is what we learned. And I think this is what you know. M It also has been touting is that it's all great if data is out there and even if you lower that bar of access where data doesn't have to move, how do I know? Right? If I'm back to sitting at Goldman Sachs, how do I know what data is available to me now in this this you know, connected data network eso we released our data marketplace, which was a very different kind of marketplace than these of the past. Where for us, it was really like a global catalog that would elect a consumer data consumer. Noah data was available, but also level the playing field. Now we're now, you know, Eagle, Alfa, or even, you know, a new alternative data vendor build something in their in their basement can now publish that data set so that the world could see and consume and be aligned to, you know, snowflakes, core business, and not where we wouldn't have to be competing or having to take, um, any kind of custody of that data. So adding that catalog to this now ubiquitous access, um really changed the game and, you know, and then now I seem like a genius for making this move. But back then, like I said, we've seen I seem like instant. I was insane. >>Well, given, given that snowflake was the hottest aipo like ever, you were a genius. Uh, doing this, you know, six years in advance. E think we all agree on that, But, you know, a lot of this is still visionary. Um, you know, some of the most leading companies are already doing this. But one What? What is your take our Are you best in class customers still moving the data? Or is this like they're at least thinking about data monetization? What are you seeing from your perspective? >>Yeah, I mean, I did you know, the overall appreciation and understanding of you know, one. I got to get my house in order around my data, um, has something that has been, you know, understood and acted upon. Andi, I do agree that there is a shift now that says, you know, data silos alone aren't necessarily gonna bring me, you know, new and unique insights on dso enriching that with external third party data is absolutely, you know, sort of the the ship that we're seeing our customers undergo. Um, what I find extremely interesting in this space and what some of the most mature clients are doing is, you know, really taking advantage of these data marketplaces. But building data partnerships right there from what mutually exclusive, where there is a win win scenario for for you know, that organization and that could be, you know, retail customers or life science customers like with pandemic, right the way we saw companies that weren't naturally sharing information are now building these data partnership right that are going are going into mutually benefit, you know, all organizations that are sort of part of that value to Andi. I think that's the sort of really important criteria. And how we're seeing our clients that are extremely successful at this is that partnership has benefits on both sides of that equation, right? Both the data provider and then the consumer of that. And there has to be, you know, some way to ensure that both parties are are are learning right, gaining you insights to support, you know, whatever their business organization going on. >>Yeah, great one. So those data partnerships getting across the full value chain of sharing data and analytics Emmett, you work on both sides of the equation here, helping companies. Let's say let's say data providers maybe, like, you know, cast with human mobility monetize that. But then also people that are new to it. Where you seeing the top use cases? Well, >>interestingly, I agree with one of the supply side. One of the interesting trends is we're seeing a lot more data coming from large Corporates. Whether they're listed are private equity backed, as opposed to maybe data startups that are earning money just through data monetization. I think that's a great trend. I think that means a lot of the best. Data said it data is yet to come, um, in terms off the tough economy and how that's changed. I think the category that's had the most momentum and your references is Geo location data. It's that was the category at our conference in December 2000 and 12 that was pipped as the category to watch in 2019. On it didn't become that at all. Um, there were some regulatory concerns for certain types of geo data, but with with covert 19, it's Bean absolutely critical for governments, ministries of finance, central banks, municipalities, Thio crunch that data to understand what's happening in a real time basis. But from a company perspective, it's obviously critical as well. In terms of planning when customers might be back in the High Street on DSO, fourth traditionally consumer transaction data of all the 26 categories in our taxonomy has been the most popular. But Geo is definitely catching up your slide. Talked about being a tough economy. Just one point to contradict that for certain pockets of our clients, e commerce companies are having a field day, obviously, on they are very data driven and tech literate on day are they are really good client base for us because they're incredibly hungry, firm or data to help drive various, uh, decision making. >>Yeah, So fair enough. Some sectors of the economy e commerce, electron, ICS, healthcare are doing great. Others travel, hospitality, Um, super challenging. So I like your quote. The best is yet to come, >>but >>that's data sets is yet to come. And I do think the cloud is enabling that because we could get rid of some of the messy manual data flows that Matt you talked about, but nonetheless, Still, one of the hardest things is the data map. Things combining internal and external >>when >>you might not even have good master data. Common keys on your internal data. So any advice for this? Anyone who wants to take that? >>Sure I can. I can I can start. That's okay. I do think you know, one of the first problems is just a cataloging of the information that's out there. Um, you know, at least within our organization. When I took on this role, we were, you know, a large buyer of third party data. But our organization as a whole didn't necessarily have full visibility into what was being bought and for what purpose. And so having a catalog that helps us internally navigate what data we have and how we're gonna use it was sort of step number one. Um, so I think that's absolutely important. Um, I would say if we could go from having that catalog, you know, created manually to more automated to me, that's sort of the next step in our evolution, because everyone is saying right, the ongoing, uh, you know, creation of new external data sets. It's only going to get richer on DSO. We wanna be able to take advantage of that, you know, at the at the pacing speed, that data is being created. So going from Emanuel catalog to anonymous >>data >>catalog, I think, is a key capability for us. But then you know, to your second point, Cindy is how doe I then connect that to our own internal data to drive greater greater insights and how we run our business or how we serve our customers. Andi, that one you know really is a It's a tricky is a tricky, uh, question because I think it just depends on what data we're looking toe leverage. You know, we have this concept just around. Not not all data is created equal. And when you think about governance and you think about the management of your master data, your internal nomenclature on how you define and run your business, you know that that entire ecosystem begins to get extremely massive and it gets very broad and very deep on DSO for us. You know, government and master data management is absolutely important. But we took a very sort of prioritized approach on which domains do we really need to get right that drive the greatest results for our organization on dso mapping those domains like client data or employee data to these external third party data sources across this catalog was really the the unlocked for us versus trying to create this, you know, massive connection between all the external data that we're, uh, leveraging as well as all of our own internal data eso for us. I think it was very. It was a very tailored, prioritized approach to connecting internal data to external data based on the domains that matter most to our business. >>So if the domains so customer important domain and maybe that's looking at things, um, you know, whether it's social media data or customer transactions, you prioritized first by that, Is that right? >>That's correct. That's correct. >>And so, then, Matt, I'm going to throw it back to you because snowflake is in a unique position. You actually get to see what are the most popular data sets is is that playing out what one described are you seeing that play out? >>I I'd say Watch this space. Like like you said. I mean this. We've you know, I think we start with the data club. We solve that that movement problem, which I think was really the barrier that you tended to not even have a chance to focus on this mapping problem. Um, this notion of concordance, I think this is where I see the big next momentum in this space is going to be a flurry of traditional and new startups who deliver this concordance or knowledge graph as a service where this is no longer a problem that I have to solve internal to my organization. The notion of mastering which is again when everyone has to do in every organization like they used to have to do with moving data into the organization goes away. And this becomes like, I find the best of breed for the different scopes of data that I have. And it's delivered to me as a, you know, as a cloud service that just takes my data. My internal data maps it to these 2nd and 3rd party data sets. Um, all delivered to me, you know, a service. >>Yeah, well, that would be brilliant concordance as a service or or clean clean master data as a service. Um, using augmented data prep would be brilliant. So let's hope we get there. Um, you know, so 2020 has been a wild ride for everyone. If I could ask each of you imagine what is the art of the possible or looking ahead to the next to your and that you are you already mentioned the best is yet to come. Can you want to drill down on that. What what part of the best is yet to come or what is your already two possible? >>Just just a brief comment on mapping. Just this week we published a white paper on mapping, which is available for for anyone on eagle alfa dot com. It's It's a massive challenge. It's very difficult to solve. Just with technology Onda people have tried to solve it and get a certain level of accuracy, but can't get to 100% which which, which, which makes it difficult to solve it. If if if there is a new service coming out against 100% I'm all ears and that there will be a massive step forward for the entire data industry, even if it comes in a few years time, let alone next year, I think going back to the comment on data Cindy. Yes, I think boards of companies are Mawr and Mawr. Viewing data as an asset as opposed to an expense are a cost center on bond. They are looking therefore to get their internal house in order, as one was saying, but also monetize the data they are sitting on lots of companies. They're sitting on potentially valuable data. It's not all valuable on a lot of cases. They think it's worth a lot more than it is being frank. But in some cases there is valuable data on bond. If monetized, it can drop to the bottom line on. So I think that bodes well right across the world. A lot of the best date is yet to come on. I think a lot of firms like Deloitte are very well positioned to help drive that adoption because they are the trusted advisor to a lot of these Corporates. Um, so that's one thing. I think, from a company perspective. It's still we're still at the first base. It's quite frustrating how slow a lot of companies are to move and adopt, and some of them are haven't hired CDO. Some of them don't have their internal house in order. I think that has to change next year. I think if we have this conference at this time next year, I would expect that would hopefully be close to the tipping point for Corporates to use external data. And the Malcolm Gladwell tipping point on the final point I make is I think, that will hopefully start to see multi department use as opposed to silos again. Parliaments and silos, hopefully will be more coordinated on the company's side. Data could be used by marketing by sales by r and D by strategy by finance holds external data. So it really, hopefully will be coordinated by this time next year. >>Yeah, Thank you. So, to your point, there recently was an article to about one of the airlines that their data actually has more value than the company itself now. So I know, I know. We're counting on, you know, integrators trusted advisers like Deloitte to help us get there. Uh, one what? What do you think? And if I can also drill down, you know, financial services was early toe all of this because they needed the early signals. And and we talk about, you know, is is external data now more valuable than internal? Because we need those early signals in just such a different economy. >>Yeah, I think you know, for me, it's it's the seamless integration of all these external data sources and and the signals that organizations need and how to bring those into, you know, the day to day operations of your organization, right? So how do you bring those into, You know, you're planning process. How do you bring that into your sales process on DSO? I think for me success or or where I see the that the use and adoption of this is it's got to get down to that level off of operations for organizations. For this to continue to move at the pace and deliver the value that you know, we're all describing. I think we're going to get there. But I think until organizations truly get down to that level of operations and how they're using this data, it'll sort of seem like a Bolton, right? So for me, I think it's all about Mawr, the seamless integration. And I think to what Matt mentioned just around services that could help connect external data with internal data. I'll take that one step beyond and say, How can we have the data connect itself? Eso I had references Thio, you know, automation and machine learning. Um, there's significant advances in terms of how we're seeing, you know, mapping to occur in a auto generated fashion. I think this specific space and again the connection between external and internal data is a prime example of where we need to disrupt that, you know, sort of traditional data pipeline on. Try to automate that as much as possible. And let's have the data, you know, connect itself because it then sort of supports. You know, the first concept which waas How do we make it more seamless and integrated into, you know, the business processes of the organization's >>Yeah, great ones. So you two are thinking those automated, more intelligent data pipelines will get us there faster. Matt, you already gave us one. Great, Uh, look ahead, Any more to add to >>it, I'll give you I'll give you two more. One is a bit controversial, but I'll throw that you anyway, um, going back to the point that one made about data partnerships What you were saying Cindy about, you know, the value. These companies, you know, tends to be somehow sometimes more about the data they have than the actual service they provide. I predict you're going to see a wave of mergers and acquisitions. Um, that it's solely about locking down access to data as opposed to having data open up. Um to the broader, you know, economy, if I can, whether that be a retailer or, you know, insurance company was thes prime data assets. Um, you know, they could try to monetize that themselves, But if someone could acquire them and get exclusive access that data, I think that's going to be a wave of, um, in a that is gonna be like, Well, we bought this for this amount of money because of their data assets s. So I think that's gonna be a big wave. And it'll be maybe under the guise of data partnerships. But it really be about, you know, get locking down exclusive access to valuable data as opposed to trying toe monetize it itself number one. And then lastly, you know. Now, did you have this kind of ubiquity of data in this interconnected data network? Well, we're starting to see, and I think going to see a big wave of is hyper personalization of applications where instead of having the application have the data itself Have me Matt at Snowflake. Bring my data graph to applications. Right? This decoupling of we always talk about how you get data out of these applications. It's sort of the reverse was saying Now I want to bring all of my data access that I have 1st, 2nd and 3rd party into my application. Instead of having to think about getting all the data out of these applications, I think about it how when you you know, using a workout app in the consumer space, right? I can connect my Spotify or connect my apple music into that app to personalize the experience and bring my music list to that. Imagine if I could do that, you know, in a in a CRM. Imagine I could do that in a risk management. Imagine I could do that in a marketing app where I can bring my entire data graph with me and personalize that experience for, you know, for given what I have. And I think again, you know, partners like thoughts. But I think in a unique position to help enable that capability, you know, for this next wave of of applications that really take advantage of this decoupling of data. But having data flow into the app tied to me as opposed to having the APP have to know about my data ahead of time, >>Yeah, yeah, So that is very forward thinking. So I'll end with a prediction and a best practice. I am predicting that the organizations that really leverage external data, new data sources, not just whether or what have you and modernize those data flows will outperform the organizations that don't. And as a best practice to getting there, I the CDOs that own this have at least visibility into everything they're purchasing can save millions of dollars in duplicate spend. So, Thio, get their three key takeaways. Identify the leading indicators and market signals The data you need Thio. Better identify that. Consolidate those purchases and please explore the data sets the range of data sets data providers that we have on the thought spot. Atlas Marketplace Mallory over to you. >>Wow. Thank you. That was incredible. Thank you. To all of our Panelists for being here and sharing that wisdom. We really appreciate it. For those of you at home, stay close by. Our third session is coming right up and we'll be joined by our partner AWS and get to see how you can leverage the full power of your data cloud complete with the demo. Make sure to tune in to see you >>then