The real world is a challenging environment for data modeling. In addition to the problems of too much data, too little data, correlated data, noisy data, etc. there are the cost and timeliness issues associated with modeling development. Furthermore, the modeling rarely exists in a vacuum so aspects of model deployment, usage and maintenance must also be considered.
This is the crucible in which the DataModeler add-on package for Mathematica was conceived, developed and refined. As a result, in addition to providing unique capabilities for data modeling, DataModeler also provides an infrastructure for the entire modeling life cycle — with an emphasis on modeling efficiency, accuracy, robustness and ease-of-use.
The FAQ is a good place to get started to see the unique advantages of the DataModeler algorithms. The selected help extracts may also be of interest. The publications are also, likely, worth exploration — especially the recent industrial modeling tutorial.