Garbage In, Powerhouse Out? (Nope.)

You spent $2M building a state-of-the-art ML platform. Hired a team of PhDs. Bought the fanciest tools. Launch day comes, predictions are wildly off. Turns out your customer data has 40% duplicates, purchase dates are wrong half the time, and nobody has cleaned the source systems in 5 years. Garbage in, garbage out. Except now it's expensive garbage with a PhD babysitting it. It's like building a Ferrari and filling it with contaminated fuel—doesn't matter how good the engine is if the inputs are trash. Data quality is the foundation. Skip it and everything built on top is a house of cards. Yet teams constantly underinvest here because it's not sexy. Cleaning data doesn't win awards. But it's what separates powerhouses from pretenders.