The data platform nobody asked for is the data platform nobody uses.
There’s an adoption problem nobody wants to admit: data teams often build for themselves, not their users.
I’ve shipped one of these myself. Modern stack. Good architecture. Documentation existed. Adoption flatlined. It hurt to watch.
We measured success by capability - what the platform could do. Users measured success by usefulness - whether it solved their actual problems.
Those aren’t the same thing.
Data teams optimize for infrastructure, pipelines, query performance. Business users need answers to specific questions, in formats they can act on, at the moment they need them.
When those don’t align, you get dashboards answering questions nobody asked. Self-service tools too complex for self-service. Data models that mirror your architecture instead of their decisions.
The uncomfortable truth: adoption is feedback. Low adoption isn’t a training problem or a change management problem. It’s a product problem.
Before you design anything, spend a week shadowing how people actually make decisions. What data do they pull manually? What do they trust? What do they ignore?
Build for that.
What’s the most technically impressive tool in your org that nobody actually uses?