Netezza's TwinFin appliance to address Marshfield Clinic's research database and computation needs

Netezza Corporation (NYSE: NZ), the global leader in data warehouse and analytic appliances, today announced that Marshfield Clinic, one of the largest private, multispecialty medical systems in the United States, has selected the Netezza® TwinFin™ appliance to address the research database and computation needs of the Marshfield Clinic Research Foundation. The Marshfield Clinic system provides patient care, research and education with 45 locations in northern, central and western Wisconsin, making it one of the largest comprehensive medical systems in the United States.

“We began working with Netezza more than a year ago. Netezza was able to provide a solution that could scale with our growing data needs and remain cost-effective without sacrificing performance,” said Justin Starren, MD, PhD, FACMI, Director, Biomedical Informatics Research Center, Marshfield Clinic Research Foundation. “The fact that the new Netezza TwinFin supports both conventional database access and also parallel scientific computation was the factor that led us to choose the new architecture.”

“With the latest spotlight and investment in the healthcare industry, it is even more critical to implement the most efficient processes and continue to improve on patient care,” said Jim Baum, president and CEO at Netezza Corporation. “Our customers, like Marshfield Clinic, have come to expect a level of innovation from Netezza and the TwinFin appliance delivers the performance and scalability required to fulfill their growth strategy.”

The Netezza data warehouse appliance is built specifically to analyze petabytes of detailed data significantly faster than existing data warehouse options, at a much lower total cost of ownership. It stores, filters and processes terabytes of records within a single unit, analyzing only the relevant information for each query. Netezza has placed the CPU power next to the data, allowing its appliances to speed through processes that would occupy most data warehouse systems for hours, or even days, thereby enabling dramatic increases in productivity across an organization.