Even if you solve the definition problem… you’re just getting started.
Now comes the operational reality:
- Engineering needs to implement tracking
- Data teams validate the data
- BI builds dashboards
- Stakeholders wait to analyze results
And all of this competes with: 👉 Roadmaps 👉 Deadlines 👉 Production issues
So what happens?
Analytics becomes:
- Delayed
- Incomplete
- Or deprioritized
Not because it’s not important but because it’s not urgent compared to everything else.
By the time data is ready: 👉 The experiment already moved on 👉 The question has changed 👉 The opportunity is gone
This is the hidden cost of traditional analytics workflows.
It’s not just complexity.
It’s time-to-insight.