As a data scientist, I’ve noticed a disturbing trend lately. My managers are so caught up in the AI hype that they’ve lost sight of the importance of traditional data science projects. It’s as if they think AI is the magic solution to all our problems. I’ve had conversations with them that might as well have been about fairy tales. ‘Agents’ this, ‘magic’ that… it’s like they’ve forgotten the value of good old-fashioned data analysis.
I’ve been proposing sensible projects with modest budgets, but they’re getting no interest. It’s frustrating, because I know these projects can bring real value to our organization. But no, my managers are too busy chasing the latest AI trend to care.
The thing is, AI is not a replacement for traditional data science. It’s a tool, a means to an end. And like any tool, it has its limitations. We need to remember that data science is about extracting insights from data, not just throwing AI models at problems and hoping for the best.
So, what can we do? First, let’s take a step back and assess our priorities. What are our goals, and how can traditional data science projects help us achieve them? Let’s focus on the fundamentals, on understanding our data and extracting insights from it. And then, let’s use AI as a tool to augment our work, not replace it.
It’s time to move beyond the hype and get back to basics. Traditional data science projects still matter, and it’s up to us to make sure they get the support they deserve.