For many businesses, embedding operational analytics in the heart of their OLTP (online transaction processing) applications is a key initiative for 2013. The leaders, of course, have already begun. The old operational data store (ODS) and operational BI were precursors as far back as the mid-90s, attempting to make faster decisions about operational matters. These initiatives have had their success stories, but they have been limited by a number of factors, both analytical and operational. The analytical issue has often been the lack of sufficient quantities of transaction and event data to effective mine. The operational aspect was the ability to get close enough to the near real-time responses required by business users and customers.
Both of these issues are being addressed with today’s technologies. The enormous growth of business on the Web in the past decade has meant that customer behavior can be analyzed through clickstreams within websites and linkages across different websites, call centers and more. Such information, analyzed in combination with transaction data, allows retailers to more effectively cross-sell, hotels to increase room occupancy and telcos to reduce churn. But, for this blog, and the above event, the more interesting point relates to how to close the real-time gap.
Traditionally, business intelligence operates on data that has been extracted from the operational environment and analytic outcomes applied back to that environment afterwards. In short, the data is brought to the analytics. This approach introduces significant delays. An obvious solution would be to bring the analytics to the data; however, prior technology did not easily allow that. I discuss this in terms of the mainframe, System z, environment, but the principle applies elsewhere too.
It is an oft-forgotten fact that 70% of all data transactions in the banking, insurance, retail, telecommunications, utilities and government industries still occur on the System z platform, due to its performance, cost, reliability and security characteristics. The inclusion of the Netezza-powered IBM DB2 Analytic Appliance within the System z complex creates a system with a dual personality -transactional performance of the original environment combined with the analytic performance of Netezza required for integrated operational analytics. With the inclusion of SPSS Predictive Analytics on Linux and Cognos on the zOS and Linux platforms, the need to move data out of the System z environment is largely eliminated. More details are to be had in the Virtual Event where IBM’s Dan Wardman and David Jeffries will fill in the technical details. See also my White Paper, “Integrating Analytics into the Operational Fabric of Your Business, A combined platform for optimizing analytics and operations”.
Irrespective of platform, it is becoming increasingly clear that when it comes to operational decisions, they have to come from the heart rather than the head!