Sarah (00:09)
Welcome to UnDUBBED the podcast that's unscripted, uncensored and undeniably data. I'm Sarah.
Fiona (00:09)
you
And I'm Fiona. And today we're exploring how Tableau is evolving within the Salesforce ecosystem with someone who's been at the forefront of connected analytics. We're joined by Kirk Munroe, co-founder of Paint with Data. Kirk spends his days helping organizations squeeze every drop of value out of their Tableau and Salesforce cloud investments. With more than 25 years in BI and analytics under his belt, he's a Tableau ambassador,
a certified Tableau expert across multiple domains and the author of a widely recognized book on data modeling in Tableau.
Sarah (00:51)
if you've ever Googled anything about semantic layers or scalable tableau architecture, you've definitely bumped into Kirk. He works with companies across North America to modernize their data strategy and empower teams to actually trust and use their data.
Fiona (01:08)
You've probably seen his work on theflelagetwins.com,
masterclass with Tableau Tim, or his collab with Tableau Visionary Hall of Famer Andy Kriebel Basically, if there's a way to make data clearing cleaner or more useful, Kirk's already doing it.
Sarah (01:25)
If you've been wondering what Data360 actually means for your organisation how Tableau Next fits into the Salesforce ecosystem, or what connected analytics really looks like in practice, you're in exactly the right place.
Fiona (01:40)
But before we dive in, remember to like, subscribe to UnDUBBED. And most importantly, please share this episode with your data teams and anyone navigating this evolving landscape.
Sarah (01:53)
Welcome to UnDUBBED Kirk. We're both excited and honoured to have you here today.
Kirk Munroe (01:59)
Thanks for having me. I'm super excited to be here today. And it's going to be a fun topic, I think. So thanks for having me on. You can now say you've had a lot of famous people on and me too. So that's up.
Fiona (02:12)
We think you're famous too. when I was going through the hackathon I was reading your blog with the twinny twins it was the only blog that was written at the time that I could find on the topic.
Kirk Munroe (02:17)
Mm-hmm.
β Yeah, I guess there wasn't a lot. I also feel a little bit like I'm the only person who's not either, yay, AI, it's perfect, or, hate that Tableau Next stuff. Do you know I mean? Like, they kind of just can calmly put it in perspective. Not the only one. I think there's some others, but we seem to be rare for some reason. I don't know why people have such a visceral reaction.
Fiona (02:47)
It's change. In my opinion, it's change. People don't like change and where the product's at right now, it's not as nearly as flexible or we're not able to bend it as much as the existing product. So it's easier to poo poo it.
Kirk Munroe (02:50)
Yeah, change. Yeah.
Right.
Right. Well, in Salesforce, think a really, not marketing it very well either, which I'm sure we'll get into doing as we go. which is unfortunate cause it's so good. least has the potential to be so good at what it does. So why would they try to market it as something else? I'm always surprised when that happens, but, yeah, it seems to happen a lot.
Fiona (03:26)
So if you were working in the Tableau Next marketing team, how would you be positioning it?
Kirk Munroe (03:32)
they kind of almost came out of the blocks with this before it was even the coding probably started on it. Like I would position it for people who work in sales force who want to use analytics in the regular flow of their work to make themselves much more effective. that's the use case.
Right? when I hear sometimes some say crazy things, like it needs a desktop edition. I'm like, why would it need a desktop edition? Like sales force right? Or, I've even heard we should do Tableau Next Public. I'm like, what? Do you know what I mean? it doesn't, unless you're going to make a marketplace out of it. you're doing this, podcast on YouTube, obviously, maybe if they made a
place for YouTube creators to be able to see their stuff. But it's not really the same as Tableau public. It's not showing off your viz skills. It would be more like a place everyone could go to make better decisions maybe. But that didn't seem to be the way they were thinking about it. So that's a couple of examples.
Fiona (04:28)
Yeah, for sure. like actually I've never thought of having Tableau Next β in a version of Tableau Public or a public facing version before. But now that I think about it, HubSpot, when you start out in HubSpot, you can get a couple of free licenses and never pay. Now they're restricted from a CRM perspective. So you don't get all of the full blown effects. β But it is free. So it's really good for
Kirk Munroe (04:48)
Hmm.
Fiona (04:58)
people who are starting out in their business. And then after a few people are on, I think you have to start paying. But perhaps there is something in that that people could get a CRM view of Salesforce for one or two licenses and a version of Tableau Next, and then it would get them locked into the ecosystem. β
Kirk Munroe (05:01)
Right.
Mm-hmm.
Yeah, that way might make sense.
but upload a bunch of data that's not in Salesforce just for the sake of showing off your Viz skills. Doesn't make it. They have a great platform for that. It's called Tableau Public right? It's the best by far. why, you know what I mean? it almost feels like internal people having to compete with each other happens at companies all the time.
Fiona (05:32)
The best, the best.
Sarah (05:42)
For sure. Now we seem to have gone down a bit of rabbit hole early on and I love
Before the call, we were talking about a potential forward statement that you'd like to share Kirk.
Kirk Munroe (05:52)
I was saying, I listened to a lot of this is less of a safe harbor software company kind of thing that we see all the time and more about, I don't know, finding financial podcasts do this all the time. And I listened to way too many of them, which is, we don't know you or your situation. So please don't buy any stocks based on what we're going to do. Talk to your own financial advisor or whatever. The equivalent, I think why we should talk about it here is Tableau for a couple of reasons. One,
Data 360, but especially Tableau Next is still very nascent and moving. So we don't know where it's going. And none of us represent Salesforce, right? So because we don't represent Salesforce, we're kind of speculating on which way it's going here, right?
in general, we don't know where AI is going. Do you know mean? It's we see, yeah. Well, I'm trying to think of who talked about this first, but I've loved it about how, although as humanity, we've been on this exponential curve from the beginning of humanity on progression. Do you know what mean?
Fiona (06:36)
No one does.
Kirk Munroe (06:54)
at any 10-year periods on so much more than the time before. But if you zoom in on that line chart, it's actually a series of S-curves. Do you know what mean? think chat GPT comes out, it's like this, and then it kind of gets flat before it goes back up again, if you zoom in on any period of time. And I think we might be in that flat area right now, to be.
Fiona (07:09)
Is that the?
Is that the trough of disillusionment that's sort of in those?
Kirk Munroe (07:17)
No, that also works. This might
Sarah (07:21)
you
Kirk Munroe (07:21)
have
encouraged while there's someone that said this other thing about, know, it was going to come, but bumpy. also though, the one that Gartner made famous, I think they stole it from someone where, you know, technology all goes like this and then it comes down and then eventually goes back up again. It certainly feels like we're somewhere near the top and it's going to overly correct, you know, and people are going to say that it's not as good as it really is. And then, you know, at some point in the future we'll...
But we'll talk about that today. think the potential is, as long as people put in perspective, think the potential is amazing, which hopefully we'll talk about. At least somewhere I think it's going to go. Again.
Fiona (07:55)
And I think that also
giving yourself β a little bit more kudos in the fact that I know that you've been up to your eyeballs in the solution and really giving it β a red hot go. So I suggest that you're synthesizing your knowledge of where the platform's at right now and the potential of where you see that it could go.
Kirk Munroe (08:05)
Yeah.
Thank you. Maybe you're right. You never know, but thank you. I'm definitely, yeah, too much, a lot of time in it for sure.
Fiona (08:33)
Kirk, we always love starting with the person behind the professional. You've had quite a journey from product management at Cognos and IBM through to Salesforce and Tableau and now running with Paint with Data. Can you take us through how you ended up where you are today?
Kirk Munroe (08:51)
The first thing is that by my nature, I'm very curious, so... And probably fairly confident, so β I don't set long... I always find a joke that's, do you see yourself in five years? I'm like, I don't know where I'm going to be in five days. β But I kind of chase what I like doing. I try to get in the vein of what I like doing and what I'm good at. And hopefully they often go together anyway. β
From the time I was very young, I was intrigued by what software could do to make our lives easier effectively in all kinds of different ways. And also, when I was at Cognos in the early kind of 2000s, I thought about becoming a management consultant, because I love that side too. Do you know what I mean? I love technology, but really on behalf of making process better.
I didn't make the leap to management consultant because they kind of, I thought that drove me crazy because I'll make all these recommendations and then have to leave and they probably won't follow a lot of them and I won't feel good about not being able to follow through. And I didn't really want to be kind of a system integrator who just built stuff because that's no fun. But wanted to see it through a little bit. So I always wanted to end with this.
boutiquey half management consultant, half system integrator that could take people all the way from the business redesign all the way to making it happen. Maybe a small scale, but I'd rather do it on a small scale, but across the whole thing than not. And the opportunity really opened up because my wife, Candy, who's a five time Tableau ambassador now, actually started the company a couple of years before I joined. So she had already kind of had it started.
She'd done all the hard work of incorporating it and all that stuff. I just got to come and do the fun stuff. Yeah. β
Fiona (10:36)
riding on a coattail.
Sarah (10:42)
Kirk let's talk about Data360. There's a lot of buzz around it, and we'd love to hear your perspective on what it actually is and why it matters. What problem is Data360 trying to solve?
Kirk Munroe (10:56)
Yeah, and a little history on data 360, because I think it also shows what it's good at. I think it's, well, it's Salesforce, so it had a hundred names, but one of the early names at least was CDP, because that's what it was. So it was a customer data platform. And you can imagine if you're, it was really targeted for marketing cloud users effectively. So for marketers who were in a B2C industries that, you know, imagine
if you do that in a multi-channel kind of way and people come to your website and they come to your store or whatever and you have to try to consolidate all those different profiles into one profile, that was what it was really good at. And recently they added the ability to reconcile accounts, which you could also imagine if you're like a bank or something and people open different accounts and their family accounts and they have business accounts and how do know they're all the same person? So that was what it was historically good at. I think...
Then what they opportunistically did when they called it data cloud, now data 360, which is kind of interesting. The one way I think that Tableau, and we can talk about how Tableau and Salesforce cultures are different in a lot of ways and similar in a lot of ways. One of the ways they're similar, which is neat, is a very kind of low to almost no code way of doing things. So they evolved it into a data lake host that's purpose built for
bringing in, I would say Salesforce objects as a primary or Salesforce tables as a primary kind of object. And then you can link out and make queries through it to, Snowflake data or whatever you want almost, right?
the way they've made it very easy to configure and bring objects in, I really like it. It probably drives the hardcore data engineer types crazy, and they probably don't want to use it. But for me, it's great. Like, as a simple example on Snowflake, which is also a wonderful product, but Tim and Guina and I are trying to work on a video for his 25.3 series on this new Tableau Cloud feature.
which is β if you have your foreign and primary keys already defined β in Snowflake or Databricks, the Tableau now you'll just be say add related tables to build a semantic model for you, which is cool. So anyway, so I'm kind of excited about Data Cloud and maybe it'll have some shortcomings versus those because
because it's purpose built, but it's great to be able to bring in, consolidate all your Salesforce data, some data from other places and kind of get a unified view, which is really good if you're running, which a lot of big orgs are like six or seven or 10 different Salesforce orgs. It's a way to also bring all that data together. β So yeah, I think it's good. think it's β for your typical Tableau user, it might be a bit of a leap, but it'd be less of a leap than trying to learn Databricks.
for your, do you know what mean, or a snowflake or something.
Sarah (13:43)
And coming from a financial background, that unified view of a customer, so important. You know, I remember just in, in, you know, banking, customer address would change, and we'd have to update it in like 10 different places, which is so painful, right?
Kirk Munroe (13:59)
Right.
Well, and what they do that I find really cool is they'll leave all those records, but then they have this unified profile that links out to them. So you don't have to change data, but you know who all those people are because you have this effectively linking objects, which is a nice way to go about it.
Fiona (14:02)
Yeah, it was.
Sarah (14:20)
And you can say this address takes priority above all others.
Kirk Munroe (14:24)
Right. And then when you're unifying profiles, you're like a government driver's license is obviously more important than a loyalty card. When you're trying to match people and you can set up all those reconciliation rules and things, it's, yeah, it's, I mean, that's, that's kind of, it's underpinnings because that's where it started.
Fiona (14:41)
Yeah, it was the thing that I was most excited about as I was going through the 12 plus hours on the trailhead. I actually found the training quite difficult to follow along. So I was trying to set up an SDO and have a muck around and data cloud and found I couldn't achieve that. But once I started to just follow along and go with the flow and not try and add my own... β
muscle memory into it, which is how I learned. I found it easy to along with, but I was so excited to see this unified customer profile because in a past life, I've been responsible for bringing a CRM online. And, you know, that kind of thing is just a massive headache for any marketing professional to be dealing with as they're trying to, you know, communicate with their clients.
Kirk Munroe (15:13)
Right.
And ETL tools don't really fix that problem or have never traditionally tried to fix that.
Fiona (15:41)
Mm.
Mm. Yeah, for sure.
Kirk Munroe (15:44)
They'll clean
up the spelling of your addresses, but they won't call those two people the same people, right?
Fiona (15:50)
Exactly. And to be able to β have multiple rules as well. it's, you know, if you needed a way to find the head of a family, you could potentially do that. Or, you know, in another sense, how do you find the unique individual, in a Tableau sense, if we talk about it, so Tableau public type things, you might want to send to my personal address. β
but everything else Salesforce related you might want to send to β my business address. And so understanding those different use cases, it can be super, super powerful.
Kirk Munroe (16:27)
Just a quick thought on the two. There's another deeper thought that we don't have to go deeper in because β it's a little early, but I think what they're also doing, which is neat is, applications are typically built on these bazillion tables because that's the most efficient way to write data into them. So like in certain kinds of things, which is analysts, we don't think about a lot, but you see these applications and you're like, why does it have 500 tables in it? Because it's the most efficient way to do that.
And then, you you'd have less tables in the data warehouse, some people denormalize them to single tables to analyze them. but now I think they're taking almost in between and they're starting to build a special type of application, which is a pull data out to give people answers as opposed to put data in application, which could be pretty cool.
I've had a good laugh about people going like SaaS is dead β because of AI. I'm like, well, where's AI going to get the data? Like someone's got to put it in in the first place, right? But now these answer based decision applications, not just a UI layer built on top of a data warehouse could be a really powerful thing. It's a thought that's not fully formed in my head, but I think that's where they're going like beyond Tableau Next.
Fiona (17:42)
All right, so Kirk, what do you see when organizations first implement Data360? What's changing for them?
Kirk Munroe (17:53)
Yeah, so I think what it enables right away, which is powerful, is they're sitting in one of two places today. They're either blind because they don't really have data other than those candidly awful Salesforce reports that you can't really report on more than one object or maybe one object away from. If you've ever tried to write those, they're super limited, right? Or...
What happens is they pull data out of Salesforce maybe, and it's in Snowflake or Databricks or somewhere. But the data engineering team doesn't really understand what they're trying to achieve. So they've got a lot of data, but it's hard to get answers back out of the data. It's easy to get data out of the warehouse, but it's hard to know what it's saying. So now, because it naturally has a bit of, or quite a bit of the application logic in it, you're much more likely
to get the answer that you would expect to get out of it. It's maybe not as automatic as one might expect it to be, but it's way better than the alternative. It takes a pretty special data engineering team to be able to build a warehouse that's usable right away that people know what's in it. I'm sure you guys deal with DE teams all the time. I'm not here to rag on DE teams, but they typically, that's not my job.
Do you mean to actually understand how people are going to use this data? Right.
Sarah (19:18)
And I think
as we get deeper into the Salesforce ecosystem, what I'm seeing is it's so customizable that there isn't really a solution out of the box for people to say, here's data 360, and this is how I'm going to use it like the customer used it before me.
Kirk Munroe (19:36)
It's an excellent point because it's not, and this one at least, it's not Salesforce's fault that it's not as out of the box as it should be because everybody customises the living heck out of Salesforce. So how could they possibly know, right? How to build these? Like I tried that customer 360 data model with a customer, the out of the box one. I don't know if you've tried and I opened it up and it's got broken relationships all over because like they're super customized, right? So.
And they're all like that.
Fiona (20:07)
But that's
the beauty of the platform as well. Sarah and I were having this exact conversation yesterday as we were looking at β some client data on their instance.
Kirk Munroe (20:10)
Yep.
yeah.
Fiona (20:20)
And
we were talking about how much it had been customised. Yes, of course, it fits their business model, you know, like a glove. And that's why people love it so much is because it works so well. And we're not expecting a cookie cutter approach to to the organisation.
Kirk Munroe (20:29)
Right? Yeah, it's so easy to do it.
Yeah, it's so easy. mean, we didn't even talk about this part, but if you're a Salesforce admin and your stakeholder comes to you and goes, we need a new field. Like you can deploy a new field on the page in like five minutes or something. Right. So.
Sarah (20:52)
Well, this was happening on the
call we were on yesterday. One of the guys was like, yeah, we need that field and goes and creates it and it's in there.
Kirk Munroe (20:55)
Yeah.
Yeah.
Yeah. And then you could get in with data loader. It's easy to populate it if it's somewhere else. So it's, and so people already use Salesforce as a database sometimes too. Like you'll see, a lot of custom objects that aren't even populated from Salesforce, but they data load in so they can get other data next to it, which it wasn't really meant to do, but data cloud it. So
Like now it makes total sense.
Fiona (21:30)
Well, it's that true single pane of glass, you know, where people can look, I'm on this page, I don't have to go to three different systems to get that information. I can just have it in one spot and it saves me time and effort.
Kirk Munroe (21:37)
Right.
All
Right, absolutely.
Sarah (21:52)
Yeah, for sure. Kirk, many of our listeners are using Tableau Classic right now. What should they be thinking when it comes to Tableau Next and when does the transition make sense?
Kirk Munroe (22:05)
β yeah, let me answer this in two parts too. One thing I hope that people do, and you know, we got to present on the analytics target at the same time in the summer, whenever that was, is like, I hope when they'd look at it and see Tableau semantics that they realize it's effectively the exact same code in Tableau classic, and they should be building better semantic models and Tableau β cloud, even if they don't use Tableau semantics. So first off, I hope they look at that and go, you know what? I've been way too lazy on my semantic.
or I didn't even think of them as semantic model. I'm a little more black and white than most people I think on when to go to Tableau next. So I think if your customers, if you work in a company or you're a consultant or you're thinking about your next first step, if the company doesn't use Salesforce, I don't envision a world where it would ever make sense to go
next, unless they fully stop supporting the product, which I don't think will happen. A new release of Business Objects came out earlier this year. That got acquired, what, in 2007 or something. So I would expect to have a classic to be around for a while. On the flip side, I would get ready because I would think sometime between, say 12 to 24 months.
when Tableau Next gets good enough. Equally, I would say anyone who's on the Salesforce platform, it doesn't make any sense not to go there. you know mean? Because from an integration standpoint, in a maintenance standpoint, because remember, if you go with Tableau Cloud as an example, or Tableau Server, it doesn't matter which one, you need someone to administer that thing. It's obviously a lot more work to administer server than cloud, but you still need to have at least one site admin.
but you already a Salesforce site admin, and it just fits into that site. so I think it makes a lot of site, but I look at it that way in terms of, is the customer primarily, the, people that are going to use it live in, in Salesforce, then it should be there. And I would think the close on this and a lot of big companies, it might make sense to have both, which we can get into for different.
use cases, which would be totally fine. And they're working harder than probably even I would on this, to get semantic models to work in classic and publish data sources to work in semantic. So they're trying to make that easy. And I don't even know if you need those connections, too, but because different groups, might sit differently, right, or for different use cases. But β career-wise, β people should not be afraid of it and should be at least somewhat looking into it because
β I would expect it to be more where the future goes, β would be more integrated analytics for sure. And there's some very specific roles that are going to exist over there that are going to be higher valued than what you could do today.
Fiona (25:10)
think you're spot on and it really excites me to hear someone else speaking positively about what the future of the platform looks like because I think a lot of the data fam.
Kirk Munroe (25:16)
Ha
Fiona (25:26)
are struggling at the moment with the positioning of Tableau Next, but I really believe that analytics is moving so quickly that it's important for us to adapt, for us to change and to seek out alternative data roles that fit within the ecosystem. So you touched on that a little bit and what you were saying there.
Kirk Munroe (25:40)
Yes.
Fiona (25:51)
Where do you foresee that there are opportunities for people who are currently visual designers in Tableau Classic moving forward into this new ecosystem?
Kirk Munroe (26:04)
Yeah, so I guess it depends how they see themselves as a visual designer. If I saw my job as writing to somebody else's spec without trying to figure out what they're doing, but my job is to make it look important and follow best practices, I'd be worried about the longevity of that job. I'm more negative on that, I think than most people are negative on that. The reason why I think to
to shorthand it is anything that you could say is a best practice. And I know people be, it's not best practice, it's, you know, but it's near best practice, right? If it can be codified, like some tech's gonna replace them, like because it can be codified. So I'd be a little worried. think we're at a point now where, but it's still a skill to know how to present data the right way if you're actually getting to people's questions.
So you're focused on the question and not the chart. Like I've never been able to get excited. I'll be candid about, my God, this is such a cool chart. I'm like, without context, it's not. It could be, it could be a really cool chart with context, right? am I helping people see something quicker that they couldn't see otherwise? Then it's a cool chart. Like in not providing any value, like very limited. So I would say,
The two areas I would say to go, and if you like the visual side is, I mean, you have to get more consultative try to figure out what people really want and then remember that you're building visuals on behalf of those problems. I've thought about it a lot. Community projects have hurt that in a lot of ways, Because...
The data sets people get are just not interesting, so they have to try to make them interesting. But you don't need some great visual to understand them, or they're so historical. It doesn't matter anymore. There's other ways to do that now and tools to do it.
Fiona (27:56)
You
Kirk Munroe (28:02)
get focused on what are the questions people are trying to answer and make sure you're answering those. So get more consultative, whether you work for a company or you're consultant or what you're going to do. And the other one, and this could be my bias, which I'll omit quickly, is I think building semantic models is going to be even way more important than that. Now, it might not turn everyone on. And the reason why I'm super skeptical of AI doing this well is because we just talked about this.
Customize the living crap out of like their Salesforce or whatever and the rules of how to Enter that data work with the system are not often enforced by the software like people know the process So someone's got to figure out that process and turn around and re codify that process somewhere else β If you're gonna expect some agent to be able to give an answer about that question, right? So I
I'm not sure if it's the same for you, but as a consultant, I feel like I spend a quarter of my time, especially on the Salesforce implementations going, okay, show me how they do that. And then your service tech, when they goes in, what do they update? Like just the product force. not like, why do need to know this? I'm like, cause I don't know what the data is. If I don't know how it got created in the first place. And then if I know how it got created, then I can probably find a way to codify it to help. β
in AI have a hope of being able to answer someone's question about it without me needing to answer the question for them. So that was probably a lot. I do think just there'll probably always be a job for being able to create pretty visuals. mean, there's information of beautiful things. There's magazines and newspapers online. There's cases for it. I'm not saying there's none. But in business, I don't think people care.
Sarah (29:30)
Yeah.
Kirk Munroe (29:54)
You know what mean?
That's more like that. Was that too dire?
Fiona (29:57)
think, think, no, that's really good. No, so there's
so, there's actually a couple of things that I want to dig into on this. β First off, I think that what your first point lends to is gathering requirements. really sitting, like spending a decent amount of time with the client or with your stakeholder. Exactly, Sarah, understanding the problem and...
Kirk Munroe (30:04)
Yeah.
Right.
Sarah (30:18)
in the
problem.
Fiona (30:26)
So any quick tips for people that you think would help them.
Kirk Munroe (30:29)
Yeah.
So I always try to start with, and there doesn't need to be more than two or three. β If I had a chance, if I was a dashboard designer, data analyst, whatever, and I had a chance to get in front of an executive, the first question I would ask for sure would be, β what are the two to three questions that you don't have the answer for today that if you did would fundamentally change the way this business runs? And then I would bust my ass to go answer those two to three questions.
Fiona (30:55)
So powerful.
Sarah (31:00)
Yeah, what keeps you up at night. That's what I always think when I'm talking to execs.
Kirk Munroe (31:00)
All right.
Yep.
Yeah, 100%. β I switched off that slightly only because I think it works too, but then you get into the risk ones, which aren't as much fun as the other ones, but they're equally important. You know what mean? Because then you get the ones if something goes wrong answers versus the fun, if something went right answers, but it also works. they're things that, and β we know with alcohol, behavioral psych, behavioral economics, people are.
They care two times as much about risk as upside. So maybe your question's the better one. I'd just rather work on the other one.
Sarah (31:40)
Good point, I'm gonna keep that one in mind.
Fiona (31:41)
Secrets,
Kirk's secrets of Kirk's life. β Keep it interesting, steer them this way. β The other thing, so coming back into the semantic models and I'm glad that you've touched on this space. I am 100 % in your camp about this is a huge opportunity for data professionals. I.
Kirk Munroe (31:47)
Yeah.
Fiona (32:07)
found when I started to spend time in the semantic layer at the beginning. God, it was tedious. It was tedious, but it really lays the foundation for me doing great data work. And what I mean by that is once I've got everything defined,
Kirk Munroe (32:15)
Yeah. Yeah.
Fiona (32:29)
available. You know, I've got my calculations there. I've got my business preferences sorted out as well. Then when I go to design a dashboard in Tableau Next or design some visualisations, I don't need 50 things on my page. I can really, I can really think about
Kirk Munroe (32:38)
Yeah.
yeah.
Fiona (32:51)
what are those top two questions that they're trying to solve and layer on the things that at a high level will help them. But because the semantic layer is behind the scenes, β you know, we can then use Tableau Agent to ask some questions of things that I haven't as the designer put on the page.
Kirk Munroe (33:03)
Mm-hmm.
Yeah, I probably like a lot of people who wanted to think of themselves as Tableau Jedi's or whatever over the years. I used to take a lot of pride in like my ability to write really complex LODs and then and stuff like that. And then you get over yourself and you realize if I was better data modeling, which probably got me to write in the book, it's like I wouldn't have to write those things in the first. Like if I have to write them, I should be ashamed of the data model I gave myself that I have to, you know, make up for the sins of that data model for. The other thing is I think I can talk, we're going to talk.
anyway, because they talk about it. I think, I'm not sure both of you are also in the tablo-semantic AI beta, but I know they've been talking about a lot anyway. It's a pretty open beta. One of the things I love about it is commenting fields is so tedious. It's even way more tedious than creating the relationships between things. what they've done, I don't know if they've done this on purpose, but I really like it, is because human nature is a funny thing. What they'll do is they'll β
Now you can just press a button and it'll auto-generate a field description. And it's funny, you look at a blank field description and go, oh, I don't want to write a field description. I click on that and it's never perfect, but it's kind of close. And I instantly go, that's okay, but it's not a great description. And then I want to update it. So I click it and I want to update. Like it's this natural so I think they got to change. So the point isn't this thing can write all your field description. Like how could it? Do you know I mean? It gets certain things wrong. It'd be like,
And orders are connected to line items. like, no, only products or whatever it is. And I'm like, that's not exactly right, but it's close. want to update it. It's actually fun. It's probably because we're terrible as humans. To fix something is fun, but to create it from scratch isn't. So I think that's going to be great. That feature is going to be really, really good.
Fiona (34:54)
to fix it.
Kirk Munroe (35:10)
We see this with GPTs think most people are like chat GPT keeps getting worse instead of better because they keep expanding the training set too much. it's cause I think cause they're going for artificial general intelligence. I'm like, and they're insulting our intelligence kind of, think because
If I was ill, I would know to go to a doctor. And if I had a plumbing problem, I'd know to go to a plumber. And if I needed something wired, I'd know to go to an electrician. So I don't know why they're not focusing GPTs on really good training sets and letting us be smart enough to know which one to use. So I only bring that up because it works in the semantic model where if you think the GPT is going to give you good answer, if you don't spend the time to tell it what everything it means and what the relationships are, it's not going.
You know what mean? It's a training set that makes them awesome at this point. Do you know what mean? Like they've already proven that they can troll it and pull it apart quick, right? But they're only as good as the training set.
Fiona (36:06)
It's interesting that you raised that. was listening to a podcast on the diary of a CEO β last week on AI and it's with a guy named Tristan. I don't know what his surname is. That's how good my memory is. Anyway, Tristan was talking about how...
they're talking about the speed and the rate of which AI is growing and what may happen to humans through that. So quite an interesting and provocative podcast, but one of the things they talked about why people won't pull the plug now on any delivery and why they continue to compete, an excuse that's used as, what happens if China beats us? And β
Kirk Munroe (36:32)
Right.
β okay.
Fiona (36:51)
Don't worry, I'm not going to get political on this. We're totally good. β So β what he said, though, is China go really deep on specific areas to make themselves great at things. So, for instance, electric cars. know, originally we would say Tesla hands down was beating the market. But if you look at what China has been doing with BYD,
Kirk Munroe (36:53)
Yeah, no. Yeah.
Right.
Right.
Mm-hmm.
Fiona (37:19)
and the way
Kirk Munroe (37:19)
Yep.
Fiona (37:19)
that they approach everything, they went deep in that space to push themselves forward in an area that will help them to grow. Whereas what we're seeing in OpenAI and Grok and Gemini, they're all trying to head for artificial general intelligence. And so just a really curious observation I felt, and it resonated with me.
Kirk Munroe (37:41)
Right.
Yeah, yeah, no, I that's a good metaphor for it.
Sarah (37:53)
For sure. We've spoken a lot around the semantic layer and how it's really important to spend more time probably building that foundation. And we find you spend 80 % of your time at least in the data preparation before you can even get to the visualization. what's your advice for data leaders who know their data models need work?
but don't know where to start.
Kirk Munroe (38:20)
in the Tableau ecosystem, they should do the masterclass that Tim and I did. It's free. Okay. no, but I think, I think it is good. takes quite a while to wrap your head around. think, β I think part of it's just a mindset shift we've already matured a bit. Okay. So Tableau products like Tableau.
Fiona (38:26)
We'll put that in the show notes.
Kirk Munroe (38:42)
matured us from, and you know, the cognos and business objects before that, but they, they matured us from, β hopefully, although I still see people doing this. It's like someone asks a question after write a SQL query, right? The reason these products got better is I don't know what question I want to ask yet. So I don't want to, I kind of want to structure this query cause I don't know. That's what makes Tableau to me so special compared to other products. And to me still way better than products like Power BI, which could be another podcast is not to dump on those, but it's not cause
Because building dashboards isn't the fun part to me. It's exploring your data. And then this is the next evolution of that. So where you're really going to show up. we tried to do this. It's hard to get it across for the type of people who would go to a session. But when Thomas and I at TC two years ago talked about multi-fac relationships for the first time as an example, I go.
It's not the modeling that's neat, but I went, I tried to make this case. I almost even tried to find an old one of those tables in the East shirts, but I go, it's funny. You know, the old Tableau demos used to be, I'm going to click around and I'm going to find out it's tables in the East where our profitability is low because of discounting. And they'd be like, that's why that's a problem. go, it hasn't sat well with me all these years because I'm like, that's where you have a problem is you have a problem.
with that measure in that area. And so what we tried to build out is I go, but the actual why, or at least a correlation, we don't have to get into the whole correlation causation, but at least a correlation to run down β would be, like I faked out support case data, inventory data returns, so that we could look and say, what else is it about tables? So we know they're not selling well, but why aren't they selling well? Like our support cases.
And it's been hard to do that traditionally because you'd have to switch between data models to do that. And then on your own, you have to visually try to figure it out. But now we can build ratios between them. Like we can pull all that data together. So you can't do that by munging one great big table together. It doesn't fit. Do you know what mean? So you have to know how to model that. And once you do, I just think it's more
I could see a world where people in the business, like even a super senior will come to you and go, that's the person we know who can open up answers to the business, not the person we go to like the old days. Who's going to go open up SAS or SPSS and come back in 14 days with an answer, and then we don't care about it anymore? So that's a long-winded way to go. think, like,
here's my example. This is what I'm looking for from Tableau next. I like integrated with Salesforce. Imagine, imagine how hard these things are like using data in the flow of your work to actually material impact how your work goes. Imagine, you're a relatively small consultancy versus small consultancy, but even if you wanted to ask a question how many
clients that we had in the past that have spent more than an average client, but we haven't engaged with in six months. Like it's crazy. We're in 2025. You couldn't get that answer. It wouldn't be that hard, but it would, it, wouldn't be instantaneous in a big company. would take a long time. Like if you know how to model, right? Not only should someone be able to ask that question and get that answer through an agent, right? Which is the exciting thing. And then the extra exciting thing inside,
Salesforce is imagine if we could go in and we're not that far off this going β Going imagine. I'm a marketing person and I go I don't know everyone who has a lifetime value with us of over a thousand dollars But hasn't in six months because I'd like to do a marketing campaign for them I should be able to ask that question get a list of customers like highlight it whatever the visual interaction is Either right click it or whatever I do and go create a marketing campaign
And it prompted me to go, how often do you want it to run? Like, what do you want their opt out to be? Done. Like that would take a marketer like weeks to do today. Like it's crazy, right? Like, and it shouldn't, like, and you can think of a million use cases like that. Do you know what mean? Like you have some kind of promotion going on and you have a hypothesis of what the ideal customer would like. You could ask for that list of customers and run. I mean,
because I'm a super curious person, you both are, imagine what I might want to do is I say, give me these, split them into two groups and run these two different campaigns so I can track them over time and see which one's a better one. People talk about running these experiments all the time and they never do because it's too hard to set them up, right? Like beyond like an ABN of website or something. Anyway, I know that was a little off the question, but it's the semantic model that's the only thing that's going to enable that.
Fiona (43:42)
right at.
Kirk Munroe (43:49)
on one side and then an integration with an application platform, which is one of the Salesforce parts, exciting to then actually go action.
Fiona (43:58)
100%. And so where my mind goes to again, and I just want to come back because I'm sure that many data people listening to that might freak out a little bit, and sit there and go, what you mean that a marketer is going to be able to query it and go and create their campaign and do an AB test and probably even create some control groups and even do some post campaign analysis to understand what the uplift is and what the best outcome is.
Don't panic is my best advice. Like go out and investigate. How do I become so good at the semantic layer? So good, because if there's no good semantic layer, the market is never gonna be able to achieve anything and they're gonna poo poo the situation and they'll turn away and they'll come back as well. But if you become the person who...
Kirk Munroe (44:35)
Right.
Sarah (44:36)
Mm-mm.
Fiona (44:50)
can sit down, can really understand the business context behind the data, which is really important to be going and defining the models and then creating that beautiful, it's like a canvas that people can go and understand, but of the data and that network of data coming together. I think it's such a powerful opportunity.
Kirk Munroe (44:56)
You
Yeah.
Right.
Right. And I think people could, it's not that hard to put yourself in an executive shoes and go, β and think about which of these people they would rather, promote or give more money to or whatever. Right. Person A is like, they build, really, visually appealing dashboards when we tell them what we want, or this person has enabled our marketing staff to 10 X their productivity, like, whatever you want to say. Like it's like, they're much more.
you know, tangible results you get out of that. And you have to be patient with it because the tech's not quite there yet, but that should make it almost more exciting because you have a chance to β be an expert on day one, do you I mean? On it. So, and it's still, and it's not overly technical. Like building these data models or these semantic models are not, like, it's not that technical of a job to do. Like you could build these things without having a clue what a line of SQL look like. I mean,
Sarah (45:48)
Mm.
Kirk Munroe (46:14)
It's kind of good to know like what SQL looks like in a way. But like, never, like I tell people all the time, they probably don't believe me. Wrote a book on data modeling, talk about it all the time. Like I've written, reluctantly like SQL three times in the last eight years. it's, it's so restrictive. It's like, Oh, that wasn't the answer I want. have to go rewrite this. Like it's crazy. Versus.
pull another pull on or like drag a little noodle to connect or whatever the case may be. So yeah, it's worth knowing. It's definitely worth knowing what is generating, whether you know the structure of the SQL or not. Like I say to people all the time, remember the semantic model in the data source in Tableau Classic, right now at least through the completely different UX.
exact same heuristics that we need today, So no one thinks like, if you pull dimensions from both of them, it's only going to give you the results that come both of them match. If you pull a measure from one of them, it will also bring all those in, including filling in any zero nulls. so Tim and I get into that in that video, which is why, not just, you know, we don't make any money off it, but I do think people should watch that because like it's completely free and actually
watch it two or three times if you have to, it takes a while to click. And we'll for sure, we'll do one on Tableau semantics too. Like we'll build the exact same thing or something very similar to show the similarities and differences.
Sarah (47:46)
Yeah. And I think just going back to the multi-fact table and building causation correlation is so valuable. I remember a case many years ago where something was going very wrong with credit cards being redistributed constantly. And the first thing was going down the rabbit hole of the courier company must be doing something.
Kirk Munroe (48:06)
Okay. Yep.
Sarah (48:13)
And it was actually through hand going through surveys that we figured out that it was actually a batch of credit cards, the magnetic strip had warped. And so when the cards were arriving at the customer, they were unusable and they were asking for them to be reissued. But that was like a good six months into that problem. And if we had a, multi-fac table that could correlate between
Kirk Munroe (48:35)
Right?
Sarah (48:41)
what the feedback was saying to what the distribution was saying, then that could have been solved so much faster.
Kirk Munroe (48:45)
Mm-hmm.
Right. And I don't know if you guys had this case when you went through, the data cloud at the time data 360 training, but one of the examples you know, customers for the lifetime value of this, or I can't remember the exact case. And then they go, you create. So this is before the semantic model, right? They're like, Oh, all you have to do as a marketer is create a calculated insight. And then they show this example. I'm like,
That would have made me happy to write SQL, because it's this β Salesforce specific kind of SQL, and you have to know what object stuff comes from. How many marketing managers could do that? I would look at it. We're all excited about it, but I think people should be excited about it. It's still very much a data job.
Fiona (49:39)
Looking at what's been announced or hinted at with Tableau Next, what do you think will actually have the biggest impact on how data teams can work with it day to day?
Kirk Munroe (49:53)
I think two things. this... β So, the two things I'm looking forward to the most is it does need to hit a bar of having enough visualisations in it to be compelling enough for people to engage with it. And it traditionally hasn't. I know they added some new chart types in the last little while. I'm not sure why they didn't go for the XY kind of thing that...
so it needs that to hit a bar. Like it's got to get high enough to hit that bar is exciting. And then the other one is now that the semantic model is a better job of both reading things like descriptions. But the other thing that's coming, which I'm really excited about is the ability to actually train the model on top of that. So you can sit there and start asking the questions people can do and then.
Fiona (50:32)
We do.
Kirk Munroe (50:36)
say that's not what I wanted it to be this. And it help you write your, it'll both help you train it, but it'll also go, well, I didn't describe that very well in the model. Do you know what mean? So like we said in my introduction, I've been in the BI space for like 25 years off and on. And, you know, everyone's been looking for natural language query for 25 years. Like it's gotta come eventually. Do you know I mean? And this feels like, you know, it finally feels like there's enough infrastructure here to make it work. So.
Yeah, that training the model thing I'm really excited about. And then, when they get enough visualization.
Fiona (51:08)
Me too.
Sarah (51:08)
I think we've
got to remember it's still only six months old. Tableau next.
Kirk Munroe (51:13)
yeah, about six months. Yeah. For like the viz part of it. Yeah. Again, I wish they hadn't released it. They should have held it a little bit longer. think, Like before we talked about what's similar between Salesforce and Tableau. I'll admit that they like traditional Tableau, at least at the end, right. In Salesforce where they frustrate me is.
Sarah (51:16)
Yeah.
Kirk Munroe (51:35)
Tableau waited for everything to be too perfect. Like take composable data sources are taking way too long to come out. But Salesforce are extreme the other way. The risk of this releasing too quick is I just hope their executives don't go like a bomb, we're not getting any revenue. So they give up on it because, because it is a great idea and it's got so many good UX parts to it already that I hope they have the patience to let it get good, which would be easier.
maybe before it was out in the market and not doing that well. And I think they have to figure out to go to market a little bit. I'm not convinced the same seller of both products is going to work. I think it's also probably causing a lot of confusion or expecting someone who sells applications on process value is going to get it. They've got to iron out to go to market too, for sure, right now. They're confusing the seller. So how can that not confuse the market?
But it's a tricky problem to solve.
Fiona (52:36)
if I had one wish, if I had a wish for this strategy, I would give it away for free right now. And the reason why I would give it away for free, I would...
Kirk Munroe (52:36)
Yeah.
Fiona (52:45)
make it a part of Tableau Cloud, a part of the SKU initially. So they'll be, you buy Tableau Cloud, you get Next for free. So they're still paying a licensing component. But what that would do is give more people access into it more quickly. If they have Salesforce, they'll want to give it a crack. It will help to surface up any of the...
potential bugs or the areas that people are saying this doesn't do this and it's critical because right now there's a bit of a tension point as far as I understand it between
the internal Salesforce product managers who have requirements for Next, and they were originally the only customer. And then the introduction and then the introduction of external clients who have a completely different set of needs to be coming through. And I think if they really want it to be successful, they need to have the clients on board, they need to gain scale and probably
Kirk Munroe (53:30)
Yeah, right.
Fiona (53:48)
deprecate the CRMA product as well. So find a pathway to migrate the customers across when there's at least parity between those two systems. And then you've got less R &D money, less support money going into multiple platforms. You've created critical mass and the product will fly.
Sarah (54:07)
So that's interesting, because you say give it away for free for Tableau Cloud customers, where I think give it away for free to Salesforce customers.
Kirk Munroe (54:08)
Yeah.
Fiona (54:17)
it's a good point. So let me go into it a little bit more. The reason why I believe that clients still need Tableau Cloud is at the moment, if they're looking at BI tools and parody, there's no doubt a Power BI conversation happening at the same time. And without having a classic version of Tableau,
to fill the gaps when Next isn't able to deliver on use cases. That's why they need Tableau Cloud as well. And then I believe that we can talk about it as Tableau rather than this component or that component. It's all Tableau, which is what I believe they're trying to achieve with the interoperability as well.
Kirk Munroe (55:03)
Yeah, I'd go slightly differently again, β which is I like a bit of both of them, but I think they would, I would only give it away free for people who are already data cloud customers. say Tableau employees at Salesforce, going back to my kind of block, they're all Salesforce, but I warned them from the start. go, my fear with this is the change in people being nervous about it aside. If you don't know data cloud.
Sarah (55:04)
Yeah, I agree.
Fiona (55:06)
β okay.
Kirk Munroe (55:31)
you're going to make a mess at Tableau Next And then, and I think they did roll it out and people upload flat files and it kind of doesn't make sense. And then like you can't, how do you schedule them? And it's very different, right? go that where the metaphor is so different is you always model live in Tableau Classic, and then you decide you're going to turn it into an extract and Tableau Next you,
model it in data cloud. We could call it extract without getting into the zero copy thing, but effectively extracted then built on top of it. Like it's a very, and a lot of, a lot of the Tableau community, like we kind of touched on it directly. They're data vis people. They're not really data people
Fiona (56:13)
so we think give it away for free while it's still building out. because no one will complain. No one will complain about something that's free, but they'll certainly give you advice on where things aren't working. And if you're responsive enough to that and designing β the roadmap around what that feedback is, because when I got in there, I was like, tool tips, can't.
Kirk Munroe (56:18)
Yeah, that part.
Right.
Fiona (56:36)
Customize the labels. I can't really customize them. There's a lot of there's a lack of control that I'm very used to in classic that I can't currently have. But then I have this beautiful semantic model that's building out behind things. And and so there's there's those tension points between both. And I believe that if we can prioritise effectively and the and the roadmap for what they're delivering, they're releasing fast. Every time I go in, it's looking slightly different.
Kirk Munroe (56:49)
Right.
Yeah, yeah, yeah,
yeah. No, for sure. think, β yeah, I agree with that part for sure. Like the, but they might say they're giving it away for free now though, because you buy a Tableau plus license and then you get a dual entitlement to the two of them. Now your point would be it's only free if you buy, maybe I might be putting words in your mouth. It's only free if you buy the highest Tableau license in the first place.
But it kind of is free if you think about it, because you can't buy tab on Excel, right?
Fiona (57:35)
But what would be the
benefit of buying Plus? I suppose there's some AI things in cloud that I would get access to by buying Tableau Plus that I couldn't have on Classic Cloud. But I'm saying just drop it. Like just get it into the Tableau Cloud license. Look, let's be honest, it's not the cheapest license out there either. there's still an investment that's going into the space. I think...
people are more accepting of limitations when they feel like they're not paying significantly for the price.
Kirk Munroe (58:06)
Yeah, yeah, yeah. Yeah.
Yeah. Although the one feedback I give them for sure is like nobody for your otherwise wants to put a buggy in complete product and production.
Fiona (58:23)
true.
Kirk Munroe (58:24)
Right. And there's been some things with it. Again, I, I just, it's hard to do, but I have so much respect for snowflakes initial launch that they took whatever it was three years and built it completely stealth and then dropped it on the market as a working product. I know it's hard to do because of the pressures to, and the capital you're burning through before you have, you know what I mean? But I don't know. Good Scotch sits in a barrel for 18 years. They could like bake a little more.
Sarah (58:27)
Mm.
Kirk Munroe (58:54)
What is that?
Sarah (58:55)
That's some good seed funding, hey?
Kirk Munroe (58:58)
yeah, they had some,
Fiona (58:59)
that's 100%, but I think that we can also...
Kirk Munroe (59:01)
Yeah.
Fiona (59:02)
start playing with things, understanding where it's going. And that's why I say we'll typically put cloud things and embed cloud using lightning web components, you know, and ensuring that there's that consistency, that backbone in there. But I think having the ability to have the end of next as well for the Salesforce clients is a nice opportunity. Even if it's we release this to certain people so
Kirk Munroe (59:13)
Yeah.
Yeah, yeah.
Fiona (59:32)
so they can play around with it, they can start tuning the models. When the ability's coming in, I think that that's going to really set people up for success. And then they may be at the forefront of changing their organisation with analytics.
Kirk Munroe (59:50)
So what they have given away, I think free for Salesforce customers, to Sarah's point is there are some pre-built apps like Field Service Intelligence, I think they might call it something. So if you're a Field Service customer, you can get all these pre-built Tableau Next limited dashboards. Again, I the problem they run into there is everyone customises the living heck of it so they don't really work out of the box the way people expect them to work out of the box.
I do think they'll figure it out in time. Like I'm more, like I think all three of us are like more optimistic on this than pessimistic on this. Do you know what I mean? As they get through it. it's like, it's exciting to me for sure. Just to be able to make, I've been waiting a long time for BI to really be actionable. I think about it that way.
Fiona (1:00:32)
Mm.
Sarah (1:00:35)
Yeah, some great points there Kirk and we could go on all day, think. Yeah. But I'm gonna move us on to the rapid fire. And what I would like you to do is that's the first thing that comes to mind and Fi and I will fire some questions at you. So I'll kick off. What's one capability you would love to see in Tableau Next that would be a game changer?
Kirk Munroe (1:00:40)
Yeah, forever, yeah. β
Yeah.
The boring one is row level security, like so that we could do that. β The exciting one is β it needs more of the char types that we've just come to love, like maps and stuff, like so that, we can build enough in it to make it to like more compelling.
Fiona (1:01:23)
Complete the sentence: in five years data analytics will be.
Kirk Munroe (1:01:30)
will be a more consultative job than it has been in the past.
Sarah (1:01:37)
What's the biggest misconception people have about Tableau Next?
Kirk Munroe (1:01:44)
that Salesforce are thinking that it's going to replace Tableau Desktop in the next couple of years.
Fiona (1:01:54)
one piece of advice for data teams trying to stay ahead of the curve.
Kirk Munroe (1:02:00)
Again, it goes back to that, get good at building semantic models and build up your, I hate to use the word soft skills, because they're really not that soft. you know what mean? Build up your interviewing skills, if you will, and get really curious about how your organisation runs regardless of what field.
Sarah (1:02:23)
What's your go-to resource for staying current with data and analytic trends?
Kirk Munroe (1:02:30)
Oh, wow. That's a good question. I am an incredibly experiential learner, which is probably why I spend so much time playing with the software before it's ready. Cause I feel like I have to experience it to get good at it and really understand it. Yeah. Ironic we're on a podcast, but listening to things without playing with things is hard.
Fiona (1:03:00)
data trend that's overhyped versus one that's underhyped.
Kirk Munroe (1:03:06)
I think that, agents are going to be building your semantic models for you. Like any day now is overhyped and, what's underhyped is the risk that AI poses to being able to build to a spec like, so when someone can define something really clear for it, how good they already are doing.
Sarah (1:03:30)
Kirk, this has been such an insightful conversation about Tableau Next, Data360, Salesforce, a few other things in between about where we're heading and what analytics is doing. Before we wrap up, what's the one thing you'd want our audience to take away from this discussion?
Kirk Munroe (1:03:39)
Hahaha
Um, uh, to, to not be. So if, the audience is the typical Tableau data fam like don't be threatened and see it as an opportunity, not a threat, like embrace it, like don't fight it. Right. It doesn't mean that, um, you should go all in on it today either. Do you know I mean? Because Tableau Classic is going to be around for a long time, but find a way, whatever you can carve out, like 5%, 10%, 20 % to learn this stuff and get really good at it.
and be excited about that. Remember that there's a whole lot of people clutching on to Excel right now. Like don't be that person with Tableau desktop, basically. Like, or SAS
Fiona (1:04:30)
or SAS β
That's brilliant. Thank you so much for joining us and sharing your expertise with our listeners today. We've learned so much.
Kirk Munroe (1:04:44)
β
Sarah (1:04:44)
Your insights have
been invaluable and we really appreciate you taking the time to chat with us.
Kirk Munroe (1:04:51)
Thank you. think, you know, like-minded, you know, consulting companies and things were looking at. So was a lot of fun to have this conversation and hopefully we can get back together in a year or two when this is like really developed and we actually see, you know, the impact that's had.
Fiona (1:05:08)
Yeah, I can't wait actually to hang out with you at TC in Tableau Confrence in May next year because I feel like we're going to be so much further ahead in those next five months. So it will be really interesting to see where the product's at.
Kirk Munroe (1:05:12)
β
Fiona (1:05:24)
And if you've loved this conversation as much as we did having it, don't forget to follow us, leave us a review, drop us a comment about what you thought and share this episode with your fellow Data Fam members.
Sarah (1:05:40)
And remember Tableau Next isn't about replacing what works, it's about unlocking even more value through connected analytics. Whether you're just exploring Data360 or planning how to make the most of the Salesforce ecosystem, the key is to start thinking strategically now.
Fiona (1:05:59)
So we're gonna include the links to Kirk's book on data modeling in Tableau, his business Paint with Data and his LinkedIn, as well as that course or that masterclass that he mentioned with Tableau Tim.
Kirk Munroe (1:06:09)
Yeah.
Sarah (1:06:13)
And until next time, stay curious, keep exploring your data, and thank you for joining us here on UnDUBBED, where we're unscripted, uncensored, and undeniably data.
Kirk Munroe (1:06:14)
Yes.
