I've worked in data science for six years, and I still question whether predictive models are more of a crutch than a tool. Last quarter, my team built a churn model that looked great in testing but flopped in production—the real-world data shifted in ways we didn't anticipate. Are we relying too much on algorithms instead of understanding the underlying business problems? That experience makes me wonder if data science is oversold, or if we're just bad at validating our work. What's your take on when to trust a model's predictions?