Publications

Aaaron Zornes from Meta Group writes about top data mining trends.

Most enterprises achieve suboptimal results when analyzing their customer base. Either they do not segment at all, or they use imprecise tools to group customers into overly broad segments. In both cases, resources are wasted on too many customers that will not provide maximum ROI. During 2002-03, predictive analytics (a.k.a. data mining) will provide large and midsize enterprises with detailed classifications to group customers into a variety of easily actionable segments. By 2004-05, such analytics will have infused "actionable" decision-making capabilities throughout the touch points of enterprise CRM solutions. For examples of early adopters, SPSS can be referenced with Siebel, RightPoint within E.piphany, and Darwin within Oracle CRM.

META Group projects that adoption of enterprise data mining solutions will be accelerated by the following:

1. More predictive models
2. Better data mining models
3. More cost-effective modeling
4. Evolving data mining standards
5. Integration within RDBMS servers

• Angoss Software: KnowledgeStudio and KnowledgeSeeker
• Computer Associates: Neugents
• Gemplus: SLP InfoWare
• IBM: DB2 Intelligent Miner
• KXEN: Knowledge Extraction Engines
• NCR/Teradata Division: TeraMiner
• Oracle: Oracle Data Mining Suite (Darwin)
• SAS: Enterprise Miner
• SPSS: Clementine
• Quadstone: Decisionhouse
• Unica: Affinium