>>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. >>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.

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