Your Data Has Answers. You Just Can't Ask It Questions.

I've been building business systems for 25 years. And for 25 years, I've watched the same scene play out:

An executive asks a simple question. "Why are sales down this quarter?" Or "Which customers are likely to churn?" Or "What's actually driving our support costs?"

Then the scramble begins.

Someone exports data to Excel. Someone else queries the database. A third person pulls reports from Salesforce. Eventually, after days or weeks, a deck emerges. By then, the question has evolved, the data is stale, and everyone involved has lost hours they'll never get back.

The Data Is There

Here's what's frustrating: the answer existed the whole time.

It was sitting in the database. Spread across a few tables, maybe joined with some CRM data, filtered by a date range. The query itself might be 20 lines of SQL. A competent analyst could write it in an hour.

But between the executive's question and that SQL query lies a chasm. A translation problem. The executive speaks in business concepts—customers, churn, revenue. The database speaks in tables, foreign keys, and joins.

Someone has to bridge that gap. Usually, it's the same overworked analyst who's already behind on three other "urgent" requests.

Dashboards Don't Solve This

The standard answer is dashboards. "Let's build a BI layer. Tableau. Looker. Power BI."

Dashboards help—for questions you've already anticipated. They're great at answering "What were sales last month?" Not so great at "Why are conversions down 12% in the Northeast region for customers who signed up through the webinar campaign?"

The moment someone asks a question that doesn't fit a pre-built chart, you're back to the scramble.

And let's be honest: most dashboards become expensive screensavers. Built with good intentions, viewed once, then forgotten while everyone goes back to asking the analyst.

What If You Could Just Ask?

The technology finally exists to do something different.

Not a dashboard. Not a report builder. A system that actually understands your business—the relationships between your data, the meaning behind your tables, the context that turns raw numbers into insight.

Ask in plain English: "Why are Q4 conversions down?"

Get an actual answer: "Three factors identified. 47% of the drop correlates with increased sales response time (2.3 hours → 4.1 hours). 31% correlates with competitor launch on Oct 15. 22% correlates with declining lead quality from Google Ads."

Not a chart to interpret. An answer. With sources. That you can drill into if you want, or just trust if you're in a hurry.

The Missing Layer

This requires something most companies don't have: a business ontology.

An ontology is a map of your business—not just your data, but what it means. It knows that "customer" in your CRM is the same as "account" in your billing system. It understands that "churn" means different things to different teams. It captures the institutional knowledge that currently lives only in the heads of your longest-tenured employees.

Building this used to take years and millions of dollars. The big consulting firms would sell you a "data transformation" project that never quite finished.

Now it can be built in weeks. AI has changed the economics. Not by replacing human understanding, but by accelerating the mapping process. By connecting systems that were never designed to talk to each other. By encoding business logic that was never written down.

The Real Product

Here's what I've come to believe: the context is the product.

The AI—the large language model that generates answers—is increasingly commodity. Everyone has access to the same models. What makes the difference is what you feed it.

Feed it raw data, get generic answers. Feed it your business context—the ontology, the relationships, the institutional knowledge—and suddenly it's useful. It understands your question because it understands your business.

Your data has always had answers. The question is whether you can finally ask it questions.