Every few years, the data world rediscovers how painful it is to get answers.
Today's "modern" data stack – born out of good intentions and developer-forward design – has become a Rube Goldberg machine of warehouse, ETL, transformation, visualization, dashboarding, and data quality layers. It takes 5 to 9 tools and hundreds of thousands of dollars a year just to ask, "Did this feature move the needle?"
And yet, most teams still wait on the data team.
Velocity suffers. Your engineers don't touch dbt. Your PMs avoid Looker. Your analysts avoid Amplitude. Errors hide across layers... Was it Airbyte? dbt? Looker? Segment? When a chart looks off, we play detective across tools rather than analyst. And while we're debugging, the dashboard is stale and the data is wrong, if it refreshed at all.
Complexity creeps in. Every tool you add solves a local pain and creates a global one. Pipelines break. Models drift. Queries timeout. "Overall data refresh" now takes 18 hours. Ask anyone in data how often they're cleaning up someone else's metric.
Silos persist. Business intelligence lives in one world (revenue, CAC, finance) and product analytics in another (retention, funnels, LTV). But the questions always cross: "Which cohort converted after the pricing change?" Good luck bridging that with today's fragmented toolset.
Proactivity is nonexistent. We have AI everywhere – from design tools to code editors – but our data still sits idle until someone asks the right question. Most data spends 99% of its life asleep in a warehouse.
A platform where insights surface before you ask. Where data is continuously monitored, modeled once, and reused everywhere. Where engineers, analysts, and PMs work from a shared canvas. Where costs go down, not up, as you grow.
We're building that platform. We're calling it Altertable. Want to see how we're building this unified architecture? Learn about our technical approach to solving these problems.
If you've ever felt the pain above, we should talk.
