You don't need a data lake on day one. A pragmatic approach to building data infrastructure that grows with your product and prepares you for AI.
Startups get conflicting advice about data. On one hand: "Data is your most valuable asset! Collect everything!" On the other: "Move fast! Don't over-engineer! Ship!"
Both are partially right. The answer isn't choosing one extreme—it's finding a pragmatic middle path that sets you up for the future without slowing you down today.
Before you worry about data infrastructure, ensure you're capturing the right data in the first place.
Product analytics: Use a tool like Mixpanel, Amplitude, or PostHog from day one. Track:
Backend logging: Structure your logs so they're queryable later:
Cost: Nearly free with generous free tiers. Time investment: a few hours.
Once you have real users and need to answer questions your analytics tool can't, set up a basic data warehouse.
Simple setup:
What to warehouse first:
Cost: $100-500/month. Time investment: a few days.
As your data needs grow, invest in proper data engineering:
ELT pipeline: Fivetran, Airbyte, or custom scripts to move data reliably.
Transformation layer: dbt for version-controlled data transformations.
Reverse ETL: Push insights back to operational tools (Hightouch, Census).
Cost: $500-2000/month. Time investment: weeks of engineering time.
Even before you build AI features, certain data practices make AI integration much easier later.
Don't just log that a user clicked a button. Log the context:
This context becomes training data for recommendation systems and personalization.
Many AI applications need unstructured data:
Store these in queryable form, not just as logs that roll off.
The best AI systems learn from user behavior:
Capture these signals from the start, even if you're not using them yet.
Vector embeddings power modern AI features. Prepare by:
"Let's dump everything into a data lake and figure it out later!"
This leads to:
Better: Be intentional about what you collect and why.
"We'll worry about GDPR/CCPA when we're bigger."
This leads to:
Better: Build privacy controls from the start. It's easier than retrofitting.
"We're moving fast, we can't slow down for documentation!"
This leads to:
Better: Document as you go. A shared glossary takes hours to maintain and saves days of confusion.
Before building AI features, ensure you have:
☐ User behavior data with timestamps and context ☐ Content/product data in a structured, queryable format ☐ Business events logged with consistent schemas ☐ Text data stored for potential embedding ☐ Feedback signals captured for model improvement ☐ Privacy compliance sorted out ☐ Basic data quality monitoring in place
Weeks 1-4: Set up product analytics and structured logging.
Months 2-6: Answer questions with your analytics tool. Note when you hit limitations.
Months 6-12: Set up a basic warehouse when analytics isn't enough.
Year 1+: Invest in proper data engineering as team and data grow.
When AI is ready: Build on the foundation you've laid.
You don't need a sophisticated data platform on day one. You need good habits: capturing the right data, maintaining basic quality, and building infrastructure as needs emerge.
The startups that succeed with AI aren't the ones who built massive data lakes early. They're the ones who consistently captured useful data and maintained the discipline to keep it clean and documented.
Start small. Think big. Build what you need when you need it.
Let's discuss how we can help bring your ideas to life with thoughtful engineering and AI that actually works.
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