Couldn’t agree more  data collection really is where the quality of every project is decided. It’s not the flashiest part of the pipeline, but it’s definitely the most critical.

For me, it starts with clarity and control: clearly defining what good data means for the project and setting up validation checks right at the point of entry not later. Automated quality scripts, schema enforcement, and early anomaly detection can save tons of downstream headaches.

And collaboration matters just as much  data engineers bring structure and consistency, while domain experts catch contextual issues that no script can.

In the end, it’s not about collecting more data, but trustworthy data. That’s what truly powers reliable AI and analytics outcomes.