As the leading source of intelligent information, Thomson Reuters delivers must-have insight to the world’s financial and risk, legal, tax and accounting, intellectual property and science and media professionals, supported by the world’s most trusted news organization. In this presentation, I will provide an overview of Big Data at Thomson Reuters and the work of its new Data Innovation Lab in particular. The Data Innovation Lab was recently stood up to make use of new Big Data technology stacks to deliver novel insights by combining and analyzing Thomson Reuters datasets across business units. One example of addressing a customer need with cross-business unit data that I will describe results from a recent hackathon effort the Lab participated in in which content, technologies and expertise from across the company were brought together to find an innovative solution to identify comparable companies across multiple dimensions, including financial, intellectual property, and news, using both internal and external data. The solution enables great flexibility for users to emphasize various dimensions as they see fit and takes advantage of TR’s Open Eikon framework, which enables 3rd parties to write applications for Thomson Reuters’ flagship Eikon platform, informed by highly-curated, highly-maintained information.
I will conclude with lessons learned from this and other efforts in which the Lab is involved. I’ll discuss how an agile approach to rapidly prototyping solutions is crucial for gaining insight and getting buy-in from internal and external business stakeholders.
Brian Ulicny is a founding member of the new Data Innovation Lab recently stood up by Thomson Reuters in Boston. The Data Innovation Lab will partner with internal teams, customers and third parties, such as start-ups and academics, on new data-driven innovations utilizing Thomson Reuters’ rich, curated data sets across many disciplines. Ulicny has a PhD in linguistic semantics from MIT, and was previously Chief Scientist at VIStology, Inc.
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