Partner Awards | Big Data Intelligence
About This Award
Partner will have leveraged one or more of Teradata’s platform technologies including Teradata, Teradata Aster, Hadoop or any Teradata Cloud offering. This solution could leverage new emerging data sources such as Web logs, sensor, etc. or compelling new analytics — digital marketing optimization, new Web path analysis, or other advanced analytics for discovery and new insight. The solution will provide a unified, high-performance big data analytics system for an enterprise and show measurable return on investment to our customers. Solution will have delivered valuable insight to lines of business and enable our customers to make time-sensitive decisions by analyzing entire sets of relevant data.
SAS & Cleveland Clinic
Cleveland Clinic: SAS and Teradata
Located in Cleveland, Ohio, Cleveland Clinic is a nonprofit, multi-specialty academic medical center that integrates clinical and hospital care with research and education. Today, with more than 1,400 beds on Cleveland Clinic main campus and 4,435 beds system-wide, Cleveland Clinic is one of the most respected hospitals in the country. The Cleveland Clinic has created an integrated, cost-effective, and agile analytics platform that is facilitating simplified access to strategic data assets across every service line; supporting the complete analytic lifecycle at scale from hypothesis generation through model operationalization and maintenance; and allowing us to generate better insights for executive and operational decision-making.
Fuzzy Logix & Gilead Sciences, Inc.
Gilead Adverse Event Signal Evaluation Rate Computation & Matching Algorithm
Fuzzy Logix and Teradata collaborated with Gilead Sciences, Inc, a US Biotech company, to implement an adverse event signal detection solution for large observational databases. Complex computations using Bayesian Poisson methods in an electronic medical record and accounting for all health transactions are now possible. For example, Gilead can now efficiently compare cohorts of subjects exposed to different medications using a complex matching algorithm. This solution has resulted in substantial time savings, an increase in the number of safety signal evaluations, and a significant reduction in the number of false positive signals.