Most early-stage founders think they need to hire a data engineer before they can get real analytics. You don't.

Here's what actually matters at the 0-to-1 stage:

1. Pick ONE source of truth for revenue data.

If your Stripe dashboard says one thing and your spreadsheet says another, you're already losing. Connect your payment processor to a warehouse (even a simple Postgres instance) and query it directly.

2. Automate the report you check every morning.

Whatever metric you're refreshing manually — MRR, churn, signups — build a pipeline that updates it for you. It takes 2-4 hours to set up and saves hundreds of hours over a year.

3. Don't build dashboards nobody uses.

I've seen founders spend weeks on a 15-chart Looker dashboard that gets opened twice. Start with 3 numbers: revenue, active users, and one leading indicator specific to your business.

4. Your data stack doesn't need to be fancy.

Postgres + dbt + a simple BI tool gets you 90% of the way. You can always migrate to Snowflake/BigQuery later when you actually need it.

I build this exact setup for non-technical founders at Dataflow Studios. One project, one flat fee, fully documented so your future hire can maintain it.