Ever felt like you're feeding your fancy ML models with junk food data and wondering why they're not performing?
That's like expecting a sports car to win races when you've filled it with maple syrup instead of premium fuel!
In this quick video, I share two game-changing recommendations that could save you from the all-too-common "great model, garbage results" syndrome:

1️⃣ Choose tools with robust data integration capabilities (make your data prep part of your modeling solution!)
2️⃣ Invest in quality data infrastructure (because even the fanciest algorithms can't turn data mud into predictive gold)

Whether you're a data veteran or just starting your predictive journey, these principles apply to EVERYONE working with data. At Pecan, we've seen firsthand how this data-first approach transforms outcomes.
Hit play to learn how you can stop trying to algorithm your way out of data problems! And let me know in the comments: what's your biggest data quality challenge?