North Star Metrics for AI Data Products
AI data products require a new north‑star metric that goes beyond adoption, token usage, or satisfaction scores. The author argues the metric should measure downstream user behavior—how the AI improves the work it was built to support. Because AI outputs are probabilistic, the metric must be derived from experimental designs that compare a treatment group to a control group and capture lift. Teams should audit existing KPIs, prioritize behavioral signals, and retire vanity metrics to give leadership a clear view of real value.
We're Missing Data: The Other Half of AI Transformation
The post argues that AI transformation in data and engineering teams is being treated as a purely technical upgrade, ignoring the parallel operating‑model shift required to sustain gains. While tools like Codex, Claude Code, and AI agents accelerate coding and...
The Token Trap: Data's Newest Vanity Metric
The post warns that token consumption has become a vanity metric for AI adoption, equating usage with impact. It contrasts two approaches: a volume‑driven model that speeds up dashboard production, and a strategic model that uses AI to free time...