Future of Data Mining

From obscurity to center stage

By Hari Mailvaganam

Data mining is the analysis of large data sets to discover patterns of interests. Data mining has come a long way from the early academic beginnings in the late seventies. Many of the early data mining software packages were based on one algorithm.

Until the mid-nineties data mining required considerable specialized knowledge and was mainly restricted to statisticians. Customer Relationship Management (CRM) software played a great part in popularizing data mining among corporate users. Data mining in CRMs are often hidden from the end users. The algorithms are packaged behind business functionality such as Churn analysis. Churn analysis is the process to predict which customers are the ones most likely to defect to a competitor.

Data mining algorithms are now freely available. Database vendors have started to incorporate data mining modules. Developers can now access data mining via open standards such as OLE-DB for data mining on SQL Server 2000. Data mining functionality can now be added directly to the application source code.

The Future of Data Mining

The complexity of data mining must be hidden from end-users before it will take the true center stage in an organization. Business use cases can be designed, with tight constrains, around data mining algorithms.