Inside TextQL's Push to Build a Truly Autonomous Data Analyst With Ethan Ding.

Welcome to Revenge of the Nerds. We’re skipping the hype and going straight to the builders. In this edition, we talked about:

• Why 35 years of 'clean your data first' promises from every BI vendor keep failing, and what TextQL does differently.

• The shift from 'ask the right question' to 'tell me what I need to know,' and why true autonomy is the next frontier for enterprise analytics.

• The Jevons paradox is coming for every data team: what happens when AI agents issue 100 to 1,000 times as many queries as human analysts ever did?
  
  Let’s dive in. No floaties needed…

Revenge of the Nerds

Ethan Ding, Co-Founder & CEO of TextQL

Ethan Ding is the co-founder and CEO of TextQL, the New York startup behind Ana. This AI analyst connects directly to a company's messy data warehouse and starts answering business questions within hours.

TextQL just closed a $17M round led by Blackstone Innovations Investments, bringing total funding above $21M from backers including Neo, DCM, and Unshackled Ventures. The company reports 9x year-over-year revenue growth and net dollar retention north of 300%.

What makes Ding worth listening to is that he is building against a 35-year-old industry consensus. Every data vendor since Teradata has told enterprises the same thing: clean your data first, and then AI will work.

Ding bets that this promise was always a dead end because the cleanup never finishes, and the dashboards keep multiplying. Ana explores the mess the way a new analyst locked in a room with a SQL terminal would, building its own context from whatever it finds.

What Is TextQL & What Does Ana Actually Do?

At TextQL, we build a product called Ana. Ana is an autonomous analyst who works with the extremely messy data enterprises actually have.

For the past 35 years, people have been sold a story about self-service: put AI on top of your databases, whether it's Teradata from the 2000s or the Snowflakes from the 2010s, and eventually, once you clean up your data enough, you'll be able to ask questions.

We built a thing that cleans up your data and runs analysis out of the box, so you can connect it to very messy enterprise environments at scale and have it work.

Why Are Enterprise Data Environments So Messy?

Data is an interesting problem. Say you work at a large bank like Goldman Sachs: you could have a table sitting inside an S3 bucket and no idea where it came from.

ETL platforms are very disconnected from source systems, and it's extremely hard to prevent people from making infinite copies of data. Everybody knows the “export to CSV and then duplicate every CSV a hundred times” problem.

As a result, nobody ever wants to damage a source of truth, so nobody works on top of one either. They always extrapolate and do their own calculations, and tracing the lineage down is very hard.

What Has Failed In Enterprise Data & How Is TextQL Fixing It?

For the past while, every initiative to make AI accessible has looked the same: you bring in a team, and they spend the next 40 hours a week labeling every metric. Revenue has to be calculated like this in this column, with this edge case. Cancellations need a subtraction, or an aggregate on some post-processing field.

And this varies from company to company. What you usually see is that those initiatives fail after a couple of months and a couple of million dollars spent on shelfware or a consulting arrangement.

We wanted to build a system that, instead of needing good context to work on your data, explores the data environment and builds its own context.

Excuse the analogy, but if I locked you in a room with only a SQL terminal and said “make sense of this database,” you would first run “select star from information schema,” figure out which tables and columns are there, and eventually piece together the contents. You would see a bike table and think, “maybe this is a bike shop.” That is how you model what you are looking at.

What Can Ana Do Today & Where Is She Headed?

Today, Ana already handles most of the work I used to do in my last job: someone asks a question, I pull numbers, and I build a visualization. It's deployed across Fortune 50s and Fortune 500s, spanning hundreds of thousands of tables, on-prem and in the cloud.

The gap between today and tomorrow is that people still have to ask questions. You still have to know how to frame a data-driven decision. “I want to email 50,000 accounts, but only spend my time on the most valuable ones. Can you triage that and figure out the top 10 I should go after?” Framing those questions is very hard.

What we're trying to crack now is true autonomy. Instead of saying, “I'm Fred, a RevOps manager, give me a commission breakdown for my sales org,” you say, “I'm Fred, a RevOps manager, tell me what I need to know. What am I not doing? Go through my CRM, my ERP, my entire systems of record, and bring back what I should know about without me having to ask.”

That's the modality the world is going toward, and it's really exciting.

What Do TextQL’s Big Clients Like Dropbox & The NBA Have In Common?

The main thing is massive sprawl. These are companies with tens of thousands of dashboards. Sometimes you still see MicroStrategy from the 1970s running alongside Domo, Qlik, Tableau, or Looker.

People have upgraded over time. Every time a new team is spun up, they pick a new tool, but the old team is still there using the old one. So you end up with 10k to 20k dashboards spread across multiple systems.

They're expensive, they're running SQL queries every other day, and you're getting metered on all of it. Being able to move that business logic at scale to other places is extremely, extremely valuable work.

How Long Does It Take To Implement TextQL?

This varies across companies, primarily depending on their security team. We generally encourage most companies to start with our public cloud offering.

If all you have is a very large database cluster, you can deploy the agent within minutes and often have business operators analyzing your Snowflake or Databricks data at a massive scale before the day is done.

That said, we work with some public financial services institutions and large healthcare institutions. Those are highly compliant systems, so we spend a lot of time with our security team configuring the appropriate permissions across the entire system.

What Drove You To Start TextQL?

If you go on r/dataengineering, or any of those forums where data engineers spend a lot of time, the first three posts on any given day are always: “My CIO brought in a new database, and now we have to migrate.” “We just wrapped up this migration after five years, now we have to migrate again.”

Any solution to this rhymes with the XKCD meme of 15 competing standards. Someone says, “I'm going to fix this,” and now there are 16 competing standards.

We want to wrap all the other databases in a different paradigm. If we weren't a database or a BI tool, what could we do by wrapping around the existing infrastructure to deliver that? That was the edge we felt we had to lean into.

If TextQL Wins, What Happens To The Data Analyst Role?

What our users are doing today is much more managing teams of agents that are bringing back insights, and then sifting through those insights to figure out what's valuable. Just because you can ask a question now doesn't mean you should. Token costs and query aggregation costs against your data warehouse mean you have to be mindful.

Say I run the analysis, “find me the top 10 most at-risk fraud transactions in the past 24 hours.” Maybe that's an extremely EV-positive decision to run, because there are a lot of transactions in that window and a lot of fraud in there.

Now you think, “I'll run this every hour so I can act on it faster.” Suddenly, you're looking at 24x the cost, but maybe the expected value is still worth it because the top one offsets every hour.

In other words, the analyst of the future is not the person writing SQL anymore. They're the person deciding which questions are worth asking, how often to ask them, and when the cost of knowing outweighs the value of the answer.

Additional Reads

• Five habits for faster product cycles: Ding breaks down the engineering and decision-making rituals that let small teams ship faster than legacy data vendors with thousands of headcount.
• Inside the architecture of an autonomous data analyst: A conversation on how Ana navigates messy enterprise warehouses, why context-building beats data cleanup, and where agentic analytics is heading next.
• The end of the dashboard era: A deeper look at why 35 years of self-service BI has failed, and how AI agents asking their own questions could finally replace the dashboard sprawl enterprises are drowning in.

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