March 8, 2021 1 min read 172 words
From Flywheel to DAG
How a flywheel becomes a DAG: Time.
Core Argument
How a flywheel becomes a DAG: Time.
Flywheels are great — people envision a smooth spin around the flywheel. Really, the dots on the flywheel are states. A potential customer snaps from one state to another.
Still — we are going in a circle. This is a cyclic motion. State transitions take time. This turns a circle into a sine wave.
Why It Matters
The insight here is that business flywheels (popularized by Jim Collins and Amazon) are conceptual models, but when you try to implement them in data systems, you need to account for the temporal dimension. Once you unroll the cycle across time, you get a directed acyclic graph — a dbt DAG.
This bridges business strategy thinking (flywheels, customer journeys) with analytics engineering implementation (DAGs, state machines, event-driven models). The founder is arguing that data practitioners need to understand both frames — the business metaphor and the technical reality.
