Most data and AI advice assumes consumer-scale data: millions of users, clean clickstreams, endless training examples. Industrial and B2B companies live in a different world, with fewer customers, long sales cycles, and messy records spread across distributors and partners. Leading marketing at a global industrial manufacturer has taught me which analytics approaches survive that reality and which fall apart. This session shares best practices for making sound decisions with sparse data: what to measure, what to stop measuring, when a simple model beats a sophisticated one, and how to be honest with leadership about uncertainty.

Takeaways:

Common analytics practices that break down in small-data environments
Approaches that hold up with limited records and long cycles
How to communicate uncertainty so leaders still feel equipped to decide