Vivienne Umusu highlights the importance of using quality data as the foundation for all applications.
We are all familiar with the old adage:
Garbage in, garbage out.
Every technology leader has said it. Every data team has lived it.
And yet, across 27 years spanning the on-premise era, the cloud era, and now the AI-first era, I have watched business habits remain remarkably resistant to acting on that principle.
Leadership consistently overestimates the quality of the data feeding the systems on which they are betting the business.
The pattern is almost always the same.
Teams build and demonstrate solutions using clean or mock data. They accelerate through a compressed development, testing, and production cycle. Only after go-live do the real data-quality issues reveal themselves.
Confidence drops.
Patches replace governance.
And the system that was supposed to drive growth becomes the system nobody trusts.
The agentic AI era does not forgive this pattern.
It amplifies it.
Read the article, You Cannot Lead What You Cannot Measure: Data Readiness for the Agentic AI Era, on LinkedIn.