Team DataFam Tableau User Group 12 Aug 2026

0:00 Chris McClellan: Welcome to Team Data Fam Tableau User Group meeting number 87.

0:04 This TUG aims to capture all the in-person and unrecorded presentations and give them a wider audience. So if you've presented at a large conference or a small conference or not presented at all, I don't mind, get in touch. We'll find a slot for you. My email is team.DataFam@Gmail.com.

0:22 I've got meetings lined up through September, but obviously there's, there's vacancies in October onwards if people need to, um, book in time for a presentation.

0:33 But today we've got Aedan talking about Beyond the Dashboard: Evolution of Published Data Sources with Tableau Next. It's a TC '26 repeat.

0:43 Uh, so I'll hand it over to you, and you can take it from here.

0:47 Aedan Radvanyi: Perfect. Thank you, Chris.

0:49 I've stayed true to source. So this is the original presentation that I had. You can even see the, uh, the date listings from, uh, the two sessions that we did at Tableau Conference. I will, uh, try and keep the slides brief so that we can get into the live demo, um, and more than happy to take questions as we go. Uh, we'll have a Q&A at the end as well.

1:07 Uh, but yeah, as, as Chris mentioned, Beyond the Dashboard: Evolution of PDS or Published Data Sources with Tableau Next. Uh, the goal of this session is really talking through, um, how you can use the existing infrastructure in Tableau Cloud, um, or Tableau Server even, and then accelerate that for conversational analytics with Tableau Next. So just a little bit about me.

1:29 Uh, I'm a– my name is Aedan Radvanyi, I'm a director for Coastal's Data and Analytics team. I run the analytics team within Coastal. Um, analytics sits within our data modernization practice, so I have directors who sit alongside me that are responsible for Data 360, MuleSoft integrations, Informatica, and Snowflake.

1:49 Um, my team and, and my responsibility typically lies with CRM, analytics, Tableau, and Tableau Next. Um, and then I do a lot of solution engineering, uh, with my team, overseeing projects and making sure that, that we're staffed across, across all the analytics, uh, engagements that we do.

2:04 Uh, my background was in core Salesforce. I kind of specialized in, in analytics quite early on, so I was a CRMA architect and then became more heavily involved in, in Tableau.

2:14 Uh, I spoke at Dreamforce in 2025, uh, and that was around, uh, the same topic. It was Tableau Next, actually with more of a, uh, CRM analytics focus.

2:24 Uh, spoke at TC, obviously, which is, uh, this session is a repeat of, and then I'll be speaking at Dreamforce, uh, in just about a month's time about the Tableau Next exam as that comes out too.

2:35 So that's me. Uh, I work for Coastal. Coastal is a, a Salesforce partner. We are a Zero Copy implementation partner, which means that, as I said, I have directors alongside me for D360 and Snowflake, uh, but we also work with Databricks, Redshift, Google BigQuery, all the enterprise data warehouses, bringing that data into Salesforce for tools like Tableau Next, uh, using the Zero Copy framework.

2:59 Uh, we have a very strong public-facing demo library. Um, I think we have a, a QR code for that later on in the presentation. But if you go to Vimeo and search for Tableau Next, I think about 19 of the top 25 videos are actually Coastal's Tableau Next or Tableau Cloud CRMA, uh, demo videos. We have a strong bench of Tableau Next implementation, uh, experts, people that have gone through an online workshop, um, that basically certifies them to work on the tool prior to the true certification.

3:26 And then Coastal just in general, um, a high number of projects, a number of expertise in Tableau, CRMA, and D360 or Data Cloud. Um, and we're publicly ranked number one as a Salesforce consultancy, uh, in a few different spots.

3:41 Uh, so what I'll be focusing on today is building upon your existing analytics investments, um, with Tableau Next. So, uh, like I mentioned, I spoke at Dreamforce, uh, last year about the pathway from CRM analytics, um, to Tableau Next, which is, is another pathway that we can look at. The focus today is gonna be more Tableau classic. So Published Data Sources can exist in Tableau Cloud, Tableau Server, um, but bringing that data through into Tableau Next so that you don't have to rework your data sets, you can just extend the capabilities to use conversational analytics.

4:13 Um, that whole framework is, is basically, uh, tied together by, uh, creating a connection point between Salesforce and Tableau. So Tableau Next sits within Salesforce. It's, uh, one of the apps within the tool.

4:26 Um, if you connect Salesforce to Tableau via your connected apps, then you have this flexibility to bring data through. Um, so what I'll show today is that connection point, creating a semantic model, um, from a Published Data Source, and then enabling, uh, what was previously Agentforce analytics is now Tableau Agent on top of the semantic model.

4:44 Um, and we'll show how that, that whole, uh, workflow plays together. If you do scan this demo, uh, it will actually show you this end-to-end in about a five-minute video. I'll elongate that slightly for this presentation just as we talk through some of the nuance as well.

4:57 Um, and like I said, the– there are different pathways to Tableau Next. Uh, I'm focused more on the, the two, uh, kind of sections on the left-hand side, the Tableau Cloud and Server to Tableau Next, uh, more so than the CRMA side. Um, but while you can use your investments in Tableau Cloud and Server to build Tableau Next, you can also develop semantics within Tableau Next and use that as a data source on the Tableau Cloud or Server side as well.

5:21 So lots of flexibility. It's kind of colloquially known as interoperability within Tableau's product suite.

5:28 Um, so with that, I will jump into a live demo. Um, so I'm gonna start us in, uh, Tableau Cloud, and this is just looking at a, um, what I would call a fairly simple Tableau Prep flow. This is, um, a number of different data sources. I'm actually gonna jump over to the Edit pane. Um, let's just make sure that our data sources are connected and flowing through.

5:48 I do see data coming through, so that should be good. Um, but you can see that, that we're kind of grounded in this, um, this banking use case. So I have, uh, an FIS core banking data source. I have FITECH wealth platform. Salesforce is one of my sources. Client master mapping, a kind of external data set. Um, and then, uh-… relationship manager client assignments as well.

6:11 So all of these data sources tied together in a fairly straightforward, uh, prep flow, um, just showing that this existing investment lives on Tableau Cloud. And you can see that we have transformations that kind of go, uh, beyond, uh, just joining the data together, removing duplicates, we're changing branch names, we're relabeling fields. Um, one of the things I do like to call out is that, um, via the Tableau Plus pathway to get to Tableau Next, you do also get access to Tableau Agent within Tableau Cloud, and that can be very useful.

6:39 So if we look at, uh, this last node, the relabel fields, where I've- I've just done a few small transformations. Can actually also use the agent to- to do some more for us. So if I wanted to, for example, create a calculated field, a well-formatted composite key using a few different fields, I can have the agent support that analysis in Tableau Cloud.

7:00 Um, less important for the kind of workflow today, but I do wanna highlight this capability. It will show you the- the calculated field as you generate it. You can make edits if this isn't what you wanted, but if I do like this format, I can apply it. It's going to create a field called composite key based on my prompt, and you can actually see that format coming through as well.

7:18 So very valuable, a- a good way of kind of, uh, adding value quite quickly.

7:22 But I don't necessarily need that. I've already got this prep flow that's outputting my published data source, and that's my banking behavioral signals. And so, uh, this is run through already, and I actually have a Tableau Cloud dashboard built on top of this.

7:35 So this is a- a kind of typical Tableau Cloud dashboard that you might see. Um, we have a KPI, we have the trend of that KPI over time. We have visuals with embedded, uh, tooltips and lots of functionality, um, which- which we come to expect from Tableau. And this is a very useful dashboard to understand the data. But if you're not someone who has strong data literacy, then there are some tools within Tableau that can support with that.

7:59 Again, I'm gonna leverage Tableau Agent.

8:01 Um, so if I… Let me drag this window slightly larger so we can see it. I'll accept, uh, the kind of preview about the information that we're gonna use.

8:10 Um, one of those, uh, tools that can be used is Dashboard Overview. So again, if I'm someone who's coming to this dashboard for the first time, I haven't necessarily worked with it before, Dashboard Overview is gonna give me a high-level summary of what the dashboard can do.

8:22 Um, it's telling me that this is an overview of customer behavior. It's talking about the various components in the dataset as well. Um, and you can see, again, this is just helping with that literacy of data.

8:33 Um, given that this is, uh, three months on now, actually, from- from Tableau Conference, I actually do have the full window to use conversational analytics in Tableau. It's one of the betas that Coast was part of. Um, but one of the other things I wanna call out is Dashboard Insights. It's another kind of pre-baked option within Tableau Agent. What this will allow us to do, um, is kind of go a step beyond the overview.

8:54 So now it's going to give me a section-by-section analysis of the Tableau Cloud dashboard. Um, and with any exception, it's gonna give me a drill down of kind of a high-level summary of- of what that- that section of the dashboard or that- that worksheet shows me. So the insight summary is a more detailed version of the dashboard overview, but as I hover over these sections, you can see that it's highlighting them in my dashboard.

9:16 So if I want detail on balance map, it's gonna call out that Illinois, um, is a top contributor con- a top contributor, sorry, for the total balance amount. As I go to the other views as well, I get more information. And actually, where there's further detail to glean, I actually get additional insights as well. So I can click into that, and that'll take me beyond kind of the surface level summary and actually the detail.

9:38 Um, and the reason I'm showing this is that detail level is really what takes a value from a dashboard, um, at this enterprise level with Tableau Cloud, where you can access it outside of Salesforce or within it, to something that we should probably bring into- into the CRM. 'Cause when we have level of detail like this and we're going down to specific account names, that's where we can really start to action on the insights we see.

9:59 And so to bring this data, this dashboard into Salesforce, um, what I've set up in the background of, uh, this- this Tableau environment is a connected app. So within settings, a connected app, um, this is a connection to my Salesforce developer org.

10:15 This is less important, um, for visual reasons, but I do wanna call this out because when we go in to build our semantic model, uh, this is essentially what will bridge that connection. So now if I jump over to Salesforce, I'm in, uh, Data 360, Data Cloud, um, and the semantic. There you can see in this development org, we have a number of different semantic models built out here.

10:34 Um, but I'm gonna create a new model, um, and just talk through some of the options. So semantic models are gonna be the basis of everything that we build in Tableau Next and everything that we'll use there. You can start with a new model, so build from scratch. You can extend an existing one. With data kits, you can deploy, um, from different orgs, but we're gonna focus on creating from Tableau, and this is retrieving your data from a Tableau published data source.

10:57 So as I click through that, it's gonna ask me for a couple of things: the site URL and the site ID, and that's the reason that we flash this screen before, because the URL comes from the top of the window. The site ID is gonna come from this connected app. And so I skipped a step earlier just to paste these in, but once you establish that connection and hit Connect, you now have every published data source that you have access to in your Tableau Cloud instance to use for semantic modeling.

11:23 Um, so what we did is we took the… If we go all the way back to the start where we had this, uh, published data source coming from our prep flow, we brought that all the way through now to our semantic model. And so you've may- you may have seen a semantic model in kind of Salesforce marketing or demo videos elsewhere where you have a number of different nodes connecting together from Data 360.

11:43 What we have is actually the published data source flowing through from Tableau Cloud. Uh, if you're familiar with, uh, zero-copy federated data access, the data doesn't technically live in Salesforce. It still lives and exists in Tableau Cloud. And so I have the ability to refresh this based on changes in the PDS in Tableau Cloud, um, but I have access to it.

12:03 I can action on it within the CRM.

12:05 Within this semantic model, I can now do a number of different things. So, um, you can see I can see a preview of the data. I get row-level information. I can see all the columns At the field level, um, what's gonna be important for semantics is actually describing our field. So for each field, we want to have, uh, contextual analysis so that when we start interacting with the agent, as I ask questions, it's able to pull in this additional context beyond just the field name.

12:29 So all of our fields are well-described. We are… Defined our, our metrics as well. These are governed metrics that are gonna exist across the dataset. So, um, this should tie back to, uh, if you remember from our dashboard, balance amount, but now we have it as a KPI. This could be a post metric on Tableau Cloud. This is a next metric within Tableau Next.

12:48 Um, so we govern those in our semantic model. You can see that we've optimized the model as well. So what this is doing is checking across our descriptions that there are no similarities between, um, the field descriptions.

13:00 We don't have all of those different nodes, so there's no missing relationships between our data nodes, and there's no fields of missing descriptions either, which is giving us an overall strong model health.

13:10 Um, and then the last step that's, that's I think very key for the agenting piece is actually business preferences.

13:15 These will be the, the terms, the, the verbiage that people use day-to-day when they're interacting with one another. And so we want to ground the agent in this terminology so that if I ask about digital adoption or online users, it knows which field is the underlying field that I want to pull from.

13:35 So this is great. This is a semantic model. This is going to power everything that we see in Tableau Next. Now if I jump over to a homepage within Salesforce, you'll see this all come together.

13:46 So you can see I have my Tableau Cloud dashboard, just like we had on Tableau Cloud, embedded in Salesforce now.

13:51 I also have these Tableau Next metrics for my semantic model that sit alongside it, and you can see that because we have a uni- a unified source of truth, my balance amount is gonna tie out to my balance amount KPI. I also have additional KPIs that I authored in my semantic model.

14:06 All of the functionality that I had in Tableau Cloud, I still have in the CRM, but the difference now is I can action at the record level on top of all of this data. So if I open the Tableau agent from, um, uh, from this toolbar, uh, you'll now see how I can actually go beyond the dashboard, beyond what I was able to do in Tableau Cloud. So ask a very high-level question here.

14:26 Show me clients at risk of leaving.

14:29 What I could potentially do in the dashboard is just click on the, the churn risk of high, and that would filter down my dashboard. Um, but there's more nuance to, uh, a client at risk of leaving. And so you can see from the output, it's pulling in the descriptions, it's pulling in the business preferences. It's identified 65 clients with a high churn risk, but it's also now looking for clients that had loan payments missed or overdraft events as well.

14:52 It's going beyond what I could typically do in just a view.

14:56 And so it's actually even telling me which accounts I should prioritize to make sure that they don't churn. Um, Horizon Associates is one of the ones that's called out.

15:04 Uh, but that's just one type of question. I think what's also interesting here is with the source information and the data source, I can see how it's pulled this together, which fields it's used, uh, which filters it's applied, and so there's a more, there's more truth and, and kind of understanding behind these prompts and, and what we return back.

15:21 Um, I can go in a completely different direction, though, within the same agent. So show me a breakdown of relationship managers and their digital adoption performance will give me a completely different breakdown of data. I'm now seeing standout performance and enrollment concerns. I can see individuals that stand out in certain areas and fall below in others, and I'll always get a visual and a breakdown among my data as well.

15:42 So I can see, um, that Casey is potentially someone who warrants some, uh, attention because she has more unenrolled than enrolled clients compared to the rest of the group.

15:52 Final question in here is, is one I like to show just for the recency piece of this. Which accounts have had more than two overdraft events but haven't had an interaction in the last two months? So this is gonna identify the accounts that we really need to focus in on. Which ones do we need to reach out to, who hasn't had a touch point recently, um, and who has also had an overdraft event?

16:10 And you can see that as well as a visual, I'm also getting key information. I'm getting the types of focus areas that I need to focus in on. I'm getting the relationship manager, so I have the right person internally to t- uh, to reach out to, um, and I'm getting the clients that I should focus on as well.

16:26 So this is at kind of a macro level on the homepage, but we can also apply this same logic to the account page.

16:32 I can use the same dashboard embedded on the account page, and I'm using that same published data source. I'm just extending that all the way through. And now the account is gonna parse as a filter in the background, and so my dashboard is limited down. I can see that, um, this is a fairly strong account. There's low to medium churn risk. There's no high churn risk, um, across the, the balances that this account has open.

16:54 Um, but again, I can go beyond what the dashboard can do with the agent. You can also see that we have these contextual Next metrics. So beyond the ones that we have on the homepage, interaction frequency score is another metric that's maybe a bit more specific to the account level.

17:08 So I open up the agent again just to show the kind of shift between a homepage and an account page. I can ask how balance amount has been trending for this account, and it's gonna pull in the account name, um, and the account ID to filter my query. And you can see that this matches what I'm seeing in the dashboard, so again, it's, it's just that ability to show that the agent can really surface a lot of what your dashboard's been able to do previously.

17:30 If I want to go beyond that and really focus in on this KPI, um, this is showing me kind of the average score across all of the regions, but if I want a breakdown, um, of how the interaction frequency score differs across regions, I can see with a quick question that South is excuse me, South is leading the way, but Central is lagging behind significantly, and this is called out in the insight as well.

17:54 It's a significant outlier, nearly 40 times lower than South.

17:58 And so my next question is, well, how can I action on that? Who is the relationship manager in the Central region? It doesn't have to be a visual. It can be getting straight to the point and identifying that for this account in this region, Morgan Hall is the person I need to focus in on.

18:12 So to do that, I'm actually gonna extend to Slack now. If I, um, use the visuals that I have embedded with Tableau Next- I can, uh, take this metric, I can share directly from the account page. Uh, you can see that we're integrated with Slack as well.

18:28 And I can send this to the account channel. So if I go to the Stella Medical Corp, and message… Let's message Kristen, who is our account manager.

18:41 Um, I see that our interactions with Stella have been sporadic across your team in various regions. Can you get connected with them to strategize? As I share that, now that message is gonna be sent and directly go over to our Slack channel now. And what we also are gonna get is, um, the embedded metric. So whether I'm in Salesforce or in Slack, I have access to the same information.

19:00 You can see that Kristen's already seen this and tried to reply. She's reaching out to Morgan, the, uh, relationship manager for that region, saying that she needs to chat. So you can see how quickly we've gone from insight on the account page directly to actioning on that information.

19:15 If we jump over to Slack, um, again, signed in now as Kristen, we can see that she's able to reply to that metric. She's also able to interact with the agent, and we can see that, you know, I forwarded along a message that has some more context beyond just the, the metric that we've seen. Um, and we can see that this is calling out Morgan as the, the lowest score for, uh, the interaction frequency across regions.

19:36 So that's how we've essentially gone kind of end-to-end with a Tableau Cloud published data source into Tableau Next within Salesforce, and then actioning on it within Slack.

19:45 Um, if we jump back into the presentation just briefly, just to round that all out. Some of the things that we focused on would be the, the zero copy data access. So, um, activating the data from Tableau Cloud into the CRM.

19:58 Um, Tableau Pulse metrics now becoming Tableau Next metrics and kind of the, the actionability that, that really drives that. Unified metrics within our semantic layer, adding descriptions, adding business preferences.

20:10 Um, and then empowering users to be able to use Tableau Agent both within the CRM and within Slack, wherever they want to action on this data. Um, and that was my presentation from TC26. I think there's some features in here that are a little bit newer, but, uh, the core of the presentation and kind of the focus is, is something that is still available today and, and is still being developed on.

20:29 So really appreciate the time.

20:32 Chris McClellan: Awesome. Perfect.

20:33 Um, just what… While you've got your screen being shared, when you were saying you pulled the published data source from Tableau Cloud into Salesforce, and, uh, you start talking about descriptions, those are the descriptions that you've already entered in Tableau first?

20:47 Aedan Radvanyi: Uh, no. Really good question. Um, so let me, let me jump back over to our semantic model. Um, these descriptions don't come through by default. So if I was actually to clear, let me just cut this out. Um, when you extend the published data source through, this doesn't, uh, kind of begin in, in the semantic model. Everything's gonna start with no description in place.

21:08 The optimized model option is a great way of kind of reviewing this, though. So by default, everything will be undescribed to begin with, but as you start layering on descriptions, you know, in the way that we work, um, at Coastal, we're typically doing working sessions with the customer to identify, you know, what is the context that we want to add to these individual fields.

21:26 But you can review these, and this will highlight, um, the individual fields that are missing that information.

21:31 I have that, um, content copied, so I can drop that back in. What you also have with Tableau now is just the ability to use AI to actually generate a description, and this is gonna look at the metadata in the column as well as the context of the other fields to suggest a, a, uh, description for that field. So there's a few different ways to get there, but it's not necessarily going to start there for you.

21:51 That is, uh, some manual work that you'd have to apply.

21:57 Chris McClellan: And, and I think Anne's question was on the top of that. She was saying, "How long did it take to prep all those tags and descriptions so the agent can work properly?"

22:05 Aedan Radvanyi: Yeah. It's a, it's a really good question. So, um, in kind of some of these demo materials, we, we will utilize some of those capabilities like, um, the AI descriptions. Um, in some of our orgs, we go into where, where companies have invested in, uh, semantics within Databricks or in Snowflake. They actually already have a data repository or a data dictionary, and so it's just a case of kind of mapping that into the semantic layer.

22:29 Um, I would say that, uh, for a typical data set of this type of size, kind of 36 columns, um, that's probably two to three working sessions with the business to really understand, you know, what does advisor rating mean, um, within your business? Or, you know, when we say branch state, how does that differ from maybe a different field that has state information?

22:49 Um, but it's, it's, it's ultimately just going through and, and kind of looking at the context of the, the, the column, um, and applying the data. 'Cause what's happening on the back end is when I ask the agent a question about asset allocation type, um, this description is feeding into the prompt that happens in the background. So whatever the context of the question that the, the user asks, it's gonna have all of this information to pull from as well, not just the field name that was maybe referenced.

23:17 Chris McClellan: Mm-hmm.

23:20 Yeah. So there's some work that goes into that, but that, that helps with the AI part as well because it's got that information to draw on.

23:28 Aedan Radvanyi: Yeah. There's definitely ways to, to, um, to speed through it, I would say. But I think the, the real value and, and the way that it's more valuable for a, a business is really putting in that time and effort to, to work through that.

23:42 Chris McClellan: Yeah, for sure.

23:45 Aedan Radvanyi: Yes-

23:46 Chris McClellan: And Jacob-

23:46 Aedan Radvanyi: … uh, Jacob, you are… Yeah, 100%. I think, um, what we typically do as we're going through this process is, um, we try and identify kind of business domains or different groups that will own elements of, of a data set or a business.

24:00 Um, and so trying to get them to agree really requires, one, a strong stakeholder that's kind of overseeing the project that will say, "No, this is the way that we're gonna decide on something." Um, but choosing power users rather than kind of the individual end users, 'cause again, you want this to be governed. You want the definition to be true.

24:17 You don't want to be kind of going back and forth on what is revenue. That needs to be agreed at kind of the business level before, uh, it goes into a semantic model.

24:28 Chris McClellan: And Paul was asking, what's the cost of Tableau Next and what is bundled with Tableau Plus?

24:34 Aedan Radvanyi: Yeah. A really good question. I don't think in the deck I go to in that. Um, this is probably a good slide to illustrate it though. Oops.

24:44 Um, so Tableau Plus is gonna be the highest level of licensing available, um, in Tableau. Uh, so if you're familiar with their license packages, Standard is kind of your base, Enterprise is level above. Plus is the, the highest packaged solution. Um, what that unlocks on Tableau Cloud is, uh, a number of support options, but also enables the AI features.

25:05 So the Tableau agent I showed in Prep, the Tableau agent I showed in the dashboards. Enhanced Q&A, so natural language questions in, um, Tableau Pulse. And then on the Salesforce side, every Tableau license gets a corresponding Tableau Next license within Salesforce. The pricing is, uh, kind of case by case handled by the Salesforce Tableau side. And so, um, I know that there are different ways that they approach that based on the size of business and kind of size of license count.

25:31 But it's the premium offering, so it's gonna be more expensive than, than Standard or Enterprise typically.

25:38 Chris McClellan: Awesome.

25:38 Aedan Radvanyi: Any, any other questions?

25:39 Chris McClellan: Any more questions at all? Yeah.

25:42 We can throw some other questions in the chat as we go.

25:45 Um, I don't think we've got any other questions, so we'll move on.

25:49 Here's all Aedan's contact details, so LinkedIn or directly onto the Coastal Cloud website.

25:56 And if you haven't done the survey, the link's in the chat. There's the QR code there as well. Um, I'm not gonna do the three-minute countdown because I don't think we need it.

26:07 Okay. So just to check the answers to the quiz, obviously, what state do you live in? Um, more people from Australia because everyone's spread across the US and Canada. Um, so there's more people from the US, right?

26:21 That's fine.

26:23 Um, what industry do you work in? So higher education, automotive, a lot of consulting, a lot of insurance, professional services.

26:32 Just showing what Tableau can be used for, which is a lot of different things.

26:37 And how many years have you been using Tableau? In this group, a lot of nine-plus, but then it filters down as well.

26:44 It doesn't matter. Everyone started off with zero years experience in Tableau, so that's fine.

26:50 And the quiz questions for today. So everyone got this one right. Can a published data source in Tableau be used as a data source for multiple workbooks? Yes, it can. That's sort of the point of doing a published data source.

27:04 Question two is, how are changes to a published data source in Tableau propagated to connected workbooks?

27:10 Um, automatically and immediately. Again, that's what you use it for.

27:16 And the next question is, what is the primary benefit of using published, Tableau published data sources? It's to simplify management and sharing.

27:25 These questions are too easy. Everyone's getting them all right.

27:28 And what happens to a workbook when it is connected to a published data source and the data is updated? The workbook's updated.

27:35 Perfect.

27:38 And I think the final quiz question: What is the purpose of a certified status for a published data source in Tableau Server or Cloud as well? It is just a badge to indicate that the data is trusted and reliable, 'cause you can turn those badges on and off as you need to.

27:55 Um, what topics should be presented at future TUG meetings is a free form discussion. That would be interesting as well.

28:03 Uh, MCP and APIs always come up. We had a bit of that today on the API side, sort of, but we'll get more in MCP stuff.

28:11 And, uh, and more TC-26 topics obviously.

28:15 And maybe the hardest question is, who would you like to see present at future TUGs, is any of the community leaders. Um, Eric Summers. Eric was on about, what? Three weeks ago for his Tableau Ops stuff.

28:26 Uh, and I've talked to Adrian before, and he always says that he's busy. I think it might be the difference between his time zone and my time zone, but I'll keep on trying.

28:36 Um, which makes the leaderboard really interesting, right? Because everyone got the right answers, and Aedan takes the top prize because it starts with an A, and then Anne is second because she starts with an A.

28:50 So I need to change that so maybe it's not done in alphabetical order all the time. But everyone got five. Everyone's in first place. That doesn't matter at all.

28:59 So as I said, all those survey questions today were AI generated, the, the five quiz questions in the middle. So if you wanna submit a question, just click on the QR code or use that link. You can post the question for every TUG that uses that system. We're trying to get more TUGs involved, or just for this TUG as well. Uh, the next TUG meeting is about the same time next week.

29:22 It's just a little bit of an adjustment. We've got Michelle and Sophie talking about Visibly, the new data badge system that they've been developing.

29:30 Uh, so is it… It's, it's Tableau enough to be on here, but it's all about how they've actually built that using Claude code, vibe coding the, the application itself, and how it works.

29:44 So again, scan the QR code, go back to the TUG website, um, and attend if you've actually got some questions to ask. Uh, tricky with this one because Michelle's in the US, Sophie's in London. We've got to try and find a time that matches for everybody, including me. So, um, attend if you can.

30:05 If you can't attend, you can always watch the recordings. They're on the YouTube channel at TeamDataFam. This TUG and a whole heap of other online TUGs, and any TUG that's welcome can put their recordings on there.

30:16 There's the YouTube, which is the full recordings of every TUG. You've got the Shorts as well, if you just wanna see the, the tiny little, um, sort of like highlights from the session. And if you don't have time for either of those, you can listen to the podcast, which is just audio on Spotify and Apple. Um, I do that while I'm driving a lot because you can listen to stuff, mark it, come back and watch the bits you actually need to see.

30:42 But if you've got any questions, email me at team.DataFam@Gmail.com and I can answer them from there. But again, thank you, Aedan, for that today. That was awesome. Uh, sort of better than seeing it at TC. Um, but again, those updated slides and just the features of how to get a published data source into Tableau Next so you can use it within that system as well.

31:02 Aedan Radvanyi: Yeah. Really appreciate you guys having me on.

31:06 Chris McClellan: Awesome.

31:07 Thank you, guys. Time to unmute if you need to chat or if, if you don't, that's the end of the meeting for today.

31:14 Aedan Radvanyi: Thank you, everyone.

31:15 Chris McClellan: Okay. Thanks a lot.

31:17 Thank you. Take care.