For five years now, we’ve been talking about the importance of knowing what’s in your data. In our last white paper, AI: Magic E-Wizard Machine or Maniacal Hell-bot? we discussed what AI is and isn’t. We covered critical data questions, like where AI tools like ChatGPT get their data (see the section Where’d This Sh** Come From?). In the section Non compos mentis, we described how bad data causes AI to lose its mind and why. And in What’s in the Water? readers learned about the challenges organizations face when they have oceans of data but no idea what’s actually in the water. You can get that paper here.
Since then, we watched the AI hype cycle take off. We’ve seen glowing predictions and stats, but we haven’t really believed them. As the inevitable failures emerge and AI sentiment starts to slide into the slough of despond, we’re going to say it again—AI results are always determined by the data fed to the model.
In this paper, you’ll gain the context for what’s failing and why. We also offer a data-first strategy for succeeding with AI projects. You’ll see how a data integrity layer is the foundation of your model and ultimately, business value. It’s going to be OK. You can do this.