Self-Service BI: Cutting Through the Hype

All right, good morning, everybody. Thank you for joining. There's quite a few signed up for this. So we're going to let folks trickle in and get started here in a few minutes. While everybody is joining, first of all, you. Great to have you here. You'll see some poll questions popping up in the chat. Just wanna know who you are, where you're from, what are some of the challenges you might be facing, tech stack, all that kinda general high level stuff. So look at the chat. That's where we'll be most active today. But, we'll get started here in a few minutes. Thanks for joining. Awesome. Some more folks trickling in. Welcome, welcome, everybody. Again, you'll see some poll chats. Feel free to go in there while we wait for everybody to jump into the webinar. Some more folks frequent in. I've covered this a few times for those of you who are still here. Polling is in the chat. Go answer some questions. Great to have you here. Thanks for joining. We'll get started here in just a couple of more minutes. Alright. We're hitting the two minute mark here shortly. Ben, what do you think? Should we start to get going? Yeah. I think we'll give it a little bit more time just to make sure everybody has the chance to to come in. It's always it's back to back meetings these days. So appreciate everybody for joining us this webinar. We'll probably go I mean, honestly, five minutes, I think, makes the most sense. So for those of you who have joined and are on time, thank you very much for that. We really appreciate it. As your reward, feel free to go grab a bathroom break, quick comfort break, grab some coffee, whatever you need. We'll get here started at five after. I'm glad you mentioned coffee. Still got some. All right, we're back on the virtual stage. A lot more of you have jumped on. For those of you that are still on, thank you for your patience while we wait for everybody to join. We are going to get started here shortly. So thank you again for attending. Hope you're all having a great week. My name is Colton Dailey. I head up strategic SI consulting partners here at ThoughtSpot. I've been here for about a year and a half. I've been working with Innerworks the whole time, and I'm very excited about today's presentation, getting you all introduced to Ben Young. You might know him before, but I've actually seen this presentation live and in person, and it went so well and was very well received that we decided to bring it to a broader audience and invite you all to see it live and not in person, but virtual. So you'll see us all active in the chat, just some housekeeping items. You'll actually see Ben and I pretty active in there while we're speaking. That's because we actually got together a couple days ago and recorded this webinar. Because there are so many of you, we wanted to make sure we're Johnny on the spot, making sure we're answering all your questions as they come in. So please be active in there. This is your time. We wanna make this as valuable as possible for all of you for taking the time out of your busy day to come learn about self-service BI. Really, what is it? Why are people talking about it? Why are companies really trying to shift their strategy towards a more self-service approach for their BI teams and analytics teams and and getting those answers fast, quickly, and efficiently in the context of this thing called the modern data stack. So Ben is gonna cover all that, here shortly, and I'll hand it over to him. So I'd be remiss if I didn't take advantage of a unique opportunity of actually speaking with my future self in the chat. So Colton, hope you're having a good week. I know you said you were gonna get to the gym every day this week. Did you actually go and do that? You did? Proud of you, bud. Alright. Enough of that. Ben, I've actually known for a long, long time. Funnily enough, we went to high school together and graduated in the same class. And here we are, what, fifteen years later, working together and and bringing a lot of BI content out to all of you. So, you'll see us in the chat. Again, enter in questions as they come up, and we'll answer them live. But Ben, feel free to take it away and take, the center stage. I appreciate that, Colton. Thanks. It's hard to believe it's been fifteen years. I like to think I have a baby face, but I think both of our hairlines are kind of retreating back to show how long it's actually been as we're working on it. So it's it's good to be here, and really appreciate everybody joining. Like Colton said, taking the time to talk through BI with us. I know there's a lot of demands on everybody's calendars these days, and yet another meeting or another webinar, the time to talk with us. So real quick, wanted to walk through just some quick background on who we are as Innerworks, what we've done, and why we're here talking about ThoughtSpot and talking about this self-service idea of BI. And as a consultancy for Innerworks, our mantra, really, our top three priorities are doing the best work with the best clients with the best people. So, really, our goal is to make sure that we are delivering at a very high level. We're working for clients that really appreciate the value we can bring, but also are trying to do great things in the world and also bringing to bat all of our bench. We have a very rigorous hiring process to where when you get one person who's working with you at InnerWorks, you get the whole bench to help you out. Really, it's our goal to help people use their data and get access to it and treat it as an asset rather than just something taking up space on a hard drive somewhere. We have worked with many clients throughout the world. We're a globally based consultancy. We have a few big names here that we'll toss out that we enjoy working with, but we also have our longest tenured client, which is a mom and pop dentist shop just down the road from our home office in Stillwater, Oklahoma. So we cover the entire gamut and appreciate working with unique and challenging problems wherever we can find Now on the partnership side, obviously, we're here with ThoughtSpot. Really enjoy working with them, but Colton mentioned the modern data stack and every component of that. So you'll see here we're very heavily partnered with Snowflake, with AWS. Got a couple others on there like Dataiku, Fivetran, Matillion. We also have worked with Tableau for a long time, and we'll talk about how all of these kind of fit together and where they go. But really, we're very excited about the momentum around ThoughtSpot, what it provides, and what it can do for your organization. Now a little bit about me. Colton mentioned us going to high school together. Since then, I've done a few things, bounced around the US and the world, spending some time in Thailand, some time in the UK, and my wife and I finally settled our itchy feet down in Maryland. That's us in the top right corner. It's actually a picture in Maine if anybody's been up to the Bar Harbor area. But I also have three little girls who take the majority of my time. They're not at home right now, so they're out with my wife. So, thankfully, they shouldn't be bouncing in the background, but we've got Mabel, Joan, and Ruth there, and I love spending time with them whenever I can. Whenever I get a little bit of time to myself, then I will also go out and I enjoy triathlon. So making the standard exercise three times harder. I really appreciate that mug. So it's a lot of fun. That's me in a nutshell. Professionally, I cut my teeth on a strategy and data team in the insurance firm, in the insurance field, sorry. And from there, I then took that knowledge and that ability to work with very technical things, cutting my teeth in Excel, SQL, and Tableau. Took that into grad school where I got an MBA at the University of Oxford. And by doing that, I found out that I was actually better at translating the technical speak to the nontechnical people than I was actually doing the work itself. So since then, I've been spending a lot of time working at InnerWorks over the past five plus years doing that translation. And as part of that, what I have worked and developed a lot of thought and content around is this idea of self-service BI. Everybody wants to do self-service. Everybody sees that it's important, and it's something that we need to do. But unfortunately, there's a lot of hype around it. So how do we figure out what is actually real, what is actually not? When self-service gets applied to fill in the blank situation, what does that actually mean? So the goal behind this content, and I've worked with a lot of people on my side to put this together. So major credit goes to Matt Hughes who runs our analytics practice as well to really create a narrative around how can we make this make sense to you. That's the goal of the webinar today. Again, if you have questions throughout, Colt and I are there in the chat, and we're happy to answer those questions for you. So we look at self-service as a concept that would actually started back in Piggly Wiggly. Not only is that fun to say, but is an actual store name. Back before the early nineteen twenties, the idea behind any supermarket, at least in the US, was you walked in, you then talked to the clerk behind the desk, and you said, hi. I would like x, y, and z. You list out exactly what you need. You put in a request, put in sometimes a ticket, and then the clerk would go behind, package up, bundle your information, and then give it back to you. And so grabbing information, it would be something like your flour, your eggs, your sugar. Whatever you needed, you got it. Now in the store, Piggly Wiggly, they actually said, you know what? We could change this. So rather than having everybody go to the desk, they gave everybody a wooden basket, and then they set up their shelves. So people would start walking up and down, taking things off the shelf, putting them in their basket, and bringing them to the clerk who then checked them out. This not only expedited service, but it made it so that people were able to have better control over their experience and make sure they got exactly what they wanted. So that's the concept of self-service was born in retail, and that quickly spread beyond just retail to all sorts of applications, including the data space. Now if you look through this list of analytics providers, you can see that we've got everything from ThoughtSpot, who we're very happy to be here with today, through to the rest of the modern data stack, Fivetran, Matillion, DBT, Snowflake, to some big player kind of Powerhouse BI tools. We've got Tableau and Power BI if you know your logo's kind of going down the side. But every single one of them is claiming to do self-service, or at least maybe not saying that self-service exclusively belongs to them as more saying this should be a core strategic component for you. So if self-service is something that's needed across all elements of the data stack, we have to figure out what that means. Let's first, though, agree on what it's not. Now few shots fired here. So to any of the Excel hardcore loyalists in the chat or in the webinar, please don't run away yet. Please don't quit. All we're saying is that when we're talking about self-service analytics at scale, it is not Excel. Now I say this as a diehard Excel user and a guy who spent the vast majority of my early career working very hard in Excel. I love it. If you wanna nerd out about how index match is better than VLOOKUP, I'm there for it. If you wanna talk about the new XLOOKUP function and how that enables things that weren't even possible with index match, I'm there for it. All I'm saying that Excel has its place, but it is not self-service analytics. And there's a few key reasons why. And before we get to those, to be honest, we could cast the net a bit wider here too. I wanna make sure that I'm not bashing on Excel. We can throw Google Sheets out there. We can throw Numbers out there. What I'm saying is that the spreadsheet interface is not self-service analytics. And We'll get to those key reasons, but one of the majority of them is that spreadsheets have been around for a really long time. So I mentioned that I went to Oxford. There, my wife was doing her master's at the same time, and she worked museum. And at Dashmolean, there's one of our earliest examples of writing. It's really small. You can see my video. It's it's only yay big. I kind of held this. And this is ancient cuneiform where this is one of the first record of writing that we have. Now if you look through and try and figure out what the translation of this says, it's actually really boring. It says, I gave you two bushes of wheat. You gave me three caskets of wine. It's a transaction ledger. There isn't anything super new or exciting we're getting out of this. It is just a kind of rows and columns. Here's this. Here's what happened. Now I don't know about you, but if we're looking at this, you can kind of look at it and trace the evolution to the spreadsheet. Right? There is nothing wrong with Excel as a tool. I love it. Again, I use it all the time, but it has to do its job, and it does its job very well, which is the ability to have a series of analyses or a series of models that make a lot of sense that you can start building off of. Anybody can come in and edit. Anybody can make a change too. It is highly, highly flexible. When I was first moving over to the database world, I got very frustrated where I couldn't mix column types in the same column. Sorry, mix data types within the same column. Right? I couldn't have a date. I couldn't have a string, and I couldn't have a number in the same column like I could in Excel. And that's because when we're looking at just Excel by itself, it has some flaws. Right? So why don't these work? We talked about kind of the mixed data types. But the thing is Excel files invariably run into limitations when we're trying to use them in a self-service environment. The major ones of these can be bucketed into really three groups, and I'll talk through these real fast for us. The first is the concept of security. Right? If I'm working on an Excel file, then I can go through and I can change whatever number I want. If I'm not happy with the way that something has gone, I can go in and kinda bump it up a little bit. Now, ideally, we can trust everybody and that never happens. That's great. But let's also think about the corollary of if I have a copy of it, I can just email it to somebody. And say I accidentally type in the wrong Ben in the email address or the wrong Amanda or the wrong Colton, whoever I'm sending it to, all of a sudden, my company's data, that key asset, is now off into the ether for somebody to be able to take and then change it to whatever they want. So we need better security rather than just files themselves. On top of that, there's also the idea of governance. It is the worst feeling in the world when you show up to a meeting and you have an answer and your coworker has an answer. They're baked off the same dataset, but because you did something different in your Excel analysis, you both present what should be the same number and they disagree. So we're at a scenario where we have dueling sources of information from the same dataset even, and we can't figure out where those came from. It derails the entire meeting. You lose the trust of the room. You lose the momentum that you had. So you have to be able to govern what's being shared. Then on top of that, there's just the scalability factor. If anybody has ever broken the fifty megabyte mark on an Excel file, you know what happens to it. You know that switching to a binary file really doesn't help in the long term. And just frankly, you run into the million row dataset limit. When we're talking big data, when we're talking large scale transactions, Excel file won't give you that. And so we have to be able to go through and somehow solve for these challenges. And in a lot of way, that's what the BI space has done because it's connected your analytics users who are working on their analytics insights, on their business logic with IT. Whenever I think of security, governance, and scalability, really, the number one thing that I'm looking for is help from IT to make sure that we can do that correctly. Right? So have we turned it on and off again? Who knows? But when we look at IT, when we look at the data side of things, this is IT's mandate, and they are very good at it. They're very good at making sure your data is secured, to make sure it lives where it's supposed to, to make sure it's governed, you have the proper access controls in there, and to make sure it's scalable. They make the investment and do the maintenance so that you're able to take advantage of all the capabilities that the modern data stack can provide. K. So it has to be a partnership here between your data users, I'll call them business, and IT going forward. I've never seen a successful BI deployment in my ten years in the industry of any group who has tried to ignore IT. You have to partner together to make this work. And at the beginning, if we're talking both the history and development of BI as well as a kind of maturity curve of an organization. So you can look at this as how BI has grown, or you can look at it how BI gets deployed inside a business or your business if you're trying to stand it up. Things work out really well. Business user comes in. They're on the left, sends a request into IT on the right. They check the boxes, and data is returned. The thing is, once that starts working, things spread, and there's more momentum that's built. People are saying, awesome. IT can get us the data, and the requests start coming in. First, it's a trickle, then it's a rush, then it's a torrent, then it's a flood. And all of a sudden, we have a massive backlog, and this poor IT guy or gal, whoever is on in tap untapped to try and figure all of that out, is just underwater. There's a huge backlog that starts growing because you have this massive glut of business users who are saying, hey. Please, can I get that data? And that's because there's actually dueling priorities here. So if you line up where our business users are coming from with where our IT users are coming from, you kinda have a fight on your hands. On the IT side, we have said, hey. We need your help making sure this is secure. But then on the business side, we then come in and say, and, by the way, I have to also be able to share it wherever I want. On the data side on the IT side. Sorry. You're coming in and you're saying, I would like this governed. I wanna make sure that I have the appropriate access to the appropriate data. But as a business user, I'm gonna then come in and say, and I'm gonna combine it with all these other datasets that I randomly found and downloaded from the web because I think it'd be a really cool analysis, and so there's tension. And then on top of that, there's a scalability factor of I wanna have a huge dataset to be able to analyze whatever I want, and I need it to load in under five seconds, please. Thank you very much. That is a nontrivial challenge to make sure that it works. And so you end up with this huge backlog. And the backlog comes not just from those challenges we were looking at, but just also sheer volume of users. If you look at an IT department head count relative to the rest of the organization, there is an imbalance that's never going to go away. You're never going to have half of an organization be IT and the other half be sales, marketing, finance, executive, fill in the blank, whatever department you want. Right? There's never gonna be a match between those two. And so you run into a challenge where you have this disconnect. So the first step we usually see and historically has been done in the industry is that IT bring we have two new players that I wanna introduce really quick. The first is the technical developer. This is someone who's come in to say, hey. I'm going to help you build out some really nice looking reports. We wanna get away from these ad hoc requests, these one offs that are coming in. Let's make it possible to build out and capture a lot of that logic to put it together into a single place. You then have a technical analyst. IT's priorities, again, scalability, security, governance, they are not supposed to have insight into the day to day functions of the business. So a technical analyst comes in. This is kinda where I cut my teeth. Right? Was saying, I get that there's a challenge on the IT side. How about I pick up enough SQL to be dangerous so I can start building my own datasets in concert with IT, but I can then use those datasets to help answer questions for business? And so things go okay for a little bit, but to be honest, the requests keep coming in, and I have never seen any organization be able to hire enough technical developers or analysts to keep up with the volume of requests from the end users. Again, if you can kinda look back at this imbalance of how those groups compare to each other in terms of raw head count to get some insight into why, but, hopefully, this is resonating with some people that are watching today. Hopefully, you've seen a little bit of this tension that's going back and forth. It's not a tension because people don't like each other or because they don't like working with each other. It's just you have different jobs for different people. And so how can we solve both of those? How can we leverage BI to get to its true potential? Now we're trying to fight this backlog and see what happens. We're trying to talk about different tools that are available, and I gave you some background at the beginning where I went to business school. Because of that, by contractual obligation, not really good. It feels like it. I have to put a two by two matrix in everything. So sorry. Here's the two by two, but we're gonna be filling this in as we go through, and, hopefully, it'll help clear things up on where this will make sense. So as we're looking at this narrative to date, I like making the distinction between the professional, the expert, or someone who hits their day job, and we've got here at the bottom of the screen. And then across the top, there's the citizen developer, someone who wants to do self-service things but has their day job on top of things too. So that split between professional and citizen is gonna be a key one. But then we also have this split between object creation. So this is building out dashboards and reports, and object consumption, which is being able to do analytics with what has been created. So we're gonna fill in different spots, different people, different tools throughout this. So going back to where we've been, self-service analytic kinda one point o. If we look at the market maturity as well as organization maturities, that poor IT individual or team usually lives in this bottom right quadrant. They can build out whatever they want. They can help people consume whatever they want because they have SQL or Python, very common these days, or Spark or R. Fill in the blank, whatever you want. I it doesn't really matter for this analogy. But the point is they're the professional, and they're the experts, and this is what they do day in and day out. So they're there in the bottom right. When they need help and they bring in additional reports or additional people to help service all these requests, generally, they are also the professionals. So they spend their day, either the technical developer we've got there on the left or the technical analyst we've got here on the right, who are using tools to try and answer questions for the citizen developers across the chasm. Right? So if we look down in the bottom left, we've got our object creation. Self-service one point o really was focused around people learning SQL, SSRS burst reporting, Cognos, IBM. Right? All of these are brought through. Crystal Reports, sorry. Throw that out there. These were highly centralized solutions that made it so people could build a report, type in a few parameters, press go, and then they would get an Excel file out the other side. The challenge was to build any of these thing, whether it's in SQL, SSRS, Crystal, or Cognos, you left behind the business users. It was too big of a lift for them to learn how to do these things. And so we had this divide that existed. The backlog still persisted because you weren't able to get to a nice state where others, especially on the business side with that higher headcount, were able to help out. Now when you look at where things went and what people would use, when they don't have a tool to use, they're gonna go back to Excel. So it is a very common pattern where we've got reports that are being built. We have a bunch of people looking to get some help, and then you just see people still using Excel. And by people, I mean nontechnical users. All that they have is Excel. That's the only tool that they have. And so it's understandable because they're not able to make that chasm. The rest of the organization's data stays locked in the hands of the professionals. And for self-service, we wanna move past that. Right? So if Excel isn't the tool to do the job, what is? Now to this point then, I feel like we need to address the elephant in the room just from an inner works partnership perspective. Right? Three little girls. I just got my YouTube music recap of last year, and the one jam that defined our year was we don't talk about Bruno. Right? So there's always something we know we need to talk about, but we don't wanna talk about it. But in this case, I wanna go back and talk about it. Grab the bull by the horns. When we look at the partners that we have on the InnerWorks side, you saw that there were two BI tools there. There's ThoughtSpot in the top left and Tableau in the middle. We have been Tableau partners at UnitWorks for the past fourteen years. We love them as a tool. We recommend them as a tool. We use it very often. We use it internally. We use it externally. It is something that makes a lot of sense that we have seen transformational change happen in the industry on, and we need to give credit to Tableau for what it did. Alright. So we don't talk about Tableau, but I will do a whole presentation about it. Right? Because when we look at self-service analytics two point o, that is what Tableau made possible. If you've ever been to Tableau conference, twenty thousand people in an auditorium talking about being data people get seeing excitement, seeing momentum, seeing movement. People are very excited about what Tableau brings to them and brings to their organization and still brings to their organization. There's a reason they're in the top right in Gartner's quadrant. It's because it has made things a lot easier for this technical developer. So when Tableau landed, it introduced this idea of a drag and drop interface for interacting with your data. It made it very easy for your business users to go out and connect to any dataset, made it very easy for for your end users to create and use visual analytics, and it still does to this day. It is a very powerful tool that makes it very possible to do a lot of different things. And credit where credit is due, it was the first make the jump across the chasm. So when we're looking for self-service BI tools wanting to make life easier for your end users, Tableau takes the cake as the marquee example of getting across to enable your citizens to do more things. Right? Now as that market has matured, there's been more things that have happened in terms of more competitors coming in there, but, really, it's important to really drill home and understand that that concept of visual analytics and the dashboard behind that were huge, game changing for the industry. It is not exaggerating to say it's the reason that I have a job today. So I'm very grateful for that just personally, but also as an industry because it has unlocked the power of BI. People started seeing the potential of what a dashboard could do. All of a sudden, something that was lost in an Excel sheet, something that was hidden behind only the professionals got into the hands of the everyday user. And visual analytics was very powerful, very easy to read and understand. That being said, this is the ThoughtSpot webinar. I thought dashboards were dead. There's great marketing going on by ThoughtSpot on dashboards are dead. It drives conversations. It drives interactions. At our client base across Innerworks, we had people ask us about this and say, wait. Hold on. Our dashboard's dead. And there's a reason for that, and there's a thing I wanna talk about. But first, with apologies to Michael Lewis, I grew up playing baseball. And so Moneyball, when it came out, hit this perfect intersection with me studying economics and then moving into the data space. Love of baseball behind that. So big fan of the book, huge fan of the movie as well. And there's a quote in there from the character Peter Brand who's here on the screen, and he was representing the actual person Paul de Podesta who was based off of played by Jonah Hill in the movie. But he talks about failures in baseball. Right? So anywhere you see brackets there, he was actually talking about baseball. But with apologies to Michael Lewis and your brilliant dialogue, there's an epidemic failure in self-service analytics to understand what is really happening. And when asked about, like, seeing dashboards or seeing visual analytics, what I see is an imperfect understanding of where analytics and insights come from. So it's worth spending some time on talking about what dashboards are good at because they are out there, because they crossed that chasm. Why did they cross that chasm? First and foremost, dashboards are great at providing qualitative judgment. If I give you a raw dataset, you don't know what's good and what's bad. But if I spend some time with my technical developer or with you as a business owner, you can come in and say, this is what good looks like. Good, bad, yay, nay. Green, red, orange, blue, whatever color scheme you wanna use, a dashboard tells you that. It also is a really good place to formalize business logic to make sure that as a group, you have a key definition and say, are we ahead of plan, behind plan? What's the source of truth there? Go to the dashboard. That has been vetted. We trust that. It also provides measurements against targets kinda similar to what I was talking about. But most importantly, if I were to categorize these, it provides productionalized analyses. Meaning that as a group, we have gone through. We have figured out an analysis that we wanna do. We have said, yes. This is accurate. Let's make this repeatable. I wanna be able to look at that every quarter, every week, every day, every hour. Who knows? The point is it's productionalized, and it's there. Here's what's happening. Now where they struggle is when we want to go beyond that. So if we're looking for a dashboard to provide hyperpersonal insights to every single person in the room, major challenge. Just as a simple example, try to build a dashboard that serves both your executive audience and your operational audience. Doesn't work. Your executive is sitting there. She's walking to a meeting. She has thirty seconds going into a meeting, usually five seconds, pulling up something on the phone. I need a number. What is it? Thank you. Done. Try to show that same dashboard to your operational analyst. They're gonna say, hey. Can I drill through? Can I see all of the underlying data that supports that? I need to drill deeper. I need to get access to literally everything. Low tolerance for clicks, high tolerance for clicks. You can play around with this whatever you want, but the point is you end up building a lot of dashboards to fit the different use cases. Also, with dashboards, you have to try and understand where you want people to go. You can build out a great narrative, which is fine. That's a great use case. But if you want it to be fully flexible, to be able to drill into whatever you want, then a dashboard invariably hits a limit. Because me as a dashboard developer, I can't choose or predict what the exact path will be for everybody. That then leads to more development time. So a new question comes in. I have to go back, figure out where it is in the data, add it to the dashboard, and you just slow down, and it spirals quickly. And then if we're looking to get to productionalized analytics, not analyses, but analytics, meaning I want more people to do what the technical analyst does, a dashboard is inherently flawed. It doesn't get you everything that you need. Now you may have seen this before on where dashboards struggle. It's kinda small here. But if you go to the ThoughtSpot blog and talk about why dashboards don't deliver on promised value, there's this metric and graphic, which I love. Starts talking about a very real use case that we see across our different book of business, where you have a business user who needs a question. Analyst says, oh, we don't have that. They need to go to the data engineer. It then goes back to your DBA. You get the content that comes back in, who passes it to your data engineer, who passes it to your analyst, gives it to your business user. Doesn't matter if it was three days, a week, two weeks later, it's out of date at that point. The business user has moved on and says, hey. Okay. Thanks. But I now I need this. Repeat the same cycle. So with apologies to ThoughtSpot marketing, I don't think that they are dead, but I do think they are incomplete. So dashboards need to be augmented by something else in order to help people truly attain self-service analytics. So we're looking at self-service analytics two point o. I've made the argument here that visual analytics and dashboards are useful. They are great. They should be rolled out in your organization. If you don't have it today, please get on that. If you twist my arm, I'll throw in some other competitors that are in here too, right, where you can have this object creation going on with other BI tools. But to be honest, even people sometimes still use Excel, and that's fine. Right? We're just bucketing it into where we need it to go and what we can provide. But even with all of this, let's go back in and check on where our friends are. Just from raw number of things on the screen, you've probably guessed that our technical developer is actually doing pretty well. Right? He's been able to go through, have all of his key reports that he's building, and then passed off a lot of that development work to the citizen developers. So no longer is he the bottleneck where everybody's coming in and saying, please, I need this. I need this. I need this. I need this. He's been able to delegate, and that work has gone out. And you have enablement going on. You have a center of excellence driving people using different BI tools. And you're getting to that point where if you're looking at, like, the object creation side, I need to build the dashboard or visual analytics, good momentum is happening. On the object consumption side, it's still dicey. Right? You have your technical analyst who's there to help. You have business users who are asking questions. You've got your DBAs that are really helping out to get the datasets in and ready to go. Is this an issue? Right? What's the volume of users between the two? Like, is this a real problem? And the answer is yes. Not just because I have a lot of fun with copy and paste when needed, but because if you look at the volume of users, especially across our book of business at Innerworks, it's roughly what we're looking at right here. You have limited technical expertise. Your business users who are very interested in building out their own content and creating their own objects is not as high or not as numerous as the people who want to consume things. So when you're looking for building out self-service analytics, you have to have a solution that provides more than just changing a filter on something that someone on the left side of this two by two has created. You have to have something that's more than just, oh, great. You wanna do this? Perfect. Pull up neutral party. Well, let's pull up Power BI. Right? Go in and start building out your own content. End users don't have time for that. They need a way to take advantage of content, datasets, and dashboards, visual analytics that have been created, but be able to go in and ask their own questions on top of those. And that's where we get to ThoughtSpot. It's a little hidden ThoughtSpot logo that's chilling in the middle of all of these users here, but they are an underserved user base no more working with ThoughtSpot. So we've got self-service analytics three point o here now. What ThoughtSpot does is it makes it very possible for your end users to ask their own questions. Makes it possible for them to drill into analysis that have been done for people to be very flexible with that end user experience, with that consumer to able to go and get your own content. So no longer are the technical teams on the other side taking ticketing requests. Their time is focused on very high value work of curating datasets, of making it so that they're able to enable all of these nontechnical users. Regardless of the spectrum they've side of the spectrum they fall on, right, they can go and build their own dashboards. That's great. Or they can go and they can ask their own questions. Right? ThoughtSpot fills in that gap and makes it possible for your end users to be able to ask their own questions, for them to be able to really question, query your data, and get back productionalized analytics as opposed to just a canned analysis. If you want as well, DBT is out there. Very interesting in terms of helping end users be able to help with the end user experience in terms of building out content that's there. We're really excited about what that does. Now at this point, we are coming up on forty minutes into a presentation. I understand that this is a lot. Right? There's a ton that's going on here in terms of different tools, different background, different story. There's a whole narrative here that we spent some time crafting, hopefully, that has cleared things up to make it easier for you to understand, but I also understand that this can be confusing. It's also every IT admin's nightmare where I'm basically making the argument here saying you have different personas, you have different groups, and it makes sense to match the tool to the group as opposed to trying to have one solution fit them all. At Innerworks, we are very much a best of breed shop. We wanna make sure that at the end of the day, you get the tool that you need to do your job. And so how can we bring this all together? What does this look like from an end user perspective, and how does ThoughtSpot make it very easy to provide this type of solution? So I'm gonna switch over at this point and show you a quick demo of how ThoughtSpot can look in a productionalized environment. So what we have here is a portal environment that is called Curator. So at Innerworks, we have a product that is called Curator. It's a wrapper for all of your analytics and your BI needs. Rather than having to remember, was that a Tableau report? Was that a ThoughtSpot live board? Was that a ThoughtSpot search, a Power BI dashboard? Curator brings all of it together under one hood. This is something that we've had multiple clients ask us for to the point that we productized it and now roll it out. It's kind of an easy button for getting access to this. You don't have to do this by any means. It's just showing you an example of what this can look like. Right? So we've got our home page with our branding. We've got our colors that we've decided on. You have a nice easy navigation experience where you're able to go through and see content that's available. You can search for your individual dashboard. So if I wanted my COVID dashboard, it brings it up. Or outside of that, you can have your home page that you've built in a no code environment with some dashboards that are showing key insights that are productionalized. You can then go through if you want, and you could drill through to a dashboard underneath. If I click on it right here from this link, it's gonna go through, and it's gonna bring up what that dashboard looks like embedded in its fully interactive form. In this case, it's a Tableau dashboard. But the idea is you've got that interactivity. You have your dashboard that you're looking at. But from there, you wanna combine that with a different experience. So all staying within the same wrapper, within the same scenario, or within the same branded experience, we have a ThoughtSpot search. Now with ThoughtSpot, you can think of it less of, like, a pure Google search that goes out and you can put in any kind of wording that you want. I like to think of it more as an Amazon search. So I have a very curated dataset, and we put a lot of time into making sure that it's ready and good to go in performance. And then what I can do is I can go through, and I can look at what's available to me down the side just looking at this simple retail demo that comes shipped with your ThoughtSpot environments. But I can also just start searching. Right? What if I'm interested in my profit and I don't know. Let's just look in twenty nineteen, and let's hit go. ThoughtSpot interprets exactly what I was going for. It runs a query against my live data source underneath. You can see this is going out, grabbing data from Snowflake, bringing it back. We're looking for my total profit in twenty nineteen, and bam. Three point two one billion. Now I fully recognize this could be something that you're looking at where this could be answered with a dashboard. I get that. That's fine. And if you only wanted to look at this, that's not a problem. But if we wanted to go through and start looking for other things on top of that, let's start figuring out where we want this to go. So if I break it out by hour of the day, see where my profit is coming from. Look at where those transactions are. Vast majority of those in this dataset, well, no dice on the hour of the day. Wasn't coming in and giving us something that was useful there. Let's go back in and try and break this out by just month. Right? Trying to figure out where that's coming in to see what my trend is. K? So, again, more points to making sure that your dataset is ready for this. If you have hour that's in there, but it's not really broken out, then probably don't surface that to your end user. But if you surface something like this, then ta da, we now have this search, and I can see exactly where my transactions are. I could surface this, and I could choose, hey. Let me pick a live board that's ThoughtSpot's version of a dashboard, and I could pin it. But then what's cool is beyond that, I can then start doing more things on top of that. Right? If this is on a dashboard, I could swap over, swap between a visual and a text item here so I can just bring up a table very quick for me to be able to use. I can go in. I can choose different chart types as I'd like, or I can even go in rather than editing the table itself. We can start looking at different information that's here. It's gonna tell me exactly what's happening. I can pull the exact query that is being generated, and I can use that other places. But on top of that too, there's a lot more flexibility that's available even if you publish something that's static like a live board. So in my Inner Burger live board demo here, Inner Burger is a restaurant site that if you'd like, we can provide the link to afterwards. But the point is this is a dashboard that shows up or Liveboard, sorry, that you can go and you can look at, and you have your different analyses that are happening. Again, prepackaged insights that are interesting to us as an organization. Let's say we're looking here, daypart name, afternoon. Right? I wanna see this bar, and that's interesting. If I am using another BI tool, I can build out a hierarchy to drill through. But in ThoughtSpot, I don't have to choose which hierarchy I'm looking at. I can go and I can say, hey. This afternoon snack thing looks interesting. Let's break this out by region. Okay. So I can see we're doing well actually across all of our regions. Midwest is doing fine. Northeast is doing great. Man, let's look at that. Let's drill down again and start looking to see if there's some sort of a trend I can break it out. Let's just say by state. Right? And we're coming through, and as I'm interacting with this data, this is the viewer experience where I'm able to view the data, but then I can start asking what questions I'm interested in. I'm no longer locked into a predefined path or a predefined narrative. I'm going in and I can choose. Let me choose and look at which columns I'm interested in. So this ability to go in and drill to me is huge. On top of that, you can add your own filters. I can add my own columns here on the side as it loads. I could even replace existing columns. The point behind having all of these available is it makes it possible for you to go in and to drill into your individual content so you can ask your own questions. There's a lot more to be had here, and I'd really encourage you at the end to go and sign up for a trial so you can start doing your own exploration. If we go back to the slides at this point, though, again, we're now forty five minutes into a presentation. How do you explain this in five minutes? That really is one of the hardest things to do for all of this. You've seen that I've tried to kind of boil this down, but it's taken me three quarters of an hour so far. My favorite analogy for this, and credit goes to Spencer Hamilton and Sarah Krasznick on this, links are there at the bottom, Is to explain your BI environment like a froyo shop. So when you think about frozen yogurt, this is sweet frog for the image that's here. It's huge by me. I live up in Maryland. What you have is an environment where you have toppings, you have ice cream. Everything is packaged and put together in a way that you can use it. You can go and you can choose. You are creating your own content, and yet you're not being forced to go one direction or another. So for instance, if you wanna go in and you wanna do my favorite, chocolate with pomegranate, and then add on Heath bars and some chocolate fudge, I can do that. I'm not boxed into a certain narrative or a certain path. I pull the chocolate handle on the ice cream and chocolate comes out. I'm not surprised. I go in and I scoop the Heath bars. I put them in. Not an issue. That's fine, and I can build out what my own environment looks like. It also scales. So rather than saying, okay. Ben can do this or Joe can do this or Amy can do this, and that's it, every single individual user can go through and choose what they want. If you wanna go in and you wanna make a sweet frog bowl just and take the Reese's Pieces scoop and scoop that in there, and that's all that you want, that's fine because it is you can be very confident that those are Reese's Pieces. You can be very confident that the data, in this case pushing the analogy, right, that you're getting out of anything that you do is exactly what you want, and then you can combine them as needed and ask the questions that you want. So think about self-service. You think about the deployment that is out there. If you need to understand where things are, I would really kind of focus on remembering two things. One, if you need an easy analogy, then the Froyo shop works. Different users walking in wanting different things, choosing different flavors, choosing different toppings, you're good to go. That's an easy one. If you need to dive a little bit deeper, then go back to the two by two matrix we've been talking about where you have your object creation versus your object consumption. How do we make nontechnical people able to do more? And ThoughtSpot really fits that bill in closing out that quadrant in the modern data stack. So, really, again, I appreciate you joining with us today. I hope this was helpful for you in seeing where the different paths lie and seeing where different tools lie and how you can use them to be successful, and then seeing how something like Curator could bring all of those together and put them in one spot where you can serve the needs of your people who need dashboards. You can serve the needs of the people who wanna ask questions. Hopefully, this makes sense, and you're as excited about ThoughtSpot as we are. There's a link up here, a QR code for you where you can go in and you can start a free thirty day trial, and you can run with that. Colton, I will turn the time back over to you. Awesome. See why I was so excited about this, everybody? Ben does just a great job of summarizing where we've come from from data BI stack, the challenges, the problems that have been overcome, where certain technologies fit, for what different use cases, and where ThoughtSpot fits into this whole modern tech stack alignment. Where does ThoughtSpot really truly shine? Ben, phenomenal job. If we were actually, like, live and in person, I'd start, like, just as a slow clap for you, but I've seen it. There will actually be more content like this. Interworks is just doing a phenomenal job with us. I'm so excited about our joint partnership together. Please, we we do have a little time left, so, we'll be hanging out live in chat. Any questions that you have that you wanna get answered, just feel free to pop them in there, we'll we'll get to them and and make sure we're we're answering everything that comes up now. Again, if if you need to jump, get a bio break, or anything between now and your next call, reach out. We we're more than happy to set up individual sessions and answer questions and talk through your specific individual use case and where we might be able to help you out. So, yeah, we'll be live in chat. Ben, thank you again. Phenomenal job. Thank you. Thank you all again for attending today. Really appreciate you taking the time from your busy schedules. Hope you I hope you got a lot out of this. And please feel free to reach out. We're always here to help. Definitely. Thanks, everyone. Thanks all. Take care. Have a good rest of the week.