Vibe analytics for the data stakeholder: A new tool for data exploration and collaboration

Let’s say you’re a sales manager. You're midway through your third coffee (it’s only 7am but, hey, we’re not judging.) Suddenly, the question hits you: Which customer segments are churning fastest this quarter, and why?

It's the kind of insight that could reshape your team's strategy for the rest of the year. You know the data exists somewhere—in Salesforce, maybe your CRM, possibly scattered across a few spreadsheets your team maintains. 

But you don't know exactly how to extract or analyze it. You could figure it out. Maybe. But you don’t have all day to futz around and find out.

You could email the data engineering team. They're helpful, but they're also drowning in requests. Your question will join a queue of six other projects, each more urgent than the last. 

If you're lucky, you'll get preliminary results in two weeks. By then, the insight might be obsolete.

This is the daily reality for data stakeholders across thousands of organizations. You're close to the business. You understand what questions matter. And you know what decisions hinge on the answers. But the data itself remains frustratingly out of reach.

What if you could answer your own questions? What if you could pull together data from multiple sources, analyze it with AI assistance, and get accurate results within minutes instead of weeks?

That's exactly what vibe analytics makes possible—a new approach to data exploration that puts stakeholders in control without requiring them to become data engineers.

What is vibe analytics?

Vibe analytics uses Generative AI (GenAI) to generate unique insights from data at a fraction of the time required by traditional analytics. The term is modeled after vibe coding, where developers use Large Language Models (LLMs) to create application skeletons from simple natural language descriptions.

With vibe analytics, you can feed data into an LLM through a data collaboration platform, ask exploratory questions in plain English, and receive accurate results. No SQL required. No Python expertise necessary. Just you, your data, and a conversational interface that understands what you're trying to accomplish.

The magic happens through four key stages: 

• Input. First, you input your data in whatever format you have—Excel spreadsheets, CSV files, JSON exports from your tools, or direct connections to databases and applications.
• Query. Second, the LLM queries this data intelligently, determining what information to extract and how to combine it. 
• Output. Third, it outputs results in the format you need, whether that's visualizations, summaries, or detailed tables. 
• Analysis and refinement. Fourth, you analyze and refine those results, either by adjusting your questions or by collaborating with data practitioners to enhance the solution.

You can do this directly using an LLM. A data collaboration platform, however, provides a myriad of additional features such as an all-in-one analytics workspace, enabling sharing and cooperation between team members and teams, a suite of data connectors, and advanced features such as data snapshotting.

What makes vibe analytics different from traditional BI tools is that it doesn't require you to understand the underlying data structure. You don't need to know which tables to join or which fields to aggregate. The AI handles those technical details, letting you focus on the business questions that matter.

Why traditional approaches fail data stakeholders

Most organizations approach data access through one of two paths. Both create significant friction for stakeholders.

Request queue

The first path is the request queue. You submit a ticket to the data team describing what you need. The team prioritizes it against competing demands, builds the solution when capacity allows, and delivers a dashboard or report. 

This process assumes several things rarely true in practice: that you know enough about the data structure to ask the right questions upfront, that the data team isn't overwhelmed with other work, that your question is straightforward enough to solve quickly, and that all relevant data already lives in your data warehouse.

When these assumptions hold, you might get results in days. When they don't—which is most of the time—you're looking at weeks or months. By then, the business context may have shifted entirely, rendering your original question less relevant.

Self-service BI

The second path is self-service BI. Your organization invests in dashboards that let you explore data independently. 

This sounds promising…until you realize three things:

1. These dashboards only work with data that someone has already loaded and modeled, which excludes the critical spreadsheet from finance or the export from that new marketing tool. 
2. The interfaces are complex enough that using them effectively requires training and practice. 
3. Whenever you want to do something the dashboard doesn't support, you're back to submitting requests.

How vibe analytics empowers data stakeholders

Both approaches share a fundamental problem: they position you as a passive consumer of data rather than an active explorer. You're dependent on others to access, structure, and present the information you need to do your job effectively.

This dependency creates delays that compound over time. A two-week wait for a simple analysis means two weeks of decisions made with incomplete information. A backlog of unanswered questions means opportunities missed and problems identified too late to address efficiently.

Vibe analytics flips the model used by traditional BI tools by putting data exploration tools directly in your hands. This doesn't mean you're replacing data engineers—it means you're no longer entirely dependent on them for every question.

An example of vibe analytics in action

Consider a practical example. You're a regional sales manager trying to understand which cities are driving the most revenue and which are adding the most new customers. Traditionally, you'd export data from Salesforce, maybe open it in Excel, and try to sort through hundreds of rows manually. Or you'd wait for the data team to build something.

With vibe analytics, you export that Salesforce data and upload it to a data collaboration platform. You ask in plain English: "Show me the top cities by revenue and the cities with the most new customers added this quarter." 

The AI generates the analysis, creates visualizations, and presents results you can immediately act on. If you want to dig deeper—say, looking at trends over time or breaking down by product category—you ask follow-up questions and get instant refinements.

This capability transforms how you approach business problems. Instead of wondering whether a hypothesis is worth investigating based on how much effort it would take to test, you can explore freely. Got a hunch about customer behavior? Test it. Want to understand a surprising trend? Investigate immediately. Need to prepare for a meeting with updated numbers? Pull them yourself.

This self-service capability also improves the quality of eventual solutions. When you do need to escalate something to the data engineering team—perhaps to turn a prototype into a production pipeline that runs automatically—you can show them exactly what you want rather than trying to describe it. A working example eliminates ambiguity and dramatically reduces rework cycles.

The collaboration advantage

Here's what makes vibe analytics more than just another self-service tool: it doesn't isolate you from data practitioners. Instead, it creates a new model for collaboration that benefits everyone.

Traditional workflows separate stakeholders and practitioners into distinct phases. You describe what you need, they build it, you review it, they revise it. Each handoff introduces opportunities for miscommunication and delays.

Vibe analytics enables parallel work. You build a prototype that answers your immediate question and lets you make near-term decisions. Meanwhile, that prototype serves as a specification for data practitioners to build a more robust solution. They can see exactly what you're trying to accomplish, understand the business logic you care about, and focus their efforts on scalability, reliability, and integration rather than requirements gathering.

This collaborative approach surfaces issues earlier. If your question isn't quite right or the data doesn't support the analysis you envisioned, you discover that while exploring rather than weeks into a formal project. Data practitioners can provide guidance on data quality, suggest alternative approaches, or help refine your methodology—all while you maintain ownership of the business logic.

The collaboration also flows in reverse. When data practitioners build out the production version of your analysis, they're working with code and logic you can understand. You're not receiving a black box dashboard. You can see the SQL queries, the Python transformations, and the assumptions built into calculations. This transparency builds trust and makes it easier to identify when something doesn't look right.

Over time, this collaborative pattern increases overall data literacy within your organization. As you work with vibe analytics, you naturally develop a better understanding of how data is structured, what kinds of questions are easy versus hard to answer, and where data quality issues exist. You become a more sophisticated consumer of analytics and a more effective partner to your data team.

Getting started with vibe analytics

Adopting vibe analytics successfully requires the right tools and the right approach. Not every platform claiming AI capabilities is actually built for the way vibe analytics should work.

An effective vibe analytics platform needs several key features. It should: 

• Show you the code behind every analysis, enabling both transparency and refinement. 
• Support varying levels of AI assistance, from generating complete solutions to providing starter code you can modify. 
• Create a collaborative environment where both stakeholders and practitioners can work together. 
• Track dependencies between different parts of your analysis so additions don't break existing work. 
• Cache data to avoid repeatedly hitting databases and inflating costs.

Several tools offer these capabilities to different degrees.  A few include: 

• Julius, which focuses on academic research and data analysis with Python and AI assistance; 
• Metabase, which  provides open-source analytics with natural language search and SQL generation; and
• Google Colab AI, which reimagines Jupyter notebooks as an AI-first solution with multimodal LLM integration. 
• Fabi, our AI-native data analysis platform that emphasizes collaboration between data practitioners and data stakeholders. 

At Fabi, we built our data collaboration platform specifically for vibe analytics from the ground up. We designed it to be AI-native, meaning every feature assumes you'll work collaboratively with AI rather than treating AI as an add-on to traditional workflows.

When you start with Fabi, you can upload your data in whatever format you have or connect directly to data sources. The platform automatically analyzes your data structure and generates Python code to load everything. 

From there, you use natural language queries to explore. Ask simple questions like "Describe this data" to understand what you're working with. Ask complex questions like "Create a customer segmentation analysis based on purchase behavior and engagement metrics" to generate sophisticated analyses.

The platform breaks complex requests into discrete steps, generating code for each one. You can accept what the AI produces, modify it directly, or ask for refinements. As you build out your analysis, Fabi tracks relationships between different components, ensuring new additions work with what you've already created.

When you're ready to share results or collaborate with data practitioners, everything exists in a unified workspace. Others can see your logic, understand your assumptions, and build on your work. The prototype you created to answer an urgent question becomes the foundation for a production solution that serves your entire team.

The future of data stakeholder empowerment

Traditional BI and analytics workflows lead to friction between data practitioners and data stakeholders. Sometimes, the relationship between the two groups can grow cold - even hostile. 

This isn’t anyone’s fault. It’s inherent in the nature of data. Until now, there hasn’t been a good way for anyone - engineers, analysts, or business stakeholders - to automatically combine, query, visualize, analyze, and interpret large volumes of data without spending days or weeks engineering a solution. 

Vibe analytics offers a new way forward. Using a data collaboration platform that supports vibe analytics, you can use natural language to explore data freely, test hypotheses, and discover insights without becoming a data engineer. This frees practitioners from one-off queries to focus on building infrastructure that makes exploration more powerful for everyone.

If you're tired of waiting for answers or working with ill-fitting dashboards, vibe analytics might be for you. Start small by exploring a dataset you already have or testing a hypothesis that's been nagging at you. 

To get started with vibe analytics, create a free Fabi account and start exploring your data today.