What Startups Get Wrong About Data Strategy

Startups move fast. That’s a good thing—until it’s not. When it comes to data, many startups treat it like a box they’ll unpack “later.” But that “later” often turns into missing insights, duct-taped dashboards, and frustrated teams asking, “Where’s the source of truth?”

Let’s talk about the most common mistakes I see in early-stage companies—and how to avoid them.

1. Mistaking Dashboards for Strategy
Building a dashboard in Looker or Tableau ≠ having a data strategy. Dashboards are outputs. Strategy is about asking the right questions, prioritizing metrics, and structuring your data to answer them.

2. Building Without a Data Model
Many teams skip over defining relationships, key entities (customers, events, products), and naming conventions. The result? Conflicting reports and duplicate logic in every query.

3. Over-Collecting, Under-Using
Startups love tracking everything—but if you don’t know why you’re collecting it, you’ll drown in noise. Focus first on 3–5 metrics that tie directly to product or growth KPIs.

4. Thinking You’ll “Fix It Later”
A fragile foundation is harder to fix once you're growing. Invest early in basic hygiene: clean ETLs, a consistent data warehouse (like Snowflake), and version-controlled analytics code.

Good data strategy isn’t about over-engineering—it's about aligning your data with business priorities from the start.

📩 Need help building a lean data foundation that scales? Let’s talk.