What most early-stage startups get wrong about their data stack (and how to fix it)

After consulting with 20+ funded startups across FinTech, HealthTech, and SaaS, I keep seeing the same data mistakes repeated over and over.

Here's what early-stage startups consistently get wrong:

1. They treat data as an afterthought
   Most founders focus on shipping features and growing users — totally understandable. But by Series A, they're drowning in spreadsheets with no single source of truth. Every department has different numbers for the same metric.

2. They skip the data warehouse
   Google Sheets and direct database queries work at 10 users. They break at 10,000. A proper data warehouse (even a simple one) saves months of painful migration later. I've seen teams spend 6 months untangling spaghetti data because no one built the foundation early.

3. They confuse dashboards with data strategy
   A pretty Power BI dashboard doesn't fix bad data pipelines. Garbage in, garbage out. I've seen companies spend weeks building executive reports only to realize the underlying data was wrong the whole time.

4. They hire the wrong first data person
   The first data hire is usually a data scientist when they actually need a data engineer. Fancy models are useless without clean, reliable data flowing in.

The fix? A lightweight but scalable stack early on: SQL Server + SSIS for ETL + Power BI for reporting. It grows with you and doesn't require a massive team to maintain.

What data mistakes have you seen (or made) at early-stage companies? Would love to hear in the comments.

I put together a free pack of 6 SQL Server diagnostic scripts to help catch data issues early: https://growthwithshehroz.gumroad.com/l/psmqnx