In Health Informatics at least this is taken even further
Data
X of Y people have some disease
Information
Based on data, I can predict disease likelihood
given some environmental and personal factors of
a patient
Knowledge
Using informed predictions, I make good inferences
about how to proceed with diagnosis
Wisdom
Using knowledge and experience I choose the right
approach for treating and diagnosing a patient
which is efficacious, healthy, and works with the
patient's actual needs
It's easy to draw these lines in other places or to call the tower a lot of woo woo able to be reduced into inferences atop raw data all combined correctly... but it serves to remind just how difficult it is to combine the right data in the right way to make the right decisions at the right times.
It also serves as a sharp counterpoint to the idea of, say, machine learning patient diagnoses. It turns out that diagnostic accuracy is terrible, but not because people are directly bad at it (even if they are) but instead because knowledge/wisdom dictates that perfect accuracy isn't that valuable---perfect care is and that can involve chasing down treatment and care avenues that would never be predicted or acting on information that is not currently in your model.
It also serves as a sharp counterpoint to the idea of, say, machine learning patient diagnoses. It turns out that diagnostic accuracy is terrible, but not because people are directly bad at it (even if they are) but instead because knowledge/wisdom dictates that perfect accuracy isn't that valuable---perfect care is and that can involve chasing down treatment and care avenues that would never be predicted or acting on information that is not currently in your model.