We have defined "subtransactional data" to represent granular business activity between transactions or other discernible events. Transaction data analysis can affect future transactions, but analyzing subtransactional data can also influence the outcome of imminent transactions. Traditional examples of low-level business performance management include network/process monitoring, radio-frequency identification tracking, and customer experience personalization, but our research indicates subtransactional data is ready for prime time. Eighty percent of enterprises (primarily in the retail, telco, manufacturing, and financial services industries) intend to capture and leverage a more granular level of business activity in 2004 than they did in 2003. Given the data volume and analytic processing implications of subtransactional data, enterprises will likely look outside traditional data management and analytic solutions to specialists like Teradata and upstart Netezza and to leading data mining solution providers like SAS and SPSS.

Bottom Line: Leveraging detailed business activity data can have a greater impact on operational and strategic business performance.