It's like we are, we're good. Let's go ahead and kind of roll it off first. We'd love to do a quick intro about inner works. If this is your first time being with us on the Interworks webinar, welcome. Thank you for joining us. We're super stoked you're here. And it works as an analytics consultancy where a global team with folks in US, Europe and Australia We specialize in helping people in teams succeed with technology, and that wording is very purposeful for us, the people in the teams always come before the technology. We're a partner of thought spot. They built a great data product that we really love and we're gonna show you why today. But what InterWorks does alongside our partners and customers is help you build strategies and really problem solving solutions to succeed with whatever technology you've invested in. We can help you get along the entire end to end analytics spectrum if you have it from data architecture and management to data prep to cloud migrations, server administration and really all the way to embedded in more advanced analytics, training and a whole lot more. So be talking about thought spot today, but I really recommend you also check out the inworks blog at inworks dot com slash blog and you'll find out a lot more about all the tech we support. And really get some great advice from our global team. We're known for our blog. A lot of people come up and say, oh, I know about it and it works for the blog. So really be sure to just check it out if you got a chance. And go ahead and if you follow us on LinkedIn, that'd be awesome because we send a lot of updates, a lot of content out there, and plus alerts on any upcoming webinars like these or events that are gonna happen. So A few housekeeping reminders, we are recording this session and it will be on our website in a few days and we'll send you an email if you to let you know when it's available and you can access it and check it out then. One quick favor we do have to ask is please use the Q and A function in Zoom today to help us keep track of the questions you have. We'll have some dedicated time at the end for questions, but we'll also be keeping an eye on that queue throughout and we'd love if we can make this really conversational and just kinda interrupt and answer those questions as we go. So That's us, but we'd also love to start off with some introductions before we dive into today's contact. My name is Ty Ketchum, and I'm a delivery architect for Interworks based in Fort Worth, Texas. At Interworks, I help clients develop and execute data strategies from planning and implementation to people enablement and sustainment. So really the whole spectrum there with tools just like thought spot. Prior to InterWorks, I spent about a decade in aerospace and energy industry in a variety of analytics roles from analytics lead, data scientists, to data whateverist, you name it. So on the data vis side, I actually come from a bit of a power bi background, which really helps me navigate a lot of conversations around thought spot power BI and where does everything fit in there, but more than anything I really love helping people solve technical problems. So that's that's the core of what we do here and what the thought spot helps us do. So gonna pass it over to you, Jack. Yeah. Thanks, Ty. Great intro. Yeah. I'm Jack Colbert. I'm an analytics consultant here at Interworks. I am based in San Francisco, California calling out of Nevada today, though. And, yeah, similar to Ty. I have a background in data analytics My focus has primarily been in the SMB and retail e commerce space prior to joining up at Interworks. Of course here, now we work with science across a bunch of different industries. My background is more so skewed towards Tableau and, you know, some data prep tooling, sequel engineering, things like that, and even some natural language processing, so, which is sort of the genesis of how I've come to familiarize myself with thought spot and why I think it's great because it leverages some of the natural language features that I've always seen is like a very powerful way to for analytics augmentation and making it approachable for end users. So again, very excited to be sharing this presentation with you today. For today, our agenda. So we're gonna cover a couple of things. First, for those of you who maybe knew thought spot or, you know, maybe you've heard of it. We're going to do just a quick overview of thought spot, the problems it's solving, and some of its core features and value positions, then we're gonna go into search analytics and, you know, building your first use cases for for search analytics and how that can look coming from, you know, maybe your traditional BI environment. So, we're going to walk through some steps, give you some tips on how to build out, flush out that first or first few use cases or search analytics. And then lastly, we're gonna show what it looks like in the wild. So we have a demo of thought spots along with some great other, like, other, like, embedded analytics tech that we have here in InterWorks, show you how it all looks. It it could look in practice. So, really excited to share this with y'all. Before we dive into the content, just wanna be curious for those who are jumping in today, you know, Have you heard of thought spot or self-service analytics? So, you know, yes or no. And of course, you know, if you have any additional context to give in the q and a, that would be awesome. Really curious to hear, like, where people are in their thought spot or self-service analytic journey. If you're, you know, a current user or if you're curious or you've heard of it, but wanna learn more. It's always fascinating to see that there's a few people who are joining in and haven't heard of thought spot. It's always really exciting because then we get to start from step one. Yeah. Awesome, Matt, the q and a, right, just getting familiar with Thosbaugh. So you've heard a bit right, but how do we make the most of it? What do we do next? What are the what's the best way, the right way to move forward? And you're in the perfect spot. That's exactly what we're gonna cover today. Alright. So, yeah, this I mean, I I would expect, you know yeah. Self service analytics and search analytics and thought spot, you know, thought spot's been around for quite quite a bit. I think, you know, with the advent of search analytics and it being such an important feature in the modern BI stack, making it easier and more approachable for your users to leverage and get insights on their own you know, it's a perfect time to be talking self-service, building your strategy. And, you know, here we're gonna talk about, like, what is what are what are core tenants or, you know, what's what what what does a good footing look like for self-service BI strategy? And, you know, how should you evaluate tooling? So A few pillars of, like, how we're thinking about good self-service. I think about it with or we're thinking about it with sick and core pillars. The first one is governance. Good self-service analytics tools and strategy have governance. So again, clear, easily to easy to implement and repeatable patterns around administering content and with that scalability. So having infrastructure around your BI tooling that can support scalable, you know, scaling your use case, from one user to ten to ten thousand. So, you know, a good tool will support that. And it'll also support internal and external embedded users. And also scale with your, yeah, your use So, you know, as your as the number of scenarios or use cases you want to tackle with your self-service analytics strategy grow, it's good to have a tool that can support, you know, your data as it grows or your use cases and your user bases. Personalization, one of the most important pieces of a self-service strategy is making sure that the insights are producing within our BI tools or BI stack are putting the insights in the language and in the tooling that is most suitable and comfortable the end user so they can act on that information. Flexibility and drill down. Another, you know, common pitfall we see with some BI tools is, you know, it it's it can be difficult for users to curate and receive the data in the format that they're looking for, making sure that all the meaningful filters that they need are applied to the data so they can make do a meaningful analysis. Again, good self-service strategy. This is imperative. And ease of sharing, you know, this is one that's real becoming more important as our you know, we're we're adding more tools to our, you know, BI ecosystem, our business applications. We need to make sure we are able to give insights to users in their preferred communication channels, in their preferred format so they can it's portable. It's shareable, and, you know, it can be shared outside of that application. And, of course, security, probably you know, there's no strategy without security. You have to make sure that users are accessing the correct data, and, you know, there's strict provisions in place to do that. And, you know, with these six tenants, we believe thought spot sits sits out of the center of this. And hits all these criteria. That's why we've really, you know, wanted to build this presentation is to kinda understand how you can benefit from building a strategy with all of these different pieces. So going past self-service, wanna go into what is thought spot for those of you who maybe knew? Thoughts spot is the platform for self-service search analytics So thought spot is a business intelligence platform that allows you to connect your enterprise data models in a cloud data warehouse. And it allows you to actually leverage natural language, search, and keywords to search your business data to retrieve answers in a visualized format that can then be shared, visualized, and embedded in other applications. It can be customized to different user needs, and it's yeah. So you're you can use keywords and GPT like search to ask your data questions, essentially. And going further into that, in the supported data connections for thought spot, you know, a lot the cloud data warehouse thought spot works with cloud data warehouses. And also there's an on prem edition. But, you know, right now, we're mostly gonna be talking about the cloud version that supports data and cloud data warehouses like Snowflake. These are some supported connections. I know there's always more being worked on the pipeline. And that's that's something we really love, you know, about thought spot looking back on the past couple of slides is is just how easy in in weight of a almost like a presentation layer that it sets it just sets on top of your data and works. Right? It lets people actually understand and use all the data you have and that you're storing and I mean, you know, you spin up all these warehouses and you spend a lot of time there, just wanna put something on it that allows me to actually use it. And we see we see the thought spot really as the tool that's allowing us to do that, do that at scale and and and do it pretty easily. So just something that has always stood out to me from from day one of us using thought spot. Yeah. You're absolutely right. And, you know, just being able to search your data live too is is awesome. I think having the live connection to data warehouses, it allows users to get that, like, point in time, those point in time answers that, you know, are not always easy to get through other mechanisms. So I love that. It's cloud first focusing on on live search. So now yeah. Because a thought spot is sort of the first of its kind in this search analytics space. You know, what is that what are the problems that are being solved. So, you know, with searcher and analytics. In practice, what this looks like is, you know, it's rather from the traditional BI space where we have dashboards and business objects that are created for users based on a collection of requirements. Search driven analytics allows users to be sort of proactive and ask questions, business questions, and queries on the fly. So rather than having to put in a request and, you know, kind of scope out what a query might look like and then have, you know, a lag time between the ask and the delivery. It's actually put into the end user's hands to go and search them their data themselves and retrieve an answer and be able to do what they want with it, visualize it in a way that's meaningful in in a in a format that is meaningful. Again, the answers to these business questions through search are can arrive in the form of visualized data, or it can arrive in the form of a table or an export. So, again, having that flexibility of what the end output is one of the key attributes of search driven analytics, how it can be powerful. You know, for those who are used to highly visual data, can build objects, build experiences that are, you know, low ink on page, you know, very polished, KPIs, things like that. But then if you come from the background of wanting to work in pivot tables or, you know, larger data tables, you can also curate that experience for yourself as well. And again, these answers can be drilled into, customized, saved, and shared with other users. Getting getting that personalized experience that we mentioned as kind of a core pillar of good self-service, being able to have it be hyper personalized and meaningful to you and shareable. And one of the biggest thing from, you know, BI operation standpoint is, you know, having a search driven analytics strategy and platform, it nearly eliminates the need for most ad hoc equal and data polls. You know, if your data is modeled correctly, you can essentially, you know, reduce that ticketing system to you know, a a very small subset of what it might have been prior to having a search analytics tool that users can approach themselves. So this is a big one for me coming from, you know, the BI, traditional BI space, you know, working with BI tool cloud data warehouse, doing a lot of dashboarding, you know, this this piece here is, you know, definitely resonates with me. Absolutely. So, again, I wanna wanna ask a couple of other questions here. Pull on this. So a couple of questions here, just out of curiosity, as an analytics professional, assuming, you know, we're all analytics professionals here. Right? We've all worked with data or reporting in some capacity Just wondering where do you spend most of your find yourself spending most of your time, and also as a follow-up there, you know, where does your data live? These two together. It's always so interesting to see that governance is never it's it's not it never gets any any hits. No love for governance, no love for us governance people. No, but I think it's something we talk about a lot and we all probably recognize that we need to to do a bit more there, but certainly, already is right. So Yeah. And maybe, you know, a lot of the time, you know, in the traditional school of the eye, maybe that we're, you know if folks are so focused on doing the reporting and the data cleansing, the data modeling that, you know, governance may fall to the wayside. And and certainly, we have some, you know, strategies and approaches to helping teams with that. But it's always interesting to see how this splits out. Jack, we have a we have a question from the q and a. You know, if you if you have an on prem database, right, does does thought spot just not work then? How how are we gonna handle that? Yeah. So there is a on prem offering from thought spot. So but, I mean, everything we're looking at today is thought spot cloud, but actually thought spot does have an on prem offering, so you could hook up to your on prem database. Yep. Right. Yeah. I appreciate you putting that in the q and a. Anything else and just throughout, just feel free to throw it in there, and and I'll happily interrupt Jack and tell him to be quiet, and we'll answer the questions and then go on. So that's great. Yeah, we definitely see a spread, right? Where do you spend most of your time? Well, I spend it all over the place because I gotta do all of that. So it makes a lot of sense. We definitely have a lot of cloud first warehousing here, which is a trend that we see everywhere. So yeah, that that checks out. That's great to see. Yeah. Also yeah. One of the things I really love about Botspot is that, you know, it it sort of it it encourages folks to do BI teams to do proper data modeling because of the benefits you get, like having well model data, really streamlines the creation of thought spot objects and search experiences. Right? So for the the BI folks who are creating these end user experiences, having a good data model to pull from, having good performance, having that security built in, it's all streamlined, content creation, and ultimately trust. So but it's it's always good to see what the spread is. And you know we we often hear okay. Well, does it does it require a perfect data model? Right? Like, does is thoughts about gonna work if everything's just not perfect? Or is it gonna work that well? And And oftentimes, I'm answering, well, is it does anything work that well if you don't have a great data model. Right? If you can be the a SQL wizard all you want, but but if you don't have it set up in the first place, no matter what you're gonna do is is gonna struggle. So I love that thought spot kind of pushes best practices natively like you said, Jack. Right? It requires you to do the things you should have been and all along. And there's no other answer where, well, it doesn't work because of this, well nothing really works if you don't have a great data model set up or at least trying to to go better in that that direction. So, yeah, just kind of inherently forcing some of those best practices and putting the focus where they where it should be all along, something that we really appreciate about thought spot. Yeah. Absolutely. So in seeing that, we have a lot and kind of anticipated, you know, a lot of folks would respond to that ad hoc reporting and data polls, and then building dashboards and data visualizations as, you know, the biggest take out of the biggest portion of your time. That'd be good to sort of delineate search analytics versus dashboarding because I think a lot of us are coming at this from traditional dashboarding background. So kind of comparing them side by side in terms of strategy and tactics, you know, search driven analytics, versus dashboards. In terms of the ownership of content creation, you can think about search driven analytics. Users are be creators and the ones who are fishing for the insights and creating the searches and the queries themselves, leveraging a user friendly business keyword friendly search interface. So recently, bot spot launched a GPT integrated GPT integration called Bot Spot Sage, which allows you to leverage the power GPT in the search experience and retrieve, you know, business insights directly from a GPT like search. So, again, it that delineating between in search driven analytics, users are creating their own content, whereas in the traditional dashboarding, BI realm. BI developers are the ones who are, you know, sort of scoping these requests, meeting with stakeholders, and then building up owning that cycle of content creation. And then, you know, when you get into the tactics of dashboard design and dashboard development processes, search driven analytics is designed to maximize flexibility in, you know, filtering, building out a drill down experience, just flexibility and exploration of the data. Whereas dashboards are sort of designed to answer a limited set of very important questions that are outlined and scoped in user interviews in sort of the fact finding processes that precede traditional dashboarding, whereas search driven analytics Users can drill into any of those attributes and customize the analyses, however, they'd like. Ideally, leveraging anything that's readily available in their data model. And again, with search driven analytics, content is highly personalized, shareable, which compared to dashboarding. Again, a lot of those paths are, you know, predefined analyses. There are things that, you know, maybe answer your your higher level questions. And, you know, if you need to get down any deeper into the weeds, you either have to find an alternative way to explore that data, or export it, or request a new view, or experience with that data. And kinda last thing here, which, you know, obviously, a little asterisk here because that's what does have a non cloud offering. But, you know, search driven analytics, is designed to query live data in your warehouse so you get that answer, like point in time, off the most recent actionable relevant data. And dashboards kind of work off a myriad of different file formats and data recency. So just kind of delineating the differences between those two. And, you know, next I wanna go into, right, like, how do we come to our first search analytics use case from this traditional dashboarding background, and why search analytics matter? And, Jack, before we jump into that, we do have a good question. And one will tee up for the Tableau side, doesn't Tableau have asked data, right, function for the end users and we might be getting to this as he mentions, but what are the pros and cons of thought spot search versus Tableau or any other one of the myriad of tools? Why do we why do we like thought spot search relative to all these other offerings? Yeah. It's a great question and, you know, a a common one. Right? I often hear these features compared side by side. Personally, what I've really enjoyed about using the thought spot search functionality is you know, it's just it's it's very intuitive. The keywords well, I've recently been playing a lot around with the Sage integration, and then been awesome just to get that natural language experience. Right? So that has been the I mean, one of the main distinguishing factors for me The other thing is, you know, search is built throughout all of the aspect all aspects of the application, which is a little bit different from mass data. Mass data has always been its kind of own experience within Tableau. It doesn't really sit within the dashboard. Experience. It's like, you know, sort of a separate entity or experience for users, whereas in BotSpot, searches at the core everything you're doing, you know, every page ostensibly that you're on in the application, you have a search experience within one click, and you can, you know, line up your your keywords and, you know, your and and essentially query your data within one click of anywhere in the application. So that's the one, I guess, that's how I'd answer. Ty, I have features to hear your thoughts there too. Yeah. Just just my my personal perspective on it, like you said, it it felt like search was an add on that I didn't ever really use in some other tools, and and other tools are great. Right? That's a thing. There's so many great. Tableau's awesome. Farby has great. Thoughtsspot is wonderful. Thoughtsspot's whole thing is it is searched. That's what they do. That's their whole mission. This is the core, their product, and it shows, right? When you use the products, if you use them all, I think you find yourself actually using thought spot search and I don't find myself using search in the other tools as much at the moment. And that's what sets it apart from me. I get a lot of requests. Okay. Well, I just wanna see the number. I just wanna, oh, I wanna figure out on my own and and putting that self-service back into the hands of the end users, the business users, it's what I've been asked for for a long time by a lot of people. And this really fills that gap and that niche perfectly. So that's what thought spot does. That's their core motive there. Yep. I would say yeah. I mean, yeah, the TLDR, their short response is that, you know, it kinda sits at the center of all of the different, you know, screens and features of the application, and it can also be, you know, embedded in you know, in an embedded analytics experience as well. So Certainly. And that's that's kinda leads into another one that I'll say we're gonna kinda cover at the end. It's can you integrate spot with Tableau, right? A lot of people don't just have one tool, they have many tools or maybe they just have one, whichever it may be, we're going to talk about some of the integration and how we see the embedding piece of it work and where does Tableau fit in with dot spot or with Power BI or or what have you. So we're definitely gonna hit on that at the end. Yeah. Definitely. But first, you know, just we wanna address, like, Why do we need search analytics in the first place? You know, coming from this background in dashboarding or sort of traditional BI building out business objects So as dashboard developers, our process sort of looks like this. We have a few stages that come from or that from ideation to execution and creating data product. This defined stage is, you know, defining your data requirements. Maybe you have a data model in place, but you need to assess if that data model is fit for new analyses. Building out visual wireframes, working with designers to make sure that whatever's being created is high fidelity, polished, then also doing some additional data discovery. Development looks like this. You're building out a v one or a v one through v three of a dashboard. Before even considering launching it to production, you know, you have this this laborious process of validating business logic, maybe some of that is net new, maybe it's existing or being modified, going through that design and UX process to make sure things are pixel perfect, and, of course, data source development, which can be one of the largest time consuming processes of dashboard and BI development. We have this refined process. This is where we're gathering user feedback, doing QA on both our visuals and our data. So engaging sort of the front line of users, right, getting that initial feedback, all leading up to a launch period where we have training, documentation, socialization sharing of this, you know, visual or your highly visual dashboard that's gone through all these stages. And then into the maintenance phase. So after launching, yeah, this process of managing change requests. Obviously, you know, dashboards are not designed to be one size fits all, so you're always going to have these sort of offshoots, these change requests, you know, triaging bugs, ad hoc reporting, sort of offshoots of what your dashboard is designed to do. So this is, like, how we typically look at the dashboard development process. Again, spend a lot of time in this loop validating logic, pixel perfects, visual experiences, and Ultimately, you know, coming up with, like, what are the priorities of this dashboard? It's it's hard to design a one size fits all approach, especially when you have all these Nuance business questions that, you know, folks have you know, they they wanna explore and they wanna dive into. And maybe you have to cut corners sometimes in BI development because you're not able to design one tool that's flexible to tackle all the questions. So you eventually have you spend more time in this phase as well towards the end because in your maintenance in in your main main maintenance process, you're, you know, modifying your dashboard to accommodate these unique needs because they're important. I mean, and, you know, it's it's but again, you can't you can only modify a dashboard to be so flexible. So again, your analysts can their inboxes can start to look like this. And, you know, if not managed well, this can really increase the the training requirements and you know, the amount of effort that analysts put into making sure users are comfortable with the tool, making sure it's useful, making sure that the information they're getting from it is accurate, and, you know, what they expect compared to a source system or what have you. Yeah. And again, you real this is where tools can have sort of a a pitfall in terms of, like, users are creating workarounds to get the information they need because they can't get it natively out of the tool. Right? So this is one of the common challenges of dashboarding that, you know, we've seen in a myriad of tools is that they're not always flexible enough to capture the more nuanced questions that often require a data analyst or someone who's extremely data savvy or some sort of feature to tackle that. And -- Right. -- this can take a lot of time and kind of take, you know, usBI folk away from some of that strategic work, some of those initiatives that maybe wanna get out after that are not tackling these, you know, more you know, the tedious having your email bombarded with, you know, bug noises. And and I think, you know, in my experience, I've probably shared with you all too much would be a lot of times build a lot of really great dashboards in the minute I create them, right, they're obsolete, or they're something else, right? There's something we gotta change and maybe we can't change and maybe it's a completely different ask. Because if we change it and add too much on, we get lost in translation and what's the point. So this So, I love thought spot because it really puts again the power back in the consumer's hands. Whatever they need that one off type thing. They don't have to go, I don't have to bug Jack, hey, give me insane sequel period to give my answer and I'll ask for another one tomorrow. It allows me to do it in my little sentence and get the answer on my own and do it on on a repeated basis. So I just love the whole dichotomy and really the whole shift in thinking from dashboards are great, and they still are. And honestly, thought spot has live boards, which are a lot like dashboards, and they're great and they have their purpose. And when used well, they're fantastic. It also has this wonderful way to get around all these problems that we're all used to having and dealing with, and it can really help lighten our load and maybe change our prerogative as a data analyst, a data whateverist, it allows us to do more of that or strategic work that we probably all wanna get to. So Yeah. Yeah. You're absolutely absolutely right. Tie. And, you know, these common challenges of a BI, especially, you know, tackling these edge cases, these questions that dashboards don't aren't always flexible enough to answer. That's where thoughts about search can, you know, be the superhero here. Again, a few of the core features and the value propositions of thought spot in tackling this problem. You know, first off, right, users can create their own ad hoc queries to, you know, leverage that natural language. This is a demo using DotSpot Sage, where you can actually use natural language to ask your data a question, and it'll return, you know, either visualize, you have the option to see a visualize data set. Or you can toggle really quickly and get a table and then, you know, have that scheduled out to your team or you know, you're a Slack channel, right, where it can be acted upon within seconds. And that you didn't need to write a line of SQL. You didn't need to build your own check or sorry, your own chart. Yeah. Using yeah. Use it using BI tool. You just used your own the the thoughts that are streaming from your brain, which is pretty cool. So, yeah, again, puts that power in the user's hands. Big one for me is drilling down on your data is super easy and intuitive. You know, this right here, I you essentially have the capability to drill down into any dimension in your data model in one click. Not even a left click just one click on a chart, you can activate and filter in on that particular point and cut it by any dimension in your visible data model, which is super powerful. It's really easy to get that lineage of, like, and monitoring and keeping track of, like, your flow of analysis as you're doing some, you know, data exploration here. Yeah. It's it's, you know, it's not just drilling down from month to week or week to day. Right? It's It's like Jack said. You don't even have to remember that you need to right click this thing because you don't. You just click it. You just select it, and then you can drill down into anything else in your data set. Which is really cool, and it brings back to why we love it so much. When you easy and intuitive, right? It makes you want to use these features. It was back to the similar like, well, what's different about their search and thought on what's different about the drill downs? Well, they're just easy to use and we find and we've learned that people actually do use these features, which again there's a lot of cool stuff out there. The coolest thing is the thing you're gonna use, and we see these features getting used on a repeated basis because they're easy to do, because it makes sense. Yep. Yeah. On top of that, highly personalized KPI and landing page experience. So when you first log in to thought spot, you can care what that you know, that first landing point looks like. And, you know, if you're not sure where to start, you know, thoughts about has built in, recommendations. So it shows you, you know, use it using usage based ranking. You can see what other users in your environment are looking at, what's trending, what's popular, if you have, you know, security set up right, you can see that on a, like, group based like, group people in your group or organization wide, which I think is awesome. Especially for those who may not know where to start, but you you you wanna see what other people are viewing. Very flexible in how you can customize this landing page experience. And again, you know, one of the core tenants of good self-service is being able to get the insights out of the application. Another great thing about thought spot is you can essentially take the information, the insights from here, or the visualizations, and send it to other business applications in just a few clicks. Even have the capabilities to integrate it with, you know, data right back and, you know, actually write and and create, like, very highly customized integrations with the data you're getting out of thought spot. So, you know, writing it back to Salesforce or you know, a marketing automation system. Right? There are ways you could do that within thought spot as well. And as Ty mentioned, you know, thoughtspot, you can create live boards to group your KPIs and your visualizations. So if you want that more, like, executive level overview, but also provide benefits of having a flexible drill down search experience, you can create a live board to have that serve as the starting point. For for your users. So I know we're yeah. We for a little bit. Gonna run through this next poll really quick, but just out of curiosity, you know, what are your greatest challenges with self-service analytics today? If you do have self-service analytics strategy, or if you'd like to start one, where do you think your greatest challenges would be? Yeah. And, I mean, even if you you don't have have that. Right? You've got data, you've got analytics, you stuff, where's your biggest headache out of these four? From data quality and trust, like, is that the number? Are we sure to to a user adoption skill gap? Is it the people, the hearts and the minds? Is it security and compliance? We don't know how to set things up. It's too complicated. We we don't trust that we're not showing right, wrong stuff to wrong people or or an integration piece. Or we have stuff all over the place. I've gotta go to different systems and local files, and I just don't know how to make it all work very easily. Yeah. Some of these results are I mean, it's pretty even compared to what I what I thought the results would be. Again, the complex data integration, Alan, surprising number of people? Again, Jack with the he he gets to hang out in Lake Tahoe and doesn't have complex data integration problems. So just congratulations on your life, Jack. For the rest of us, totally agree. Right? That's a big a big hicc up IC. And of course, the user adoption, skill gap, the hearts and the minds, the training, a tool is only good if someone uses it. We certainly see that and The good news is we can do a lot about that. So Yeah. And that kinda launches us into, you know, building our first use case search. We kind of went through, right, sort of why we need search analytics. We have all these edge cases that, you know, these cases where our BI objects, our business objects are not meeting all of the complex, nuanced, specific needs of our users, and That's where building your first use case for search comes into play. So again, just let's go through the like, let's imagine we're a BI team. You have a dashboard. You know, it meets all of our requirements, clear KPIs that we've scoped out with our users, visual best practices, and predefined analysis paths that have been determined valuable by our user base. So, you know, we have all the right dimensions in there for the analyses that we've identified as relevant and useful for our user base and ostensibly answering all the important questions. So we spend all this time going through this process, and then come launch, we get these sort of additional questions. Right? These KPIs like, for instance, let's just say Yeah. In this in this example, you're a restaurant with lots of franchises, and you'll always have folks who are gonna ask, well, I wanna see this one level deeper, or I only wanna see, like, data in this time period. Right? So in a traditional BI, you know, ecosystem. Right? We might be able to accommodate, you know, two or three requests write a quick query or export the data and land it in a way that can meet these or create a calculation that can you know, create a flexible, like, date analysis period. But then with that, you know, users are always going to have more questions. Right? There's gonna they're gonna build upon this. Users will always wanna drill down and understand more. And then, you know, maybe they're serving the data to other users in presentations, reports, and they're getting asked these questions. And then the chain between source data and questions, you know, the gap widens. Right? And this is ultimately where the search analytics your first search analytics use case starts. Thinking about all of these more nuanced specific questions that cannot be answered, or, you know, your tool cannot be designed to capture all these questions. So, you need an alternative approach that can. And can meet users technically where they are, and also give them, you know, answers to these more nuanced questions. And I think it's it's great because, you know, it's what's the point? Right? Well, the point is the answer. The point is the end user, us, getting what we need. It's not, I love looking at beautiful things, but that's not necessarily the point. The point is that I can get an answer, I can get it now, and I can make a decision and forward and do it again throughout the day and throughout the year. So if we focus on what we're really trying to do here, that's where we wanna get into and, okay, how does thought spot help me get there, right? So a few steps into sort of feeling out your first use case or identifying and building that out. First step is to understand. So, you know, looking at all of these unanswered business questions and grouping them and, you know, understanding for for all these different questions that are be being asked of you, you know, what is the size of the user base? What is the overall business cost, which is tough to assess, but usually, you know, in the in terms of time, that's the biggest one. Like, what are the costs of not having the answers to these questions? And what is the risk overall? Of not having these answers, those are important questions to pose when you're understanding, you know, what you want to focus on for your first search use case. And ultimately, how users wanna act on this data, right, is a dashboard or a live board or a visual form of data the right experience, or do they need something that, you know, arrives to them in a business application elsewhere? So these are all important questions to ask, because maybe your users are not getting they have the right information, but they can't get it in the format or the location that they need it for it to be effective. Second step is to prioritize and organize. Again, you know, the best way to go about this and the easiest way to go about this would be to organize these common business questions by data attributes, sort of build out and sort of draw guardrails around your use case. Right? So Are they using similar dimensions, sales, or is the audience similar, right, by business or departmental alignment? And also being able to prioritize and organize by user base. So maybe you have a use case where, you know, there's thirty to seventy users who annually, who need to get this one query and there's no current solution for that versus a user base of five hundred or five fifty one hundred users monthly, you know, either one of those is a good place to start, but, you know, recommend starting where you're going to have the highest user impact with the low effort. So, you know, identifying those areas where the data is ready and the user base is large, that is like a perfect set up for, you know, your first search analytics use case. And again, that activation method, how they're gonna act on the insights is also imperative understanding that. Step three is to build, again, you know, assuming that you have your use cases defined, making sure that you have a business data model that supports all these to be answered, oftentimes, you know, in some BI tools, you'll have people pulling data and have complex data integration to support your use cases. You wanna make sure that all of your data that will help users answer these core questions is supported by a central well governed data model with security built in And again, you know, the analyst job in thought spot is to build in that the business friendly keywords in making the data model searchable So users can leverage a natural language to get their, you know, their answers and leverage that search experience. Step four, test and validate. So as you build out your searchable worksheets and your visualizations, you know, putting bring together a task force with a few core power users you can, you know, test the outcomes, making sure they're matching what the users expect, what's useful. So, again, in BI, we have that sort of test and iterate process. We're always gonna have that, but what's great about BotSpot and a search analytics tool is you really tighten that loop and you shorten the time from creation to testing, and, you know, again, subsequently testing the insights. So you can gather that group of test user making sure the search experience is optimized and that you have your, you know, keyword synapse and that your data is consistent and secure so they don't it can be launched out to a larger user group. And lastly, you know, launch and monitor, making sure that your end users are trained on search. They know all of the different business keywords. It's supposed to be a gift. But, oh, there it is. Now it's going. Making sure end users are trained on search, and they understand like what the full suite of capabilities and keywords they can use are. And then yeah. You know, just creating tutorials and resources as you would with any other BI launch support the unique needs or the questions that these users might have. And last thing, I wanna inject before there. I wanna just say my personal anecdote for anyone that cares. The the the peep the the cases that that I've seen work really well They spend a lot of time nailing that data model, which is great, again, just best practice. So they spend a lot of time beefing up and building that data model and they spend a lot of time training their people. Right? They don't just train the power users. They don't just train the data teams that are gonna be monitoring it or administer administering it. It's they get their core users in a group on a consistent basis, right, and they train, and they talk about it, and they share. So like any other tool, thought spot is going to be something that if you water it, it will grow, but it's really easy and people can get behind it and understand it because it's all natural language, and they don't have to go read a science book to understand. So from my perspective, it's that build and then the training, right, the launch of monitoring. That's where we see a lot a lot of benefit, right, that exponential growth. So Yep. Yeah. And again, this piece here, thought spot has a very, you know, very, like, detailed granular way of tracking adoption across your thought spot ecosystem so you can target, you know, the users who, you know, you expect it to be power users who, you know, maybe are not adopt is fast. So it gives you a very precise way of seeing, you know, where your efforts are working and maybe where you want to focus your or recalibrate your efforts and revisit this process of, you know, creating your search experience. And lastly, alignment. Making sure that your your search interface is aligned with other tools. Right? So with that, what we're showing here is a way that, you know, our initial dashboard that answers a few of our questions, right, or overarching big numbers that we may need to visit on a quarterly or monthly basis can be supported by a search experience. So users can get the benefit of visual analytics and creating their own self-service search analytics in the same experience. And this is all being done in our in our Works product curator, which is an embedded analytics ecosystem, which we're gonna show you really quick wanna make sure we save a couple of minutes for any questions towards the end of this, but I just wanna quickly show you what this could look like in practice because I know we have some new folks who see that spot live. Awesome. Yeah. So I'm gonna quickly show so imagine in this experience we have, you know, a dashboard where that has our overarching numbers, and then we have our search experience here where we can leverage our natural language search. So thought spot, we can select a data source and very quickly begin searching. So let's just say we wanna see the top three where you use the franchisee example. Each state sales. So notice how as I'm typing things in, it's giving me recommendations. So, you know, if if I don't know where to start my search, I can get some recommendations of what other users are searching on, and it can help me start my journey. And then if you have a more ambiguous field as well, like, like sales, for instance, it'll give you all the it'll give you multiple options for, you know, different versions or related fields to, like, sales. Right? It'll give you other measures that you can search by. And you can see here, you can use things that require complex calculations and other tools. You can just type that right into the search bar here, and, you know, it'll allow you to fire up a search in seconds. And Yeah. No. It does. One thing to note on this too, right? It's obviously, it's thought spots engine under the hood. It's converting your words, your natural words that kinda makes sense to you into a query that get goes and gets the job done. You also hit on it very briefly, Jack, but it'll it'll use usage based ranking to say what other people care about in your organization. I think that's super cool because it gives someone the ability to get started on their own and diving into things that people actually care about, people actually view in your organization. I think that speaks a lot to the culture thing in advancing that and self starters and We've just seen a lot of people go farther and it makes it less intimidating using the product because they build it to kind of learn on itself and grow with you as you as you grow in your journey with it? Yeah. Absolutely. Yeah. So in the same situation or in the same example, Right? You have the ability to toggle back and forth between, you know, visualizations and an actual table, which know, is very familiar for users of Excel or those are who are used to consuming sort of raw underlying source data. What's great about this is in either format, you can actually drill down into any data point and expose any of the other dimensions in your underlying data model. So in here, if you wanted to drill down on I'm in California, so I'm gonna drill down in California. We have this ability to one click drill down and we can cut this by any other available dimension in our data model here. So let's just say we wanted to see types of restaurants or yeah, stew restaurant type. Okay. Maybe it was a bad example because it's null. But it was easy to get there. And that's kind of the point, right, is like, hey, we'll spend the time getting the data model set up. It's the tool makes it easy to get to what you're looking for. And that's the name of the game. Make it usable. Right? So we have some questions too in the Q and A, just briefly, Jack, maybe we're turning on time, but maybe do you have anything else to add about, you know, the Tableau ask data comparison? I know you touched on a little bit earlier, but anything you wanna add onto that and how it kinda compares and is different to thoughts spot. Yeah. So kinda going back, I think the the primary difference for me is that you know, the search experience within thought spot is embedded in every part of the platform. And, you know, search searchability is sort of, like, accessible within one click of any part of that spot. And, you know, it's kind of the it's the it's part of the content creation cycle as well. So I think that that's probably the biggest distinguishing factor for me is that It's sort of the your heuristic of thought spot is that you start with search, and that's used to create your data visualizations, your live boards, that then turn into these other business objects. So -- Right. -- you're essentially taking these streams of consciousness. You can build them act on them, and then pin them, and then that can help create content. Whereas, a lot of times, you know, when I've I and I thinking more, like, strategically, I see, you know, ask data as, like, supporting dashboards rather than using the process of creating data objects, it's usually used as like sort of an ad hoc, you know, tool on top of or to supplement the dashboard. Whereas in the search analytics experience, you can actually use it as a part of your development and your content creation cycle. Yeah. Definitely a lot of other differences, but, you know, that I think that's the one that people need to be for sure. Users who have experience with both or evaluating both. It puts search and it puts answers as the focal point, right? Not as the side point in addition to it. It's what you're coming to thought spot to figure out right, that answer, the search in your head. That's why you're here and that's why thought spot exists. A couple other things that I just wanted to share before we close it out. We talked about earlier cloud on prem, right, that sort of conversation. I know we demoed a lot of cloud stuff and we talk about a lot of cloud things, but thought spot does have embrace connections, embrace is kind of their terminology to connect on prem sources, so SQL Server, what have you, all the things, So it definitely has the ability to do it there. And the other thing we always like to talk about is, okay, I want to get started with thought spot. Think star schema, think good data modeling, right modern good best practice data modeling. That's the way to go, that's the way to get started, kinda rapid start, and go run, have fun, ask us questions, we love to poke around and bug hunt and do all the things. So yeah, Jack, any final words for you there. No. I don't have any. Those are those are all great questions though. And of course, if you have any other questions that we didn't address, you can hit us up directly, and we're happy to take those offline and answer those. But as we're closing out, you know, best practices, you know, this is all recorded, so you'll be able to access this recording up on our YouTube at some point in the next couple of weeks. Right? That's right. Links, good stuff. You'll have it. You'll check out the webinar. You'll see the links. Just check your inbox for it. We do have a poll coming up that we would love to understand what you're coming here for, which we're hoping to get out of it for. And again like Jack says, if we didn't get to all the questions or if you just have another one, there's actually going to be a survey that'll automatically pop up when the webinar ends in your browser. And if you have like twelve seconds to there, I'd really appreciate it for your feedback also so we can get some of those questions and follow-up with you personally afterwards in you those answers and give you a bit more context around it, right? Not just the answer, but would love to have a conversation. So if you did love our session, remember my name's Ty, if you hated it, Jack. His name is Jack, right? So, but really if you have any other questions, feel free to drop them in that survey. I promise you we'll get back to you. We'll we'll scan the chat. And like I said, we don't just want to get back to you with an answer. We want to get back to you with the why behind what we're talking about here and what we believe in because thought spot certainly makes it easy for us to be believers and advocates. So thank you all for joining us. We're right at two PM, so I hope you all have a wonderful afternoon, morning day, wherever you're at, and we'll talk to you next time. Yeah. Thanks y'all.