Business battles are fought in real time, and IS must keep pace. Real-time business intelligence infrastructures promise a never-ending stream of fresh information, insight and decision support to frontline knowledge workers.
Nevertheless, real-time business intelligence has not graduated to enterprise primetime yet. Most production business intelligence implementations rely on data warehouses, which consolidate operational data loaded via scheduled batch transmissions rather than real-time updates from source databases. As a result, many organizations have rich stores of historical data in their data warehouses, but few contain information that is refreshed continuously.
A traditional data warehouse operates in store-and-forward mode, introducing latency into data delivery to reports, dashboards and other business intelligence applications. Most of today’s data warehouses have been optimized for specific latency-producing operations: extraction, transformation and loading (ETL) of data from operational database management systems (DBMS); retention of that data in persistent repositories; and retrieval of that stored data into reports, graphical dashboards, multidimensional online analytical processing cubes and other business intelligence outputs.
It is possible to retool data warehouses to support real-time business intelligence. Some data warehousing vendors have begun to address these requirements in their products. Doing so requires that data warehouses — as