Getting Started with Data Infrastructure

Most businesses collect data. Few actually use it well. The gap between having data and deriving value from it comes down to infrastructure — and it’s more accessible than you might think.

Why Your Current Setup Is Probably Broken

If you’re pulling reports manually, stitching together CSV exports, or relying on Google Analytics as your only source of truth, you’re leaving money on the table. Here’s what a proper data stack looks like.

The Three Layers

1. Collection

Every user interaction should be tracked as an event. Button clicks, page views, form submissions, purchases — all of it. Tools like Segment, Rudderstack, or a simple custom event pipeline feed this layer.

2. Warehouse

Raw events go into a warehouse (BigQuery, Snowflake, DuckDB). This is your source of truth. Never transform at the collection layer — collect everything, transform later.

3. Modelling

dbt transforms raw events into business-meaningful models: sessions, user journeys, cohorts, revenue attribution. This is where the magic happens.

What You Can Do With It

Once the foundation is solid:

• LTV modelling — predict which customers will spend more over time
• Lead scoring — rank prospects by likelihood to convert
• Churn prediction — identify at-risk customers before they leave
• Ad optimisation — feed predicted values back to Google and Meta

Getting Started

Start simple. Pick one critical event you’re not tracking properly and fix it. Track purchase, with all the properties that matter: product, value, channel, user ID. Then build from there.

The goal isn’t a perfect system from day one. It’s a foundation you can trust, that grows with your business.