Instead of a horserace between product features, this approach puts the focus where it should be: on value to your business. It recognizes that the value of a new tool depends on the other tools already available, and it forces evaluation teams to explicitly study the impact of different tools on different users. By creating a clearer picture of how each new tool will impact the way work actually gets done within the company, it leads to more realistic product assessments and ultimately to more productive selection choices.
I couldn't agree more. For the past 10 years, since the Elder and Abbott review of data mining software presented at KDD-98 (on my web site) I've tried to think of ways to summarize data mining software. The obvious way is by features, such as which algorithms a product has. The usability of a tool is another characteristic to add, as John, Philip Matkovsky and I wrote about in "An Evaluation of High-End Data Mining Tools for Fraud Detection". I've also described the different packages by the kind of interface (wizard, menu-driven, block-diagram, command line, etc.).
It's not easy to provide a summary in this multi-dimensional view of data mining tools. Sounds like an opportunity for predictive modeling!