Usually data mining is viewed as the final manifestation of the data warehouse. The ideal is that now information from all over the enterprise is conformed and stored in a central location, data mining techniques can be applied to find relationships that are otherwise not possible to find. Unfortunately, this has not quite happened due to the following reasons:
1. Few enterprises have an enterprise data warehouse infrastructure. In fact, currently they are more likely to have isolated data marts. At the data mart level, it is difficult to come up with relationships that cannot be answered by a good OLAP tool.
2. The ROI for data mining companies is inherently lower because by definition, data mining will only be performed by a few users (generally no more than 5) in the entire enterprise. As a result, it is hard to charge a lot of money due to the low number of users. In addition, developing data mining algorithms is an inherently complex process and requires a lot of up front investment. Finally, it is difficult for the vendor to put a value proposition in front of the client because quantifying the returns on a data mining project is next to impossible.
This is not to say, however, that data mining is not being utilized by enterprises. In fact, many enterprises have made excellent discoveries using data mining techniques. What I am saying, though, is that data mining is typically not associated with a data warehousing initiative. It seems like successful data mining projects are usually stand-alone projects.