#278: Is AI Good at Data Analysis? That's the Wrong Question? with Juliana Jackson

Summary

Podcast Summary: The Analytics Power Hour #278

Title: Is AI Good at Data Analysis? That's the Wrong Question?
Date: August 19, 2025
Hosts: Michael Helbling, Moe Kiss, Tim Wilson, Val Kroll, Julie Hoyer
Guest: Juliana Jackson (Associate Director of Data and Digital Experience at Monks; Co-host of Standard Deviations Podcast; Author of "Beyond the Mean" newsletter)

---

Overview

This episode explores a widely debated question in the analytics community: Is AI (specifically Large Language Models or LLMs) actually good at data analysis—and is that even the right question to be asking? With special guest Juliana Jackson, the hosts dig into the realities and misconceptions about how AI is being used (and hyped) in data analytics, the industry’s pressure to adopt AI, and what analysts should really focus on as the field evolves.

---

Key Discussion Points & Insights

1. The Origin of Juliana's AI Analysis Rant

[02:42] – [06:40]

• Juliana shares the story of starting the "Standard Deviations" podcast and how Simo Ahava became her co-host.
• Both hosts and guest express frustration with the oversimplification of analyst work, especially when people claim LLMs can replace genuine data analysis.
• Quote (Juliana, 08:26):
  "I keep on seeing a lot of people on LinkedIn dumping a lot of [data] into ChatGPT... and they're kind of like minimalizing the work an analyst does and how much experience does it actually take to be able to look at numbers and draw conclusions."

2. LLMs and “Probabilistic Uncertainty” in Analysis

[13:48] – [16:52]

• Discussion about the fundamental differences between LLMs (probabilistic outputs) and classic statistical analysis (deterministic).
• LLMs generate different responses to the same prompt, while real statistical methods are consistent and reproducible.
• Quote (Tim, 15:02):
  "If I put a prompt into ChatGPT and immediately put the exact same prompt in, I'm going to get a different response. ...Uncertainty is built into them, which is unnecessary for the task of crunching numbers."

3. The Real Root of Pressure: VC Hype and Industry FOMO

[18:32] – [22:21]

• Juliana argues that much of the push for "AI everywhere" stems from VC-driven tech hype, not actual business needs.

• AI is often deployed as a feature to attract funding or market attention, not necessarily because it's beneficial or needed for the task.

  Quote (Juliana, 18:32):
  "The actual problem is the VC bullshit that's happening... I wouldn't need an LLM on my CRO tool to think about the hypothesis. Why? Just let me do my A/B testing in peace."

• Pressure on analysts and marketers to “innovate at all costs” is making many feel vulnerable and uncertain about their career value.

4. The Pitfalls of “AI = LLM = Magic” Thinking

[24:14] – [25:14]

• AI has become synonymous with ChatGPT or LLMs, ignoring the wider field (like programmatic ads, predictive modeling, etc.).

• This is a dangerous and narrowing perception in the industry.

  Quote (Tim, 24:14):
  "There's a more dangerous narrowing of, 'Oh, AI equals LLM equals chat interface for anything.' That's an unfairly narrow definition."

5. Self-Service Analytics, Chat Interfaces, and Business Abstractions

[25:14] – [30:09]

• There's a renewed industry push for self-service analytics and chat-based interfaces as the next big solution.
• While abstraction makes things appear simpler to business users, genuine complexity in business data remains; not everything can be (or should be) automated.
• There’s a risk that LLMs, eager to offer answers, won’t probe for necessary details—missing business context and nuance.
• Quote (Val, 25:14):
  "The business will have an easier way to deal with an abstraction than with statistical methods... I do understand the need for abstracting a lot of what we do."

6. Anthropomorphizing and Emotional Attachment to AI Tools

[28:53] – [32:48]

• Analysts (and people in general) tend to anthropomorphize AI tools, which can create comfort and attachment but also over-trust.

• LLMs are seen not just as tools, but as “colleagues” or sounding boards—a throwback to early chatbots like ELIZA.

  Quote (Juliana, 32:48):
  "If we take it to the analytics world, it's very nice… to be able to talk to somebody that gets it… without you feeling that you're an idiot. It's a tale as old as time, like we are the problem."

7. The State of Analytics Community and Vendor Hype

[34:36] – [37:24]

• Juliana laments the decline in vendor-sponsored education and honest thought leadership.
• Vendors, driven by hype rather than substance, are “taking advantage of blurry lines” in the market to sell AI tools that may not actually help analysts.
• Quote (Juliana, 36:58):
  "Automation can scale outcomes, but not competence, and AI cannot fix broken value propositions for vendors. If users didn't come before AI, they're not going to come after AI."

8. AI as an Amplifier, Not a Shortcut

[37:24] – [41:48]

• AI can help juniors or accelerate certain tasks for experts, but it doesn't replace foundational knowledge, context, or fair critical thinking.

• Moving from “zero to one” in skill may be impressive, but “one to two” (or expert level) requires deeper learning still not matched by AI.

• LLMs can be a shortcut, but not a substitute for understanding data, business context, or analytics methodology.

  Quote (Michael, 39:10):
  "LLMs are prediction engines... they're usually giving you some average that gets you further than you would get yourself… But I'm not an excellent developer with AI. An excellent developer is thinking about all kinds of other things... The AI is not thinking about any of that."

9. Multimodal AI & Practical Recommendations

[41:48] – [47:09]

• Discussion on the promise of multimodal AI (combining language, visual, and other modes) for future analytics.
• Juliana strongly recommends using the right-sized model for the job—small language models for specific tasks, LLMs for exploratory or ideation tasks.
• LLMs should be used for discovery or proof of concept, but for production-level analysis (especially unstructured data), smaller, more controllable models are preferable.
• Quote (Juliana, 42:02):
  "Language models are good for language... I will never choose to use an LLM to do any type of unstructured data analysis. I will always go for a small language model... because you have more control."

---

Notable Quotes (with Timestamps)

• "I keep on seeing a lot of people on LinkedIn dumping a lot of [data] into ChatGPT... and they're kind of like minimalizing the work an analyst does..." — Juliana Jackson [08:26]
• "If I put a prompt into ChatGPT and immediately put the exact same prompt in, I'm going to get a different response. ...Uncertainty is built into them, which is unnecessary for the task of crunching numbers." — Tim Wilson [15:02]
• "VC bullshit... I wouldn't need an LLM on my CRO tool to think about the hypothesis. Why? Just let me do my A/B testing." — Juliana Jackson [18:32]
• "There's a more dangerous narrowing of, 'Oh, AI equals LLM equals chat interface for anything.' That's an unfairly narrow definition." — Tim Wilson [24:14]
• "Automation can scale outcomes, but not competence, and AI cannot fix broken value. If users didn't come before AI, they're not going to come after AI." — Juliana Jackson [36:58]
• "Language models are good for language... I will never choose to use an LLM to do any type of unstructured data analysis." — Juliana Jackson [42:02]

---

Memorable Moments

• Juliana’s self-deprecating humor and honesty about feeling "lazy" yet obsessive about proper methodologies.
• Debate over why analyst jobs matter and shouldn’t be replaced by shallow AI promises.
• Multiple hosts openly sharing industry anxieties about keeping skills relevant in the age of AI.
• Juicy inside stories about podcasting in the analytics industry.

---

Practical Takeaways

• Don’t conflate “AI” with “LLM” or “chat interfaces”—AI is a broad field.
• LLMs are great for exploration, ideation, or for those with some knowledge. For production analysis: use statistical/machine learning tools.
• Business pressure to “innovate with AI” may be more about trends than actual business value; analysts should focus on fundamentals and critical thinking.
• Vendors and organizations should invest more in genuine education, not just feeding the latest hype.
• Use small language models for specific, repeatable analytics tasks—you get more control and reliability.

---

Timestamps for Key Segments

• Intro and Welcome: [00:00] – [02:16]
• Juliana’s Podcast Origin Story: [02:16] – [06:40]
• AI/LLMs and Data Analysis: What’s Missing?: [06:40] – [13:48]
• Probabilistic Pitfalls of LLMs: [13:48] – [16:52]
• Why is the Industry Chasing LLMs?: [16:52] – [24:14]
• Democratization, Chat UIs, and Business Users: [24:14] – [30:09]
• Human Attachment to AI Tools (Anthropomorphism): [30:09] – [32:48]
• Vendor Hype and Lost Thought Leadership: [34:36] – [37:24]
• AI as Amplifier, Not Short-cut: [37:24] – [41:48]
• Multimodal AI & Model Selection Tips: [41:48] – [47:09]
• Wrap-Up & Last Calls: [47:09] – [56:25]

---

Hosts' & Guest’s Tone

• Candid, sharp, and irreverent: No one shies from critique, and Juliana’s blend of humor and bluntness keeps things lively.
• Reflective yet practical: Numerous admissions of industry anxiety but always grounded in actionable advice and appreciation for foundational skills.

---

For Further Exploration

• Juliana Jackson’s “Beyond the Mean” newsletter – Deep dives, rants, and actual research about analytics and AI.
• Standard Deviations Podcast – Co-hosted by Juliana and Simo Ahava, focusing on analytics and digital experience topics.
• TED Talk referenced: "This is What a Digital Coup Looks Like" by Carol Cadwalladr [47:57]
• MeasureCamp Chicago Event and the Ologies Podcast (mentioned in Last Calls)

---

Bottom Line:
Rather than ask “Is AI good at data analysis?”—the real questions are WHAT problem are you solving, WHAT tool or model is best for that job, and HOW are you ensuring critical thinking, context, and community? LLMs are powerful, but they’re not magic—and the future of analytics depends on how wisely we integrate new tech, not how quickly we jump on the hype train.

---

Transcript

[A]

Foreign. Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

[B]

Hi, everybody. Welcome. It's the Analytics Power Hour. This is episode 278. You know, these days, every device in your home, for some reason, wants to connect to the WI Fi. I mean, does my coffee machine want to write poetry or something? Or give me sports scores? I guess sometimes the capabilities and features available might not fit the function of the thing. And maybe this is an intro to a podcast about where AI might fit into data analysis. So recognizing that AI is most likely here to stay, let's, I guess, dive into it. First, let me introduce my co host, Val Kroll. How. How's it going?

[C]

You always do that to me. You give me the intro.

[B]

No, I do it to everybody. In fairness, I love doing the how you going? Ever since I learned it many years ago. I'm good, but I TR to, like, only use it on Australians. Okay, well, good. I need to get you a more Chicago. Hello. How's you guys?

[A]

That's so New York.

[B]

Okay, we'll work on that. Never mind. You'll work on it. We'll workshop. We'll workshop it. All right. And Tim Wilson, big old Howdy, partner.

[D]

How y' all doing?

[B]

There you go. Get back to your Sour Lake, Texas roots. And I'm Michael Helbling. And today I'm really excited about our guest. Juliana Jackson is an associate director of data and digital experience at Monks. She's also the co host of the Standard Deviations podcast, and we all love her newsletter, beyond the Mean on Substack. And today, she is our guest. Welcome to the show, Juliana.

[A]

Thank you. And I should answer with this very Eastern European accent. So I can fit this. The same stuff that you guys were doing. You see, I can actually.

[D]

You're European.

[B]

What? What?

[A]

I was trying to fake the Eastern European accent when I speak. You see, I can speak like this, too, if I want. That's how I actually sound.

[B]

I like it. Well, it's. This is actually sort of a long time in the making, because I think.

[A]

Really?

[B]

Yeah, I think so. Like, all of us have been reading and sort of a lot of your recent newsletter posts and passing them around to each other and talking about them. And so we always find that when that happens, we're sort of like, okay, we need to get this person on the show with us so we can have a conversation. So we're really excited to have you today. And I think our first and most burning question is, how did you get Simo Ahava to do a Podcast with you.

[A]

No, I'm just kidding. Listen, listen. So every. Every measure camp that I used to go, like, maybe a year or two years ago, everybody was telling me, so nice that you're doing. Simon lets you do his podcast. And, like. And I'm thinking about Alban specifically. Thanks, Alban. If you're listening to this. So what had happened was actually the podcast was done by myself in the beginning, and I actually had Tim as a guest, and we had one of the most esoteric dope episodes ever. We're talking about segmentation and horoscope and causation and correlation was so cool. I have always been a big fan of Tim, and I still can't believe he talks to me sometimes. And he knows that because he told me to stop, which I'm clearly not doing it. But anyway, so I was working at CXL back then, so they were sponsoring the podcast. It's pretty expensive to run a podcast, as you guys know. And then it was like the Spotify wrapped. And actually, for some reason, people were listening to me on my own and doing the way I was doing it before, which was more, again, esoteric, more, you know, whimsical and shit. Oh, you're gonna lose your pg, by the way, after this.

[B]

No, no, we're explicit, so just keep it.

[A]

Okay, great.

[B]

Bombs away. We love it.

[A]

Jim Stern. It says it's a sign of intelligence. So whatever Jim Stern says, I will follow. Yeah. Anyway, so I posted the results from the Spotify rep. Actually did pretty good for somebody that, you know, I don't know, irrelevant person that I still am. And then I was speaking at the DHL Analytics Summit with Tim Coppens. And then Simo was speaking. He spoke before me. So while I was getting ready to go and speak with. To the. To the event, Simon messages me on Slack. He was like, hey, you know, like, I've been thinking about it, and I think I would love to do the podcast together with you if you'd have me. I'm like, what? I'm like, oh, shit. Message my husband. I was about to go live. My husband was in the next room. I'm like, oh, shit. Simon Havard wants to do the podcast with me. He was like, who the fuck is that? I was like, don't worry. You know, don't worry about it. And I was freaking out, and I said, let's talk about it after I finish this, because I was generally going live. I cannot tell you what the hell I was talking about at that event, because I was like, oh, my God. Oh, my God. Someone wants to do a podcast with me. Why? Why is this a joke?

[C]

It's all you could think about.

[A]

Yeah, like, I couldn't think about nothing afterwards. It was something. I don't even know what I was talking about. So, anyway, I finished the talk. I feel bad for you till if you're listening to this, I'm sorry. I really enjoyed the event, though. It was very nice. Thank you for having me. It's a sign that I was never asked again. But, yeah, so I messaged Simo afterwards and I'm like, fuck, yes, obviously. And, yeah, we just ended up doing the podcast together and he's been sponsoring since, and we didn't know at that time how this will work, and we still don't. If you're listening to the podcast, it's just, I think while we're very different as people, we're also pretty similar in terms of banter. So we kind of just play off each other. And he has obviously his topics about GTM and server side and data privacy and all the stuff that I have no idea about. And I just am more into my commercial analytics, more data storytelling and AI and mobile apps. So it's kind of a good fit, I think. Yeah. And we've been doing it, like, for three years. I think it's now close to three years. I think ever since. Yeah, three years. Three years. Almost three years. And for some reason he still wants to do it. For some reason people still listen to it. And for some reason I have an episode that I should have edited a month ago, but. Sorry, Team Kaufman. Yeah, but that's a very long winded answer.

[B]

No, it's great.

[C]

I love the origin story.

[A]

Yeah. So I did not convince him I'm not Alfred. Okay. I'm tired of being told that I'm Alfred. Not Alfred.

[B]

So recently, I think the genesis for this topic came about from something on your newsletter where you're talking about sort.

[A]

Of.

[B]

Can large language models do data analysis? And where does that sort of. You kind of broke it down into a number of different ways, and I sort of want to dive into that just a little more deeply because I think there were some really important distinctions you made that were really, really important about sort of, okay, here's LLMs. Here's data analysis. Like, think. Think holistically about that. So, like, first off, sort of what brought the topic to you in terms of, was it just sort of everything that's going on in the industry? Were there specific things going on that were kind of like driving that to help you to start to think about it.

[D]

Were you being triggered by specific interactions?

[A]

Better.

[C]

Better way to put it.

[A]

I mean, it's a mix of things. It's funny because the last message I sent to Tim yesterday is that I got triggered by somebody on LinkedIn, so I'm writing something about agents. So. No, I mean, it's a mix of things, I think. First of all, I'm a consultant and part of my role is to make sure that I test all these AI systems and products and services to make sure that once I go in front of a client with a solution, I am able to speak confidently about it and make sure that I am a trusted advisor and not just trying to shove people, different products and services on their throats. And I. Oh, you're actually.

[D]

That means you're not. You're not doing it right.

[A]

So, yeah, I guess. I guess.

[D]

What kind of consultant are you?

[A]

A pretty shitty one, I guess. I don't know. But. But yeah, so like, I've been working with language models since 2021, so, no, 2022, when I joined Monks, and I was always happy about using. So I'm not saying large language models, I'm saying language models. Very big difference. And I was very excited because I was always struggling to analyze unstructured data and obviously they're very, very good at that because you are giving them this unstructured data and they're transforming it into numbers so you can finally use your brain and come up with something. I've done this for a lot of clients and on different types of data sources, but I keep on seeing a lot of people on LinkedIn dumping a lot of into ChatGPT or to Cloud or whatever they're using. And they're kind of like minimalizing. Is that the word? Yes, minimalizing. The work an analyst to do and how much experience does it actually take to be able to look at numbers and draw conclusions? So I guess I was just a bit triggered and I did what any other triggered person would do, just go and read science literature. That's right, I showed them. And I was talking to Jason Packer. SHOUTS TO Jason Packer. I was talking to him. I talk a lot to Jason, actually. He's a poor guy. So I was talking to him about it and I went through all the science papers from this year that were people basically just trying to do data analysis, tabular analysis, using LLMs. And the only way I noticed that anybody can get close to it is if they use a mixed mixture of Python of code and LLMs obviously, I knew the answer to that before because I tried it myself. I actually dumped a fake Data set, a CSV into ChatGPT and I asked it, what does this data say? Do an exploratory analysis. The exploratory analysis was actually pretty spot on. But then when I started to go granular, I noticed that the results are not there. Then I just started to research what people are speaking about this. Then I got very angry because there's, I think a big problem that we have right now in the industry is the way we look at innovation. So before, innovation used to mean that you are somebody that's looking for outside of conventional wisdom to solve a problem. You had a problem that you wanted to solve, so you were seeking outside of the conventional wisdom, ways of doing and going about something. Now what we're doing is we're actually having solutions that are chasing problems that don't exist. Why do we need a new way to. Well, it's my perception. So why do we need the new way of doing data analysis when we have very proven statistical methods and calculations? Like, why the hell was I struggling to pass that stats course and listen to what's his name to Georgie Georgiev or going to town whistling stuff? Why do I have to do all that effort to figure out what the fuck my cov is that's solved. It's a problem solved. I don't need an LLM to tell me how to do this because it's the same as vibe coding. I can understand vibe coding to a degree, but it's kind of the same concept, like, why are we creating new ways for problems that are already solved? Because if it is just very infuriating to me, the conflation that happens in this industry, and it's people that should know better, it's people that are digital analysts. And I'm sorry, if you're listening to this, I don't care. You can be mad at me, that's fine. But we should know better. And you have people with platforms, on social media, platforms that they should use for good. Instead, they're just, you know, contemplating these doomsday theories and propaganda and whatever the hell you're calling it. And it's. It's very infuriating. So I did my research. Obviously, a lot of the stuff there is subjective. I could be wrong. I always say that in my, you know, don't take my stuff as gospel. I'm just somebody that learns out and then unlearns and then I download it on people. But, yeah, that's kind of like how it started. And in general, my newsletter is kind of like everything that I write is basically just some sort of frustration that I have and I end up just writing about it.

[B]

Hey listeners, ever feel like your data pipelines are messier than your inbox? After vacation, say goodbye to chaotic integrations and hello to FiveTrans. FiveTran delivers automated, reliable data pipelines that are as easy as hitting subscribe on your favorite podcast. With hundreds of Pre built connectors, FiveTrans streams your data seamlessly into your favorite warehouse, keeping everything accurate, fresh and ready to use. No more manual maintenance or midnight data drama. Just clean, reliable data when you need it. So don't spend another minute wrestling with your data. Visit FiveTran.com APHToday for the latest news and events and discover how FiveTran can streamline your day to life. That is F I V E T R-A-N.comAPH for the latest news and information about FiveTran. Okay, let's get back to the show.

[D]

Well, in that and you went pretty deep in that the one post which we will definitely link to. But the like the. I mean there's some pretty funny things when you're like, oh, you know, people tell me they dumped some CSVs into ChatGPT and the results were shit and you're. Or the results were terrible and you're.

[A]

Like yeah, no shit, but punchline.

[D]

Oh yeah, yeah, but I think you like I felt like I'd sort of seen it and people were kind of saying it. But you called out very, very specifically that we all kind of know that LLMs in an overly simplified way are doing this probabilistic kind of next word. It's inherently probabilistic. Yet when we're doing stuff with data, there's probabilistic when we're predicting things. But when we run if I run a linear regression on a set of data today and use R and you run a linear regression on the same set of data using Python, we're going to get the same basic model. There's not trying to be like oh, and kind of mix it up so that it's natural language.

[A]

Yes, it's the same results, but it also can be a bit different. I mean, it's different on how you would do it in Python versus R, but the mechanics.

[D]

But there's like a very descriptive reason. Or maybe if I ran it in R today and if I ran it again in R, if I put a prompt into ChatGPT and immediately put the exact Same prompt in. I'm going to get a different response. And so there's that. That was like one slice that I was like, oh, yeah, yeah. We still have this fallacy often in the business that the numbers and the data are objective and there's a failure to sort of think about uncertainty. But now somehow we're wanting to take LLMs, which have kind of uncertainty built into them. It's like, well, that's a different and unnecessary aspect of uncertainty for this task, which is crunching numbers. So I don't know. That just was one of the parts of that that I was like. That is very useful to point out to people, even as people. A couple episodes ago, when we had bi polynzema on and we were having that discussion about natural language queries, I think that's kind of similar to trying to solve the wrong problem. There's this idea out there that that's what we need to do is to just have natural language business questions get converted to models or queries. And that seems like it's focusing on the wrong. The wrong part of the process. We need really good questions, which I think LLMs can actually really help with. I don't know. That's not a question. I just kind of went on my own little.

[A]

No, but listen, you're right. I have some thoughts if I can share them.

[C]

That's why you're here.

[D]

Okay.

[A]

I don't know. I feel like it's so hard to be a guest because I'm used to be a host. I don't know how to guess. Like, I'm like, yeah, exactly. We should ask this bitch what she's thinking about. Stuff.

[B]

Hey, I'm the facilitator of this one. Okay, Juliana? So, like, you just rest easy.

[A]

Okay. So to watch what you said, Tim, I think. Okay, so I will speak for myself. I'm a very lazy person, so I'm always going to look for the easy way out. I think most of us in this industry are inherently lazy, so we're always going to look for a way to fix something faster, to optimize the way we work, because that's how we're wired as analysts or analytics people. I'm speaking for myself, but I suspect some of you guys would probably feel or relate to anyway, so I would love to exist in a world where I can just have thoughts and put them into a model and just get my results and move on. And then I will play, you know, Diablo for the rest of the day. That would be so good for me. I would love that. But the problem right now is that the actual core of the problem is a bit deeper. So we have. Do you guys remember Silicon Valley?

[C]

Yes.

[A]

Right. You know the series, Big fan. So you remember. Great. So you know Son of Anton, right?

[D]

Oh, yeah.

[A]

Okay.

[D]

That's the AI who winds up deleting.

[A]

Yeah, exactly. So Anton was the server solution. And then we had Son of Anton, which was the AI in the show that basically brought, I don't know, 30 pounds of hamburger meat. Anyway, so what I'm trying to.

[D]

I just watched that clip last week.

[A]

It's hilarious. It's, it's. But it's so relevant to where we are right now. So the actual problem of where we are right now is the VC bullshit that's happening because we, you know, venture capital is killing, basically investing hundreds, thousands of millions of dollars in these people. And that makes people from the top. And it comes to the lower market to vendors in the industry. It comes like this thing, oh, my God, I need to put AI on this stuff. We need to put AI on this product. So we are thinking of, like, we see these people trying to obviously add AI to anything that they're doing. Like, for instance, I wouldn't need an LLM on my CRO tool to think about the hypothesis. Why? Just let me do my A B testing. Let me do my A B testing in peace. I'll come up with my hypothesis. I'll run my test. I really don't need an LLM to tell me how to create a variation. I don't. But this is an example. Or like this GA4 Insights. Come on. Conversational analytics. It's so triggering. Even now. I always think about that meme with Kelly Rowland typing in the Excel sheets. That's exactly how. But that's conversational analytics. You're writing text into an Excel sheet. That's it. Period. So the problem comes from the top of the market. It comes from VCS and so on. And us here in the world, we're dealing with people that have the stories made up and they're trying to convince other people is classic business stuff. Now the problem is it creates this pressure on analysts, on marketers or technical marketers and so on to keep up with that stuff. You need to keep up with the times, you need to adapt, you need to make sure that you're using it in your workflows. And for sure, I am truly excited about using artificial intelligence. I've been for a long time. And to some degree, all of us in analytics have been using artificial intelligence with programmatic ads. We have been using it with a B testing with predictive customer lifetime value. And this is where the problem is. We and I wrote about this intensively. We think that AI is just large language models and that's where all our problems come because if you have that conflation in your head, you're going to basically ignore everything else that happened until 2022 when GPT 3.5 launched and that's it. And then you're going from that. So I understand people are trying to use these models to do better analysis or to be faster or to do, I don't know, I want to think that people have the best intent at heart when they're trying to do these things. But I think there's a huge pressure that comes from upmarket, there's a huge pressure that comes from vendors and there's a huge pressure that stakeholders will inherently put on people working in different teams or in agencies to innovate at all costs or to do at all costs. And it sucks and it's kind of depressing me. And I found myself quite often and even like the other day when I oh, this is sad. I feel vulnerable sometimes and affected about how, how much do I need to do as an analyst and learn to be able to feel that I'm still valuable to the business or if I'm still valuable to my clients. And I feel like it's kind of like such a disruptive, disruptive phase in the digital space for all of us. I don't know if any of that makes sense, but I'm pretty vulnerable right now. So that's kind of like how I feel about this. So yes to what you said. Yes, we should use statistical methods because that's how it works. But yet here we are dumping private personal customer data into GPT and ask for customer segment. Why not?

[D]

Well, and there was a point and I mean I perpetually am like 4 to 7 years behind actually understanding distinctions or even between data and analytics. I think I was probably 10 years behind understanding that distinction. But a lot of time I think that's because there's inconsistency in the way people use it. And I hit a point five or six years ago where someone, and it's like the Venn diagrams were, were shown to me that said machine learning is all of these statistical methods and programming and you're doing models and AI is when you actually enable that machine learning to actually do something, to take an action. And I don't know where now as you're saying AI, when people hear AI now and they think ChatGPT or they think an LLM. And partly I think even now people are thinking of agentic. AI is becoming like, well it's a cool word to say so I'm going to have this agents and I'm like it's used kind of all over the place. But when you say AI is not, I mean it's fair to say LLM equals chatgpt or cloud or perplexity. AI is getting equated as well in some cases just magic cool stuff as you were just talking about that we just got to slap the label on it for all sorts of technically unrelated reasons, but we need that label. I think what you were getting to is there's kind of a more dangerous narrowing of oh, AI equals LLM equals chat interface to anything that I. And that's like an overly and unfairly narrow definition of AI is that that's.

[A]

Better than whatever the fuck I just said. So thank you for that. But no, that makes sense and that's 100% how it's perceived and it's very dangerous.

[C]

Yeah, I think one of the threads also back to the Colin Zima episode about BI is he was talking about this balancing self service versus the controlled that the analysts have within the tools and it seems like there's like I'm curious your thoughts on this Juliana, that democratization and making it accessible to the business users and it's just about getting the right permissions and the data clean enough so that they can query to get everything they need. Do you think that part of this is a response to past failed attempts at putting all the data in the hands of the business users to get them to get insights to rain from the ceiling? Is it specifically about failures of self service that people think, oh the chat interface that's going to be the thing that cracks this nut? Or do you think it's not? As that's a really good question.

[A]

I have two answers to it. So I think in general the business will have an easier way to deal with an abstraction than with statistical methods and calculating, I don't know, the P values of the world and having to deal with SQL and BigQuery and so on for a business, from a business perspective it's easier to deal with an abstraction which is the chat interface. It's so much easier. And I saw clients lit up, for instance, when we build a SQL query writer that was running into their reviews and you basically were writing in the SQL query that I want to see what's the sentiment for the reviews in this period. And then the chatbot was giving you the SQL code so you can just put it and run the SQL. I do understand the need for abstracting a lot of the stuff that we do because for the business, truthfully, businesses don't give a shit about the complexities of our job. Like they do not. They only care about what do I need to do, how much is it going to cost me, what's the conflict and what are the next steps. So I do think, yes, from a business perspective, it does make sense.

[D]

I think there's something important and when that analysts have actually also we've lived in that. When somebody says why? When I talk to this analyst, I just ask a simple question and they have a bunch of other questions. They want me, can you give me revenue for less? Can you pull leads for me? It's like, well, I need to know the timeframe, I need to know the region, I need to know, et cetera, et cetera. So I think there are aspects of the data complexity. Every aspect of data complexity represents some underlying complexity of the business. So I do feel like that is often misguided. A good analyst can say, based on my experience of the person, their role, the nature of the question, my knowledge of the business, my knowledge of the data, I can safely make a bunch of assumptions about how I'm going to write this SQL. And there are aspects of that that I want to hide from the business. But it's not like that's complicated just because it's just gratuitous, meaningless, unnecessary complication. It's complicated because the business is complicated. And so that's where it's an aspect of the misguided, this desire to. I want to ask a simple question. I don't want to have any probing beyond that of my question. It's this, the. And I've had this line for years when people would come by and say, hey, I got a quick question. It's like, well, the length of your question has proven no correlation to the complexity or the effort required to answer it. And there's a piece of that where I'm like, now people are looking and it's like, oh, because the LLMs are sycophants, I can ask a super simple question and it doesn't come back. If I don't tell it to probe for more, it is going to run off willy nilly.

[B]

So wait a second, Tim, is that why all of your questions on the show are so long? It's like a reaction to the quick question.

[D]

Finally You've come into my.

[B]

Okay, I'm sorry, Julie, love to hear what you.

[A]

No, I think Tim is right, as always. I would sound. I would sound like. I would sound like. That's a great thought. You should write about this.

[B]

Wow, you're so smart and intelligent.

[A]

No, but you. You are right. This. This shows to. This points actually to the biggest problem that I think it is. The second biggest problem. The first was, you know, what's coming from, you know, enterprise VCs. The second one is we tend to anthropomorphize everything that we work with because, of course, we're humans. So you see that there's so many people that use LLMs as a therapist, and it's not a funny thing. It's not a funny thing. These people go through some serious shit and they feel like they have nobody to talk to, so they end up anthropomorphizing them. These are more extreme examples. But if we take it to the analytics world, it's very nice, I think, to be able to talk to somebody that gets it and is able to give you instant answers or to be able to talk to you at the same level as you are without you feeling that you're an idiot. Because I do feel like an idiot most of times, that's fine. But it's. So it's nice to be able to talk to these models and, you know, like, I brainstorm a lot on different topics or like, again, like, one of my biggest hobbies is to read science paper. So I put my science papers into notebook LM and I'm trying to parse.

[D]

Them and mine them for more evidence of how. Of how lazy you are. As you were just saying, you're like, I'm so lazy. So I'm just going to go read another scientific paper about deep analytical models.

[A]

Listen, I'm putting them in. I have adhd. Tim Olson. I have adhd. So for me, it's very hard to keep my attention. Like you can see right now in this podcast, like, I'm killing myself right now to be able to stay on point, which doesn't work. So anthropomorphic, Right? So as an analyst, we. Using these things, we tend to get very attached to them. And it's. I felt it on my own skin many times when I discovered small language models and I discovered hugging face a few years ago, I lost it. It's probably how people felt when they discover stack overflow. I was like, oh, my God, can you actually do this? It was this emotion wheel. Plushkev. I think, I hope I'm spelling it right. If not, I'm sorry, but it was this emotion wheel. So to be able to analyze emotion in text, you need to be able to have data that is annotated by a lot of people. You have to have huge data set of annotated data to be able to give it to a model, to fine tune it and then process some content. So it's like, oh my God, this is already here. I can just copy this shit in my collab and just feed it something and I can use Claude to write the code. That's amazing. So I get it. But one thing that happened for me, and I hope people will take away something positive out of this, is that it made me realize what are my knowledge gaps in terms of understanding how stats work, or machine learning or data analysis. So it for me, what did for me, it sent me back to school, it sent me back to read, it sent me back to study. So I never like one thing I can say when I write. I never write from the perspective of somebody that knows better because I don't. I'm learning shit and I like to give it back to the community for free. And I'm like, yo, that when I discover knowledge graphs, I did the same, but I got attached. Like I use them as part of my routine. So what happens is in, in the analytics space, and back to your question, Val, is that we tend to use these tools and we kind of become attached to them. That's. That's kind of what it is. And it happened in the 60s with the Eliza chatbot, if you remember, and remember that Eliza chatbot and people started developing feelings and stuff for a fucking chatbot. It's a tale all this time, like we are the problem.

[C]

So quick aside, I have to share and we can cut this out later, that anthropomorphized. Oh, this is one of my words I have trouble saying. Anthropomorphizing is one of the reasons why I had to live in a single in college in our dorm room, because my roommates didn't understand why I wanted the DVDs to be organized alphabetically because it made them happy. And so I did a lot of living on my own.

[A]

So I get the attachment part to it.

[C]

So back to the chat interface, making it accessible. Perhaps this is some nod to the self service and I think in reaction to some of the things you were saying too, Tim, it's only a half step away to be like, oh yeah, we'll just put a chat interface on it. Put a bird on it for my portly ADEW fans. If the existing relationship between the data teams and the analytics and data teams is. There's just a certain amount of aggregation or filtering that needs to be applied to the data to answer my question, for me to know the truth or the answer to my question. So it's so reductive that it's like, oh, if this is already how we operate, I ask you a question, you go pull a number or two for comparison and boom, like, now I know what to do. Now that answers all my questions. It's so easy to sell. Like, you're talking about Julianna, like, for the VCs to come in and say, what if we made that even faster, easier? You can do a thousand a day versus just the time it takes the analyst to pull it. So I think it's like building on existing broken models and relationships of working, which is why I think it's like so easily able to prey upon these audiences for why that this is going to solve everyone's problems 100%.

[A]

Then it's, it's, it's, it's, it's, it's actually infuriating to me. Like, I lost so much respect for so many people in the last year just because, like, I'm sorry, I'm just being honest. Like, you guys knew what you got yourself into when you invited me here. No, sorry, I just lost. No, I'm genuinely.

[B]

No, I'm, you know, it's. I understand. Never meet your heroes.

[A]

Yes, I, I like that as well.

[B]

No, no, no, I, I'm, I'm expressing a thought that comes from my own journey as well.

[A]

No, it's real.

[B]

You look up to people and then as time goes on, you're like, I can't look up to them anymore.

[A]

Or there you invite them into your podcast because you think they're, you know, they're cool, but they're bitches. I lost respect for a lot of people because we are not doing enough education as we used to do. I. I wrote about this recently. There used to be a time when vendors were doing so much educational content on their blogs, they were doing courses and webinars and there was so much educational content for the community. I'm thinking about the Krista Satan's of the world. Like when, Remember when she was running the community for ga? So much, so much stuff. It got us all together. There will never be times like that. And even in. In CRO, the only people that do content that is useful are just the guys At Conductrix. I'm sorry, like, I'm saying this with all the responsibility. I was, yeah, I was gonna do like a Mac, but, like, I'm begging, I'm begging him to write, like, please, I'm just saying, please, just write something. Just. Just go on the blog. Help me, Help me figure out something in life. So there's, like, very few people that still do content that inspires and help the community. And what's happening right now is what you said, Val, we're preying on the lack of education in terms of AI. I'm not saying you guys are uneducated. This is so bad. Delete this. We are playing. There's a lot of vendors that are riding the hype and instead of educating the market and being there for them, they're just kind of like taking advantage of the blurry lines that exist, exist, and they're just trying to basically sell. That doesn't, you know, make sense. And I had this note on my phone, which I tried to post on LinkedIn, but I didn't want to get blocked. But I'm going to read it to you guys right now. Where is it? Jesus. Where? Oh, I sent it to my husband and he was like. My husband shouts to my husband.

[D]

He was like, he still. He's still caught up on, like, who is Simo Ahada?

[C]

Yeah, he's still researching that.

[D]

Yeah.

[A]

Because I wanted. Okay, I said, automation can scale outcomes, but not competence, and AI cannot fix broken value. Props for vendors. So if users didn't come before AI, they're not going to come after AI. So this is not changing anything. So I have this just here for my husband and I said, sending it for me, don't worry about it. I just had this random thought and I wanted to post it, but. Yeah, I'll shut up now.

[D]

So that's this other part that concerns me that, as you're saying, is trying to oversimplify to the point that I don't have a base of knowledge. Going from a base of knowledge and using an AI tool. Or I think about. My son's a software engineer and he's like, yeah, I use code assistant. I use cursor all the time. Because he deeply knows how to write code. And so it's a very, very good supplement if somebody who's reasonably fluent in SQL and understands the company's business data structures is using it, that's like one use. But somehow we kind of conflate that with. Oh, and the natural extension is that some random person can come into a random company and say ask a random question and that's what's going to come back. They're going to be able to safely run with is kind of terrifying. So it's like there is a spectrum, but there's somewhere. There's a. There's a line that needs to be drawn that says no, you still need to learn and learn in the broadest. Learn the tools, learn the data, learn the business, learn how to think, learn critical thinking. And maybe that's just. I'm extending your rant that it's just getting dropped in as though AI is the shortcut. Because AI can do this cool thing. So let's just extrapolate it it to say, oh, it can do everything else.

[C]

Michael, not to put you in the spot. No, that's good. Michael, not to put you in the spot, but remember when we were talking before about like, if you're at zero, to get to one is impressive, but if you're at one, getting to two, like, do you remember when you were talking about that? Those nuggets of wisdom. I feel like that applies here.

[B]

LLMs are prediction engines. So they just take everything they know and they try to predict what it is that would be the best answer. But there's good and bad answers that they're incorporating into that training set. And so they're usually giving you sort of some average that gets you further than you would get yourself. Right. Like, because I don't know how to write a lot of code, so it can write code for me and does awesome. And it's so cool. Like, I go from zero to like, yeah, I go from zero ability to write code to like, I'm impressed. A developer. That's incredible. Now what I'm not is an excellent developer with AI. Excellent developer is thinking about all kinds of other things about architecture, about speed, about latency, about all these other things that matter to how that code is going to work and interoperate and all those different things. I'm not thinking about any of that. And the AI is not thinking about any of that. We're just klutzing along. So when you take that and you bring it to an analytics context, there's a bunch of things an excellent analyst is doing and those sort of like operations or tasks are things that like, yeah, wow. It's amazing. An AI can go compare some data sets and show you a little graph and all those things. That's pretty decent. But it's kind of like that sort of really junior analyst that sort of is like comes in with like this amazing insight that time on site goes up when the page is confusing or something, you know, it's like, okay, thank you.

[A]

Thanks. Thank you.

[B]

So you know, and you're like, oh, I'm, I'm happy for your excitement. We're not going to present that to the client, you know, because it's dumb, you know, but then you, but you, but then you go through that process. And so actually this is sort of like a last thing I want to kind of jump into. So thank you for teeing me up. Valid. One of the directions that I see AI going is sort of multimodal or multimodal AI, which basically is sort of like, okay, so we're talking about how dump your data into an LLM and it tries to analyze it. It's not good. What do you think, Juliana, about as multimodal comes online and we start leveraging different LLMs for different parts of that process, Is there a brighter future for analysis of data when we could leverage this thinking for this and this process for that and so on and so forth? Could that sort of a step out into past where we are today?

[A]

This is a great question. I have so much I want to say, so I'll answer to your question first. So language models are good for language.

[C]

News flash, you heard it here first.

[A]

I think we should use LLMs sparingly for discovery, for proof of concept, to try new things, to test drive something. I will never choose to use an LLM to do any type of unstructured data analysis. I will always go for a small language model. And the reason why I would do that is because you have more control over how that small language model is going to perform over time. Time. I genuinely hate using LLMs and I know that sometimes you're forced in life because Google put vertex AI and they put the. They allow you now to prompt inside vertex AI and then you can create magic and do topic classification, all the statistical analysis things and it's great. But the F1 and precision and recalls and the accuracy calculations afterwards are hard and you have to manually go and yes, no, yes, no. Oh my God, look, we have 80% after six weeks. And I would always choose a small language model because. And I speak from experience here I was working with crassy mirror and it took us some time to tame it. We use distilbert. I love distilbert for text. It takes time. You need to build the vocabulary, you need to do some topic mapping, you need to do some things next to it, which I'm terrible at Code. I'M mediocre. Mediocre at best. At best. With Claude, I'm good and it looks pretty, but I cannot code. But I. What I can do is I have the critical thinking and the commercial experience, so I can challenge the person that does the code and the person that shows me that data and I can work with it. So I would say if you want to analyze video, you want to analyze images, you want to analyze GIFs and other, you know, you know, media formats, don't use an LLM to do it at scale because it's not going to work. It's very hard to pipeline an LLM because if, let's say you do a dashboard with an LLM and you do some sort of task like topic classification or analysis of some specific creatives, you create a looker Studio dashboard or a Power BI dashboard. It's good for a one off. But try to pipeline that because data changes over time. Data changes the format, what's the word for it? The shape, the context changes for the data. How are you going to automate that? How are you going to do a pipeline in BigQuery that's going to go and analyze that data and make sure that you do it over time? With a small language model, you can, I built with Crassy mirror a pipeline in 2023 and it never crashed because the model that you were working with, you can control, you can fine tune, you can change, you can feed it new data, you can do few shots, you can do one shots where you can give it different ways of looking at the data. So it enriches the vocabulary that it uses. So it's a bit different. I get. So I don't know why I care so much because I'm not saving lives, for fuck's sake. But I'm generally like, I also asked Jason like one time, like, why do I care so much? But I generally do care about what we do as an industry and as a community and what we tell each other and what we advise each other. And I think, yes, use LLMs to prove something, do it to test. I use them all the time. Deep research, for example, I found ways to automate deep research and do something before a pitch, before an rfp. They're so good to do a market analysis. So good where I, for instance, I'm writing something about agents and I asked like, what are all the valuations and how did they change in the last years? I'm not going to do that manually, I'm lazy. But I'm going to use the deep research for it. And then I'm going to manually read that and be able to do a visualization in my collab and show people, hey, this is what it is. And then I get people to hate on me on LinkedIn because the pyramid is not to their taste. Yeah, yeah, I know. You know. You know who you are. Multimodal is already there. I've tested it on YouTube videos. The way it would work with an LLM is not the actual video that you analyze is the metadata of the video. And you would analyze the title, you analyze the tags, the description, and so on, and you would infer from those what is actually going on. Now, there are models that can actually analyze the video, but it's very expensive and way more complicated. So an easy fix is to use the metadata. Like this is how. For instance, I do similarity analysis between search intent and the search query and the content of the website. So I create a function that looks at the website, page title, excerpt description, the tags, and everything. And then I look at the search query and try to map to what extent the search query is found into the content of the page. So I think, again, if you want to go multimodal, you should. And that is already there. It's just you have to choose the right model for the right job in an LLM. It's definitely just good for language and not for other types of data sources.

[B]

Oh, that's awesome, Mike. Job great. Okay, we do have to start to wrap up. We could keep going for a long time, but we've got to start to.

[A]

Wrap up, and I feel like I gave you nothing.

[B]

No, this has been a. I'll tell you later.

[A]

I gave you nothing. I'm a horrible guest. Welcome to a new episode of Standard Deviation.

[D]

Okay, after the show, we're gonna give. Michael's gonna give his stern lecture about this negative.

[B]

No, it's not gonna be stern. It's gonna be heartfelt, felt. Okay, let's break back in. All right, we do have to start to wrap up, but one thing we love to do is go around the horn, share a last call, something that might be of interest to our listeners. Juliana, you're our guest. Do you have a last call you'd like to share?

[A]

You could always read my shit, but I actually saw a video today. It's a video from I said to go to. Okay, so it's called this is what a Digital Coop Looks like, and it's a TED Talk by Carol Cadwallader. Is the person from, I think, the Guardian or the observer that Wrote about the whole Facebook data privacy stuff that happened a few years ago. And she did a TED Talk recently. My homie Sean David sent it to me and I watched it. And basically she's talking about how all of this that's happening right now in the market with AI is a coup for data. And basically, data is the crack cocaine in Silicon Valley. And it's what everything that's happening right now is to make people complacent and lazy and use these models and use this technology to kind of cancel their ability to think, which it's very fitting with where we're at right now. And I'm going to share the link with you. I think it's a great talk. And she talks about how all our IPs and all the stuff we create is being taken by these models is used as training data. And again, it's just basically the message that she sends is that we are, you know, the future is already here and it's happening. This is not something that's going to happen in a few years. This is already starting and I can definitely see it. And it's kind of a scary place to think, but I want to end up with a high note. So you can read my article about the UI Act.

[B]

Actually, what's funny is I did read that article and it was really good.

[A]

Good. Are you still alive after reading?

[B]

I mean, there were some parts where I was like, oh, but that's not you. That's the depth of the whole thing.

[A]

Shouts to a shout to a Paul, to Shivan, to Robert Bar, to Fabrizio for reading it. Those are the privacy experts. I was like, please, guys, read this to make sure I don't say stupid.

[B]

No. But it's. It's really good. And it's also kind of interesting to sort of. Of get an understanding of sort of like, okay, yeah, there's some really important distinctions that have to be made. So, anyways, hey, thank you. That's a great last call. All right, Val, what about you? What's your last call?

[C]

So I'm gonna pull a Tim. I have a twofer today. So the first one is I have. And I know Julie has too. I think she got me hooked on the Ologies podcast with Ali Ward. There isn't a topic that she talks about that I'm not interested in. But there was one recently on Salugenology. What does anyone know? Cellugenology.

[D]

What's it called?

[C]

Cell.

[B]

Is that.

[C]

No. Okay, so it's the opposite of pathology, which is the study of disease or Sickness cell E Genealogy is the study of health, which is super interesting. Exactly. That's the to health.

[A]

Exactly.

[C]

So it breaks it down and it's like it's all salad. No, there are actually five categories like movement, nature, art, service and belonging. And it' to social prescribing which NPR has been doing some topics on which I'll. We'll also put the link to that. But it's all about like things you can do to help improve your health. Not a replacement for, you know, medications or other means to get healthy. But anyways, thought it was really interesting. Then my second one is last time I'm going to do it. My plug for MeasureCam Chicago. It's about three weeks away. I have the honor of being one of the volunteers on the planning committee and let me just tell you, we are supported by 10 gracious sponsors to make the attendee experience amazing. We're feeding you breakfast. We're making sure that you are well caffeinated. There's going to be a third party show at the end. The first ever live performance of the third party show with Josh Silverbauer. And then we're headed to dart club and we're going to rent out the whole first floor so people can play darts and hang out and free food. So anyways, amazing event. Please register. Would love to see you there.

[A]

You guys in US do it better than us in Europe.

[B]

I don't know, I've never been. I've never been to a European measure camp.

[C]

Me neither.

[B]

I really like to go.

[C]

I was like there's got to be some play about like hitting your target. Like if, if no one makes jokes about this.

[B]

Well, there's a difference between accuracy and precision, Val. The classic. All right, last call time, Tim.

[D]

Well, I will go with just one. And it is a YouTube video that came out a couple of months ago. I think I picked it up from flowing data, but it's called the Plea the Incredible Story of Smallpox and the First Vaccine. And it's really just a data visualization in video. It's got just some clever ways of illustrating numbers. It's got kind of this plinko board that sort of comes down. So it's like 25 minutes long. It is actually pretty interesting around kind of the history of smallpox and the first vaccine. But it's also just from a data visualization, data storytelling. Like I mean really, really finely crafted and well done. So just kind of a interesting watch and kind of impressive. What about you Michael? What's your last call?

[B]

I'm so glad you asked. So a recent paper I came across, you'll notice that I reference Christopher Barry's newsletter all the time, so. So shout out Christopher. I get a lot of my last calls from him, but recently they did a study where they put together a MacGyver set of tasks. And so immediately I was enthralled because growing up, MacGyver was my favorite show. And yes, I am Gen X, don't worry about it. But basically the thing was there was practical challenges that force the large language model to think creatively, like substituting things or using things not for their specific context. So basically checking to see whether or not an AI could creatively solve a problem using sort of innovative or out of the box ways of thinking about things. And humans are still much better at doing that than LLMs, so just wanted to throw that out there. Anyways, it's an interesting read and kind of neat to see what people are trying to do, but I think it's also cool to watch to see like, okay, yeah, can we help LLMs get more creative with solving problems or thinking outside the box on things? Because then maybe there's dangerous potentially. Yeah.

[A]

Remember AI overviews when they were telling you to make pizza with rocks?

[B]

Yeah.

[A]

They should have started there.

[D]

Well, and to clarify for the Gen X listeners, of course, you're referring to the OG Richard Dean Anderson MacGyver, but did you ever watch the reboot MacGruber from, I don't know, five years ago?

[B]

Oh, no, I did not see that. But no, I mean the old school MacGyver. The real MacGyver.

[C]

Yeah.

[B]

Me, TV, like paper clip and a chewing gum wrapper and like solving real world problems.

[A]

Yes.

[D]

And. And I will now age myself. That when I said they rebooted it about five years ago, apparently it was in 2016 that they rebooted it.

[B]

Oh, all right. Yeah. Never saw it. All right. Juliana, thank you so much for coming on the show. It's fun to talk to you and I think the reason why is because you're like us. You are smart, passionate, a little bit too self deprecating. And that's a nice mix. And so it felt, really felt nice. It was awesome. So thank you so much for coming on the show. I appreciate your research, your insights, the things you could share with the community. I love how you're sharing your knowledge as you're learning and pulling these things through in your work and it comes out like you're. Yeah. So thank you so much. It's been a pleasure having you.

[A]

Thank you for having me.

[B]

Yeah. No, it's our pleasure. And as you've been listening, you've probably been thinking, oh, man, I want to learn more about this topic or I want to interact with these folks. You can reach out to us. We'd love to hear from you. And you can do that on the measureslack chatgpt group or on a LinkedIn, or you can email us at. Contact analyticshour IO. And you can also find Juliana Jackson in a lot of those same places. And also she has a newsletter, which is exceptional, called beyond the Mean, I think. Yep. Okay. Make sure I didn't get that right.

[A]

Beyond the Meme. That's how I should have named beyond the Meme. Well, it would have been more fun.

[B]

There's nothing wrong with having two newsletters and, of course, the Standard Deviation podcast that she co hosts along with Simo Hava, who asked to join her podcast. Let's get the record straight. And so, anyways, there's lots of ways to. To reach out and interact with her as well, so.

[A]

But please don't give me feedback. Nope. No feedback.

[B]

Positive, positive feedback.

[A]

Unless it's, like, extremely positive, like, oh, my God, Juliana, you cured me from anxiety. Or. Or I woke up this morning better because of you. Other.

[B]

I was about to ask an LLM to do a bunch of data analysis, and your insights stopped me from making that terrible mistake. So there you go.

[A]

Only that.

[B]

That's what. That's what we want to hear. All right, well, anyways, please reach out to us. We're delighted to hear from you and those kinds of things. And of course, as you're listening, we'd love to give, have you rate and review the show on the platforms that you listen to it on. That helps us out quite a bit. All right.

[A]

No way.

[B]

Algorithms. We like the feedback, Juliana.

[D]

We'd like it.

[B]

We won't tell you. We won't tell you. It's okay. No, I'm saying about the show in aggregate. Okay.

[A]

It's downhill from here now.

[B]

Yeah, it's fine. But there's one other thing I think is very important to say, and I think both of my co hosts agreed with me, Tim and Val, that no matter how you're trying to use LLMs, the thing you should never stop doing is analyzing.

[A]

Thanks for listening. Let's keep the conversation going with your comments, suggestions and questions on Twitter at analyticshour, on the web at analyticshour IO, our LinkedIn group, and the measuredchat Slack group. Music for the podcast by Josh Crowhurst. Smart guys wanted to fit in, so.

[D]

They made up a term called analytics. Analytics don't work.

[A]

Do the analytics say go for it no matter who's going for it. So if you and I were on the field, the analytics say go for it. It's the stupidest, laziest, lamest thing I've ever heard. For reasoning in competition.

[C]

Cuz we're doing the YouTube shorts.

[B]

I just love that Tim was like oh, it's like what?

[A]

Who cares?

[B]

I know, but which LLM like seriously, just whatever. And Tony, listen out of work pal. He's our editor.

[A]

He's.

[C]

He's had some comments.

[A]

Comments about Michael's audio yet. Michael's.

[B]

That's not even what I was referencing. I. That's not even what I was referencing. I was like. Cuz we're five and a half minutes into recording and we haven't started yet.

[C]

So Tony's like takes material hopefully.

[B]

I know. Oh I trust me, I am so.

[D]

On board to do YouTube.

[B]

Yeah, yeah. I love it and I'm amazed at everything that's happening with those. And I will try Val. I will, I will. I'm here to learn.

[C]

You're here to try.

[B]

As much as another couple of years.

[D]

Before he's like ah, you. It's just Tim whining.

[B]

I'll just be like how did you convince Simo to do a podcast with you? I think that's probably the thing on everybody's mind.

[A]

Actually it was the other way around. Really.

[B]

That's fascinating thought. That'll be my first question.

[A]

Okay, perfect.

[C]

Rock flag and AI is more than LLMs.