Healthcare & Life Sciences
40+ billion clinical and operational data points about 13 million patients are analyzed using Cloudera.
In traditional IT environments, clinical, operational and financial data are managed in data silos. Meanwhile, with the movement from paper-based to electronic health records systems and the increase in usage of machines and medical devices that produce a steady stream of data, the volume and variety of information that organizations in the health and life sciences space want to capture and analyze have skyrocketed.
Cloudera allows healthcare companies to process and manage this data while saving IT infrastructure costs via training, software (CDH and Cloudera Manager), and expert support and services.
Genome Processing & DNA Sequencing
According to the National Human Genome Research Institute, it costs about US$10,000 to sequence one genome using standard technology, and this process generates about 100GB of compressed data. Apache Hadoop has made it affordable for scientists to process and store this data, helping to reduce the cost of sequencing a genome to less than US$100.
Cloudera makes it possible for life sciences organizations to process DNA information cost-effectively and at scale by simplifying Hadoop deployment and management.
Read the press release:Cloudera Chief Scientist Jeff Hammerbacher Teams With Mount Sinai School of Medicine to Solve Medical Challenges Using Big Data.
Clinical Quality & Cost Analysis
If healthcare and life sciences companies can integrate patient data, drug effects data, clinical data, R&D data and financial records, they're more likely to identify patterns that drive better, more proactive healthcare. Furthermore, if healthcare companies can share blinded patient data and integrate social media content into their data management systems, they'll have an even deeper pool of information from which correlations can be made.
Cloudera offers a solution that makes it easier for these organizations to bring together large volumes of detailed data from numerous sources, in a variety of formats, into a single, flexible and scalable system for long-term storage and analysis.
Read the case study:Streamlining Healthcare Connectivity with Big Data.