Both say they were able to bring business intelligence closer to business processes, so business managers and IT staff alike can now detect problems and make decisions within a time frame that is most effective. This approach moves away from the time-honored BI tradition of gathering lots of data and then analyzing it later. Similarly, in traditional data warehouse analysis, reporting tools generate canned reports each month for detailed views of, say financial performance, and analysts later trudge through the mounds of cleansed data.

A Smarter Data Warehouse

By comparison, approaches like the ones taken by Coursen and Hayes—often dubbed operational BI or inline process analytics—let managers make decisions with little or no delay based on current analysis. What these solutions don't do is replace people as the center of that decision making.

Generally, the approach to cross-process operational analytics that Coursen took is the more common one, notes Matthew Liberatore, a professor of operations and decision technology at Villanova University, currently leading a new group in BI and analytics.

Coursen continues to rely on a data warehouse as the repository of mounds of enterprise data, extracted and transformed into common formats, with common context and analytic rules applied. But he differentiates what data is collected, staging more time-critical data so it's gathered more often. He also adds some operational data that might not otherwise have been collected. That lets him update the data warehouse with certain data on a daily or even more frequent basis, then run operationally oriented analytics using Tibco Software's Spotfire tool against just the timely data sets.

The production data, for example, travels immediately to the data warehouse as it is generated, so the production analysis tools can run constantly, looking at results from all fabrication stages at once to identify issues.

In essence, the data warehouse handles multiple types of analysis while remaining a single repository for IT to manage, reducing complexity. "I can leverage all my previous investments by having the data all in one place," Coursen says, instead of trying to retrofit a common analytics system into multiple applications and keeping them integrated over time.

Like Coursen, Gustavo Rodriguez, IT director at Mexican regional airline Aeromexico connect (formerly Aerolitoral), has several key application suites that handle key operations. So he needed a way to analyze processes across them for the midmarket airline company. Rodriguez also implemented a staged data warehouse that updates and analyzes maintenance fleet status, commercial and finance data, and several other operational indicatorsdaily. This helps business and operations managers adjust schedules and fares quickly, based on factors ranging from changes in referrals from partner airlines to the effects of bad weather on passenger bookings. Some data—such as passenger information and fare yields—is updated hourly for analysis by Bitam's BI tools.