Garbage In. Garbage Out: Please Don't Blame the Models ... at Least Not Yet.
When I saw last week's Martech piece on why trust belongs at the center of your data strategy (https://martech.org/why-trust-belongs-at-the-center-of-your-data-strategy/) by Jay Mandel, it brought back a recurring situation.
One of the easiest mistakes in analytics is blaming a predictive model when the real problem started much earlier.
George Box famously said, "All models are wrong, but some are useful" and I'd add one more thought: useful models become far less useful when they're built on unreliable data.
This is where systems thinking matters.
Poor predictions rarely come from one bad dataset. They come from commercial systems that allow inconsistent definitions, broken processes, stale data, disconnected platforms, and weak governance to quietly compound over time.
The model is simply reflecting the system that produced the data.
At Provalytics, we see this repeatedly. Organizations invest heavily in sophisticated AI and predictive analytics while underinvesting in the data ecosystem that powers them.
That is why we built Agile Insights Modeling (AIM).
AIM isn't just about building better models. It's about creating a repeatable learning system where data quality, model performance, business outcomes, and continuous refinement all improve together.
Before replacing your forecasting model, ask a different question.
Is your model failing...
...or is it faithfully describing a system that is?
Good models deserve good data.
Great decisions require great systems.
Because in analytics, the model usually isn't the first thing that needs fixing.
Cheers
Todd