The Tools Matter: What Fixing an Outlet Can Teach Us About AI and Data

Home ownership comes with one universal truth: something always needs fixing. I remember my first condo years ago, when a faulty electrical outlet forced me to learn some basic wiring. I didn’t have the right tools, but I cobbled together what I could find and somehow made it work, albeit poorly, and after way too long.

Fast forward nearly 30 years. Last weekend, I replaced another outlet. This time I had every tool I needed. The job took minutes, and the result was cleaner and safer. Experience certainly helped, but the real difference was having the right tools and knowing how to use them.

Today, artificial intelligence is the shiny new tool everyone wants to use. The promise is irresistible: do more in less time, automate the routine, and focus on what really matters. But here’s the truth: AI is only as strong as the data it learns from.

We’ve entered an era where data is everywhere, yet clean, accurate, and well-managed data is harder to find than ever. Governance, maintenance, and hygiene aren’t exciting topics, but they’re the foundation of meaningful AI outcomes. Without disciplined data practices, AI systems simply learn bad habits faster.

Think back to being told as a kid to put your toys away—not just shove them into the closet. The point wasn’t neatness for its own sake; it was knowing where things were when you needed them. The same goes for data. You wouldn’t waste 30 minutes hunting for a missing drill bit when a well-organized toolbox could have saved you the time.

Data behaves the same way. Messy, inconsistent data might work for small, low-risk tasks, but when decisions have strategic consequences, poor data is costly: wasted budget, poor customer experiences, and lost trust.

Strong data practices are operational, not optional. They mean enforcing standards, documenting lineage, reducing bias, and measuring quality. Product teams must treat data as an asset. Program managers should budget and plan for it. And change leaders need to prepare people for new, data-driven ways of working.

Like any good toolset, data and AI require care, discipline, and respect. The work isn’t glamorous, but it’s what makes the whole system hum.

(And if you haven’t seen Admiral McRaven’s “Make Your Bed” commencement speech—go watch it. You’ll get it.)