What is emergent analytics, and what’s a key ingredient for making it a successful effort in your enterprise?
To answer the first question, think of emergent analytics as a semantic software architecture that lets organizations get enterprise intelligence out of highly distributed data. And think about achieving that, not with a data warehouse that requires moving data from multiple systems to another place for analysis – where it almost instantaneously becomes out of date – but by enabling business units to describe their own information entities and artifacts that characterize their domains. They accomplish that using RDF descriptions organized in OWL ontologies. The result is that information assets can continue to live where they always have existed, but now, real-time data across systems can be graphically displayed and related to other data, opening the door to greater insight and better analysis.
That’s a powerful notion for organizations ranging from government institutions to enterprises that have been on acquisition sprees. In fact, the Department of Defense is involved in a project to do just that, enabling analysis across its various departments’ human resources systems. “It’s a 100 percent semantic technology-based approach to integrate data across HR domains – the Army, Navy, Air Force, Marines and other agencies, and it’s been very successful thus far,” says Michael Lang Sr., co-founder, CEO and chairman of Revelytix.