Dissecting performance issues with Random Forest
There are several things you can do, even though giving a detailed descripton of your datasets would help a lot:

The first thing is that you should is to thoroughly compare the two datasets:


Are the same features present in both datasets?
Is the data collected in the exact same way? As an example, if age is a feature make sure that it is not recorded differently. Let's say one dataset could use the categories (&lt;25, 25-50, 50+) and the other the exact age.
How do they differ in size?
Plot the distribution of each feature in both dataset: do they differ significantly, and why? You could test the hypothesis that a feature is drawn from the same distribution in both datasets.
Look for correlated features: correlated with each other and with the target distribution 


The amelioration of your algorithm will entirely depend on the answer to these questions. If you observe significant differences between the two datasets then your historical data will not be a good training set for this task.

Independently from that, your 100% accuracy is very suspicious and your algorithm is probably overfitting.