Data Products Need Empathy, Not Just Pipelines
A data product is not successful because it loads. It is successful when a real person makes a better decision.
We celebrate green pipelines. The DAG ran. The freshness check passed. The data loaded. And then we wonder why nobody uses the thing we built.
A pipeline that runs is the floor, not the ceiling.
Adoption is not usage
A login is not a decision. We measure "active users" because it is easy, and we avoid measuring whether anyone's work actually got better, because it is hard. The hard metric is the real one.
What happens after they see the number?
This is the most important and least asked question in BI. The user reads the metric — then what? Do they know what to do? Can they explain it to someone who pushes back? Can they find the exception that matters to them?
Watch users work before you design for them. You will be humbled, and your product will get better.
The hidden product work
Empathy in data products is not a soft skill. It is concrete work: shadowing users, learning their vocabulary, understanding the decision and the workflow around it, and respecting the messy reality they operate in. That work is the difference between a dataset and a product.