Tackling an Industry Issue
Prescient, a Chicago-based risk management and technology firm, recognized a breakdown in the information exchange between travel risk management firms and their clients. While firms were generating in-depth, informative reports on specific cities, traveling clients weren't reading them. The reports were too long, with data that was stagnant, having been generated days, weeks and even months prior. Prescient saw the need for real-time risk information and turned to Big Data to develop an industry transforming solution.
"We recognized early on that this data was going to grow in a way that demanded deep, post-collection analytics. We needed to have a framework with a very rich toolset, that would allow us to develop those analytic workflows over time and therefore adopted a Connected Data Platforms strategy," said Mike Bishop, Chief Systems Architect, Prescient. "In today's world, our clients' primary concern is keeping their employees safe when they travel. With Hortonworks' help, analysts can complete two detailed assessments every day, down from producing one every three to four days which is a 700% increase in productivity."
A Connected Data Platforms Strategy
Early on in the process, Prescient realized it needed to make ingestion of data simpler. Taking data from incoherent, unstructured sources like RSS feeds and news sites, Prescient uses more than 49,000 data sources to monitor risk levels. To cope with the volume of unstructured data, the company turned to HDF for its data ingestion needs. HDF was the only option to provide a clean user interface to help manage data at scale, as well as low ongoing maintenance requirements. By pairing HDF with HDP, the company can securely manage all data types, from any source and in any environment. This allows Prescient to combine historical trends with real-time information, leading to extremely relevant and comprehensive risk assessments – and ultimately, the ability to empower clients to travel smarter.
Prescient and Hortonworks continue to innovate together. As it stands, manual effort is still involved in filtering out real threats from false positives. Prescient plans to leverage machine learning and Apache Spark to create self-learning algorithms that will reduce the number of false positives.
Prescient Case Study and Video: http://hortonworks.com/customers/prescient/
Connected Data Platforms: http://hortonworks.com/products/
Connected Data Suite: http://hortonworks.com/info/connected-data-suite/
Hortonworks Customers: http://hortonworks.com/customers/
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SOURCE Hortonworks, Inc.