To all of you who are joining us today, today, hey. Thank you for for joining us on this actionable dashboards webinar. My name is Jack Hulbert. I'm an Analytics Architect here at InterWorks. I'm based in San Francisco, California. And here, I have my colleague, Mat Hughes, who's our Product Architecture Lead. And, yeah, really, today's webinar is about, you know, leveraging reverse ETL and automation, to make your dashboards more useful, drive impacts, and, you know, creating more action oriented behavior with, your business intelligence and, dashboarding tools. And we're going to talk you through some some patterns, some ways that, you know, we like to think about that. And, you know, if you have questions throughout the presentation, I'll be here to answer, any any anything that comes in the chat. So and we'll also have some time at the end for q and a. But, yeah, if you have any questions, please just feel free to to drop them in the chat. Okay. Perfect. Yeah. And a little bit about InterWorks, we are a data and analytics consultancy. So we offer support and, services in, you know, AI readiness, generative AI applications, business intelligence. Obviously, we do a lot of work with some of the, best in breed business intelligence and data platforms and help our customers build, you know, analytical applications, high highly resilient business intelligence systems. And then we also do things, you know, all of the supporting services around that, governance, design, strategy, performance optimization, you name it. And then we also deal with, you know, infrastructure that support those systems. You can go ahead, hit next slide, Matt. Yeah. And, Matt, just wanna pass it over to you, you know, introduce yourself, and, yeah, I'll I'll hand it over to you. Yeah. Thanks, Jack, and, thanks everyone for joining. My name is Matt Hughes. I'm a product architect lead at InterWorks. What that means is that I tend to work with a lot of enterprise organizations to help them, build and deploy complex analytics systems, custom projects, you know, things like that. And so today, we're gonna be talking a lot about a lot about dashboards. We're gonna be assuming that, you know, the folks here are are coming from of a data and analytics background and that you're familiar with dashboards and why you would want to build them and why your users would want to use them. And so what we're gonna be talking about today is some of the ways that we see dashboards, falling short, for both users and for organizations. This is not meant to be a a a dashboards is dead kind of conversation. That's not how we intended, and and that's not the way that we wanna frame this up. We we think that there's a lot of value for, BI products like dashboards. What we wanna talk about today, though, is we wanna talk about kind of a subset of those use cases that, require a little bit more than what you can do in a dashboard, talk about what's possible, help you understand some patterns, and then and then talk about ways of actually actually implementing them. So if we think about what that looks like, I think it's really helpful to start with a common story that that I think we're all very familiar with if if we're in the world where we're building dashboards and decision support tools, for for our business users to use. So here, you know, we have a common enough use case. Think about a dashboard that you're using for, sales prospect targeting. Right? You could imagine a, a set of filters. You could imagine a scatter plot that helps you look at all of your sales leads and then, model which ones are are likely to, to to to yield good results. You could imagine some kind of interactivity so you can drill and look at details. I think this is the kind of dashboard that we're all very familiar with. Right? It's a decision support tool that a user goes to. They perform analysis to target the sales leads that they want to do something with. You know? Maybe that something is, creating a new marketing campaign targeting those leads. Maybe that something is sending them an email. Maybe that something is just an internal process. But regardless, the the dashboard here, what it's doing is it's pulling in disparate data. It's applying business logic to that disparate data. Right? It's it's helping us understand which of the sales leads we care about versus the ones that we don't, which ones are we happy about versus which ones are we not, and then it adds interactivity into it. And so if we think about the cycle then of what the user is doing, they go to the tool, they perform the analysis, they do the interactivity, And then, ideally, what they get at the end of that is a is a list. Right? And and and what do they do with that afterwards? It depends. Right? And and I think that's actually what we wanna talk about today is is what do they do with it afterwards. I I really meant what I said at the beginning today that this isn't a, a dashboards is dead conversation. I think we see a lot of that happening in the in the space and in the industry. I I think that we think that there is actually value in in what dashboards are doing here. Right? That it's displaying data quickly. It's allowing explorations. It's it's pulling in these disparate data sources. I think the problem is is more in the experience of that that common story we were talking about. Right? That, you know, you you are bringing data in from a source. You're you're putting it into something like a database, and you're allowing for that analytics. But if you think about what this user is doing, they're working in a business system. They're trying to come up with an answer, and then they have to leave. And they have to go find the dashboard, and they have to go use it, and it's kind of like a separate tool. And then once they get the answer, well, then what do they do with it? Right? When they when they filter down to the to the result set of the prospects that they wanna target, what do they do with that dataset? Well, we build a lot of dashboards for for a lot of folks, and we help a lot of organizations, you know, go through this sort of analytics journey. And I think a common thing that we see is that the thing that people wanna do with them next is, they wanna get down to this target set of my, prospects that I wanna go target, and then they wanna dump them out to Excel and then do something with it. Right? And a lot of times, you know, I see this kind of this meme and this idea in the analytics community of, you know, why do my users wanna dump this out to Excel? Why don't they wanna use this this this glorious tool that I built for them that uses all the visualization best practices that isn't just a bunch of a bunch of data in a spreadsheet. And we tend to view this as a as a symptom. Right? The the dumping something out to Excel means that we're not meeting users' needs. Right? That they're getting to a point where they're they're getting an answer, but we tend to view just getting an answer as as not quite sufficient. Right? That we we wanna get an answer, and then we wanna actually go, change something about our business, make a decision, make an action. We wanna action the insights so that we can actually get business impact. And and that, frankly, it's not enough to just, you know, get the answer. And so we'll talk about today why that's a problem, you know, and and what we can do about it. But but I think in the short term, the the kind of surface level problem there that I'm describing is that the in that experience, in that story, the dashboards are are failing the user. And and the reason that it's failing it is because they'd like a tool that that helps them perform the analysis, come up with the targeting, and and, ideally, action the things too. And and instead, it it does part of that. Right? It brings in the data. It allows for the exploration, and it it it is visually appealing, but it doesn't do a very good job of, taking the answers that you get and then doing something with it. Right? And doing something with it can mean a lot of things. It could sometimes, you know, what a what a user needs to do is they just need to provide more information about, you know, what they're seeing. Right? And and that's kind of like a write back, use case that we see quite a bit. Sometimes they wanna drive action in other tools, and and, you know, that's the the example I was talking about before. But regardless is is they they typically can't do that in, internal BI tools. Right? The, the the Power BIs of the world, the Tableaus of the world. There there's a there's an incentive for those tools to not really integrate well with other systems and and to just kind of allow for the reporting. So what this means is that not all dashboards, just to be clear, but but some dashboards for some use cases, we should be thinking more about what's possible, and we should solve the user's problem for them in a way that is is viable. So at the surface level, I think that's part of what I'm saying here, which is, your users will be happier if they can get to a point where they're getting to the insights, and then they don't have to do more manual work in Excel afterwards. But but they can instead, you know, push a button and then and then action these things in other systems. We'll we'll talk what that looks like. I think that's the the surface level observation, though. And and don't get me wrong. I think that's a a good enough reason to try to fix this. I think that we should be making users' lives better. We should be making them more effective, and and I think that we should be building, you know, analytic products that allow them to do that. But one thing that we've seen, is that as a lot of organizations are exploring, generative AI to automate some of this work that we're talking about doing, the analysis, the finding of the insights. What we're seeing is that there's a key missing piece around the actioning of the insights that organizations are are suffering from. And so there's a there's an even better reason to to to build this better system that we're talking about here. So let's talk about the the way that that lack of integration is is hurting the entire organization rather than just an end user. And so if you think about how you would operate operationalize analytics, Right? I think there's this is a similar cycle that we often see, but you imagine a person, the person kind of like the story that we're talking about already. They go out and they source information from the business. They apply business logic to it. They get insights with a human involved, analyzing, and and using that dashboard. And then there's that manual kind of black box that we were talking about before. Right? That, when you get to the point where you get to the dashboard and then you get insights, but then you don't know what happens next, that is a is a key problem here. Right? Because we don't have a dataset around those actions. And so what we what we really need, if you think about, the rest of the stops on here, we we tend to have a data around doing these things. But a lot of organizations are really missing this this part that you see in blue here, the, the so what of, the analysis, the, you know, if I find targets that I think that I should target, well, what should I do next? Should I, you know, run a discounting campaign? Should I run an education campaign? You know, there's a whole whole set of kind of what next actions that we should we should be able to figure out. And oftentimes, in a lot of organizations, that is just a just just a black box because it requires manual work, so we don't really understand what's what's happening there at scale. So let's talk about ways that we can solve some of those dead ends that that we've been alluding to, so far. So I think the the first method that we tend to see for trying to build a feedback loop to get better action tied to insights is really just a a basic, ability of, doing data input. Right? You you see this a lot in the in the Tableau world. You see this a lot in kind of basic BI use cases. It's it's what we think of as write back. Right? What this tends to mean is that people doing analysis, will either, a, need to provide some, you know, color commentary about marks that they're seeing or about insights that they're seeing or or, b, oftentimes, we'll see a a process involved. Right? That that an organization will have KPIs. They they have operations built around those KPIs, so so they're monitoring them on a regular basis. They're making changes to their business. They're, keeping track of what those changes are, and then they're they're looking to see if those changes change the metrics. Right? If you're going to do that, oftentimes, you need a way to provide some kind of data input alongside your analysis and to collect that from the people doing the analysis. And so if you if you watch the screen here, here's an example, like, a really basic data input example where, you have a trend that you see. You have some kind of commentary about that trend, and then you have a way of submitting it. Right? And then if you'll notice, you can see here that what it's doing is it's taking that comment. It's putting it back in the data itself, and it's making it available in the analysis. And this is the kind of the world's most basic version of this, right, to to show how this works. I don't think there's actually a ton of value in putting the comment in the tooltip except for the fact that it it demos well. But what organizations tend to do here is to take that human commentary and then put it back into a dataset where they can cross reference it against the KPIs. They can show different points in time what they've tried to do about it, and then they can also, move away from this freeform text and get to a point where they are, choosing from sets of actions and sets of things that they would do. And then that gets you into a world where you you're starting to operationalize this and you have a very useful dataset at the end of it. Right? But that's kind of a a really simple use case. I would think about this as being necessary for operationalizing any sort of KPI approach. And then we often see other sorts of, like, miscellaneous use cases around writeback. So it it could be for other things besides adding business context to metrics. It could be for things like, letting a user make different selections of cohorts and pushing that back into a database and then building aggregations off of those cohorts. It could be letting a user build out different scenarios for scenario planning. Right? My my point is is that oftentimes, for some advanced use cases, you need something that's more than just a read only presentation of data. And a lot of the native BI tools like a Tableau or a Power BI don't really do that out of the box, but it is very possible to engineer an experience like this, and it's something that we we do quite a bit. Or if you're listening to this and you're thinking, oh, I actually I I want to do this sort of write back all the time. I wanna do a lot of complex scenario planning. I wanna actually make this a first class feature of the tool. There are some modern BI tools out there right now that are really focusing on this kind of experience of not just making the data and the analysis a one way street and instead making it a a valuable feedback loop. And so, you know, for those sorts of, for for those sorts of tools, if that's something you're wanting to do quite a bit, you could reach out to us, and we could talk through, you know, what those tend to look like and who's doing a really good job of that. But I think I'd look at Sigma analytics as kind of an obvious version of that. Right? They really leaned into this idea of making the data a a two way interface and making it part of part of analytics. That's one version of this, and I think that's the version that we tend to see most often whenever people are starting to explore these, like, more complex versions of dashboards and more complex versions of data activation. But I wanna talk about a more complicated version here that, you know, instead of right back, maybe we think about it as more like right forward, I guess. But if we if we watch the animation here so here, what we can see happening is I've selected some marks, and then, bear with me for a second while while Tableau updates based on those mark selections. But what I'm going to do here is I'm gonna select some marks, and then you'll notice there's a send data button up there at the top. I click on the send data button. I name the, the list here for the marketing campaign that I wanna create. And then what Tableau is doing behind the scenes there is it's taking those marks that I've selected, and it's sending it out into HubSpot. It's creating a new campaign, and then, we could even trigger it from there if we wanted to. Now that specifically might not be relevant to to things that that, you know, you're trying to do or you're trying to enable your users to do. Right? I think it's a it's a fairly niche use case that assumes that, you know, you care about pushing things into a CRM like HubSpot. It assumes that you're doing targeting for email campaigns. But my point is is that most organizations now, most companies have this growing plethora of SaaS tools that they use, and they use those to runs their they use it to run their business. And you could replace HubSpot and a CRM here with, Workday as an ERP or Salesforce as a CRM or, Airtable or Monday as generic purpose business process apps where you'd be writing these things to. My point is is that there's a lot of these SaaS endpoints and SaaS tools where, we're doing analysis in Tableau, and you can tie them together and action them, in a way that is actually solving your business problems rather than adding more work for your for your end users. Right? And so it is worth pointing out here, and and I I tried to make this point a couple times before, but I'll I'll I'll I wanna try to make it as explicit as I can now too. The what we're not saying, we're not saying that you would do this for all of your dashboards. Right? We're not saying that every dashboard that you built should be integrated into a business system and set up more like an application. And if you're not doing that, then you're not meeting your user's needs. That that that's not what we mean at all. I a lot of use cases with a lot of folks where they get a lot of value out of dashboards as a read only decision support tool that helps them action something. But we typically see a small number of high value use cases inside organizations where they really do need to integrate the analytics they're doing with their different business processes. And for those, it makes sense to do some engineering to allow for this sort of experience. And so the reason that we wouldn't do this for everything partly is because it this is not a a thing that you build with a self-service tool like Tableau, at least in in in Tableau's current state. Right? Meaning that, typically, there is going to need to be some, like, understanding of application integration, APIs, you know, that sort of thing in order to make these things work well together. In fact, philosophically speaking, we tend to view this as the the next kind of, technical unlock that we're gonna start seeing with analytics tools. Right? That if you think about Tableau, and what it did at the very beginning, it it said, what we're going to do is we're gonna take all the things that we would normally have a dashboard do that normally requires an engineer or a developer, Right now, we see instead of a lot of organizations, they have purchased or contracted with an increasing number of SaaS business tools. Those SaaS business tools don't talk to each other. They they don't have automations between each other, and, there's no talking to the analytics hub in the middle. And so what happens then is, companies that wanna try to tie all these systems together, that wanna make their analytics products, actually action into these systems, they tend to view it as an engineering problem. Right? Either either, a, they don't realize that's possible because it's not something that, you know, Tableau or Power BI does out of the box, or, b, they might realize it's possible, but they understand that they're gonna need a, you know, a developer to do the API integration. And those API integrations tend to be brittle, and, you're you're gonna have a hard time finding a developer to do the do the work inside your organization. And for all those reasons, we just we we we don't tend to do it, or we don't tend to view this as possible, or we don't tend to promise our users that that we can do these sorts of things. In a lot of ways, that was that was what building interactive dashboards was like at the beginning of, you know, whenever Tableau started becoming popular. What we're seeing is we're seeing, tools that make connecting all of those different SaaS tools and automating them together and and building these sorts of well integrated systems. We're seeing those, become more popular. We're seeing them become more capable, and, we're seeing them become a lot more viable for organizations. And so this tying together of the SaaS tools, I think, is is is the next round of where we're gonna find some value for for the things we're building for our users. And we're we're seeing some, technology pop up that allows for this sort of integration between the different SaaS tools to be possible. So let's talk about what this looks like if we do this in a repeatable way. Because in this version that I'm showing you right now, what I'm mostly showing you is here's how to build a better dashboard. Right? A better dashboard that, allows for actioning in a system and and allows for some automations. But, that doesn't prepare you for the the automation of the actioning that we think a lot of people are gonna wanna do later. And so we should talk about how you can do that and what that looks like. And so if we're thinking about making this repeatable, this is where, we think that some of the this newer technology and some of these newer products are actually really helpful and and really enable, some new patterns here. So think about a dashboard with a human involved, and that's what we've been talking about so far. You've got a dashboard. You've got somebody there making, making decisions or making insights from that dashboard. And then, what we've also decided today is that you need to capture those decisions somewhere. Right? That it's not enough for those decisions to just live in someone's head and then go get action manually, but we should understand what someone has targeted, what they would like to do, and and then and then store those somewhere for analysis later. Right? I put I put Snowflake up there. It could be any database, but I think Snowflake works really well for this for a lot of reasons. And then for some use cases, you could probably stop there. Right? You've made a better dashboard that solves your user's problem, and it allows them to action insights without dumping data out to Excel and without, you know, becoming a manual headache for them. But, we tend to think that for these sorts of use cases, the real end game here and the real end goal is to, take the human out of the loop and and to try to automate the actual decisions that they're making over time. So, you know, we we've seen this growing category of tools. You you might have heard them be referred to as a lot of different things. Reverse ETL tools is one of them. And and out of that category, we really, we like what Census is doing there. We like what they're building, and and we like that their their philosophy around this is more about a universal data platform to tie all these things together than than any one single use case. But Census is a tool that works really well for recognizing scenarios that happen in your data warehouse and then actioning or pushing, data from the warehouse out into your SaaS tools, like, in this case, HubSpot for that example that we were talking about earlier. And so if what you end up doing is you build the decision support tool, you make it so that it meets your users' needs so that they can then action those from the tool itself. You can then use that to build up a dataset of those actions. And then at some point, you can just start using a tool like Census to take scenarios in the warehouse and then customers in the warehouse and then automatically target them for new campaign ads, in in, you know, whatever CRM tool you're using. And so let's talk about each one of these steps if this is something that you're you're interested in doing. So couple points I've already made. First of all, we we don't want to do this for all dashboards. Right? This is some engineering effort. We want to do this for a subset of dashboards where this is worthwhile to do. And so you might be looking at this and and trying to figure out, okay. Well, what are those dashboards? How how do I get started? You know, that sort of thing. We have some demand signals that we tend to look for in the usage data of, like, your BI tool, like your Tableau environment. Those demand signals are workbooks that have been created that are, I guess I'd call them, like, ad hoc filtering workbooks. So imagine a large number of filters and then, kind of a target output and then an Excel data dump of that target output. That pattern is something that we see a lot that that indicates this kind of, manual breakpoint where you would wanna integrate this with a different system. And so if you wanna actually translate that into something you could look for in your environment, I would use either the the admin tools in Tableau cloud or the repository on on prem. And what I'd be looking for there is user behaviors, and the user behavior I'm looking for is a is a data export and a data export that's, like, a high percentage of my, like, usage. Right? And so what that tells me is that I have a tool out there. People are coming to it. They're filtering it, and then they're dumping the data out somewhere else. I would be looking for those workbooks as, the first things to go talk about with users to help understand what they're trying to do and see if, there's a there's a better way there. Last of all, it's worth pointing out that, you know, I I kind of alluded to this before. The BI tools out of the box don't do a very good job of this. Right? They, you need a mechanism for extracting the data out of the the dashboard and then doing something with it. And so that either means embedding your dashboard in something like a web layer, so that's very easy to do, Or it means using an extension, and then and then having that put in there. So if you're watching this and and you're you're saying this is very interesting, I would like to build something like this. But at my organization, we don't do any sort of embedding and we don't allow extensions, reach out to us and let us know. We can talk through ways of getting started here and and, like, ways of getting started in very low risk ways. But I would generally view those as a as a prerequisite. And then once we've once we've targeted the dashboard, we then want to store the user behavior, store the user actions, and store the decisions that they that they get. Now not to state the obvious, but, like, if we had to manually create a dashboard that wasn't actually integrated with any other systems and we are just going to try to build up a dataset of those actions, the user would never do that. Right? Because they'd have to manually input it. They'd have no reason to. But if we've built an integrated dashboard that actions something in a different system, if the user clicks the button, then now they have an incentive to use it. And then what we want them to do is to use that so we can store the data about what they're doing. Right? And and we wanna do this for two reasons. I think that that, you know, the reason that I alluded to earlier is that we want to build up a dataset that we can then use to inform the automation rules that we build in a in a tool like census. Right? So what we wanna do is we want to understand, like, what the user saw on the dashboard, what they were targeting, and then and then what they actioned. That's our main goal here, but it's also worth pointing out that, you know, philosophically speaking, we tend to think that internal analytics teams and internal data teams should be thinking about the dashboards and the things that they build for their end users as products more than more than anything else. And a a key understanding, if you're if you're going to do that, is you you need to understand how your users are using the the tool. Are they using the feature that you built some engineering around in order to to do this integration? And so most BI tools don't give you that level of granularity. Most BI tools tell you, like, you know, John Smith came in and accessed the dashboard at this time. It doesn't tell you what he did in that dashboard, what he clicked around in, and, you know, did he use this, you know, automated button that that we put some engineering time into doing. And so what we've learned over time is we've as we've done this over and over again is you think that what we should be capturing is the obvious information. Right? The things that the user clicked on to action in a different system, the relevant metrics that led to that targeting, maybe, like, something like a process campaign name or something that they type in. But if what we actually wanna do is build up a dataset that we can use to engineer these automations over time in something like census, we wanna capture a lot more than that. We wanna capture the user information. Right? The filter state of the dashboard, what all filters do they have in place, what all parameters did they have in place, and and what it what, like, what did that lead to for a result set? And then if there's any input variables in the dashboard, we we wanna select that too. And so what this means is is that what we what we wanna be doing here is building up a much larger dataset than than probably what we're expecting, but that dataset is really valuable and useful over time. So let's talk about that part here. Ideally, what we'd be doing is we'd be taking these, decision tools, and we'd be transitioning them to automations. Right? The the in a perfect world, we'll get the human out of the loop. We don't have to have them, manually futzing around with the dashboard and then going and manually doing something in a system. We can analyze their behavior of that dashboard, automate it, and then allow that analyst and that user to do more interesting higher value things to monitor the output of those, to monitor the the benefits, you know, those sorts of things. And so that's where a tool like census comes in. We think that, you know, if you imagine sorry. If you imagine, just the dashboard and the SAS tool for just a moment, you know, you don't really need census to do that. That's something you can do with the BI tool, with with a few different integration capabilities and and the SaaS tool itself. But if what you really wanna do over time is, you know, increase the throughput of these decisions and these actions, to action them much faster and much closer to, you know, real time, then we think that what we what we really wanna do and the the best end goal is to not make this a dashboard that a human needs to interact with, but is actually, like, a set of scenarios that we've built in census that then target customers and then push those customers out into, different campaign, platforms like like the CRM. And so while it it it it's actually an improvement for your end user to build a dashboard that helps you action these things and and push things into other tools, we think that it's a it's a better benefit for your organization to actually automate these in reverse detail tool like census. So, I do realize that we've covered a lot and that a lot of this can feel like an a big engineering project if this is something that you've done before. I think my ask is is if this resonates with you. If if you've built dashboards before and as you're building them, you're thinking my user needs more of an application and and less of a dashboard, reach out to us. And what I don't mean by this is reach out to us, and and let's kick off, like, an engineering project. Right? I think that a lot of what we're talking about here today, the treating analytics like products, understanding your users' needs, and then expanding your understanding of what's possible so that you can automate these things. We're seeing a lot of companies push into this with, their adoption of generative AI with their need to get more value out of their their data assets. And so if this is something that's interesting to you and you just want somebody to talk about this, like, from a thought leadership perspective, definitely reach out to us. We have solution architects that have been building these things, that started as kind of custom implementations, and and now they've they've become productized a little bit more. And, we're we're very happy to just talk through, like, what the pieces look like, what the patterns look like, and and how you can get started without turning this into, you know, a project or or a sales pitch. I'll stop there, and and I think we've got a little bit bit time we had a little bit of time left here for for questions or q and a. I haven't been paying attention to the q and a session, so I don't know if, Jack, if you're getting those as we went. But if we have any that are still outstanding, we'd be happy to talk through this. Yeah. We had a few I mean, more just, conversational, but, you know, I I was asking around to see see if, you know, anyone in the audience has attempted to, you know, create sort of this, like, action or action oriented behavior in their in their tools. And, yeah, it's like it's nice and, like, validating to see, you know, alerting is a common place to start, like, wanting to get dashboard feedback. User feedback and dashboard is a great place to start, and I think, you know, we can definitely help. We have very, like, clear paths of how to, you know, bill for those kind of behaviors. But, yeah, really curious. And thanks, Matt. Yeah. If you if if there are any other questions, though, we can take them in the q and a or or the chat, however y'all want. I guess, while we're waiting for any other questions to come up, Matt, I'm just curious, like, what other what other kind of use cases are you like, do you see pop up in this space? I mean, I know we talked a lot about, like, internal tooling. Yeah. Like, what what are the other, like, common starting places you think people or teams are wanting to get after first? Like a prototype or concept or, you know, the first integration or automation project? Yeah. I think there's two major use cases we didn't really get into here. And the reason for that is because I think they're common enough that we we wanted to to start with some ones that that were a little bit newer and a little bit more kind of art of the possible. We get a lot of folks that are asking about basic alerting and the kind of insight distribution. Right? If you think about integrating your dashboards or analytics with with other systems, I think the first thing that most people tend to think about is, how do I, you know, push things to Slack? How do I push things to Microsoft Teams? You know, that sort of thing. So report distribution is, you know, when you think about integrating all these systems as a use case that that often comes up, we didn't talk about it here that much because that's kind of the low hanging fruit that a lot of the BI tools are doing out of the box. Right? Like, you've got, Tableau integrating with Slack natively. They've really pushed on their Teams integrations this year in a way that that was good to see. So report distribution is something that comes up a lot. I just don't think it's, it's a solved problem in a lot of ways, and so we didn't really talk about that. And then the other use case with dashboards that that we see quite a bit is, like, runtime or live predictive modeling and predictions and and that sort of thing. That's that's another use case that we see quite a bit where, you know, what you're wanting to do is instead of taking some items and then pushing them into a different system, you're instead wanting to take some some inputs and some items and then evaluate them and predict some things about them and then and then present it back to the end user. Mhmm. That's another kind of integrated use case that we see quite a bit. We didn't talk about it here because I think Tableau has some some perfectly okay patterns around Tabby in in doing those. But what I would say is is that if if folks are, exploring those kinds of, like, live evaluation use cases, you could also reach out around that. I think there are some better ways of doing that than Tabby even if we didn't get into it today. Yeah. I definitely have heard that latter one. You know, and being able to capture, like, scenario states, that's the way I mean, it's challenging. Right? Like, if if you have, you know, twenty inputs to a model, like, how do you know, like, which combination is optimal and being able to save all those different states and compare them against each other is, that that's really great. I can definitely think of some verticals where that would be really useful. And, yeah, to your early earlier point, you know, yeah, the starting off with, like, messaging and alerting as, like, the initial use case, I really liked how you tied it back with, you know, using tools like Census to be able to connect that action or, you know, what would maybe prompt a message using Tableau's native integration. You can take it one step further with these tools, push it into a, you know, governed data source, and then use that to trigger behavior in the actual, like, source system. So, you know, you're maybe you're you're maybe taking, like, two steps out of the equation there, and you can, put the user or the person that needs to, you know, take action in back into the source system without needing to go in and look at the dashboard or the message and then go, you know, may seem like small time savings, you know, when you look at it under a microscope, but in aggregate, like, that could be hugely beneficial, especially for, you know, marketing teams or prospecting teams. I mean, those are great starter use cases, but I I can think of kind of other applications where people are doing, like, repetitive tasks based off of alerts, as a part of their their core work, using data. So, yeah, really like that. I haven't seen any other questions come through on q and a. Yeah. Just give it a few more minutes just in case, you know, Matt and I have been answering a little bit about some other, you know, things we're seeing in in the field. Yeah. So please feel free if you have any questions. And, yes, this is all being recorded. So, you know, as a follow-up, you will you will be able to, get this recorded if you wanna look back at it later. Oh, looks like you've got somebody with a raised hand. Let's see here. Oh, cool. Yes. Hi. This might not be, related, but when you go to the census, the actual census web household income or what state you want or whatever. Household income or what state you want or whatever. And that's kind of what I was I'd like to do for our users is let them choose what variables they wanna look at and and kind of create their own table. Like, they can create their own time frames that they look at and the variables that they choose. But just through the dashboard tap, softwares, I just I can't figure that out. It seems like the only thing I can think of is it would have to be web based. So I didn't know if you could speak to any of of that. Yeah. I'd be I'd be happy to. You know, internally, we will often refer to the use case you're talking about as, like, analytic ad libs. Right? Because what what it tends to look like is it's the user just needs to, like, be able to build a query out of, a bunch of things they select from. Right? So if I'm understanding right, it would be something like, you know, show me blank metric, and and maybe you they could choose a lot of those. But show me blank metrics cut by blank dimensions for blank time period. Right? Which is which is why we tend to think of it as as the the analytics ad libs. The in a lot of ways, what you're talking about there is just is just any sort of user friendly ad hoc analysis. And I don't think it's, I don't think it's crazy to say that a lot of the BI tools like Tableau don't do a very good job of that. So if you're if you're wanting to build that really flexible ad hoc analysis experience, I think there are some tools that, allow you to do that without necessarily needing to build the dashboard front end. And so think about, you know, building the data source or the or the semantic layer that powers the dashboard and then exposing that to the end user. And then that allows them to pick and pick all their metrics and slice and dice there. So there are there are a few tools out there that do that. I would look at, something like a, well, if you're already using Tableau, that's the intention of what Pulse is doing to some degree. But you could also look at Pulse, ThoughtSpot, Sigma. You know, those sorts of tools are are all meant to try to get to you where you can do a flexible, like, ad hoc analysis experience for your end users without needing to predefine it all in a dashboard ahead of time, which I I think is what you're going for there. Thank you. I'm still my voice can be heard. I appreciate that. I got Pulse, Sigma, and was it HotSpot? ThoughtSpot. Yeah. Yeah. So Tableau, Pulse, Sigma, and ThoughtSpot are are, I would say those are all products in this growing category of metric first reporting that allows people to do the kind of slice and dicing that you're talking about without building a dashboard first. Thanks. Of course. Looks like we had a question from audience. Matt Albacete was asking about the reverse ETL landscape in general and and build versus buy. And and, Matt, I I apologize. I'll make your question a little bit more complicated and because I think that when we see customers try to figure this out, they're trying to decide between specialized tools like a like a CDP or something like that if that's the use case that they're using, general purpose reverse ETL tools like a like a census, or they're also frequently looking at and and I and I hate this kind of buzzwordy term, but, iPaaS tools, integration platform as a service tools, like your, your MuleSofts of the world and your your trade dot ios and your your Zapiers and all of those. I tend to think that those three categories are have a lot of tool overlap or I'm sorry, a lot of function overlap and and and maybe just have different customers or different different venture capitalists. I think that we're seeing a lot of organizations right now, realize that a platform like Census is better for them than trying to build this because building is brittle and is going to result in an in a lot of kind of custom integrations. But I would I would in your use case, I would be thinking about this, and I would be separating between data movement and data integration, whether that's reverse or not, and then event movement and event integration. Because what we tend to see is that a lot of these tools want something closer to streaming, closer to real time, and something that will allow you to, action something like a customer behavior almost as soon as it happens. And and I think that's closer to, like, event integration than data integration. One of the reasons that we like census for this is well, I'll give you I'll give you kind of, like, two main reasons we like census for this. One is most of the reverse ETL tools in the market are just, customer data platform tools or, CDB tools in disguise. Right? They're really focusing on this marketing use case, and we think that's a little bit myopic, and we think there's a generalized, use case here. And so we like Census because they're trying to be a more generalized platform rather than just, trying to displace segment at a at a bunch of different customers. And then the other reason we like them is because they allow for this real time event integration workload also. They you can action things very close to real time in ways that are, ways that are really helpful. Hopefully, that's helpful. If if you wanna talk about it more, I think we're very happy to talk about this landscape. And so, you know, if anyone's interested in the tooling landscape or wants to have that conversation, we'd love to talk through, like, your specific use case and then and then help you understand the landscape. They're both great questions. Well, cool. I, yeah, I didn't see anything else in in the q and a or the chats, Matt. So, you know, I think we could probably go ahead and and wrap this up here. So for those of you who haven't yet scanned the QR code, you know, definitely, if you wanna talk to one of our solutions architects, do that. You can also reach us at interworks dot com. We also have a blog that has, you know and I've linked a couple here in the chat of some concepts we've covered today. But, you know, a lot of this content and, you know, these topics were always you know, we're creating content on in our InterWorks blog. And, yeah, we have our video library where y'all can catch this webinar replay if you wanna revisit it at any point. And, yeah, I mean, thank you, Matt, and thanks to everybody for for joining us today. This is great turnout, and, yeah, hope everybody has a great rest of your week. Thanks, everyone.