Why So Many Analytics Projects Fail

In my years working on analytics projects, I've seen a common pitfall: failing to consider operationalization. Here's why this matters:

🚀 Operationalization must be considered from the start. At eXelate, we needed to scale audiences probabilistically. But our servers could only handle boolean logic, not complex ML operations, limiting our options for scoring. Instead of waiting for upgrades, we immediately adapted using association rules which could be supported.

This wasn't optimal, but it was operational and improvable. It showed that early engagement and flexibility can turn potential roadblocks into effective solutions.

🔑 Key Takeaway: Engage early, adapt to what's possible now. This approach saves time and creates implementable, iterable solutions.

In the GenAI era, ensure your solution will be acceptable and usable upfront, avoiding impressive but impractical PowerPoint presentations.