Vibe Analytics: When Everyone Becomes an Analyst (And Analysts Become Everything Else)

1. Introduction: The Vibe Shift

So, vibe coding is a thing. I won't spend this post arguing why vibe coding is a thing, because that's not really the purpose here. To be honest, the whole thing is in a weird space right now. On one hand, you see the super enthusiastic people posting about how they built a million-dollar app over a weekend. Definitely not true. But on the other hand, you see very experienced developers pointing out how this whole thing cannot really work. And then in the middle, you have developers saying, well, it's really good for prototyping, but not much more.

Look, I'm not a professional developer (although I studied computer science), so I don't have a strong opinion on this matter. But I've been testing these tools for over a year now, and the speed of progress is crazy. When I compare the apps I was building a year ago to what I'm building now - it's wild. And if I project this forward another 12 months, we'll be having a completely different discussion.

So I'm not going to dive into where we are right now, because it's not really relevant. What I want to explore is this: software development might be the first area where we're seeing new paradigms evolving. There are several reasons why it's happening there, but the interesting thing is - if vibe coding is becoming a thing, what about vibe analytics?

And I want to collect some early thoughts about this. Nothing here is solid, nothing is properly tested. It's just an early thought experiment. So let's look into vibe analytics and see where this might take us.

The thing is, if we're honest about it, analytics has always been in a weird spot. We've built these complex data stacks, created elaborate dashboards that no one really wants to look at, and spent countless hours modeling data that only a handful of people can actually understand. And now, with LLMs reshaping how we work with code, it's natural to ask - what happens to analytics?

I've been thinking about this for a while now. Not in a "will AI replace analysts" kind of way - that's boring and probably the wrong question. But more like: if the barrier to building software is dropping dramatically, what happens when the barrier to doing analytics drops too? What happens when anyone can just dump their data somewhere and get insights? And more importantly, what happens to us, the people who've been doing this analytically-minded work for years? Where do we fit in this new world where everyone can theoretically "do analytics"?

And here's what I think might happen: analytics, as we know it, is becoming invisible middleware. It's becoming this background layer that just works, like plumbing or electricity. You don't think about it, it's just there. The whole data modeling, the ETL pipelines, the carefully crafted metrics - all of that becomes infrastructure that hums along in the background. It still needs platform engineers, but far fewer people are doing the heavy lifting.

But here's the interesting part - while analytics becomes invisible, analysts themselves are evolving into something completely different. We're not going to be the people building complex data models or insights anymore. Instead, we're becoming operators and strategists. We're moving into the actual business, into growth, into product, into marketing. We're bringing our detective mindset, our pattern recognition skills, our ability to ask the right questions - but we're applying them directly to business problems, not data problems. And we have some superpowers that we can bring to the business.

2. The Evolution of Analytics Enhancement

Phase 1: Chat with Your Data (Why this isn't vibe analytics)

You definitely see the first attempts at enhancing analytics with LLMs. Most of them revolve around this cringy concept of "chat with your data" or "have a conversation with your data." I mean, yeah, it's the obvious one. We see ChatGPT working, we know we have all this data that no one can really use, and we're stuck building these ridiculous dashboards that no one wants to look at. Plus, let's be honest, most people don't actually understand what the dashboards mean unless you have a really good analyst explaining it to them.

So the obvious step is to say, okay, let's make it conversational. Let's let people chat with their data. And look, I've tested some of the early tools. It's definitely cool to see how well they can return some initial results. But this is just enhanced analytics, not vibe analytics. It's like putting a chatbot on top of your existing mess and calling it innovation.

The biggest problem is - and this has always been the case - the core challenge in analytics isn't getting answers. It was always about asking the right questions. Good analytics teams spend most of their time figuring out what questions actually matter, what questions will lead to real business impact. Once you have the right question, finding the answer is usually pretty straightforward. Most analytics initiatives fail not because we couldn't find the data or build the dashboard, but because the questions we were answering had no real connection to business outcomes.

So this whole "chat with your data" thing doesn't solve the fundamental problem. Sure, someone from marketing might have a specific question that's been bugging them for months, and they can finally get an answer without bothering the data team. That's nice. But they're still asking the same limited questions, just faster. They're not suddenly asking better questions. And that's why this isn't vibe analytics - it's just the same old analytics with a conversational interface slapped on top.

3. How Vibe Analytics Actually Can Look Like

The Marketing Campaign Example

Let me give you a concrete example. Say the marketing team runs this big initiative - they've created an industry report, spent good money on a survey, compiled everything into a shiny PDF. It's a classic lead magnet play. People download the report, give their email, and hopefully, these are decision-makers who might buy your software. The marketing team runs campaigns around it - some Google Ads, LinkedIn thought leadership pieces, the whole thing. It's a big thing.

In the traditional approach, you'd have data scattered everywhere. Web analytics data about landing page visits, source information in GA4, leads in your CRM with form responses, maybe some enrichment data, campaign performance in various ad platforms. If you have a really good analytics team, they'll sit down with marketing, understand the whole initiative, figure out how to connect these data sources in the warehouse, maybe extend some models, and eventually produce a nice dashboard. The premium version is when an analyst manually compiles a report with insights and recommendations. Everyone hopes for the best.

But here's what vibe analytics could look like. Someone from marketing sits down, creates a new chat - maybe in Claude or ChatGPT - and just dumps everything in there. The initial strategy docs, all the campaign assets, the Google Ads data, LinkedIn Ads data, GA4 exports, HubSpot data, even qualitative feedback from sales calls. Everything. Then they ask the obvious question: "I want to do a post-mortem on this marketing campaign. What went well, what didn't work, where can we improve?" No mention of making an analysis of the data.

The key difference isn't just that it's faster or easier. It's that you're combining context with data. The LLM understands what you were trying to achieve, not just what happened. It can see patterns across disconnected data sources that would take weeks to model properly. And most importantly, it can help you ask better follow-up questions. You're not limited to the pre-built dashboard metrics anymore. You can explore, dig deeper, ask "why" and "what if" questions that would normally require a new analytics project. That's when analytics stops being about delivering answers and starts being about discovering insights.

Does it have flaws? Of course, it has. Can it go wrong? Of course it can. However, this could also be possible in the old model. The major shift here is that data becomes just one ingredient in the mix and not the focus topic like in classic initiative reporting.

From Analytics to Operations

And here's where it gets really interesting. When you start doing this, you realize vibe analytics very quickly becomes vibe fine-tuning. It's not just about analyzing what happened anymore - it's about constantly adjusting and optimizing in real-time. The line between analysis and action basically disappears.

Let me paint you a picture. You run initiatives - could be marketing, growth, product, whatever. Every two weeks, you do a check-in. You pull all the data - and data here means everything. Analytics data, sure, but also qualitative feedback, customer interviews, support tickets, strategy documents, asset performance, even Slack conversations about the initiative. You dump it all in, run a full analysis, develop next steps, and implement them over the next two weeks. Then you repeat.

This isn't traditional analytics anymore. It's operations. You're not building dashboards for other people to maybe look at and hopefully make decisions. You're directly involved in the fine-tuning process. You're asking questions, getting answers, and immediately turning those into actions. The whole thing becomes this continuous loop of sense-making and adjustment.

What this means is that everyone starts becoming either an operations manager or a strategist. If you're in the weeds, constantly fine-tuning based on data and context, you're doing operations. If you're zooming out, looking at the bigger patterns, thinking about where to place the next big bet, you're doing strategy. The traditional analyst role - the person who sits between the data and the business, translating one to the other - that role just evaporates. We all become operators or strategists, using data as naturally as we use Slack or email.

Ok, I think, you get the idea, that I am hopelessly optimistic. But why not dream about what could be possible?

4. The Product Manager Parallel

This reminds me of something from my product days. The product manager role is actually kind of weird when you think about it. It basically exists because there was (and is) this massive disconnect between engineers and the business side. You needed someone to translate business needs into technical requirements, to decide what should actually be built, to bridge these two completely different worlds. The PM became this necessary translator, this bridge between two groups that couldn't really understand each other.

But here's the thing - I've worked with some companies that don't have product managers at all. They just work from first principles. Designers design features, developers build features (and sometimes vice versa), sales people suggest features based on customer feedback. Product development happens through this complex, organic system where everyone contributes. It's messy, but it works because everyone understands both the business context and has the tools to actually build things.

Now imagine where vibe coding takes us. When everyone in a company can potentially build a feature, when the sales person can prototype their idea over a weekend, when the customer success team can fix that annoying bug customers keep complaining about - what happens to specialized roles? The PM role might just dissolve because the bridge isn't needed anymore. Everyone speaks both languages. And I think the same thing can happen to analysts. When everyone can analyze, when everyone can pull insights from data as easily as they write an email, the specialized analyst role stops making sense. We don't need translators anymore - we need people who can think analytically while doing actual business work. Guess who that will be?

5. The New Role of Analysts

Guardrails and Metrics Systems

But here's the thing - when everyone can build features, run campaigns, and make data-driven decisions, you need incredibly solid guardrails. Think of it as an insurance system for the whole company. You need a rock-solid metrics system that tells you if the company is heading into danger territory. This isn't about vanity metrics or nice-to-have dashboards (or any granular analysis no one really cares about - who clicked on this navbar item). This is about fundamental indicators that scream when something's going wrong, when someone's experiment is tanking core metrics, when the business is drifting off course.

This kind of foundational metrics work becomes even more critical in a vibe world. It's not sexy work - it's deep, careful thinking about what actually matters for the business, how different metrics relate to each other, what the real leading indicators are. And it requires constant fine-tuning as the business evolves. Someone needs to maintain this insurance system, to make sure it's actually catching problems before they become disasters. This might be where some analysts end up - not building reports, but building and maintaining the fundamental measurement infrastructure that keeps the whole vibe operation from going off the rails. It's like being the person who designs the traffic signals and guardrails on a highway where everyone just got their driver's license.

The Detective Mindset Advantage

What analysts bring to the table is this weird detective mindset. We're really good at finding strange patterns and then obsessing over them until we understand what's actually happening. We see a weird spike in the data and we can't let it go. We need to know why it happened, what caused it, whether it's real or just noise. This investigative instinct, this pattern recognition ability - that doesn't go away just because analytics becomes invisible middleware. If anything, it becomes more valuable when applied directly to business problems.

Think about it - when analysts move into growth or marketing or product, they approach problems differently. A traditional marketer might see a campaign performing well and scale it up. An analyst-minded person sees the same thing and immediately starts asking: but why is it working? Which specific segment is driving this? What happens if we isolate this variable? Is this performance sustainable or are we just capturing low-hanging fruit?

We bring this systematic, investigative approach to everything. And in a world where everyone can run experiments and launch features, having people who can spot patterns, investigate anomalies, and really understand causation becomes incredibly powerful. We're not analyzing data anymore - we're analyzing the business itself, in real-time, with all the context, and with the ability to immediately act on what we find.

Data as Invisible Middleware

So what happens to the actual data infrastructure? I think we'll still need core people working on what I'd call platform engineering for data (Robert Sahlin has a great post about it:https://robertsahlin.substack.com/p/the-golden-path-revolution).

Someone needs to make sure data flows into the right places, that it's accessible, and that it's reasonably clean. But this is really foundational work. Here you lay the foundation that vibing is even possible. You're not building complex data models (for reporting purposes only) or intricate business logic anymore. You're just ensuring the foundational data is there and shaped to meet people's needs.

The whole idea of complex business models in the data layer might just disappear. Think about it - we built these elaborate models because different teams needed different views of the data, and SQL was too hard for most people to use directly. But if an LLM can understand your business context and work with raw data to answer your specific question, why do we need these pre-built models? They were always a compromise anyway - they worked great for specific use cases but were useless for others. We will still need an excellent model for the source layer. The data warehouse becomes less like a carefully curated museum and more like a well-organized storage room.

6 - Pipedream or what

To be honest, no idea if that can play out like this. But we will see change, massive change. And it is a good idea to think about what this change could look like. Running through scenarios.

And most importantly, test and experiment.

At least, this is what I will do next. Run the kind of scenarios I described before - combining marketing, product, and data, and come up with operational and strategic insights. I will keep you posted.