Does the World Need Another Data Tool?

I ask myself this question more often than I'd like to admit.

There are over 400 companies in the "modern data stack" landscape. BI tools, ETL platforms, data warehouses, analytics engines, visualization layers, orchestration frameworks. You name it, someone's funded it. The data tooling market is worth tens of billions of dollars and growing. The last thing it seems to need is another entrant.

And yet, here I am, building one. So either I'm delusional, or the question itself is wrong.

I think the question is wrong.

The Saturated Market Illusion

From the outside, the analytics market looks saturated. Hundreds of tools. Billions in funding. Every conceivable problem seemingly addressed by multiple well funded companies.

But here's what that landscape chart doesn't tell you. Most of these tools serve the same customers. They're built for companies with dedicated data teams, six figure analytics budgets, and the engineering bandwidth to stitch together a stack of specialized tools.

If you're a Fortune 500 company with a 20 person data engineering team, you're spoiled for choice. Snowflake or Databricks for your warehouse. dbt for transformations. Looker or Tableau for dashboards. Fivetran for ingestion. Airflow for orchestration. Monte Carlo for observability. The stack is deep, the options are plentiful, and the budget exists to make it all work. More importantly, those companies have the analytical expertise to know what to do with those tools. They have people who know which questions to ask, which metrics actually matter, and when a number is signal versus noise.

But what if you're not that company. If you're a startup founder trying to add analytics to your product, or a small business trying to understand your own data, or a team of two engineers who need insights but can't hire a data specialist, that landscape chart is useless to you. Those 400 tools might as well not exist. And even the affordable ones (Metabase, Redash, Google Sheets) assume you already know what to look for. They give you a place to put charts. They don't tell you which charts matter.

The market isn't saturated. It's saturated for a specific customer. Everyone else is still underserved, not just on tooling, but on the expertise those tools assume you already have.

Why I Know This Personally

I didn't set out to build a data tool. I set out to build a customer success product.

The goal was specific. Detect patterns to figure out whether a customer is likely to churn, or whether there's a way to expand revenue from them. To do that, we needed to track metrics across various data points and determine the health of each customer. Usage patterns, support tickets, billing data, product engagement. Stitch it all together and you'd have a picture of who's happy, who's at risk, and where the opportunities are.

This sounded easy. It wasn't.

The problem hit us almost immediately. Every single business tracks their metrics in different ways. They use different tool stacks. Some have everything in Salesforce. Others have a patchwork of spreadsheets, databases, and SaaS products that don't talk to each other. There's no unified way of analysing data across these businesses without either paying for incredibly expensive tools or losing a significant number of nights of sleep trying to stitch it all together.

We tried. We really tried. But building a product that fundamentally depends on data analytics is impossibly hard when the analytics infrastructure underneath assumes you're already a data expert with a big budget. We weren't. We were a small team trying to build a customer success product, not a data platform. And even when we got tools connected and data flowing, we hit a deeper problem, we didn't know what to analyze. We had usage logs and support tickets and billing data. But which patterns actually predicted churn? Which metrics were noise? That required analytical judgment we hadn't built yet.

That product didn't fail because the idea was bad. Plenty of companies need better customer health tracking. It failed because the gap between "we have data" and "we understand what the data means" was wider than we ever imagined, and every tool that claimed to bridge that gap gave us charts, not answers.

The LLM Bet

After the customer success tool, I stepped back and looked at the problem differently. Maybe the issue wasn't which analytics tools to pick. Maybe the issue was that configuring all of these tools was the core problem. Too many knobs, too many integrations, too much assumed knowledge.

So I did what most founders in this space have done recently. I thought, if I just add an LLM and let users ask what they want in plain English, they'll finally get the answers they need. No SQL expertise required. Just ask a question, get an answer.

So I built Doorbeen.

I carried the lessons from the first product with me. The biggest one was that users were scared of connecting tools to their production databases. They didn't trust a black box with their data. So I made Doorbeen open source. Full transparency. You can read every line of code that touches your data.

It was a better product than the first one. The open source decision was right. The natural language interface felt magical in demos.

But here's the worst thing. Outside of the demos, most of the time the agent failed to give users answers that would actually help them significantly. It could generate SQL. It could return results. But the results were often wrong in subtle ways like wrong joins, wrong filters, wrong assumptions about what a metric actually means in the context of that specific business. The model could write queries, but it didn't know which questions to ask in the first place, or whether the answer it produced actually made sense for that business. It had access to the data, but not the expertise to interpret it. Same problem as every self serve tool before it, just wearing a different hat.

And when it falls apart, users don't know. They get a confident looking answer that's quietly wrong. That's worse than no answer at all.                          

The Epiphany

That's when it hit me. Text2SQL isn't just "not enough." The whole approach we take to data analysis might be wrong.

We keep trying to make it easier for people to interact with the existing infrastructure by building better UIs on top of warehouses, natural language on top of SQL, drag and drop on top of complex pipelines. But the infrastructure itself was never built for these people. It was built for data engineers and analysts. Adding a friendlier interface doesn't fix a foundation that was designed for someone else. And the deeper problem is that the foundation assumes you already know what to measure, how to segment your data, and when a number is real versus noise. It encodes none of the analytical thinking that makes the tools useful in the first place.

Maybe what we need isn't another layer on top. Maybe we need to rethink the foundation itself, one that doesn't just make data accessible, but encodes the expertise to make sense of it.

Data analysis needs to work for a world where it's not just built for select human beings who are already experienced and have access to the right tools. It needs to work for everyone and that includes the AI agents that are starting to take so much weight off our shoulders. If an agent can't reliably analyse data because the infrastructure doesn't give it enough context, that's not the agent's fault. That's the infrastructure's fault.

We need to build a world where data analysis is accessible to all human beings and agents in a fair way. Where nobody feels dumb because they can't figure out how to get answers from their own data. Where nobody feels left out because they can't afford the tools or don't have the technical background.

This isn't a feature problem. You can't solve it with a better chart library or a smarter chatbot. It's a foundation problem.

The Real Question

So does the world need another data tool? is the wrong question. The right question is does the world need a data tool for the people that existing tools ignore?

And the answer to that is obviously yes.

Look at who's been left behind by the current analytics landscape.

Product and Design teams who need constant insights from data to understand how users behave and where the product should go next. But they rarely have direct access. Instead, they fall at the mercy of data teams, waiting days for answers to questions that should take minutes. The people closest to the product are the furthest from the data.

Startup founders who need analytics in their products but can't afford a six figure stack and a data engineer to run it. Even when they find affordable tools, they don't have the analytical background to know what to measure or which patterns actually matter. I was this person. Twice.

Non technical teams who've been promised "self serve analytics" for a decade and still can't get a straight answer to a simple business question without filing a ticket with engineering.

Small engineering teams who understand their data but don't have time to become experts in dbt, Airflow, Looker, and the rest of the modern data stack alphabet soup. They can build the pipeline, but knowing which questions the pipeline should answer is a different skill entirely.

These aren't edge cases. This is most of the market. The analytics industry has been so focused on serving the top 5% of companies that it's forgotten about the other 95%.

What I'm Betting On

I'm betting that the next wave of analytics isn't about more powerful tools for data teams. It's about accessible tools for everyone else.

Maybe I'm wrong. Maybe the market really is saturated and there's no room for a different approach. Maybe the 95% of companies currently underserved by analytics tools will stay underserved forever.

But I don't think so. I think the analytics industry built an incredible ecosystem for a narrow audience and mistook that audience for the whole market. I think I'm not the only founder who's pivoted away from a good idea because the data infrastructure underneath was too hard. And I think the rise of AI agents is about to make the foundation problem impossible to ignore.

The question that keeps me going isn't "does the world need this?" It's why didn't this exist when I needed it the most?

I still don't have a good answer to that one. But I'm building towards the future where hopefully nobody else has to feel that way.