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Certilytics Named Cloudera Data Impact Awards Finalist

Award Recognizes Industry Leaders in Enterprise-Scale Machine Learning

Certilytics, Inc. has been named a finalist for a Cloudera Data Impact Award, a recognition of Certilytics’ industry-leading technology ecosystem that processes massive healthcare data sets to produce sophisticated predictive intelligence for health plans, PBMs, government entities, and provider health systems.

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Certilytics was named a finalist in the “Data for Enterprise AI” category, which honors organizations that “have built and deployed systems for enterprise-scale machine learning and have industrialized AI to automate, secure, and standardize data-driven decision making.”

The other finalists in this category are Bank of America, Experian, and Gazprombank OAO. The winner is set to be announced Nov. 18.

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“Congratulations to Certilytics for their placement as a finalist in the annual Data Impact Awards, which recognizes the best of the best innovative organizations running the Cloudera platform,” said Mick Hollison, CMO, Cloudera. “Each of the finalists embodies what the Data Impact Awards stand for—recognizing organizations that have through a data-led culture, superior technology, a pioneering spirit and the belief that things can be done differently, and better, showcased that what is thought impossible today, can be possible tomorrow.”

The selection highlights Certilytics’ incorporation of Apache Hadoop software libraries available from Cloudera into a highly scalable data processing and analytics framework, the Certilytics Predictive Healthcare Analytics Ecosystem, which processes more than 30M patient lives per week.

“We are thrilled to be recognized by Cloudera as an industry leader in the field of healthcare predictive analytics,” said Certilytics Chief Technology Officer Elton Tavenner. “We have embraced a modern, open architecture that avoids the pitfalls all too common in the healthcare sector—and allows us to evolve with our customers as we manage and analyze data at massive scales.”

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