DATA SECRETS Podcast

What's NEXT for Tableau? Data Secrets LIVE from Dreamforce 2025 with Chris Williams

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In this live episode of the DATA SECRETS Podcast, host Nathan Settembrini sits down with Chris Williams – Tableau Ambassador and keynote speaker at Dreamforce 2025 – for a candid, on-the-ground conversation about the evolution of Tableau, its integration into the Salesforce ecosystem, and the game-changing potential of Tableau Next.

Chris shares behind-the-scenes insights from his Dreamforce keynote, breaking down how the new Tableau semantic model is providing a unified foundation for analytics, AI, and actionable insights across Tableau and Salesforce, plus how it’s all connecting with Slack and Teams for deeper collaboration. We dig into:

  • Why semantic models are the “puzzle piece” connecting raw data to real business meaning
  • How self-service analytics is becoming accessible to all personas, not just data pros
  • Tableau horror stories, real-world wins, and what happens when sensor data goes silent
  • The secrets of uncovering hidden insights that actually drive business decisions and save serious money
  • The importance of community, mentorship, and the #DataFam in the next generation of data careers
  • Practical tips for breaking into analytics, and why business context is your secret superpower

Whether you missed Dreamforce or just want to hear what’s next in the world of data, this episode is packed with stories, advice, and actionable takeaways for anyone looking to level up with Salesforce and Tableau.

🔗 Connect with Chris Williams on LinkedIn

If you’re ready to make smarter decisions and unlock the secrets hiding in your data, this is your backstage pass. Don’t forget to like, subscribe, and keep sleuthing!

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Nathan Settembrini [00:00:00]:
Welcome to a very special episode of the Data Secrets podcast. This is our very first in real life episode that we shot in San francisco at Dreamforce 2025 with my good friend Chris Williams, who's a tableau ambassador and he was just on stage that morning at the Tableau keynote and he got interviewed by the CMO of Tableau. He did such an awesome job and just wanted to just talk. Just a couple guys talking about all things Salesforce, Dreamforce, Tableau, Tableau next. Hope you enjoy the conversation and we'll jump right in. Your Data has secrets, secrets that could change everything if you only knew where to look. Welcome to the Data Secrets podcast.

Chris Williams [00:00:55]:
Hey, man. So what are you getting out of Dreamforce this week?

Nathan Settembrini [00:00:59]:
What am I getting out of Dreamforce?

Chris Williams [00:01:00]:
Yeah. What do you look to, what do you look to build off of from this week here?

Nathan Settembrini [00:01:04]:
Yeah. So our why for coming to Dreamforce is so Salesforce bought Tableau a couple years ago and you know, we love tableau obviously, right. And so we see it as an investment in the kind of the future ecosystem that Salesforce is creating by pulling Tableau into, into Salesforce world.

Chris Williams [00:01:34]:
Right.

Nathan Settembrini [00:01:35]:
And so what they've been doing kind of systematically over the past couple years is taking the best of Tableau Classic now they're calling it, right. And putting it into Salesforce where you know what they say, like 490 of Fortune 500 companies are on Salesforce or some stupid number like that.

Chris Williams [00:01:57]:
It's insane.

Nathan Settembrini [00:01:58]:
And so they just have this great distribution with Salesforce and Tableau's mission to help people see and understand their data, injecting that into the Salesforce culture. But now they've taken a step further, right, with Tableau Next and Agents and Agent Force and all this stuff, which I'm excited about. I know you're excited about it. So you go from data to insight and then insight to action. Right? What are you excited about?

Chris Williams [00:02:38]:
Well, it was really good to see this morning during the tableau keynote about how that semantic model can be reused across all levels of Tableau. So Tableau Classic, you have got Tableau Cloud, you've got Tableau Next. They all operate off the same semantic model. Ultimately, you're going to have the same sort of AI experience across all those brands. And this is what we've been looking for from Salesforce in Tableau. Upon the acquisition, we wanted to be sure and understand what their long term vision is. And I think it's coming into fruition now. Having all that interact with Slack is also a big bonus. With teams on the way. So understanding and being able to have self service analytics across all Personas for everybody, anybody can go in there and take a look at their data, visualize it and make some decisions off of it.

Nathan Settembrini [00:03:31]:
Yeah, explain for, because not everybody in our, like people in our world know what semantic model is and like why is semantics so important? And how would you explain it to maybe somebody who's not a data person?

Chris Williams [00:03:44]:
Well, think of the semantic model as how all these objects interact with one another. So if you have database tables, it's a matter about how these database tables relate to one another. And ultimately, as you're putting these pieces together, think of it as a puzzle. A puzzle is only really meaningful when it's complete. All of these pieces all interlock with one another. The completed puzzle is actually what I would call your semantic model because you understand what the borders are, what's happening in the interior. And then on the whole what you're seeing is a complete story being told. Because when you're done with your puzzle, you get the big picture. And it's exactly the same way that we deal with, when we're dealing with analytics. You build a model so that a picture can be seen. So we're building the same thing. How's that?

Nathan Settembrini [00:04:36]:
Pretty good. I normally, the way I, that I really like the puzzle metaphor, the thing that I've been, the way I've explained it is it's the meaning layer. Yeah, right. It's the kind of the interpreter and the, it's, what does this measure mean is good, you know, up is good, down is bad, that whole thing. And, and the reason why it's so important is because AI like any sort of LLM, like it must first like understand what the data is.

Chris Williams [00:05:17]:
Yeah, I mean what you're trying to get your AI engine to understand is what are you asking the questions about? That's why the model is so important. And you want to have a robust model, but you don't want to have a model that's just flooded with useless information because then you're going to be asking questions about nothingness. We don't want that. But you want a focused collection of everything that you want to, everything that you care about, data wise, whether that be KPIs, whether that be KPIs within, in certain dimensions. All of that just needs to be in your semantic model and have a relationship so that you can not just ask one question, but ask the secondary and a tertiary question. That's when you get the supporting argument for whatever your thesis is for what your data is telling you. Yeah.

Nathan Settembrini [00:06:09]:
Yeah. For the people who did not attend Dreamforce or may have missed the tableau keynote, our friend Chris Williams here was on the stage representing the datafam. I'm just curious, you know, that was three hours ago, how you feel like, how.

Chris Williams [00:06:28]:
Oh, I'm still on cloud nine, man. Yeah, it's the Data Fam. For those who are not familiar with tableau or the concept of what that community offers, the Data Fam is what everybody strives for when it comes to not just analytics, but just inclusion altogether. The datafam really has invited me and showed me a lot more about what analytics means and how to truly translate numbers into a store. It's not just a dashboard. The dashboard should tell you something and it should lead you to do something else. So one of the things I wanted to make sure was clear through keynote today was that we have a community of willing and able helpful individuals and professionals to help you get started, to help you keep going. And then obviously, when you're off and running and you're running the rates with everybody else, help somebody else turn around, give it back. Make sure you mentor, make sure you teach, make sure you share the wealth, because that sets the stage for the next generation of data awesomeness to come. Follow us because, you know, we need more in this industry.

Nathan Settembrini [00:07:44]:
Awesome. Yeah. It was so fun to see you up there, and you did great.

Chris Williams [00:07:48]:
That's great.

Nathan Settembrini [00:07:48]:
Well, I knocked it out of the park.

Chris Williams [00:07:50]:
You're part of this fam. You're part of my development as a professional person. So, you know, thank you for your part in my journey, because. Thank you, man. Yeah, it's awesome.

Nathan Settembrini [00:08:01]:
Yeah.

Nathan Settembrini [00:08:03]:
Appreciate you, man.

Chris Williams [00:08:04]:
Absolutely appreciate you.

Nathan Settembrini [00:08:06]:
Yeah. So this is your on the road version of the Data Secrets podcast with Chris Williams. I am curious. Okay, so real quick. So on the Data Secrets podcast, the whole point is that secrets live in your data, and our job is to uncover those secrets and not just get the insight out of there, but also be able to take action from it. So I'm curious if there's a story, and I'm putting him on the spot right now. We did zero preparation for today. As you can see, we're in the brain date area of Dreamforce. We got stood up by our brain dates, and so we're like, let's put these brains together. Anyways, so I'm curious. Okay, so one of the things in Data Secrets is just like a story where you found some really important insight in some data, and it led the business that you Were supporting to make some better decision, better informed decision that made a real impact on the business. Is there a story that comes to mind?

Chris Williams [00:09:16]:
Yeah. I mean, when you're doing your analysis, you have a primary goal usually that you're shooting for, but everybody knows if you've been doing any sort of analytics whatsoever is that you will always come across something else that will draw your attention. And sometimes that second thing that draws your attention should be your primary thing.

Nathan Settembrini [00:09:37]:
Yeah.

Chris Williams [00:09:38]:
So I had an instance where I was doing something for a major fast food company. We had some sensors that were in the refrigerator and they kept dying. Okay. And so they were losing food.

Nathan Settembrini [00:09:53]:
The fridges were dying or the sensors dying.

Chris Williams [00:09:54]:
Fridges were dying.

Nathan Settembrini [00:09:55]:
Okay.

Chris Williams [00:09:56]:
Because the sensors were dying.

Nathan Settembrini [00:09:58]:
Ah, okay.

Chris Williams [00:09:58]:
And so instead of analyzing the sensors themselves, we analyzed the company that sold us the sensors and then turned around and figured out that, okay, they sold us some other things where we had some faulty equipment as well. So instead of just trying to diagnose the initial problem, you also have to look at what is the source of that problem and try to figure out what the best way to take a look at that is. And the only way to do that is not make it subjective, you make it objective. Because you're using the data. Yeah. To tell you what to do next. So we use that data to open up the floodgates and try to engage with other companies that have. That sold different materials in different refrigerators. And then we were able to go ahead and figure out, hey, maybe we should go with this more reliable brand and move vendors. And ultimately that saved them a lot of money. It saved that store a lot of money because they were always closed because they had food that was spoiler.

Nathan Settembrini [00:11:00]:
Interesting. So were you able to. Because the data, like, was it one of these things where the, Sorry, the sensor is like, if it dies, it doesn't tell you that it died. It just would like not be reporting anything or.

Chris Williams [00:11:18]:
Yeah. So I mean you would actually. It would have streaming information from the sensors. So when you didn't have that information, you had a gap.

Nathan Settembrini [00:11:26]:
Yeah.

Chris Williams [00:11:27]:
You would then figure out, okay, what's going on?

Nathan Settembrini [00:11:30]:
Yeah.

Chris Williams [00:11:31]:
Why is there a gap? And how long. When did that gap start? What time is it now? So if you could figure out the delta within those. The time now and when they actually stopped getting data, then you're in a situation where, okay, I've been down for X amount of time. What's going to happen to all my food in that fridge?

Nathan Settembrini [00:11:51]:
Right.

Chris Williams [00:11:52]:
And you know, it's a lot of money. You Got to get more food. But you also have to make sure that you have a functioning fridge. So we determined that by the difference between how long when we got our last bit of data to when it was discovered that it was down to really figure out what our average downtime was with this.

Nathan Settembrini [00:12:17]:
Interesting, interesting. Is there a. So one of the segments in the podcast is the data horror story. So this is where everything went wrong or the data misled you and a terrible decision was made or production went down and everything. Yeah. So what, is there a horror story that comes to mind for you?

Chris Williams [00:12:39]:
I don't have a particular horror story. It was more of a. Oh moment where you're trying to put a, you know, story together and then again, you have an absence of data.

Nathan Settembrini [00:12:53]:
Yeah.

Chris Williams [00:12:53]:
And then with an absence of data, you create outliers. Then everything gets inflated or deflated depending on what your. Your KPI is. It's just now a matter of, okay, is this real or is this faulty? Yeah. And so if it's faulty, then you've got to make that sort of declaration, say, this is not exactly what we should be using. And so thankfully, we had other pieces of data where we had a more stable idea what numbers we should be getting, and then we were able to make a better decision before we made a very bad, impactful, negative decision.

Nathan Settembrini [00:13:35]:
Nice.

Chris Williams [00:13:36]:
So we caught it before all hell broke loose.

Nathan Settembrini [00:13:38]:
Yeah. That's good. That's what you want to do, right?

Chris Williams [00:13:41]:
Yeah, sure, absolutely.

Nathan Settembrini [00:13:42]:
Catch it in uat. Yeah. Well, cool. Well, this has been really fun. Is there. Okay, so for anybody out there, who is it, you know, you said give back to the next generation, people coming around or coming behind us or like, where. What direction would you point people in right now who are kind of maybe just they're interested in data, they're interested in tableau, the ecosystem. Like what. Where would you point them?

Chris Williams [00:14:09]:
So what I would say is, if you're just getting started in this whole fun ride of data analytics, what I would tell you is that bring every knowledge of everything that you know about your, you know, vertical, whether it be healthcare, whether it be automotive or whatever, bring all that with you, because all that data knowledge is going to serve you well. When you're understanding how all of these data objects work together, it's really important because if you know what you're doing with that particular data set and that type of data, you're going to be just fine. Bring everything with you. Don't have amnesia. I use that a lot. And just make sure that don't overthink the analysis that you already do on a day to day basis, you're just putting an actual title to stuff that you're already doing. If you know how to solve problems, you can do analytics.

Nathan Settembrini [00:15:05]:
I met a girl the other night who grew up in the banking world and like in the branch and she figured out that her favorite part of her day was doing some of the like reporting stuff at the end of the day.

Chris Williams [00:15:18]:
Yeah. And.

Nathan Settembrini [00:15:19]:
And she's like, I want to make that my career. And so that business context has served her super well because all she had to do was. So there's generally, there's three legs of this stool. There's the business context, there's the technical acumen. So like, can you make the tool do what you want it to?

Chris Williams [00:15:38]:
Exactly, yeah.

Nathan Settembrini [00:15:39]:
And then there's the design component where it's can I make it beautiful? Can I make it functional? Can I make it intuitive to use and like, from a user experience perspective. So you've already got one of those legs more than likely.

Chris Williams [00:15:53]:
Right.

Nathan Settembrini [00:15:54]:
And it's just a matter of investing in yourself and the other two, so.

Chris Williams [00:15:57]:
Exactly.

Nathan Settembrini [00:15:58]:
So, Chris, thank you for this. This has been really special.

Chris Williams [00:16:01]:
It's always an honor, buddy.

Nathan Settembrini [00:16:02]:
Yeah, man.

Chris Williams [00:16:03]:
It's always an honor if people want.

Nathan Settembrini [00:16:04]:
To connect with you. Find you on LinkedIn. Chris Williams.

Chris Williams [00:16:07]:
Yeah, find me on LinkedIn. Chris Williams. Yeah. I know it's not the most unique of names, but he's the only one we can. What we'll do is we'll. We'll post it. Post a link on. On. When you post this up, we'll post a link.

Nathan Settembrini [00:16:21]:
Put it in the. Put it in the description. Don't forget to like and subscribe. This has been a Dreamforce edition of the Data Secrets podcast. We'll see you keep sleuthing.

Chris Williams [00:16:31]:
Take Sam.

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