Fiona (00:08)
Welcome back to Undubbed, the podcast that's unscripted, uncensored, and undeniably data. I'm Fiona.
Sarah (00:16)
and I'm Sarah. And today we're diving into something huge for anyone in the data space. Tableau Next.
Fiona (00:23)
That's right, this isn't just another Tableau release. It's a full rethink on what analytics can be.
A rebuild from the ground up, think agentic analytics, an intelligent semantic layer, and Data360, which has zero copy from your cloud, all working together to make insights not just visible, but actionable.
Sarah (00:47)
And today we're breaking down what this means for decision makers, how it changes the role of analytics teams and what is possible when dashboards don't just show data, they do something with it.
Fiona (01:01)
And as always, we'll unpack the human side because tech only works when people actually use it. So let's talk about the future of analytics and why Tableau Next might just be the biggest leap since Dashboards themselves.
Sarah (01:18)
So Fi, Tableau has been leading the analytics world for years. So what's actually new about Tableau Next?
Fiona (01:27)
Great question, Sarah, and perfect to start on. So Tableau Next is really about Salesforce's building of an analytics product from the ground up.
and all integrated with their Salesforce core platform. So everything is in the GUI. Previously in the Tableau ecosystem, we have Tableau Desktop, which is a thick client that we all go and install. And if you're in large corporate organizations, that can be a little bit painful because they either have to package it up or give you special rights to do that installation. And now we've got a tool that does all of the development.
through the GUI. So really interesting to see how they're bringing together a different way of doing things. But from the insiders that I chat with at Tableau and from the things that I've pieced together online as well, they're really aiming towards parity from the Tableau desktop space and bringing that through into Next. But right now it's a brand new product that was just released in June of this year.
So it's definitely not at parity. It's a simplified version. You really have to think about how you're going to communicate with data. But there are some exciting new additions into how we go about our analytics workflow.
Sarah (02:58)
So quite a change there for us seasoned Tableau people that are used to say Tableau desktop and almost shy a little bit away from Tableau web edit, I would say. So we're kind of starting in that web edit space.
Fiona (03:15)
Absolutely. the old executive in me really loves the fact that, this can be something that's fully GUI based rather than having that fit client that is installed because it is quite problematic. Especially if the launch is done from the business and not from IT as well. There's a lot of things to get around. So really exciting to see how that comes together.
Sarah (03:42)
Yeah, and I think we'll dive into it a little bit later as well. So much updates happening. So not having that thick client behind the scenes, not having to reinstall the desktop all the time as well.
Fiona (03:51)
Mm-hmm.
Yeah, and if you're someone that has a CRM analytics, CRMA background, Tableau Next is probably a little more familiar to you as opposed to you being a Tableau Classic or Tableau Core user. Because just like CRMA, it's all browser based as well. β There are more limited
visualizations, although from what I understand that's actually changing quite rapidly because Tableau are doing releases, I believe monthly in this space. So really interesting for people to be able to move as well from CRMA across into the Tableau Next space, which is something that I believe all clients will need to do in the future, whether that's in a year's time or a few years time to be seen.
So Sarah, why don't you run us through the four layers, the data, semantic, visualization, and action layer that's in Tableau Next, and walk us through a little bit about what those mean in plain English.
Sarah (05:06)
Yeah, sure. I'm actually going to share a slide here.
Okay, so within Tableau Next, we have four different distinct layers. We have the data layer, the semantic layer, the visualization layer, and the action layer. Now, the data layer is the foundational layer, and it helps us unify all the different data sources. There's some really amazing stuff that this layer allows us to do in terms of almost getting to that single customer view. So it lets us...
select which parts of the data that we've got within our entire system and look at which ones are important to us under which circumstances. This also allows us to pull in different data sources outside of Salesforce, like Snowflake and Databricks. And it allows us to do that with zero copy. So really exciting space there. The next layer is the semantic layer. Now think of this as the translator.
So it helps us convert what is technical into business language and have all those traditional joins together. The third layer is the visualization layer. This is something that we're most familiar with coming from Tableau. It's the interface, it's the dashboards, it's pulse, and also now we have the agentic AI space there as well. Now the final layer is the action layer. Now think of this as the executor.
This helps us trigger
workflows and really take the action on what's happening.
Fiona (06:42)
Yeah, there's a lot to take in there and get through. First up, the data layer or data 360 as it's now known, previously known as data cloud. The CRM geek in me from many years ago is so excited to see that there's finally a way that we can identify which parts of which records are we going to use for which purposes and really lends to
that single customer view. And an example of that is if, for instance, in the Tableau space, some of us may use personal emails for Tableau Public, and then work emails for registering our licenses, or perhaps even attending some of the events. Tableau and Salesforce are now able to identify with specific rules, how they should go out and reach us and target us for different marketing reasons.
In the past, you know, having worked on many CRM projects, this is problematic. And it also is something that's really left up to the technical people to be able to define and try and get there. But now it's much easier with the way that you can use rules within Data360. And I'm excited to see how organizations actually reach out to their customers.
and are more specific and accurate and personalized in their targeting as well. The other space that I wanted to, like I guess every space I'm really excited about, maybe with the exception of the visualization layer, which is kind of funny, the semantic layer, as you mentioned, it's really where the business context comes in. And recently you and I did some work on the Tableau Next Hackathon so that we could get much, much closer.
and familiar with the product. And this is where I spent a lot of time β reviewing our data, bringing our data together, defining our data. So those definitions become really important when you start to use the Tableau agent or concierge for querying the data and giving it the business context behind the definitions. So
Think of how you use ChatGPT, for instance, in your day-to-day life, and you might say to ChatGPT, tell me how, or explain to me like I'm five, or those kind of questions. In order for Salesforce and Tableau Next to be processing your queries and structuring them correctly, it can't just have something that's like calculation underscore one, two, three.
to understand what's in the data. needs that business context to understand how things should actually be processed. really exciting to see that there's spaces in the semantic layer to be able to pop those definitions in, but also give it context like if this data is going up, it's bad, or it's good, or it's neutral.
And I haven't seen that in other platforms as well. So it's really exciting to see that we're getting much more control as data professionals over that area, as well as the holistic business preferences that comes in Tableau Next as well.
Sarah (10:17)
And I will say in that space as well, it's very business friendly. So it's less on the technical, which is a great addition as well, because it's business that are gonna understand those individual rules.
Fiona (10:31)
Absolutely. Very easy, very easy to put together. And interestingly for me, as I was putting it together, I was sort of using a bit of chat GPT, along with, β you know, my own thinking to think about how should I describe this information so that it's easy to process, but also gives that context as well. And then the action layer, which you have
the data pro, the concierge and the inspector
All playing different roles. So the concierge lets you ask questions in natural language. The inspector is surfacing proactive insights and the data pro is helping with building and modeling the visualizations. So you've got all of this additional help that in parts we've had in traditional tableau where we've been surfacing the insights and we've seen the evolution of that happen within Pulse. But
This is really looking at every single space that we can start to bring in the information and surface it to the users. Really exciting.
Sarah (11:41)
Very exciting. I see you skipped over the visualization layer there Fi in your kind of summary. Is that just because you feel it's got a bit to go?
Fiona (11:53)
But let's be honest, we don't have the same flexibility that we used to in Tableau Classic. And so as someone who's been using Tableau now for over 10 years, that can be a little difficult to process. How am going to bring this information together? But they are changing really quickly. So I think because we're so used to something that's so advanced, there's
There's less so in there, but there are some really interesting things that I do love in the visualization layer, which is...
When we've talked about dashboards classically, so we've talked about going in, helping your stakeholders understand, you know, see and understand their data. The challenge is that we tend to complicate things because they want to be able to answer any question that comes through.
Well, we don't have to do that in Tableau Next, which is super exciting because of the way that these four layers actually come together. And let me explain that in a little more detail to give you context. So when we talk about dashboarding, we often give an anecdote around it should be like the dashboard in your car, understanding your speed. It's relatively simple. There might be a warning light that pops up. β
We're certainly not trying to make it like the dashboard of a 747 or an A380 But that makes it much more complicated for people to really understand what they're trying to do. As developers, we know exactly where each knob is, what each thing means, but with someone who's less...
experienced or hasn't actually been building it, it takes time and when there's so much on the page it can overwhelm people and slow that adoption or even halt it completely. The beautiful thing about Tableau Next is you no longer need to do that. So you can really skinny down your dashboard development and the reason why is because you can actually use the Tableau Agent or the Concierge to ask questions
that go deeper that relate to the semantic model that's available. And what I mean by that is when you've built your dashboard and you've brought your visualizations in, you've done it using the new semantic layer where you've modeled your data, you've provided all of the context behind it. And then you might put your speed.
your fuel up on the dashboard and having that as your minor metrics. But underneath you might want to know is my fuel efficiency improving over time or is it degrading over time? Now is that important all the time to know about your car? No it's not but you might be curious about it. Now that data is all available in that semantic layer
And so you're able to go and write a natural language query and surface that quite quickly. So it means that you're not actually having to think about the hundred possible questions as how you can visualize them and jam them into a dashboard and do it in a way that's not overwhelming people. All you need to do is ensure that that data is available in the semantic layer, that you've defined it well and given it the context.
And then your end users are going to be able to self-service and take it through. So quite a shift in the way that we're developing.
Sarah (15:44)
Yeah, for sure. So instead in the past, we've had to think of everything and try and put that down somewhere that's not overcrowding the dashboard and you know, maybe having drill paths and click throughs and so forth. But what we can do this way is we can as you said, a well built semantic layer can actually help the people find their way themselves without having to surface it all and having that ability to, as you call it, skinny down your dashboard.
Fiona (16:14)
So before we shut the slide down, I think it's important to acknowledge something else that's on the page here, which is the Tableau marketplace. Really exciting to see that clients are now able to have a private marketplace where developers internally can share their applications and to be able to reuse anything that's developed locally for their teams as well. But
As a consulting org, β what I'm really excited to see is that they're bringing together in the future a public marketplace, which is a way for people to develop applications that would work, for instance, maybe with a company like Xero. And then you could just plug and play by using an application that's developed by someone else. And it means that you can get further faster. β
using the IP and knowledge of other organizations.
Sarah (17:08)
Yeah, some really exciting times there, I think for freelancers and consultants potentially in that space.
Fiona (17:15)
Spot on
Sarah (17:18)
So we've been hearing a lot around agentic analytics, which is goal-orientated, autonomous, and available 24-7. What does this really mean for businesses?
Fiona (17:31)
That's a good question. I think it's actually, in my opinion, developing as we're, as we're seeing the improvements on platforms like Salesforce, like Tableau Next.
Where I'm really loving the Agente Analytics is what I touched on before, which is to be able to dive deeper into my dashboards and understand more about what's happening without having to have that actually visualized on the page. So during our hackathon,
One of the things that I visualized was a heat map of risky driver behavior by hour of the day and by day of the week. And interestingly, you know, things had peaks in the middle of the night from the zero to perhaps 4 a.m. space, there was there were more risky behaviors happening. So that might be sharp breaking or
speeding or different things and I was asking questions to the Tableau agent you know why might this happen and it talked about driver fatigue it talked about you know a number of different things which obviously you know does tie in when you start to think about it that makes sense people are tired at that time of day and getting things done and there's less people on the road as well so they might you know be a bit more lead foot
and driving a bit harder. So for me, being able to have that information at my fingertips, but not having to answer any question that pops into a manager's mind β means that we need to get better in the data preparation space. So I think β it means that businesses will be really hiring for people that understand that
business context, but can apply it in a data sense as well so that it can all be prepared. And I think that businesses are going to need to also
not always believe everything that they say, so continue to be curious and ask why as well, because things don't always go right in AI. We often talk about Chatty G and Chatty G's hallucinations. So I think that people who are using the analytics really have to get curious, it's telling me this. Does that feel right in relation to my business?
I was really excited to see at Dreamforce recently that Tableau is starting to bring in different ways to train the agents as well. So being able to not only thumbs up or thumbs down whether something's right, but actually give context behind this is the reason why it's correct or this is the reason why it's not correct. And all of that information is then fed back into the model.
which we all know is really important for training the right outcome.
Sarah (20:37)
Yeah, some great stuff there. I think going back to our example on the hackathon a little bit, having the ability to then layer in the weather data for us in our example was really important. And just showing that in there and then leveraging the agentic analytics further, it could really kind of start to pick up on maybe there were other things that were happening within different weather patterns as well.
and starting to build that story out. And I really like how you said, it's starting to layer in different responses and do we agree with that response or disagree with that response and where it's going and building up that knowledge base as well.
Fiona (21:19)
So with that said, Sarah, what's the human angle here? How do we make sure that AI agents amplify people's work and not replace it?
Sarah (21:29)
So agents are brilliant at processing data and spotting patterns, running repetitive analysis. Honestly, all the boring mechanical work that we typically don't even want to do anyway. Now, some things that they can't do well is understand organizational politics, make strategic trade-offs, and have the difficult conversation about why someone's pet project shows a negative ROI. Have you been there before, Fi?
Fiona (22:00)
No comment...
So Sarah, what's the human angle here? How do we make sure that those AI agents amplify people's work and not replace it?
Sarah (22:11)
So agents can really handle all the boring mechanical stuff, all that repetitive things that we've been doing for years and really hated anyway. What I love about where we are now is humans can do all the soft decide. So the judgment, the politics, the projects that we have to do because it's a political statement, the strategy. I feel that we're in a great space to move a lot faster.
and iterate a lot quicker and go through what is important and more on that softer side, take a few risks, see if it is going to work or not and put the agents to work to maintain all that boring stuff that we don't wanna do. It can't in my mind replace emotional intelligence, but it can do all the boring stuff. How do you feel?
Fiona (23:08)
Yeah, I think there's certainly a difference that I've noticed in myself about how I'm going about building things. And this is both in what I call Tableau Classic, which is Tableau Desktop or Tableau Dext as well, where in the past, you know, I'd really be hammering out the calculations, you know, where I get things wrong and, you know,
Googling and doing everything else now and next I can use Einstein or I can jump out and of Tableau desktop and go into chat GPT and give it a Framework behind what I want the calculation and it will walk me through I say I get a good hit rate and that's probably about 90 % accuracy in terms of Being able to write those calculations really quickly
And what that means is I'm more productive as a developer and I'm able to do more elegant things with the calculations as well and spend perhaps some more time going back and thinking about how I can optimize the experience, whether that's in the performance that the dashboard's running at on Tableau Classic.
or whether that's in how I'm actually approaching things from a calculation, like not using a fixed LOD, like it's the hammer for everything because it does give you a lot of control, but it does slow things down as well. So I think in myself, I'm seeing a total adaption to how or where I'm spending time.
So I really believe that the way that I am working now is significantly different to the way I was working three years ago. I also believe that these areas that didn't exist before that I'm spending a lot more time in. So we talked a little bit about the semantic layer previously.
which is really doing all of that definition upfront. As a good analyst, I like to be able to document my calculations, et cetera. There's more space for me to be able to do that and provide more context as I'm starting to build out the calculations. So in Tableau Next, for instance, upfront, I define, is it a dimension or is it a measure? What kind, you know,
what is the kind of format, so I'm thinking about ints and floats and a lot of different things that data people will understand right upfront. That previously was an afterthought when things weren't working when I dragged them onto the page. So more time spent in areas that were unexpected that make things a lot smoother for the end user to be going and developing.
Sarah (26:09)
Yeah, some really interesting insights there, particularly around how you're leveraging Chatty G as we affectionately call it.
Fiona (26:18)
Yeah.
Sarah (26:20)
And you're right, I think it's a great time right now to almost get it to critique all your work because I think that's where the value really comes in and leaning, having more space to work on those softer areas like the emotional intelligence.
Okay, so let's talk about adoption. Fancy features are great, but how do organizations actually build a culture where people use this stuff?
Fiona (26:53)
Back to our old friend change management. coming in and understanding why it's important to change, creating that appetite for change as well, and then giving them the knowledge that they can actually apply that change. So teaching them how to do it, measuring that.
reinforcing the behaviors, all great things that help people adopt the change. What I every organisation that I've been in is nuanced in the way that they want to run the analytics operations. So it really depends on your organisation and what your needs are. Are you going to have a free for all where everyone's able to go in and do the development? β
might not be in the Tableau Next space, perhaps the best place to start. I feel like starting small with a POC is a great idea. For Tableau Next specifically, I recommend being a Salesforce client.
because one of the really unique propositions is being able to get access into that Salesforce data, as well as all of the other beautiful data that's in your environments as well with zero copy. So zero copy is all about leaving your data where it exists. And so that could be your Databricks environment, your Snowflake environment.
but being able to model that and pull that through into your next environment and to be able to enrich it with what's already in your Salesforce platform as well. So when it comes to organizations, building a culture where people actually use this stuff, I think starting small, understanding what a really good use case is to bring that Salesforce data together with data that's probably outside.
of the Salesforce ecosystem and being able to solve a unique problem that's never been solved before. And when you start to do that, you start to get people excited, making sure that they feel confident and asking those agentic questions, β using the concierge or perhaps looking at the inspector insights that come out on the metrics, making sure that people feel
confident enough in themselves, you'd be surprised how many executives actually aren't confident with data. It's a really big thing to build that confidence in people. So start small, solve some problems and make sure that you give people the ability to actually use them as well.
Sarah (29:43)
Yeah, yeah, some great stuff there. it's like make it relevant, make it easy and make it visible. Right. And when we say relevant, it's like, look at your detractors almost and think of maybe that sales manager out there that says, you know, we don't need this and our organisation, maybe show them how they can hit their targets, leveraging.
Tableau Next, and then you'll see the clog start clicking and they'll change. And if you make it easier, I love how it's all integrated. So if they're working within Salesforce now, they can have these dashboards appear within their workflow, which I think is great. And that makes it super visible.
Fiona (30:05)
Mm.
Absolutely, it makes it visible. It's really important for creating trust in the organisation. And one other thing that really helps to create that trust is that semantic layer. So one version of the truth, being able to be shared across departments. How important do you think that is for scaling a data culture?
Sarah (30:46)
So important, we've always worked in areas where you've got silos and the HR department holds on to their org structure and finance hold on to their profit and loss. And, you know, the poor salespeople are in the middle trying to figure out, you know, how we're going to get that information or marketing's come up with a new product and it's not shared with finance, you know, all that kind of stuff that we see happen in the big organizations. If you can start working together and having this
semantic layer that has the intelligence built into it to say, hey, when I've got my marketing hat on, these are the things that are important to me. And when I've got my finance hat on, this is what's important to me. think bringing that all together and having the confidence to use that is a real game changer.
Fiona (31:35)
Agreed.
Sarah (31:36)
When you look at Tableau Next as a whole, where do you see the biggest ROI for leaders?
Fiona (31:43)
The biggest ROI that I see for leaders by leveraging Tableau Next, there's a few places that I'm really excited about. First up, having that semantic layer and giving that business context. So it essentially makes analytics accessible to anyone who can ask a question, are really important. So the biggest ROI I believe is that,
they're not going to have to hang around and wait for all of the analysts to build everything out. If the analyst has built the semantic layer, has the information that's accessible, has defined it well, being able to get the information that then informs their decisions and informs their actions will really see some great returns and quite swiftly as well.
I also think that there's an opportunity to get out and out of the lengthy builds of dashboards, I think is still important. Don't get me wrong. You know, I think having your standardized monthly reporting that you're able to.
take into your board meetings, review, or even quarterly meetings, review with them how the business is performing, still super important, but it's the dashboards which are really helping people to understand campaigns or different performance that we can be
A little more perhaps carefree about the build as such, not having to jam the hundred metrics into a dashboard. As long as we're modeling that data out and providing the context, it's going to be much easier for people to start asking the questions that matter to them in the particular moment.
Sarah (33:29)
And I feel as well, there's a lot of unknown unknowns. And I think having these layers built in the way that they are allows for a lot more discovery and questioning the data and discovering these unknown unknowns. So I see some massive potential in ROI here.
Fiona (33:54)
Yeah, those unknown unknowns are kind of like a, there's a moment and we've all had this moment where there's this, something's unlocked in the way that we're thinking, we may have been listening to something on the radio in the morning and it's triggered another thought like, β
I wish I knew this. Previously, it's much harder to get into the analytics pipeline. There's quite a lengthy build in terms of getting not only the data model ready, but all of the different visualizations and making sure that we're selecting the right visualizations. Now, as long as the data model's there, we can go and query it.
Sarah (34:32)
Yes, a lot more capacity for self-service.
Fiona (34:36)
Staying in the flow.
So in that flow, do you think that analytics will move from something that we check to something that runs continuously or quietly in the background?
Sarah (34:50)
100%. You know, we used to build monthly, weekly dashboards, I think they'll become a thing of the past. And it will be more around this has happened. what is that outcome leading to? And what action am I going to take from that and having all the data at our fingertips in order to, ask concierge and to come up with with our next steps forward.
So yeah, I feel that we will really move forward and become a lot more data informed and acting on it faster. And that will only happen if it's really integrated into everything that we do and we can see the data as it's happening.
What's one quick win use case you'd recommend for leaders wanting to provide the value of agentic analytics fast?
Fiona (35:39)
Yes, start small. I suggest reaching out to your account exec, if you're on the Tableau platform, reaching out to your account exec and asking, can I get access into the next platform? I want to do a POC specifically if you're on the Salesforce platform.
because that integration is something with the power of Data360, being able to unify your customer profile and being able to bring that together with all of the other data that you've never been able to see before. It's really going to unlock some powerful things for your organisation and personalization. So do a POC, get stuck in, see.
what Tableau Next can do and what it can't do on the flip side, get to understand that a little bit more as well because it's not going to give you the same or parity with what you're experiencing in Tableau today but there are some things that are much further ahead than what you're currently experiencing so it's a really great opportunity to have both.
Actually that brings me to the next thing. recently at the hackathon there was an outright winner, Sarah. Do you want to run us through? Okay.
Sarah (36:59)
There was, I just, I might bring up a slide on that.
Fiona (37:04)
So Sarah, recently we talked a bit about the Tableau Next Hackathon and I know that we didn't end up finishing our submission because we were a little busy with work. But I just wanted to pause for a moment because we started to talk about both Tableau Classic and Tableau Next and the ability to bring those environments together. Who took out the grand prize of the Next Hackathon and what was it about?
Sarah (37:34)
Yeah, so the ultimate winners were from Biztory actually, and they did a really great β use case, which integrated Tableau Next, Tableau Classic, and Slack, and bringing all the analytics right into the workflow. So some really awesome stuff there around interoperability and taking it all to the next level.
Fiona (38:03)
Yeah, and so what interoperability means is how each of the previously siloed applications are now all able to work seamlessly together. So it's knitted together in a way that the stakeholders are unaware of how it's all coming together. And I really, really loved β their video because things like tech can sometimes be a little dry. So I love that the Vistry team let their β
Imagination's run wild and shared a few laughs with their submission as well. So for those of you that are watching via YouTube, there is a QR code that's on screen that you're able to scan. We'll also put it into the show notes.
Sarah (38:49)
Yeah, some really awesome stuff. And I loved looking at, many of the entries in the hackathon and seeing how people were pushing boundaries with that. So lots of information there that we'll share in the show notes as well.
So last question, Fi, if you're advising a chief data officer today, what's the one piece of advice you'd give before diving into Tableau Next?
Fiona (39:14)
great question. The one piece of advice that I would give a chief data officer or a data leader before diving into Tableau next is.
Don't just go all in on one platform. Think about how your environment with Tableau Classic or the cloud platforms can work to your advantage with Tableau Next as well. So it's not an or proposition. I like to say it's an and proposition at this stage in the future when there's more parity, you could definitely go down that space, but it's brand new.
and it's just gone GA this year. So if you're thinking that you're gonna get a product like Tableau that's been around for 20 years or so, it's not there yet. But the great thing is they're putting so much investment in this space. Things are changing really quickly. So give it a crack. Think about a proof of concept. Think about how you could be bringing together your own unified customer profile.
and really starting to nail some of the great questions using the agents.
Sarah (40:25)
we've already spoken to some people that are very forward thinking that are really excited about this space. And I think it's right what you're saying and don't expect everything from this tool, but lean into where it's heading and lean into what it can do today. And I think getting to those unknown unknowns in your organisation is somewhere where you can potentially make some some quick wins straight away.
So there it is, Tableau Next, not just a new feature set, but a whole new mindset for analytics.
Fiona (40:59)
this is where it gets exciting. When data, semantics and action come together, analytics stops being something that you can look at and becomes something that works with you.
Sarah (41:11)
So if today's episode got you thinking about what's possible for your business, you know where to find us. Head to dubdubdata.com or connect with us on LinkedIn.
Fiona (41:23)
And my favourite part of the show, if you liked this episode, please hit follow, leave a review and share it with someone who still thinks BI stops at dashboards.
Sarah (41:36)
You'll find the links to Tableau Next resources and our dub dub data breakdown in the show notes.
Fiona (41:43)
Thanks for tuning in. Stay curious, stay data informed, and we'll see you next time on Undubbed.
Sarah (41:50)
Bye!
