Data for Business Builders — Core Patterns

Original thinking from a 2020 Notion doc titled “Ultimate Guide to Data for Business Builders.” The premise: at a certain point in a company’s growth, the founder’s job shifts from building the product to building the business — and the data challenges that come with that shift are predictable and solvable.

Pattern 1: Operational vs. Reported Numbers (Snapshots)

The core tension: front-line operational numbers and “reported” numbers diverge over time. You weren’t crazy when you reported the numbers the first time — the underlying data changed after the fact (late-arriving events, retroactive corrections, backfills).

Solution: Snapshots. Capture the state of key metrics at the time they were reported, so you can compare “what we said then” to “what the data says now.”

Key fields for snapshots:

• The metric value at snapshot time
• A reported_at timestamp
• Handling for hard deletes (deleted records that disappear from current state but existed at report time)

This connects directly to analytics-engineering and the concept of incremental-models — snapshots are the mechanism that lets you have both a current-state view and a historical-state view.

Pattern 2: Version Control for Business Definitions

Business definitions change as the company evolves. The problem isn’t changing definitions — it’s changing them without showing stakeholders the impact.

Solution: Environments. Build the new definition in an isolated environment without touching production. Show the old and new side-by-side. If stakeholders reject it, scrap the environment. If they approve, promote it and tag the change.

This is the same mental model as dbt environments and git branching applied to business logic. The key insight: showing people how the data will look under a new definition is far more effective than talking about it hypothetically.

Pattern 3: Walking Back to Source

When inheriting or auditing an existing data setup:

• Trace stored procedures, queries, and ETL jobs back to their source systems
• Create a denormalized data model from existing Excel spreadsheets or reports (reverse-engineering the implicit model)

This is the “transition guide” pattern — meeting a business where they are and building a bridge to a proper analytics-engineering setup.

Why This Matters

These three patterns (snapshots, definition versioning, source tracing) are the recurring problems every growing company hits. They’re the building blocks of a data-infrastructure-buy-in conversation: here’s what breaks, here’s the predictable fix.