Powering Decisions, Driving Actions | Tableau Keynote, Dreamforce 2025

Before every breakthrough, there's a question, a challenge, a moment that demands clarity. Because progress doesn't just happen. It's discovered in the patterns in the connections and the data. Every question holds a story, a story waiting to be told. A story that always has a better narrative when it's told through data. Because data isn't just numbers and charts. It's insight. It's truth. It's the engine of progress. And now we're at the edge of something new, something revolutionary. An era where data and AI work side by side to imagine new possibilities, to see what couldn't be seen, to do what couldn't be done. This isn't about technology alone. It's about unlocking potential. Your potential. The potential to see clearly and move forward faster than ever before. The ability to create personalized experiences at scale. The power to lead with confidence in every moment, to create a world where every story is heard and every person is empowered, turning information into action and action into impact. This is your moment, your opportunity to make a mark. The future is not a distant point on the horizon. It's something you shape right here, right now. So what will you do with it? What story will you tell? Data doesn't just reveal the world. It helps you change it.

Please welcome Chief Revenue Officer Tableau Terry Hatfield.

Hello, everyone.

I am so excited to have all of you here, and I'm so excited for us all to be in this room. Whether you are a trailblazer, which we all are, or you're part of the Data fam, let me hear it.

All right.

If you weren't part of the Data fam, you are now. You are in this room with us to talk about what is so important, which is how do we power our decisions? How do we drive actions? That video was inspiring to me, but also a little bit of a wake up call because we think about all of the challenges that we have to get there. This isn't something that's the future. Even from last year, we are literally in the present. All of this is available and accelerating around us, and we need to figure out what to do with it. I don't know about you, but it's somewhere I want to go, together with all of us in this room and beyond. Let's talk about some of the challenges first. Then we're going to talk about how Tableau solves some of those, which is hugely important to think about. Number one, Data. I've been with Tableau for 13 years, and data has changed tremendously. So in 2010, there was 2 zettabytes of data. In 2035, there'll be over 2000. So if you don't know what that means, outside of the fact that it's a two with 21 zeros, basically it's every LLM, every email, every slack message coming at you all at the exact same time. When we think about data, it isn't just about the amount of it. We're actually figuring that out. We've got a lot of tools to do that, but what we really have to figure out is how to get signals out of the data. How do we get the answers that we need to move forward? Now, moving on to the next challenge, we've got AI. You'd probably say, okay, Terry, AI is amazing. Why is it a challenge? It's not necessarily a challenge. It's something that we need to work through. AI is here to bring us speed. AI is here to bring us velocity. But at the same point, what we have to remember and what we need is to be able to provide context. Whether you're an IT in this room or you're an executive or you're an analyst, context is one of the most important things. Utilizing AI to get IT to do what we need to make the decisions that we need. And if you've never gotten to a fight with an LLM, I suggest you try it, because it's interesting what you can get out of it. So you think about it. We got to have that context. But almost as important as the ability to provide context to the data that we're working with is trust. When we think about trust, when we are talking and communicating with data, we need to make sure the data is the right data that we need to make the decisions that we're about to make. We think about the moment in the place that we're in right now.

And.

It'S in a place where we've got a ton of dashboards, a ton of information, a million ways to answer a question, insights. But insights alone aren't actionable. You have to be able to take it to the next level. You have to be able to ask that next question. And how amazing would it be to not have to ask somebody else the question? You yourself can ask it. We think about business right now, and what we have is a decision gap. We've got data all over the place, tons of it. Every answer you could possibly have there. You've got insights, dashboards, thousands of them. Reports, thousands of them. Everybody wants to give you a different data point, but are you in the same room as the people that are making the same decisions. Can you connect with them? Are you able to get all of that information at once when you need to have action or is it delayed? That's the decision gap that we have. That's where we need to go from having data to having action and to being able to take that action. So why Tableau? Why with us? Why Salesforce? We've been doing data for 20 plus years. Over 20 years. The mission of Tableau has always been to help people see and understand data. That's not going to change. When we look at information, when we look at dashboards, we're still going to have that. We still want clarity. We still want to be able to see things and visualize them and make sure they tell us what we need to. But that next step right now is we have to be able to acting on that information is what we are doing Together Next. When you think about Tableau Next, it was a vision last time we were here together and now it is a reality. Tableau Next gives you trust in your data. Tableau Next allows you to work with context. When you put that together with a 247 trusted advisor, action happens. We're able to make decisions, we're able to move faster. When you see that video and you think about reality that is now possible. Tableau Next is built for every application, workday, netsuite, SAP doesn't matter what you're working with. The goal of Tableau has always been to be able to work with applications in anywhere. It's built for any use case. If you're in the public sector, if you're thinking about case volumes and you're in campaigns and marketing or you're looking at revenue or you're working at any company, any use case that you have can be done. You've got reports and information that do it and it doesn't matter what industry you're in. I talk to people in healthcare all of the time trying to make a difference in everybody in the world, all their patients, all the people that they're working with. In retail, where they're trying to change the business and it's dynamic in financial services, it doesn't matter. All of us have the same issue and we all need to go in the same direction. So when you think about what it was before, picture being in a meeting where you know there's a dashboard for that. I'm pretty sure we have that information. Well, we need to have a plan for sales. Let's go do that. We need to have a plan for marketing.

Okay.

You do that. Let's come back, and then once we get all the information together, we can decide what to do, what's next. That's the world we live in right now, which is great. There's a lot of insights. But imagine being in that same meeting and in that meeting making a decision saying, you know what? We see some risks in our revenue. We think it's these top 10 pieces of business. What are the risks? What's actually happening in those opportunities that we're working with? Oh, these are the risks. Great. You see it in the flow of work. Then you say, you know what? I'm going to take an agent and I'm going to send a note to all of the people who are working on that.

Right.

While I'm sitting there with some suggestions on things that we can consider. And at the same time, from a marketing perspective, hey, you know what? We want to boost some of our opportunities with Tableau.

Next.

Great.

Let's have a marketing campaign. All in the flow of work, back and forth, the information, decisions made. You leave the meeting, you're great. That's what we all want to have happen. So now let's talk about how that can be done. I'm about to introduce a man with some amazing shoes on right now, our chief product officer, Suther Jones, to tell you how it works. Come on up Southern.

Thank you.

Thank you all. Thank you, Terry. And Terry's right. One thing really resonated with me, and she said, insights alone are not enough. And every single person in this room has been had that situation that's not very comfortable situation. You're looking at data kind of like that before picture, you see some insights, but you're hesitating. You're not sure if you can take action. You can't take decisive action. That's the decision gap that Terry just talked about. Well, good news. Here at Tableau, we're on a mission to close that decision gap. And in the very near future, every single one of us will have that 247 trusted advisor. And I get to unveil all the product capabilities that are going to make that future a reality. All right, so let's start. So is it one product that does it all? No, it's a rethinking of the way that we do analytics, accelerating a path from data to semantics to visualization to actions. And yes, those are the four layers of Tableau. Next, many of you may be looking at this going, hey, Southard, Yeah, I got all that. We do some of that today. I'd say Great, that's smart of you. That's a best practice approach to doing analytics in your organization. But what I'm going to share with you in the next five minutes is the unique things that tableau next does that you're probably not doing today. That's actually going to help you close that decision gap. So it all starts with agents. We developed an agent for every single person in your organization built specifically for the job they're trying to get done. So if you are a business user like Terry talked about, and you just want to ask a business question and you're in Slack or you're in Workday or you're somewhere else, you should just ask that question and then you get a natural language response and a visualization and a recommended action. That's an agent we built for all the business users. And what about the analyst? Probably the most important job in your company, the analyst. What about them? They need agents too. We built that for them as well. Agents that help them manage semantic models, build semantic models, manage data models, build visualizations, build dashboards, accelerating the work of the most important person in your company, that analyst. Now, those agents, they're great. They're excited. We're all very excited about it. You couldn't step anywhere at this event without hearing about agents. But they feast on one thing and they die without it. And that's data. That's where the open data layer comes in. All of you have some level of data layers in your organization. Many of you have started on a journey to bring those all together into some sort of new modern warehouse. That's great. I commend you for that. But all of you also have an uneasiness, just like I described at the beginning, that something's missing. And I commend you on that because you're right. Something is missing. Let me give you an example. You're currently analyzing your revenue performance. You're trying to figure out what is a driver of your revenue performance. Really important. So you got your CRM data, your ERP data, your general ledger data, maybe your quoting data. And you do all this analysis and you get a dashboard and you're ready to make some decisions, but you're still not feeling great. That's right. Your instincts are right. You should trust them. Because a lot's missing. Like 80% of the data is missing from that analysis. What's missing? What about all your contracts and PDFs? What about all your call logs and voice transcripts? What about all your emails? What about your training records? That's all in unstructured data. And that wasn't in your analysis. So unfortunately, your analysis is incomplete. So what you need is what comes with this tableau. Next, semantic layer is the ability to bring structured data together with unstructured data without moving it. That's right. You do not have to move the data. You can have maybe your unstructured data in GCP or structured data in Snowflake, some of it in Data360. You can bring it together without moving it. You do not need to move it. That's unique. That all comes together and gives it meaning in semantics. You heard a lot about context in this keynote. Lots of people talking about context. Why do you need context to make agents sing. The semantic layer gives your data context. Once again you say, hey, I've done semantics. They've been around forever. We have rethought semantics. We've rebuilt it from scratch with AI infused in it. AI can help you build your semantic layer. It can help you manage your semantic layer, giving you the ability to move much faster. But we did one other thing. Semantic layers give you one single source of truth. We all love it. It's the business metrics you all speak. We all love that. But wait a minute. What about the agility that the analyst demands? What about the what if scenarios? What about the ad hoc experiences? How do I do that? We did that. Every analyst will get their own personal sandbox. They can connect it to the govern semantic layer, run experiments, figure out what's right or wrong, see if an insight is useful or not. If it's good, they can promote it. If it's not useful, they don't have to do it. That's agility. With governance, that's not what you're getting most. Modern and current semantic layer, you're going to get that with tableau. Next, all that actually comes to life with visualizations. So tableau, leader in visualizations, we're going to double down on it. We're going to continue to enhance our visualization library, add more visual thinking capabilities, so that clarity of a decision comes to life. When I see that visual, what makes it different? As Terry talked about this, it goes in the flow of work and it has the context of it. Every visualization we build now has a wrapper on it so you can place it inside of the application where you're working, workday, Koopa, Slack, and it understands what you're talking about at that moment in time. It can deliver a visual for you. So if everyone here is a leader or team manager, if you're in Workday or PeopleSoft, you go to your manager page. Do you see an insight that tells you which top performers have low compa ratios? You probably don't, but you should. Tableau Next is going to give you that. And yes, for all of you that work in teams, I really apologize. I'm sorry. Too bad, it will be in teams too. That's coming very soon. Last part is Actions. BI usually stops at the visualization layer. No, we have a full blown workflow engine built into Tableau Next. That's both pro code and no code. You want to write a bunch of code, write APIs, great, we got you. You want to have a drag and drop flowchart builder, great, we got you. We have pre built Actions in every single one of the applications you work in. Maybe you want to close a service request in Salesforce, maybe you want to change in order status in SAP. Maybe you want to promote somebody in Workday. Those are buttons you can click and take action right from an insight. That's also something unique you're not doing now. All right, I could ramble on forever about all the product capabilities. They only gave me eight minutes, which I'm going to go over. But here are 100 plus features that either delivered this year are going to come really soon. Data Fam, where are you? All right, so we got your back. We got rounded corners, we got custom color palettes, we got ShowMe 2.0. We go tableau Agent on server. Yes, Server is still here. And we got this thing called interoperability. Interoperability. It's a big word. I can barely say it. Stand on here on the keynote. What is interoperability about? Well, every single one of you has a product from us. Most of you do. Tableau Server, Tableau Cloud, CRM Analytics. You're like, tableau Next sounds great. Do I have to migrate? No. Migration is like a four letter word. Not exactly close enough. No. Tableau Next plugs into your existing product so that conversational analytics experience that you want, that I just talked about inside of Slack, that plugs into your existing tableau deployment or your existing CRM analytics deployment, you can leverage your existing investments. Now, we didn't think about interoperability just from a Salesforce analytics perspective. We also thought about it from the fact that maybe you're adopting semantics from one of the new modern semantic layers like Snowflake's Cortex or dbt. Well, just two weeks ago, we announced the Open Semantic Interchange. That means that if you have semantics in Tableau and semantics in your Cortex, you Don't need to copy and manage them separately. They talk to each other. One multiple vendors, one semantic layer across your organization. So your agents see one semantic layer, whether or not it's three or four different vendors, semantic layer. They all talk to each other. It's open standards. We're agreed to with Snowflake and DBT and a couple other leaders in the industry. This is where the industry's going. If the person you're working with is not doing that, I suggest you think about it. All right, so last thing. Announcement. I'm most excited to announce. I've been talking about insights and the flow of work. What if insights came to everybody whether or not they're working or not? What about your kids, your grandparents? Where do they go to get insights? They go to ChatGPT and they go to Claude. What if they could connect to Data right from ChatGPT, right from Claude, upload a spreadsheet or connect to data and start having a conversation with it right there in ChatGPT or right there in Claude? Well, that's. We're announcing today, coming very soon to you. You go to any of those environments, you can connect to data and start having a conversation with your data right there. Now, if that isn't seeing and understanding data and taking action for everyone, not just people who are working, but I don't know what it is. So super excited about that. All right, So I can talk about this forever. I'm over my time. Yeah, I'm pretty fired up that, too. But what better way to actually understand how we're doing this than seeing a customer who's actually using a lot of these features in action? So, indeed is a longtime tableau customer. And we're going to have a little film here that shows how they're using Tableau to close that decision gap.

Roll.

The film indeed has tons of data, but if you don't have it connected, you can't see what it's trying to tell you.

How can we run the business better? How can we experiment better? How can we leverage the insights that we're getting directly from this data to make better decisions? There's latency that is often unacceptable. You can't test opinions, you can test data.

And now with a digital workforce of agents, the game is complet changed.

We chose Salesforce because it is one unified platform for how we run the business. The most powerful aspect of Tableau is it's limitless. We're building a foundational understanding of our metrics that can then be used by.

Tableau dashboards people inside of Indeed can make good decisions about how to proceed with campaign decisions, sales and sales motion decisions. The other benefit is the ability for us to bring together all of the Data, Data Cloud, AgentForce, Marketing Cloud, Sales cloud, service cloud, all connected to Tableau so that the experiences that we provide are fueled by data are personalized to each of our employers and our job seekers.

Tableau Next allows us to shift where we're spending our time in meetings in the future. You're having the pre meeting in Slack where we are delivered insights so that when we jump into a meeting, we're all empowered and we've got shared trusted context. Having one platform that is trusted, it allows us to make faster decisions for our business.

So in the future, building agents connected to Tableau provides us the ability to bring together all of the data across different parts of Indeed to fuel these agentic experiences.

Because we are data driven. The center point is Tableau. The future is going to be an agent making, making sure that we're shaped in the right way to deliver the best outcomes for our customers.

All right, It's a great video and welcome to the stage. The star of that video, CIO and CSO of Indeed, Anthony Moises. Anthony, thank you. Thanks for joining us.

Thanks for having me. Not sure I'm a star just yet, but thank you.

Oh, that's pretty good.

That's pretty good.

I'd give you the nomination at least.

All right, sounds good.

So you've been a long time tableau customer. You guys have a really big and complex business. Maybe you can share with the audience a little bit of how you're using Tableau today to help Indeed achieve its mission.

Yeah, absolutely. As I said in the video, I think one of the things that's most important about Indeed is the centering on the mission. We have a mission to help people get jobs. And that sounds super simple. And maybe the idea is simple, but the idea is very, very difficult. And one of the things that we do to leverage within the business is like how do we make decisions for job seekers and employers? How do we go from curiosity to clarity as fast as possible? And this is where Tableau comes in. It is really that sort of orchestration, that command center for me in the way that I think about the performance of business.

That's really cool. I love that. From curiosity, clarity. I think many people would love to have that. So last time we talked, I had shown you some of those features I just was yammering about five minutes ago and you got excited about it. So what we wanted to do is do something a little different. Thank you to our amazing demo team. John Denby, Mark Sanders, please give it up. We built a demo of what a day in the life could look like for indeed with some of the new capabilities. Obviously we scrubbed the data, so not going to show your live data, but let's roll the demo and give an example of what it might look like. So imagine that instead of you going into your executive meetings, looking at the dashboard and having a conversation, you woke up and you got your metrics right there in Slack where I think where you guys work and it tells you what's changed in your metrics, why it's changed and gave you a quick, you know, if you will, snapshot of what's happening even before even Daven starts. What do you think?

Yeah, it's pretty incredible. I think this is all live.

It's all live.

So fresh up to date.

Yes. In fact, it's not just a JPEG image, it's actually a real working object. In fact, if you click on one of those objects, you can actually see everyone else who's having a conversation about that in your entire company. So now it's not like separate conversations in different rooms. It's all one place having a conversation around analytics.

Yeah, the threaded conversation is pretty cool. That's pretty high context.

That's really cool.

That's really cool.

Yeah, we're pretty excited about it as well. Up there it says CSAT and it's going up. Congratulations. That's. The numbers aren't real, but I know you guys are much higher. I know it's going up, but you know, if you were looking at that, what would you, you know, what would you want to do?

I think, you know, the way that we start our executive team meetings often is we, we actually go to the second and the third question. So I'd like to take this and go to the second question.

Go for it.

What do you want to ask for an example? Let's, let's look at the last 90 days or 180 days for prospects. Let's look at the trend for just prospects.

All right, can we, can we ask that question what the trend is for prospect accounts for csat? All right. Can analyze weekly trends on CSAT for the past 180 days. So what's happening right now is a bit subtle. Looks simple. So you define prospect accounts a very specific way, unique to your business. In fact, the way you define CSAT is unique to that business. So we're going through that semantic layer. We're understanding the context of the conversation. You're just in the metric you just came from, issuing that query down to probably Snowflake, where a lot of your data is, and maybe across to data 360 as well, and then giving you a natural language response and a visual response that comes with that. So what do you think about that? Would that be something you guys want to use?

Yeah, that's pretty incredible. And it's pretty quick. Yeah, pretty quick.

It's not bad.

Yeah.

Engineers, hats off. Yeah.

Woo.

So you know we are on Tableau Cloud.

Yes, I heard.

No migration. That's pretty good. Yeah, that's pretty good. Are you sure?

Am I sure? You know, I'm sure we'll even show it to you. So you're right. So everybody who's a tableau customer, you have something called a published data source, a pds, you can connect directly to that from Tableau Next. And I'm not moving my data, I'm just connecting to it now. Connect to it. What it does is it creates a view into that semantic model, your pds, and represents that in the tableau semantics. There it is. There's the view of the published data source. And now I can start asking questions when I'm in the context of an existing tableau dashboard. So imagine I have a beautiful tableau dashboard, something like a beautiful Sankey diagram. That's my existing tableau cloud. And I can start asking questions through Tableau Next. Agent. Right there. Right next to it.

Fantastic. All right, I've got the curve ball. Uh oh, we're an Einstein. We're an Einstein analytics shop.

Einstein Analytics.

Wait, have you changed the name again?

Einstein Analytics. CRM Analytics. Yeah, we had a few of those customers out here. Yeah, we changed. No, the name is still CRM Analytics. Yeah, that's an amazing product, by the way. We did the exact same thing for that. So in CRM analytics or Einstein analytics, there's a thing called a data set, very similar to a published data source in Tableau. You connect that to data Cloud, you put your semantics with it, you can start having conversations with it along with your CRM analytics right there in the same place. So that's getting leveraged right now without migration.

No migration. I'm sold.

All right, so I just want to thank you for your partnership. You've been amazing to work with. Your team's been great to work with. We're really excited about turning what was a fake demo into a real life scenario. We've got a pilot going with you right now on Tableau Next. So I just want to thank you again for your time.

Yeah, thank you. Appreciate the partnership.

All right, thanks so much, Anthony. All right, so we saw some really great examples how a customer would use this. You heard about the products. Now you want to say, well, how does it really work? Can you sell it in action? Yes, we're going to show it in action. In fact, I'm not going to show it. Someone smarter than me is going to show it. Please welcome Murali Krishna Prasad, President CTO and owner of some amazing shoes. We're going to do a demo.

Those are the Tablo shoes right there. All right, thank you, Sadhur. Thank you, Anthony. You saw that awesome set of demo from Indeed. And I'm going to take some time to show you behind the scenes of how all this kind of comes together. So the first thing that we have done with Tableau Next is the ability to define predefined semantic models. And that's what we call as a C360 semantic model. So out of the box, we have already pre created all these semantic models for you. So that means you know exactly how the accounts rated, case and contact and everything else that comes with it and including all the industry models as well. So you have right out of the box. You don't have to spend money building all of these things. Now this is the reason why when Sadat said, you know, I can actually have these metrics and dashboards in the line of work, it just works. So if you are a salesperson, you can actually have the metrics right there in the, in your sales cloud under leads. I can actually see all of these things. If you're a marketing cloud person, the marketing religion dashboards is all powered by the semantic models And Tableau next right there for marketing, if you are an agent developer, all the agent analytics that you saw in the agent force keynotes and everything is all powered by the same dashboard and you can go extend it yourself. If you are a vertical industry like you are doing financial loans using our financial services, again, you have the dashboards right there in the line of work. Finally, if you're Carmen the data analyst, you also have this data analyst dashboards, right, all the analysts, sorry, dashboards right there in the line of work in service cloud as well. Now look at here something interesting. I'm Carmen the service analyst and I'm looking here, I can see the speed to answer call response times, FCR rates, all of that stuff. Those are awesome metrics to have. But these are still very reactive metrics, meaning I'M looking at post facto what's going on. What I would really love to have is being more proactive. I would love to know what's the average NPS score and all the customers with critical tickets and open pipeline and so much more. We saw that in the indie demo where we would love to see this so that you can be more proactive about what's going on with all your cases. So now with Tableau next, you literally can just ask the question and get the answer. But while it is working, I'm going to go behind the scenes from Samantha to really the data analyst Persona and how many data analysts out here, you guys are all sort of magicians right there.

Yes.

Now we're going to see behind the scenes of how Tableau can see are just able to answer that question for you. So let's go back to that C360 data model that we all have. But remember, one thing is missing. In this model we don't have the NPS survey, we don't have the product usage that's not coming out of the box. So I need to go add it. But which IT admin will allow you to go mess around with their production instance. Like ah, I'm gonna add some data models, semantic models. Which one will have you?

Nobody.

Right. This is why we built this thing called personal orgs. So now every one of you can go if allowed by your admin to say get me a personal Org. So you'll get all the metadata, including the semantic models and a zero copy of all the data that's available that you can access in that main org and you can personally, it's like your own personal sandbox. You can play around with it, bring any more data, create your own dashboards and then share it back with your admin if you like. So here I have this personal Org, which is why I can now go and say extend the semantic model. So I'm literally extending the main model from the main org into my own personal sandbox. So now I'm going to go to the data object and say let's bring in the product usage that's sitting in S3. Some of my NPS course here. I'm just going to go add here and I'm add it to the model. But one thing that you all know, in any semantic model creation, it can be a nightmare figuring out all the joins, relationships, the metrics and everything else. And this is where we bring in the agent capabilities. Right there you saw something slide right in the analytics agent. What it did was the moment I added it, it already figured out the right relationships. We call this the auto semantic creator model. This is able to give the relationships and the insights. They all look pretty good. I'm just going to say apply all just one click. I got my new semantics extended from the base model. Not just that, the other big problem with all these things is optimization. You want to make sure the joins are right, otherwise the queries are going to be slow. Right? But with AI, we go one step more. It's not just about doing the traditional thing we all know about. Oh, did you get the foreign key, primary key right? Did you get the join right? Et cetera. But AI also needs descriptions when that LLM looks at that query, remember we talked about in the indeed case, like CSAT scores, et cetera, the LLM needs to know, okay, this is what this particular model means or this particular attribute means. And so what this new semantic optimizer is able to do is also generate missing descriptions so that this model is not just perfect for running the query, but also perfect for your LLM. So I'm just going to go apply this and my semantic model is ready. Now you have to go test it out. For that, we've also built this testing center right out of the box. So now you can go and test all your semantic queries. Just make sure the queries are returning. Right. And you can also add questions or import questions. I've already created this question here. The average NPO score for all customers. Great number. Looks interesting. 62. What is it?

Right?

Not quite. Why? Look at this. This is where the data analyst Persona, you guys are going to be super important because you're looking at it and you say, what? When I asked this question about the average NPS customers for Open Pipeline, it gave me the answer for all opportunity stages. Now this is the most important thing. When you go to an analytics agent. This is not like your regular agents just answering something. It's okay if it hallucinates, not the end of the world. But if your analytics agent's giving you wrong result, you're going to get screwed in your business. So you've got to make sure the data is right, the answers are perfect. There is no error for hallucination here. So now I got to fix this because what the agent is thinking that it's going to go look at all the opportunity stages, but I'm going to have to teach it to say, no, no, no. Open means is only stage three through five. Now if you go read the literature all around, the most answer they'll say, spend millions of dollars, train an LLM, fine tune an LLM, and it might give you the right answers. You don't need to do any of that stuff now. So as an analyst, you can literally choose a reason why this is wrong and put in the description of how to fix it. In this case, I'm going to instruct to say open means opportunity stages three through five. And this will be recorded and stored with the semantic model as metadata so that when the question comes, we will create this as a prompt to the LLM so that it's able to generate the right SQL query to go answer the question. All right, with that, let's actually save this. You're done. It's saved. And now let's go back to Samantha, to Carmen, back. It's a service thing. And let's see what happens. You see the answer come back and it's 58. The right answer this time. Right. Okay. So this can also be done in Slack as we actually showed before. Right? So I can have all the conversations in Slack. And also another interesting thing that comes up in Slack, look at this. Suddenly something just popped up in Slack. What's happening here? Tableau next is our agent is also monitoring all your metrics. So it's not just creating the metrics, it's monitoring the metrics and it's saying, there's a new trend detected. It's up 88%. That sounds awesome. If it only was my sales. Unfortunately, it's my case handling time. That is not good. All right, so because this is Slack, I can literally go here and ask my agent the question of what's driving this sudden spike. And the agent's going to go analyze all the data that you had because you had created all those semantic models, the data analyst had done all the prep behind the scenes. It's coming back saying, you know what? The service rep, the human service rep availability in Southeast region seems to probably be the reason why you have all this problem. So now I can quickly then in Slack say, let's go create a swarm around it. I'm going to go work on this issue. I'm creating that swarm channel right there. I'm going to pull all the people involved, bring all the people to make sure we get that Southeast thing taken care of. Few days passed and things are in a much better shape. And what was going to be a crazy case explosion here, we were able to go fix it. And life's all beautiful again. So that is the power of Tableau Next, what you saw was A lot of things I want to quickly summarize. You saw the ability out of the box semantic models that was created for your entire 360. The ability to take a personal org so you get your own personal sandbox. The ability to then extend the model, but also use AI so that it automatically tells you all the relationships and insights. And the AI that optimizes so that you get the right description on it so the LLMs can work on it. A powerful testing center that makes sure all of this thing can be tested. And as data analysts, you can get fix the things so that you get the right data, not some hallucinated data or results. And finally, that proactive ability for the agent to go tell you what insights are actually going up and in all surfaces as well. That is the power of Tableau next. All right, now to end this, I want to bring Rekha to the stage to really talk about how all of you as community is actually delivering this impact. Rekha.

Thank you, mk, Anthony Sadud, our incredible demo team, for showing us what's possible with Tableau and agentforce. Okay, this is the fun part of the presentation and I get to do something really cool. Guess what? I'm going to tell you about our secret sauce. Are you ready? Our secret sauce is nothing but our data fam community. For over 20 years, they have been learning, they've been shaping, they've been growing and shaping the future of data and analytics together. Just look at the stats. It's a thriving community. We have 5 million plus Tableau Public users, 50,000 plus user group members, and over 400ambassadors and visionaries right with us. This is the power of datafam and this is how we roll and this is how we operate. Thank you, DataFam, for doing everything that you. Look, there's one thing that we know datafam does extremely well, which is you give them the right tools, they will wow you with creativity and real impact. Take our hackathon for example. We rolled this hackathon about a few months ago and we had over 4,000 plus participants, hundred plus projects. I can guarantee that our judges have had an extremely tough time because every single project was just so cool, so impactful, and we decided to judge them on creativity, design and impact. So, without further ado, let's get the drumroll going so we can announce the winners. The first one we have is the most creative. This is Team Bratwurst. They did something really fun. You know, they were able to look at the solar and EV forecasts and created a smart, energy, efficient Management tool. Think about real impact, Think about the creativity. So that is Team Bratwurst. So congratulations. Thank you for your incredible project. Okay, we are going to do three of this, so get your claps rolling pretty fast. So the next one we have is Most Impactful, and this goes to Agentic Analytics Readiness. They built a tool to actually monitor your AI tableau content, so it's actually ready for the Agentic era. So this is foundational. Everyone needs this tool, and it's right here. Thank you. Team Agent Tech Analytics Readiness. The next one we have is the best design. You know how chaotic the hospital checkout process is and how many times we've all been frustrated. Well, they decided to take their matter in their own hands, so they changed that. So they built a tool to make that seamless, to make that more efficient and actually build trust between the hospital management and the patient. So kudos to this team. They've done it really, really well. So we have one more grand prize winner. Okay, are we ready? This one goes to Tableau. Next question. This Rockstar team literally felt like an extension of our team because they took interoperability to a whole new level. They were able to pull up in slack both Tableau Next and Tableau Classic set of products. And you can ask a question and get your answer. That's interoperability in action. So shout out to Team Tableau. Next question. What you just saw today was candidly just a snapshot. Like I said, 100 plus projects, really creative, really impactful all along. So congratulations to the winners, but Also thank you, DataFam, for showing us constantly the art of the possible with tableau. So I get to do another fun thing in this presentation, which is bring up one of our incredible tableau ambassadors, Chris Williams. Hi, Chris. I've had the good fortune of learning to work with you over the last prep calls that we've had. I love your sweatshirt. I love everything that you've done. Thank you for joining us today. So quickly introduce yourself and tell me your reactions to those Hackathon submissions.

Well, thank you so much for having me, Rekha. This is wonderful. So I'm all being up here. Thank you for having me. It's a great representative. DataFam here. This hackathon is amazing. I've been doing this for 30 years. I've been. I'm dating myself right now. So we have. I started my career during the Y2K phase, so I was doing Cobol RPG, CL, and all those languages are relatively linear, manual effort, a lot of precision. Fast forward to today. And what an innovative world we're dealing with right now. So look at the hackathon that we have and the innovation there. It's been nothing short of outstanding. So now we've kind of leveled up. We've gotten to a point where we have manual processes before and then you have those give some basic statistics. Now we have AI driven cloud based analytics that are providing a lot of insights with speech need.

Right.

And that's exactly what we're looking for to do now. When, you know, talk about how I evolve and how people evolve, it's like you have to have to continue to upskill. You have to upskill, you have to reskill. I'm a firm believer of that. And this is something that you have to do in order to keep fresh in the industry. Learn those new tools, learn those new techniques, be able to go ahead and stay fresh and stay relevant within the data environment. And it's also been very interesting to see this in first person. You look at everything that's happening, you look at how analytics have become much more accessible and much more impactful. Anyone can go and engage with their data, create visualizations and then answer questions based off of that. Isn't that what it's about? Not just looking at the data, but also answering the questions about it. So it's been really excellent watching this first person.

I love that. And also, not to date you again, but you've been doing this even before Tableau was even founded, so amazing job. Thank you for everything that you've done in this journey. I also want to get your thoughts on Tableau next. What did you think about that? What's top of mind for you on that innovation?

So let's step back and talk about agentic analytics real quick. So agentic analytics is basically designed to help empower business users to operate with speed, you know, with automating routine processes and providing, you know, insights really quickly. So how does that affect us? This is my world at Concentrix. I do this every day. I eat, breathe and sleep it. So, you know, you have Tableau next. You have Tableau, you have Salesforce, you have Slack. All of these technologies brought into one spot to provide the platform and provide the area where you can get actionable solutions right then. And not just the insights to provide those solutions, but also to just actually go and do this, which is the whole genesis of what prescriptive analytics is about. Right? You're doing something based off of what you see. And so that's exceptionally important. You know, earlier in the presentations we talked about closing the gap between insights and action that has to happen. I mean, we have dashboards that need to be brought to life. And those actual dashboards will tell us, no matter what your Persona is, what to do next. And this is leap years from where I started. Yes, I know I'm old.

No, not with that post. I don't know if you all caught that post. That was pretty incredible. Last question for you. I know you've given back selflessly for the community and we are so grateful. And I know that datafram has played a big part of your career, professionally and personally, and there are a lot of folks getting started. So what would be your advice to all of them?

Well, I've. I don't know where I'd be without the Datafam, to be honest. I started off 15 years ago developing dashboards, really just focusing on the data and putting a dashboard on just numbers on the screen and having those say something. The Datafam really taught me how to bring those to life, tell a story. We're in a use case driven world and we have to have anything that we produce from a dashboard to actually tell a story so we can solve a problem. That's how we get adoption. My best advice is not just learn, but pay it forward. Take your experiences and share the wealth with others. I do that by being a tableau ambassador and a user group leader for the Portland Tableau User Group and Black and Tableau User Group. I'm a user group mentor. I do teach at local colleges and universities, try to bring students into the DataFam world. I also do a lot of mentoring. And you know, we do this also for creating a center of excellence within Concentrics to do this. So I always stay hungry, I always stay ready. If you stay ready, you don't have to get ready. And so this is one of the mantras that I live by. And you know, datafam has really given me the confidence and the platform to really learn from everybody. So no matter what I do, I always, I may be teaching, but I'm also learning from everybody who I talk to. And that's made me such a better professional and a better person.

I love that. Big round of applause for Chris. Thank you. Chris.

Thank you for joining.

Appreciate it. Thank you. So you heard, Chris, a lot of words of wisdom there. Thank you. We have a community on Slack, it's called the DataFam Slack community. We launched it back in April at Tableau Conference and we have over 15,000 members right there. So you have a question? You have your answer right there with the community. So please join us. Let's continue the conversation. And we can't wait to see what else you create with tableau and agentforce. And with that, back to you, Terry, to wrap it up.

All right. Okay, everyone, first off, I want to say a huge thank you to the tableau team, our tableau product team, our customers. Tony, thank you. Our marketing team and all of our data fam are getting us here today and we want you to experience more when you're here this week. So come join us in the campground. Come join us in the tableau lounge. Come to our upcoming tableau sessions. We want to see you there. We want to talk to you. We want to answer your questions. And if you want to find out more thinking about questions that you have around structured data, unstructured data plan for what you're doing in the future, getting you to where you are at in terms of data to action. Join us at tableau conference in San Diego May 5th through the 7th. We can't wait to see you there and we want you to have a great rest of your dream for us and we'll hope to see you soon. Thanks, everyone.