Unlock the Power of Agentic Data | Tableau Conference 2026 Keynote Replay

Please welcome Executive Vice President and Chief Product Officer, Tableau, Southerd Jones.

TC! Hello, TC! Woo! Welcome. Welcome to the beautiful, sunny city of San Diego, and welcome to the world's largest and greatest data and analytics event. What makes it the greatest? All of you, this community, the Data Fam! Woo! I want to start by thanking you, thanking everyone here in person for taking time out of your busy schedule, traveling long distances. We've had people from Australia, Japan, Europe, Africa, South America. Everybody has come here for this community. And thank you for those online. I think we have like 20,000 people on Salesforce Plus. With us. Thank you for joining us virtually. And I want to take a moment to thank a few special groups, a few groups that make this community really what it is. So when I call out your group name, please stand. I want to thank our Visionaries first. If you're a Visionary, please stand up. Be recognized. Woo! Thank you so much. For leading us forward with your innovative thoughts and driving our product forward and driving this community forward. There's a couple of you I want to recognize specifically because you've been a Visionary for 5 years and you've done some other amazing things for us, and that makes you a Hall of Famer. So, Tristan Guillon, yes, Tristan, thank you. Zach Bouders, Zach, thank you. And Jen Dainer, Thank you. Next, if you're an ambassador, I'd like you to stand. Please stand up, ambassadors. Visionaries, stay standing. Ambassadors, please stand. Woo! This is so cool to see. Your evangelism, your ability to bring people together and share your passion has made this event what it is. Many people are in this room because of you. So thank you. Thank you. Great appreciation and gratitude. Stay standing. Next, we want to thank Tableau User Group leaders. If you have organized a user group, if you presented at a user group, or if you've just been somebody who's taught others, please stand up and be recognized. Come on, sir. Thank you. Thank you. It's a passion for sharing and teaching that makes this community what it is. Without you, people wouldn't be building data skills, which are some of our greatest life skills. So a huge thank you as well. You've all taken us on this amazing journey. You can stay seated now, but thank you. You all brought us to this great journey. We've been on this journey for quite some time, and the journey started for many of us as a first-timer at an event. So if you're a first-timer, I'd like you to stand up now. Who here is at the Tableau Conference first time event? First time, stand up. Wow, really? This is awesome. Look at this. Look at that. Wow. Wait, wait, stay standing. Turn to your right or turn to your left. Give a fist pump or shake hands. Right here. This moment is what this event is about. It's about sharing your passion, it's about meeting new people, it's about making acquaintances, making friends. Some of you might even meet your future spouse. That's happened. It has happened. So thank you for coming out and stepping outside, outside your comfort zone and making yourself available to meet new people. That's what this event's all about, that's what this community is all about. You can take a seat, thank you. So this journey started in Seattle. It started with 187 people at our first event. Imagine that, 187. There's like 6,000 people here, there's 20,000 online, 187 people to where we are now. And that is, that journey went across the globe. And because of you, we've reached so many different people. Because of you, we've been able to reach underdeveloped countries. We've been able to reach new and unknown places that had never even worked with data before. Because of you, we are in 48 countries today, over millions of people, 210 user groups, 57,000 user group members, 70,000 events on a monthly basis. And almost every single one of them is because somebody here online is organizing it and setting up and sharing their passion for— what we all want to do is help people see, understand, and act on data. Such a massive thank you. And that's the power of this community. That's what this event's about. And that's what we're here to talk about over the next couple days. Now it's not just about the community. It is a little bit about this product called Tableau. And you guys have made that Tableau product great. You started by asking us for Tableau Public, so we made Tableau Public. Thank you for all those who pushed for it. And thank you, Jack McKinley, for this really cool viz on Tableau Public. It shows all the features we've been releasing over time, and it's beautiful viz. That's just an example of what Tableau Public is. It is often the place where all of us said, brought light to the causes that we have passion in our hearts about. I still think it's one of the best properties online there is today. It wasn't just public. You asked for something else. You said, hey, can you make Visual Analytics better? Can you bring us Tableau Free Desktop? Yes, that's what we did. That's what we did. And get this, 100,000 people have downloaded Tableau Free Desktop since March. 100,000 people. Who previously had not been exposed to Tableau and now are being exposed to Tableau. And that's because of us listening to you and together leading forward this evolution. It wasn't just that. You asked for a bunch of features in our product. You asked for rounded corners, custom color palettes. Yes, rounded corners. Road time areas in Tableau Maps, my new favorite feature. All of them because of you and us together leading that charge. But today, today you can't go anywhere without someone talking about AI. And you know what? You know who's leading that? We are. You are. I see you out there. I see you on LinkedIn, vibe coding, connecting to Tableau MCP, building custom apps, leveraging Tableau AI. I see you leading the future. And yet there's a lot of uncertainty because there's so much hype. But that's something I want everyone here to take away. In the next 3 days, you're going to learn a lot about how AI can accelerate our mission to help people see and understand data. But there's one thing AI cannot do that all of you have already done. Anytime someone logs into Tableau and looks at a viz or a dashboard or a metric, do you know why they trust it? Do you know why? Because someone here has gone to great lengths to make sure it's correct. Someone here has said, I'm gonna make sure the join is correct. I'm gonna make sure the level of detail calculation is accurate. I'm gonna make sure the visual representation is right. I am personally gonna make sure that someone can trust this because they make key decisions on that. AI can't do that, only you can. So the next couple days you're gonna learn a lot about AI, you're gonna learn about Tableau features, and you're gonna learn about how together we're gonna accelerate our mission with AI. Now right now I introduce somebody who's gonna help you, I'll take you on that, on on that little journey. But first, he's somebody who has spent his life in Tableau making key business decisions based on information and knowledge that came from Tableau. So with that, I want to welcome our new GM for Tableau, Mark Recker. Woo! Thank you.

Thank you, Sutherland.

Wow.

I am thrilled to be here at TC. This is my 55th Tableau conference. 5th day in this GM position. And I want to start with a thank you. A thank you to our customers, a thank you to our partners, a thank you to the incredible Tableau team. But most importantly, if you're part of the Data Fam, can you stand up? Can we give them a really, really loud thank you?

Woo!

TC is about you. It's about the Data Fam. And listen, I've been looking forward to this day for a very long time. It's actually the first thing I told my team. I said, we have 55 days until TC. You can ask them, they're right over there. But I wanted to be here in rainy San Diego as we shape the future of Tableau together. Now, I've had the opportunity to meet many of you in in the last 55 days, but not all of you, 'cause we know data. There's thousands of people here. That would be statistically improbable. Just yesterday, I had coffee with some folks. I ran into folks and saw the power of this community and this conference. But you all don't know me yet. So what's the first thing you should know about me? Well, I have been data obsessed since before I can remember. I was the kid scouring through box scores trying to understand patterns in signals to understand why my Minnesota Twins were always losing, which was mostly bad pitching and bad hitting. It's a bad combination. But I love data. I love it. I love the visuals, the stories it tells, how it sparks curiosity. And the, the core belief that I have that has shaped my career is that when you can see you see and understand data, you make better decisions. And so for the better part of the past decade my day has started in the same place. You want to guess? Tableau. A cuppa Phil's coffee in my Tableau command center. Now the Philz part is an unhealthy and expensive habit, so I don't recommend it. But Tableau— Tableau would tell me what changed, what needed actions, what decisions were ahead in my day. So I ran my business on Tableau. I grew my career on Tableau. So I think I can safely say that I have been the Data Fam's number one data fan.

Woo!

Now, today, We have more opportunity with data and analytics than we have ever had before. And that is an incredible, incredible opportunity that we get to channel for the next 3 days here. And that's why we come here to San Diego every single year to innovate, to be curious, to push the boundaries of what we can do together. Now, last year, We showed you this slide. We introduced Agentic Analytics, building on the 20-year journey of the Data Fam and Tableau. Look at the eras that we've conquered before: self-service analytics, visual, augmented. And in each of those eras, it was you, the Data Fam, that took data and turned it into insights. In the form of vizzes, dashboards, alerts, notifications, triggers, so that a human could take them and make decisions and take actions. But guess what? The audience for our work is expanding. In the future, it's not just going to be humans. It will, but it's going to be humans and agents. Agents you create. Tableau Agent, third-party agents. And that requires more from us because we know this: data alone is not enough for an agent. In every era of analytics, there has been a human on the other side of those insights who had the intuition to look and say, hmm, that's not accurate, because they have context. Raise your hand I would expect every hand to go up. If you've ever looked at a dataset or a dashboard and said, God, that is not accurate, I'm going to have to spend more time on that. Yeah, we've all felt it. Guess what? An agent doesn't know that because they don't have context. And that is exactly why 89% of leaders say they've experienced inaccurate or misleading AI outputs. Show me the other 11%. So, what does that mean? What do agents actually need? They need more. They need knowledge. They need metrics. They need definitions. They need semantics. They need published data sources. Is this sounding familiar? It should, because for the last 20 years, it has been the Data Fam that has been architecting knowledge inside of Tableau in the form of— and this statistic shocks me every time— 33 million semantic data models that you built in Tableau. So while this era requires and inspires new ways of thinking, new ways we can orchestrate data in analytics, it requires one thing above everything else. And that's knowledge, the very knowledge you've built in Tableau. And so that's why I'm excited to unveil to you where we're headed with Tableau based on what you've already built inside of it. So today, Tableau is your Agentic Analytics platform. Now, every layer of this platform is powered by AI. And show is way better than tell. So we're going to show you for about 40 minutes. We have some fantastic demos, and we're going to show you that it's in every product experience, not just Tableau Next. I will repeat that. Not just Tableau Next. I figured that would be— popular. It will be in cloud, in server, in desktop. But before we show you— yeah, you can, you can do applause. All right, before we show you, you have to listen to me because there's 4 things I want to highlight that you're going to see. The first is our knowledge engine, the ability for you to take structured and now unstructured data and and in natural language turn that into knowledge and semantic data models. Number 2 is a knowledge graph, so you can take and understand your entire enterprise's knowledge base, because we know there are not insights or actions without knowledge. And this is point number 2, our decisions engine. This era is no longer about insights decoupled from actions. It's about insights and actions in real time by humans and agents alike. And you will see that. You will see conversational agentic analytics in Cloud, in Server, in Next. You will see Tableau Pulse. You will see third-party agents. Driving decisions, because that's what we do with knowledge. And the third point, and this is very, very exciting, and I think it's aligned to Tableau's founding principles of democratizing data and trust, which is you can take your trusted knowledge anywhere, wherever your organization is working through headless analytics and MCP. Will Sutton is going to show you. You can take it to Claude, you can take it to ChatGPT, to Slack. You can even take it to Microsoft Teams, Salesforce, and Tableau. So that gives you the power as a data fan to tell your organization what they need to know before they even understand they need to know it. And this fourth point, which I think in the AI era is more important than ever, is our platform is secure, governed, composable, and extensible. So you can safely take your trusted knowledge wherever your organization is working.

And guess what?

Any assets or knowledge you're building elsewhere, you can bring that back in to your trusted Agentic Analytics and knowledge platform. Okay? So, what does this mean for us? I had a favorite line in the video. It was never about the data or the dashboards. It was about you, the data fan. And in this era of AI, with all of this noise, that rings truer to me than ever before. Now, we showed you this slide last year. But frankly, we were a bit nebulous about it. And that's okay, because a year later, it's getting clearer and clearer. We have many fantastic roles inside of the data fam. And in the future, they're likely going to converge a little bit in an empowering way, turning you into a more technology-forward function. Now, you have always been a trust enabler, an outcome whisperer, a decisions orchestrator. But in the future, you're an agentic architect, you're a decisions architect, you're a knowledge architect, and that is empowering you to drive more impact than you ever have before. You are the composer driving your company's entire analytics and data strategy. And that is a multiplier to the massive impact you already provide to your organizations. So what does all of this mean for us, for Tableau, and for the Data Fam? It means the same thing it did 20 years ago. It means opportunity. And so for the next 3 days, we'll start building that future together. As we always have through every era of analytics. So, guess what? The tell part is over. We're going to show you across every product experience how we architect knowledge, how we power decisions, and how we identify actions. But before we do, I would like to end the same way I started. The last 55 days have been incredibly inspiring to me. I'm wildly excited about our future, but I'm most excited to build it with you. So thank you to the Data Fam. Thank you to our customers. Thank you to our partners. Thank you to the incredible Tableau team. And with that, Souther Jones.

Woo! All right. Thank you, Mark. And— Just as Mark said, we are going to show you product now. That's the fun part. But we're going to do something a little bit different this year. Instead of Tableau product or engineering people showing you product, you get to see that with devs on stage coming up soon tomorrow. Today— yes, I'm excited for that too. Today we're actually going to have people from this audience, customers of Tableau and community members like you, show the product. So I'm going to start. By bringing on stage someone who's been with Tableau for quite some time. Please join me in welcoming the VP of Global Retail Analytics, Chad Stroup. Chad, come on up. Chad, good to see you. Thanks so much for being here. Really appreciate everything you've done. Disney's been an amazing leader and at the forefront of data analytics for quite some time. So really appreciate you coming here to I Present.

Yeah, thank you. Thank you. I appreciate it. If we're going to do this journey, we got to do it right. And so what I brought was Stitch. I heard that might be your favorite character.

Is that supposed to be me?

This is you, right?

Chaotic, mischievous, chaotic, problem causer.

It's in trouble, but a disruptor, right, of the market. And in the end, everybody loves Stitch, right? So here we go.

Oh boy.

You might need to do that.

You can hold it. I'm going to be Groot.

Oh boy.

Like baby Groot. Deeply rooted in data, but continuously growing. We got to do it right.

This is awkward.

And a special thanks to my account manager, Eddie, here. He's the Force, so he's got a baby Grogu. Yes, let's do this.

Thank you. Now that I'm embarrassed and red-faced. I'm supposed to ask you some questions. No, in all seriousness, you know, you've always pushed us in terms of where you want to take Tableau and how your users are using it. Maybe you can share a little bit about what we could do better with Tableau today to help your users be even more powerful.

Sure. Thanks. Well, great, Mark, for sharing the early vision here. It's exactly what we're looking for. So at Disney, what we're thinking about is how do we get away from the reporting layer to a decision layer? Decision layer. Exactly what you kind of mentioned there. So for us, it's really important to be connected to the systems that are making the decisions, right? Today there's a bit of a gap. Our users are in the tool, they're looking at dashboards. Then our location planners, for example, might be going down Main Street early in the morning, walking through the Emporium. They saw a dashboard, said, okay, here's our top sellers, here's out of stock, But then they gotta go back to the office and then they gotta make the decision. What do I do? How do I, you know, make that guest experience better? And so what we're asking is how do we get that closer and connected to the systems where I'm actually making change? So that's for us is if we can do that continuous loop, bring us closer to that real time, that would make a big difference for us.

Well, that is good to hear and, about 3 and a half minutes, you hopefully will show that to you.

So I'm excited. Thank you.

Uh, so the other conversation that you can't escape these days is AI. How do you see AI change in the way your users interact with data?

Yeah, good question. I'm, you know, we have some of the best data I think around, right? We understand our customer very well. Um, but we're excited. I mean, really excited about Agentic AI. Conversational AI, and really using that with Tableau. So today our dashboards are great at answering the question they were designed for, but what it doesn't do is answer the next question, right? And there's always a next question and a next question. And so for example, we have great dashboards that are monitoring our real-time sales in stores, looking at conversion. Conversion might be working well for an item, it might not be working well for this item. Why is that? You know, I want to be making, you know, ask that second question. Is it, are we out of stock? Maybe this item sold great and we in stock, but now it's not selling. Well, guess what? It might not be on the floor. So how can I use Agentic AI to, you know, help me answer those second questions? And really the way I think about it is Tableau and AI together become my personal data scientist right there by my side. I want to give it a name. I want to call it something. It'll be my best friend, right? And help me really answer those questions real time.

Well, hopefully, yes. Do you agree with that? Yes. Hopefully we can do, show you that today. And, and, and I think that's what we all want. We, A, we want to create that trusted advisor, but be under the covers. We want to know that we can trust what that agent is telling us, which is the key thing there. All right, so last thing is, as I said, you've always been wanting to help us bring us towards the future. Maybe you can do a little future telling. 3 years from now, imagine coming back here in 3 years. How would you see— how would you like describe the way that Disney is using analytics 3 years from now?

Yeah, well, great question. In my mind, we're not talking dashboards like we talk today, right? I want to be naturally conversing with Tableau. I want to be, you know, really connected to the systems like I talked about. You know, I want Tableau and AI to be continuously monitoring my business, looking at trends, looking at park traffic, looking at out-of-stocks. And I want it to really help me answer that question that I hadn't thought about yet.

That's right. Letting you know what you didn't even know to ask or something like that. Right.

Exactly.

Well, we look forward to doing that with you. So thank you again for your partnership. Thanks for embarrassing me, Stitch. Really great to see you here. Thanks so much.

Appreciate it.

Woo-hoo! All right. Next up to show you the product, someone who I think is a perfect person to show you the product because he himself has been in Tableau probably longer than most people in this room. He was an early member and huge fan of data and analytics, specifically with Tableau itself, and he can tell you his story. And today he is the Chief Data Officer at Salesforce. Please welcome Michael Andrew.

All right. Boom. Everybody, I am so excited to be here. Today to share what we're doing at Tableau. And let me start with this. I've been now using Tableau for almost 20 years. Back in 2007— yes, I'm a little old— I discovered, oh my God, I can see my data. I started my career like so many of you as a data analyst, looking at data, making charts, finding those insights that could move the business forward. And then I built a business on Tableau. I was a customer. I paid for Tableau Server. I was providing analytics to some of the best brands in the world, like Nike and Google and Verizon. And I made sure I had Tableau Server. I made sure every single analyst in my business had what they needed, had Tableau to do this analysis. And then when I joined Salesforce in 2019, there was something peculiar. They didn't have Tableau. I was like, what's up? Come on, guys. And believe it or not, I took about 40 emails, a bunch of meetings, escalation all the way to the CIO so my data science team could get Tableau. I was successful. We got Tableau. That's right. We started using it to do some real insights. And then a little later that year, Salesforce kind of wised up and said, well, maybe we should buy the company. I don't know if I had any influence on that, but I'm going to give myself a little bit of credit maybe. And what I'm really proud to say is now, More than 7 years later, as a chief data officer at Salesforce, we run our business on Tableau. Every single day, tens of thousands of employees are logging into Tableau. They're viewing the dashboards, they're making decisions, whether they're in product, whether they're in sales, HR, operations, everybody. And I would love to show you all of our real internal data, but that might be a little irresponsible and not fitting our trust value. As the chief data officer of a public company. So today we're going to show you this story through Bolt Bikes, a really awesome imaginary company that sells awesome e-bikes to everybody so that we can show you what Tableau of the future looks like. Now to do this, please give a major shout out to John Denby, our awesome demo driver. All right. So we're looking at Bolt Bikes and sales are going pretty good. So here's my e-commerce sales having a lot of great acceleration, but that only tells one story. I kind of want to know not just e-commerce, the whole thing. Now, in the past, when you want to join this data, let's say data in one database, data in another database, you have to blend the data. What we are now announcing is something called composable data sources. Where you can take different data, different published data sources, and make them as one. Actually do the join, materialize it.

Yeah, yeah.

I know, excited. I'm excited too. So look at this. So now we're joining our, of course, for some reason they had two databases. That never happens in the enterprise, right? You never have data in different places, do you? And so we're taking now the web data with the retail data all in one view, all explorable. That's pretty cool. Composable data source, everybody. My team is super excited. And so what can you do with it? Well, now you can make super awesome Tableau dashboards like this. Beautiful. You see the whole world. So I'm looking at it. I'm seeing my sales. I'm seeing things going around. But maybe I have some questions, questions about, well, how are we doing? How are these bikes being sold in San Diego? Now, if you had to do this today, maybe you'd create a filter, maybe you create a tab. But now we are offering to all of you Agentic Analytics right in Tableau Cloud, right in Server. Now you can ask the agent this question, well, what was shipped in San Diego last month? And what did we get? It's thinking, it's thinking, it's coming, it's computing.

Boom.

We can see that San Diego is the most popular for e-bikes. Yeah. Shredding some of the trails around. And all of this is grounded with more than 95% accuracy. Because I don't know about you, we've had some vibecoded dashboards showing up around Salesforce, and they're basically totally wrong. Beautiful, but completely inaccurate. So I really care about making it accurate. And you can see all of this is sourced on that data. But how are we getting to that answer? How are we enabling these agentic analytics? Behind the scenes, we are offering to all of you an analytical knowledge graph. This connects to all your data. It understands it, the semantics, the maps. Don't worry, you don't have to build this. We're building this for you automatically. Now, you can modify it, you can tune it, it will keep self-learning, and you can adjust it. You can feed the intelligence. And it's this grounding in the knowledge graph that gives the context so you can trust the Agentic Analytics.

Amazing.

I can't wait to see all of you use this. Now, why don't we switch over to Tableau Next and let's talk about a semantic model. Semantic models, while amazing, maybe sometimes are a little tedious to build. So we're offering you new tools to speed up your semantic models. Call it AI to build your AI, where it can suggest fields, it'll check if you have conflicts. We have our little Einstein magic kind of wizard, and here we go, optimize the model. So all of this again is to help you more quickly get to putting your intelligence to describe your data, to model your data so that you can ultimately power trusted analytics. And why do you want to do this? So you can put Agentic Analytics in the flow of work. So why don't we switch over, and here we are in Slack. And here's Jennifer, our awesome CEO of Bolt Bikes, and she wants to know some questions, right? Well, what's really happening? How, how, what are the top bike models doing? And right there in Slack, she can ask the question, it's gonna think, it's gonna again use that same trusted knowledge graph, the trusted data, to be able to pull it up, Get an answer. And there you go. So electric bikes are crushing it. Mountain bikes doing okay, I think. But look at that. You also have a recommendation. And what's unique about this is it brings the insights and conversations your teams are having with the intelligence. But you don't just want the one answer. You want to be able to take that intelligence and turn it into action, turn it into recommendation, begin to make a decision. And then you can produce this, your report automatically right there. Again, grounded in the trusted data, having the key insights, and having strategic recommendations right in the flow of work. All of this. So again, you pick your data, you connect it, the graph builds it, and here we go. So what did we just show you? We just showed you a lot. So, So we're going to go to the slides now, and let's recap. We showed you a few things. We showed you the composable data sources, which enable you to now bridge all of your data into one data model you can work with. We showed you the conversation analytics in cloud, in server, coming to desktop. We showed you the automatic analytics knowledge graph that understands your data, maps, builds it. And we showed you how you could bring all of that into Slack. So all of this is to ensure that all of you can keep having your data grounded in the trust and knowledge you do every day, but empower that AI-driven agentic analytics with 95% accuracy. And I'll just say a personal story. This is literally what we're worried about at Salesforce. We have those dashboards. We have a few, maybe, sales leaders kind of excited. Like, oh my God, if I coded this thing and we look at it and we go, yeah, but every metric is wrong, right? And that's the danger. If you just kind of use the AI itself without this grounding, you're going to get hallucination, you're going to get security, you're going to get gaps. We want to make sure that all of you and all the work you and the data fam put on your data can make sure that your company runs and has trusted decisions. So we just showed you how you can become the architect, the knowledge of your company. And Rekha is going to come up and show you Power Decisions. But first, we want to cut to a film from Enjin, one of our great customers, of how they're using Tableau to succeed. So let's take a look.

Woo!

Role of analytics is like, what's the role of food and water?

Incredibly important.

Over 30,000 businesses, over a million travelers are on the platform now. Engine is a modern travel and spend management platform.

It's a very big space ripe for disruption. Things come together not in weeks, months, years. Things come together in minutes, hours, days. We were all doing the best we could with the tools and resources we had available to us at the time.

There was only so far you could go with that.

Salesforce is the operating system that makes it seamless. It's the platform that the world runs on.

Agentic Analytics and Tableau changed the way that we think about data. It's not just for my team, it's for the whole company.

Without it, they would be different silos.

We wouldn't be unified. AI is only as powerful as the systems that you connect it to.

With natural language, I'm able to tell it how I want to join some tables, and all of a sudden it builds a semantic model for me that I can then deploy, then have another agent go and analyze data without me having to do anything other than type. In sentences. It's the ability to interact with data in ways that were otherwise not possible. It changed everything.

CSAT goes up, costs to deliver go down, customers are happier, we're getting them answers faster.

What's the trade-off?

There's no trade-off, like. And now Slack is essentially an extension of them.

Slack to us is more like an operating system. I'm able to talk to Slackbot and get all of these details ready for me so that I have them in front of me.

Having that information available to all in Slack right then and there at your fingertips gives us the ability to iterate that much faster. I can interact with our analytics agent right from my phone and be like, "Hey, what's going on with this? This looks funky. Like, tell me what the deal is here." And it can dive into the data and give me some insights. To have data to inform the direction you build and then have data after you build to inform your iterations, to inform if you made the right bets. It's going to be revolutionary. It's going to be game-changing.

We're going from the dumb software era to the the smart software era.

That's literally what's happening in real time.

It's an incredible opportunity for all these businesses. If you run on Salesforce and Tableau, you're nuts if you don't.

Thank you. Um, that was an incredible story. So many companies, so many organizations like InGen have been using Tableau to power their day-to-day. We spent the last 10 to 15 minutes talking about architecting knowledge. That is critical. That is the foundation for AI. Great. Now, how do we take that knowledge? How do you take your data? How do you take your semantics, your context, and actually power decisions? And that's what I'm gonna show you in this next chapter. But hello, Data Fam. This is Rekha, and in my role, I get to be in a lot of meetings where big decisions happen. You know what's the catch? Unfortunately, with access to all the AI tools, all the reports, all the dashboards, the whole decision-making process itself is slower, it's harder, and it's more disconnected than it needs to be. Hmm, I'm sure we all know a better way to do this, right? To do that, let's zoom out for a second. Like I said, all of us have access to all your tools, your Slack, your Teams, OpenAI, ChatGPT, you name it, you have it. You all have a question, what do you do? Go to that, ask a question. The problem, every time you ask the same question, you get a wrong answer or a different answer from any one of these tools 99.9% of the time. Same question, different answer, across the tools. That's the problem. The problem right now is not about just getting access to your data. It's also not about getting the answers. It's about getting the right answers at every single time. Now, is it all of us? Are we going to go back and check every single tool to find the right answer? Do you have the time to do it? I doubt it. So there must be a better way, and that's why we have Tableau. What Tableau does is we are able to build models based on your data. It's your trusted knowledge, your data. Now, composability and extensibility have always been in our DNA from the day we started Tableau. And if you were to break down those jargon words, what it actually means is you can use Tableau to power every surface, every agent, and every workflow based on your trusted knowledge. That is our knowledge engine in action, and that is an absolute game changer and one that gives up our productivity in extreme ways. And that also reimagines our decision making, right? Now think about it. You're working wherever you are, and you're able to pull up your insights, and you're able to take action right there. No toggling. No going back and forth, and all of those answers are correct and accurate because it's based on our data, your data. So here's the fun part. We took all of this powerful knowledge and put it in the hands of all of you. What do you all do? You create magic. You go beyond creating dashboards and you create magic with this. You reinvent new ways of working. On how we do, how we operate. The best part of Tableau, and I've said this every time on every stage or every one-on-one meeting I've had, is Data Fam. You're so central, you're so critical to our strategy, and that's how we do it. That's our secret sauce, and that's how we operate. So I'm really excited to show you our new Data Fam AI and Analytics Showcase. This is where your every project, your every breakthrough, your every big, bold idea that you've all what you created is on stage. So go ahead, scan, get inspired by that QR code and that page that we've built because you never know, one of your big projects can be on this very stage at the keynote next year. That's our promise to you. So speaking about incredible AI showcase, I have the distinct pleasure of welcoming an incredible visionary from the community who has done some amazing things. To do that, please welcome Will Sutton on stage. Hi, Will. So excited to have you here. Thank you for joining us.

Thank you for having me.

Okay. A little birdie told me you were a past IonWiz champion. So what's changed since then?

I kind of feel that everything's changing, but also nothing is changing at the same time. I started using Tableau in 2013 and I didn't really know what I was doing, but it was thanks to the folks here. The Tableau community gave me the skills and knowledge to go and take on that contest. I stepped away after that contest. I thought, well, this is it. My career is set. I've got a long, happy life building dashboards. Yeah, how wrong was I? The next year ChatGPT was out. Huge buzz in the market about that, but it hasn't stopped. And I feel even now, like every week, every day, there's something new coming out. And while I see a lot of change happening there, I still come back to this group here. They're the people that give me the grounding to say, what skills do I need? What things are actually going to shift the dials? What my clients need to know about this stuff. These are the people that help me stay on top of this.

Oh, wow. So a lot has changed. Also not a lot has changed. That's cool. You also built the Tableau MCP on LangChain. You were the first one to do that. What drove you to do that? Did you do that?

Yeah, so I was part of this project. I think it was a lot of curiosity of like, well, what would happen if you took the Tableau environment and just connected it up to AI? And I want to give a shout out to Joe Constantino on the Tableau side. Yeah, Joe put together the initial initiative to bring together Tableau developers and community developers working on this project. It's really exciting. A lot of like testing things out. And we landed on Chat with a Data Source, which is just, again, it feeds into curiosity. We always want to know a bit more of a data source. And so having another way to do that with a chatbot is really helpful. And now this has become a staple of the Tableau MCP server.

It has. Thank you for doing that for us. And it's so powerful to watch watch that in action too. We are all really excited about AI, agent tech analytics, and all things Tableau. What are you most excited about?

Yeah, I've got a lot of excitement for the future, but one thing that's been burning away from me is vibe coding with Tableau.

Oh, yes.

Now I love creating charts in Tableau. I find a lot of joy in that. But one thing I don't love is I don't love recreating charts in Tableau.

Fair.

If you've ever had to migrate from a different tool to Tableau, there's no fun in that. There's no creativity, there's no joy. So I feel that's a really good opportunity for an AI to come and help accelerate that process.

Yes, that sounds fun.

Speaking of that, can I show you a few things?

Oh my gosh, yes. Do we want to see that? Yes, please take it away, Will.

Hi folks, I'm Will. I work as a consultant at the Automation App. I lead on AI implementations and enablements for our clients. Recently, I stepped into a new role, and it wasn't what I was expecting it to be. It's been long hours. I've had people screaming at me day and night, and you should see some of the crap I have to deal with. That's right. I became a parent. Oh boy.

Oh boy.

I find myself forever on the go nowadays. There's never enough time in my day. And so anything that makes me a bit more productive is a big win for me. I've come on at Bolt Bikes. I'm here as an analyst helping build out their retail inventory dashboard in Tableau Desktop. There we go. There's my viz. It's coming together. I'm fairly pleased about this. I've got this together. And I've had one more request come in from the CEO, no less. They came over to me. They're on the desk. And they were really excited. And they started drawing out this viz that they wanted me to build. I was like, well, it kind of looked— yeah, it was looking like this. I think, oh, that's a double chord chart right there. There's got to be some calcs. The trigonometry alone is making my head hurt. And I think, well, I love their enthusiasm. I don't want that to go away. But I don't think I have enough time for this. But I know someone who does— my mate Claude. So here I am in Claude. I'm going to go and pass over this sketch over to Claude. And I'm going to ask it now, can you go and make this viz come to life? So what will happen here is we'll start building out a Tableau viz extension with Vibe Coding. What this will do— so Tableau viz extension, really good for these more complicated vizs that you just want to see how it looks. So this is really fun, a way of actually just getting an idea of what that prototype's going to look like without having to do the heavy lifting. And then, yeah, I come back later and here we go. We've got a viz. I've been dragging and dropping this in Tableau. So the AI here has done a lot of the heavy lifting for me and I've now got that viz ready for us. Okay, because I've saved so much time building this viz out, let's go and get this published. You know, let's go and see what the stakeholders say about this viz. So to do that, I'm going to jump back into Claude and I'm actually going to ask it, hey, can you go and get this embedded on our Tableau portal for our retail partners? So Claude's going to work away. It's not just, it's going to go and write the embed code for us and also go and get this published on our Tableau site for me as well. So this is a real nice competitive advantage for our retail partners. Now they have the data right where they need it, right on the shop floor, and they can go and interact with this data answering the questions where they use their data, where they have their analytics.

Cool.

Now we're not just at Bolt Bikes, we're not just shipping charts here, we're not just shipping bikes either. We're also shipping the Tableau MCP server too. So I have a follow-up question from a distributor and they can use the Tableau MCP server to answer that question in natural language for them. So here I am, I've connected up to Claude again. What's going to happen here is we're going to connect up to Tableau MCP. It's going to find the relevant content for this and then it's going to start querying that data source to answer that user's query. This is really powerful for me and the clients I work with. We've seen so much of this being a major like win-win in these cases 'cause this is saving me a lot of time. This would be a request that comes into my inbox, can now be handled by this, gives me a lot of time and value back back in my day, but also for our stakeholders, they get immediate answers to their questions. So here we can see a full product breakdown here. Okay, so this has been conversational analytics, but the really nice thing about MCP is it's here in Claude, it can be there in, in Tableau with you as well, but it doesn't just have to be tethered to a desk at all. Hello?

Hey there, mate.

I'm your BI analyst here at Bolt Bikes, ready to help you shred through some data.

What can I dig into for you today?

How are my top-selling e-bikes at the moment?

Let me pull that for you properly.

Your top seller right now is commuter e-bikes with about 19,800 units shipped in the last 30 days, followed by lifestyle at roughly 10,500 units. Catch you on the next wave.

Thanks, dude. Uh, could you break that down by region for me?

West is absolutely crushing it with over 10,500 commuter units, while East and Central are pulling in solid numbers too. Commuter bikes are dominating every region, but West's also got strong lifestyle sales at nearly 6,000 units.

Catch you on the next wave!

Thanks, bro, take care.

Catch you on the next wave. Okay, there you go.

The data you want at hands-free, on the go. So whether I'm in the office, on the road, or back at home with the kids, I can have the access, the insights, and data I need. So this has been really powerful, what we've seen here today. I feel the real thing you've got here is choice. Previously you saw a lot of decisions that you had to go and make manually. Now you have an automated alternative that you can run at scale here. We showed you embedding analytics. That's where we took basically a sketch. I have a notepad full of ideas that I now can go and pass over to an AI to see what it's going to look like and get that into production within minutes. Next, we went back to Tableau NCP where you can have that conversational analytics. With your AI assistant or on the go. And I'm really pleased to say that all of the stuff I've shown you is available now and wherever you are on server, cloud, or Next for Tableau. Now, if you've enjoyed some of this, I want to give a big shout out to devs on stage. We've got our hosts here, Sophia and Lauren here. I have had a little preview of what's coming up and honestly it's really amazing, cool stuff. I'm super excited for that. So do not miss that session. So this has been Powering Decisions. What we're going to do next is go and move over to MK to show you Identifying Actions. See you all, data fam.

Thank you, Ralph.

Wasn't that amazing?

All right.

I'm MK. I'm the president and CTO for engineering and all engineering in Salesforce. Great to be here to meet you all. Now, one thing that both Michael Andrew and Will was talking about is how long they've been in the analytics sort of business. And we know the analytics business itself has been changing from the spreadsheet era, the data warehouse era, the dashboard era. Now to the agentic era. But one thing has been constant through all of this. That has been Tableau. We've been with you in this journey as the analytical world changed to make sure we are there for you to guide you through those changes. And this is why over 97% of Fortune 100 companies trust and use Tableau. Tableau. But it's not just the big companies. Everyone from the startup, SMBs, enterprise, everyone works and uses Tableau. Now, in order to show some of this and how these agentic actions are going to come live, let's actually switch to the demo. John, are you ready?

All right.

Now, first, this is Tableau Next, which you're seeing there. First, we saw Michael Andrew showcase a a lot of the sales demand forecast and so on. Let's do a quick inclusion of that. We're going to pull in all the sales forecast in there. Let's upload the file right there and you're going to quickly see the sales come up pretty fast. DC26, there you go. That's the sales demand right there. And voilà, in a minute you have a dashboard that you're going to see. Now, interesting thing. What Chad also mentioned was often the sales figures as just sales. What about the actual warehouse inventory? And now with the power of Tableau, you can bring together your sales and your warehouse. So it's no longer just your sales forecast that is showing up, but your warehouse demand as well. So now you have one operational plane on which you can actually see everything happening from your inventory to your sales forecast and everything in between. That is your operational system. But that's not all. Often sales is a lagging indicator. When somebody comes to your shop and doesn't find that Root or your toy, you're already kind of done, right? They're going to go to a competitor. But most often all these demand forecasts are stuck in unstructured documents. It could be like this analyst report or other kind of things. And these were always separate from analytics. Not anymore. Because today, with the power of our agent platform in Tableau, you can simply upload these documents or even create a whole pipeline of all of these unstructured documents. And with that, you'll be able to quickly see, as you can see here, he's gonna upload the document. And now we have one unified lens on which you have your structured data, your unstructured data, and all of them integrated. And that's how you see that, in fact, that AI is recommending and actually telling you root cost analysis right there on what happened. You got a lot of demand on your West Coast like we saw San Diego, but you know what? Inventory is sitting someplace else. And you can see in that fancy graph there as well. But because this is built on all of those semantic and powerful models, we just don't have to see the visuals. We can actually open up the Tableau agent right there and actually ask the questions. John's gonna type now and actually ask the question as to, you know what, show me the forecast, what is going on? And this agent is now looking at not just that sales data, not just that inventory data, but also all that unstructured data to make sure this demand forecast is actually right. And so as you can see here, it gave you actually a beautiful dashboard as well, right there embedded. And of course you can do it in all the things like Will showed in chat, in, uh, Claude or in like, uh, ChatGPT, wherever you want. Here it's embedded right there for you in that dashboard. But now this is interesting. Now I see that there is a problem. Tableau is no longer just a passive dashboard anymore. With agents, you can now connect them to all the actions in your enterprise. So I can actually say, please move that 500 units from, you know, Dallas-Fort Worth to the West Coast right there. So you're moving from a passive dashboard to an active system right there in one pane of glass. And you could say, okay, you know what, I have hundreds of products. Disney probably has thousands of products. You can't keep like checking, oh, which product, which inventory, what's happening all over. Can we automate it? Yes, we can. Now you can actually use the same Tableau agents with all the actions between, with our agent force. You can now simply say, go to auto mode. It's gonna automatically make all of these actions happen. And if you go, you can see that all those notifications are popping up right now. Right there where the agent is acting on your behalf and doing it automatically. And soon, if you can sort of fast forward a little bit, uh, John, if you see what happens, as you can see here, now you see this beautiful network. It's all balanced, all green because the agents are working on your behalf and making this move. Remember, these agents weren't just working on some raw data. We had actually created all the semantic models. You have created all those semantic models and made sure the agent had the right accurate data. It's not just some white-coded dashboard that's just thinking some random data. This is real accurate data. And also you see this beautiful Sankey diagram below that is showing how the distribution is working. 'Cause these are all new charts and dashboards that we have now added to Tableau Next. All right, let's switch to the slide. What did you see here? You saw a bunch of things. Let's switch to the slide. Slide deck. What you saw is first we were able to bring in data, your sales data, your inventory data, exactly like what Chad said, which was a big gap. Now you can bring all that together, live data. You can also bring your unstructured data. So it's not just your structured data anymore. So you can actually validate all of your, uh, theories. And then we were able to create that unified model on top of it. And with that unified model and lens, you were able to convert what was a black box logistics and shipping, all siloed into one unified operational platform. And more than that, we were able to then run agents to go analyze automatically, create these autonomous agents that can work on your behalf. And finally, we wanted to make sure it's humans and AI working together. So we had all those awesome Saki diagram, other chats to make sure the AI is actually is actually working right. This is the power of agentic analytics. It's not just some dashboard on the side, not just some conversational thing, but everything coming together into that unified plane. So you can move from just passive visuals to active analytics on the same control plane. All right, with that, Sadaat, do you think we can go even bigger?

There's one more thing. All right, let's see what that is. All right, all right. Alright, one more thing and then we'll bring it home. So, I do wanna show you again one more small thing we have been cooking up. But before I do that, I wanna do a quick recap. So at the beginning you saw Michael Andrew demonstrate conversational analytics on Server and Cloud and Next. Yes, not just Next, it's in Server and Cloud. And that's not just an AI tool, It's actually grounded in a full knowledge graph. You also saw that, and I saw many of you taking pictures being like, what's that, what's that, when do I get to play with that? Very soon. Actually, it's coming in July to cloud and then server a little bit in the fall, but yes, you'll be able to get access to all of that. Now what else did you see? Not just AI features. You saw a bunch of other things. This is a very short list. I said, can you put the full list on the screen? They're like, it's a keynote, Sutherd. Well, come on, put all of it on there. Well, we need a Tableau Viz for that. That was earlier in the presentation. This is just a short glimpse. Everything we're doing, cloud, ServerNext, desktop, and even public. You see the Help Agent went to public. I don't know if anyone's seen that yet already, but we're bringing also Tableau Agent stuff to public as well. So every single one of these areas is getting invested, meeting your customers, your partners, yourself, where you are. And that's our goal. Now you heard Mark talk about automation app automation app. Our knowledge engine, our decision engine. I mentioned at the beginning of the keynote, all of us are here to help create knowledge, turn that knowledge into insight, and turn that insight into an action via decision. That is what we're all really, really good at. And that's why there's millions of you leveraging Tableau to do that. And our job is to give you the tools to help you do your job faster, better, and with even more passion. So that's hopefully what we've been able to impart on you today. Um, but there's one thing. How do you control all those agents? And we just saw a bunch of agents in the last 20 minutes. How do you control 'em all? So we have this idea, it's called an Agentic Analytics Command Center. So we're gonna show you a demo and I'm gonna be really clear, this is a vision demo. None of this is baked, all right? Literally created in the last couple weeks. It's our idea, it's our vision. We want your feedback. Share it, tell us how you think. Slack me, Slack Mark, actually Slack the engineering team because they're the ones that actually do all the work. So huge shout out to Suresh and his team, everybody else, all the work they do. Um, they're, they should be getting all the credit, right? So let's jump into the demo. So imagine this, you have a lot of agents working on your behalf. When you log in, what's your homepage? You actually get a homepage that looks like this. It tells you how your agents are performing. It gives you some to-dos. I'll get back to that. It tells me where work's being done, both in production and development. Often we got a lot of things kind of in that development stage that, you know, that incubation stage, other things that are actually being utilized. And I see my health monitor. Let's open up that monitor. Let's take a look at that. So what I see here is a view of all of my agents, but not just a conversational agent. Maybe they are metric agents, ones alerting agents, maybe they're data pipeline agents, all of them. I get at, and what we're giving you is a view on how to establish performance of them. Efficacy, that means is it doing the job it was intended to do? Thumbs up, thumbs down, any of those type of things. Adoption and trust, are people actually believing what they saying? Are they having not great conversations? Are they coming back after they use it? And lastly, perhaps what's important, data integrity. If the data underneath is not right, then no agent can be trusted. So this is the view. We're monitoring all of those agents on everything. In fact, if you scroll down, you can see on red and yellow, I don't know, my agents in production. I can see the sessions, how many people are logging in, the health of all of them. All of that is here. It's a nice, beautiful one-point view. It's a lot for me to digest. Even me, even Stitch can't digest all of that in one second. But what if you could just tell me what things I should be taking action on? 'Cause right at the beginning, I have these little to-dos. So this is the system looking behind the scenes and finding out, hey, are there agents that people are using, some people really engage with, and not enough people are using? Are there agents that aren't performing well? And then it recommends actions for me to take. My first one is, here at Bolt Bikes, we want to make sure that our carbon footprint is as low as you can possibly make it, so we have a metric on it. A couple people are using it a lot, but not everyone in the company knows about it. How do I get out the knowledge? So I click on that. It gives me a suggestion. It says, hey, you should get these other groups to sustain— to subscribe to this metric. I can accept the suggestion, and off it goes. It sends a notification via Slack. And yeah, it'll, it'll work in Teams too, Will. So yeah, but if you want Slack, it's much better. And it'll send out the notification. I don't have to do anything. My work is done. And I've just increased the performance of the agent because I got more people to engage. My next one is it says you have an agent in in testing, it's in my production, it's not quite in production yet. It tells me my adoption trust is good in a UAT, data integrity is good, but efficacy is not great. My thumbs down rate is 15%. That's not good enough to send to production. I need to make it better. So I can see it against efficacy, trust, integrations. But actually let's dive into lineage. Let's take a look at what actually makes up this agent. So what, how do you, what, what's inside of an agent? Everybody talks about agent. What's inside of an agent? This is a view of everything in my agent. At the top I see where the agent's being used, Slack, Salesforce, Google Workspace, Claude. Then in the middle I see the agent and I see where it's being grounded. Those are the semantic models, those are the context. And at the bottom I actually see the data that it's pulling from. This is one view of how my agent's working and it highlights the area where I have some challenges. So my customer success agent, which is allowing someone to ask questions about customer success, is struggling in some areas, is struggling. And it tells me across this knowledge graph, I got all the information I'm doing, what do I need help with? So let's jump down and show what's red, which is down below here, my customer satisfaction topics. This is a semantic model on customer satisfaction. If I drill in, it gives me some suggestions. Now it's gonna come up and tell me, hey, people are confusing, or the agent is confusing what to use when you ask for customer health, should it use NPS or CSAT? Good question. We should tell it, otherwise it's gonna get inaccurate answers. So it actually recommends something, and I can actually tell it, hey, this is what you should be using in that situation. This is a perfect example of when an agent will give different answers, but you know how it should be answering. Not only can I accept that clarification, I says, use, use that MPS and label them both so that they know which one am I looking at. I also say, make sure that you're using that exact same thing across different regions. Don't try to mix them. So I'm gonna ask it a question. So not only can it does me suggestions, I can actually ask it question, and I can ask that question, ask it to react to that, and it will give me the answers I want. All right, now let's X outta here, and I wanna go down and show you one last thing, and that is that it's not just this agent that was confusing or getting confused between CSAT and NPS. I have an issue on my pipeline. I have pulled data from my customer health data source, and it tells me I've only refreshed this once a week. That's no good. I didn't know that. but it tells me that, it says, "Hey, do you wanna do it every day?" So yeah, in fact, I might wanna come back and actually do it in real time, but this is something on behind the scenes letting me know the issues with how I make my agent perform better. So that's what we call a command center. So again, this is just our thoughts. We'd love to get your feedback on it. Slack me, send others that. It's how all of us can make these agents perform better in the same way that we wanna make our dashboards perform better, our business perform better, the same concept. So we can go back. And what I would hope all of you do, go and explore all the sessions, learn about how you can use AI to accelerate our vision to help people see, understand, and act on data. Learn how you can build new vizzes, learn how you can leverage desktop in all the new ways. Most of all, get to know everybody here, share your experiences, make this event really what makes it so great. With that, I'm gonna turn it back over to Mark. [FOREIGN LANGUAGE] All right.

How was show? Pretty cool? Awesome.

Okay.

So I was told to go fast because we're running late. But— Okay. We have an amazing TC planned for you. We have devs on stage tomorrow at 9:00 AM. Who's excited for that? Tomorrow we're bookending the day with Iron Viz at 4:00 PM. Who's excited for that? And who could possibly be excited for date night out with Dylan Francis? All right, we have an amazing, amazing week here. I will end it the same way I ended it before, but a huge thank you. This is going to be a vantage-tastic week, and I only have one more thing. We'll see you all here next year in San Diego for TC27. Thank you very much.