Business issues drive project development. Datamining often yields specific results rather than general rules. User-friendly GUIs mask the complexity of systems. Complex results are not always easily understood or interpreted. Successful use requires statistical, database analyst, and business analyst skillsets.
Data quality is critical to results.
Dataminers still require knowledge about statistics and the mathematical algorithms, neural networks, decision trees, and data visualization techniques that enable the tools to detect relationships and patterns in the data they grab from databases. Most important, people have to understand why they're using them. But enterprise-wide software that runs on parallel servers and mainframes can cost from several thousand dollars into the millions for customized datamining systems that also require outside consultants. Customizable datamining apps are available for relationship marketing and fraud detection. According to IBM, Intelligent Miner supports several different algorithms in one package and is scalable across large databases.