Code Is Cheap. Data Is the New Bottleneck

A year ago, the conversation was always about engineering capacity. We need another developer. The backlog is too long. Can we outsource this feature?

That conversation is changing fast.

Tools like Cursor, Claude, and Google AI Studio have made writing code dramatically cheaper. What used to take a developer days now takes hours. The bottleneck has quietly moved somewhere else—to data.

Data Can't Be Generated

Here's the difference: code can be generated on demand. Data cannot.

When someone asks "what's our retention rate by acquisition channel?" the answer doesn't come from writing code. It comes from whether you tracked acquisition sources properly, whether you defined retention consistently, whether your data is clean enough to trust.

No AI tool can help if the answer is "we never tracked that" or "it's somewhere in a spreadsheet from 2023." Data has to be collected intentionally, over time, before you need it.

The Three Data Problems

Most teams hit the same walls.

They didn't track it. The feature shipped but nobody added the events. Now you want to know how users interact with it and there's nothing to query. You can add tracking today, but you've lost months of history.

It's scattered everywhere. Customer data lives in the CRM. Usage data lives in the product database. Revenue data lives in Stripe. Marketing data lives in five different platforms. Getting a complete picture means stitching together sources that weren't designed to work together.

Nobody can access it. The data exists, it's clean, but only two people in the company know how to query it. Everyone else waits in line or makes decisions without it.

These aren't engineering problems. They're organizational habits that take time to change.

What Good Data Infrastructure Looks Like

Teams that move fast have figured out a few things.

They track everything early, even before they know they'll need it. Adding an event takes five minutes. Realizing six months later that you should have been tracking something costs you those six months of data.

They centralize where it makes sense. Not necessarily one giant warehouse, but at least clear answers to "where do I go to find X?" A product manager should be able to ask show me signups by campaign this month without knowing which system it lives in.

They make it accessible. The data team shouldn't be a bottleneck for basic questions. When anyone can get answers themselves—through dashboards, natural language queries, or simple SQL—the whole organization moves faster.

The New Competitive Advantage

Two companies want to build the same feature. Both can ship it in a week. But one company knows exactly which users to target, has clean behavioral data, and can measure impact the day it launches. The other has to guess, wait, and run manual analysis weeks later.

Same code. Same timeline. Completely different outcomes.

The companies winning now aren't necessarily the ones with the best engineers. They're the ones who treated data as infrastructure, not an afterthought. Where questions like what's converting best right now? get answered in seconds, not days.

The Shift Is Already Here

Code used to be the constraint. Now it's abundant. The limiting factor is whether you have the data you need, organized in a way people can actually use.

The organizations that recognized this shift early are already moving faster. The ones still treating data as someone else's problem are falling behind without realizing why.