Train a model using multiple data sources
Add all data together, and make sure you have features representing all possible insights. In your case one feature with age/maturity (young, adults...). Lets say you fit a decision tree (or Random Forest, gradient boosting...) the model will decide whether to do a split or not on this feature if it contains meaningful information.
If you combine you should be able to have more data and the models will work better.
In case you have different columns in data1 than in data2, consider leaving them as NaN, and then treating them properly.