>>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.