by Dr. Thomas Hill, executive director analytics, Quest
One way to look at how predictive modeling technology will transform the healthcare sector is to compare it to other industries that were the earliest adopters—and automaters—of such methods. In this Health Data Management article, Dr. Hill asks whether healthcare data science and predictive modeling could be similarly automated? And what exactly would that look like?
by Shawn Rogers, chief information officer, Information Management Group
Speaking recently at Cloud World, Shawn addressed the great business opportunity afforded by hybrid data environments, where the cloud presents an interesting convergence of technologies and capabilities that enable data processing from almost anywhere—often with the purpose of applying advanced analytics that lead to insights not previously understood.
- Video clip #1: Where does the cloud fit strategically in hybrid environments?
- Video clip #2: Are advanced analytics the holy grail of hybrid cloud computing?
by John K. Thompson, general manager advanced analytics, Quest
In his latest article at ODBMS.org, John Thompson explains that the data scientist skills gap will not deter data-driven organizations from achieving the benefits of predictive analytics, thanks to their willingness to pursue collective intelligence as a practical, collaborative workaround that is powerful enough to "change the world."
Three Tips for Surviving Today's Complex Data Landscape
by John Whittaker, executive director, product marketing, Information Management Group
In this article contributed to Data Center Knowledge, John acknowledges that many organizations collect a disparate mix of structured and unstructured data, and he spells out three information management priorities for DBAs to maintain efficiency and achieve successful integration with analytics downstream.