Jason Prentice leads the data team at Panjiva, where he focuses on developing the fundamental machine learning technologies that power our data collection. Before joining Panjiva as a data scientist, he researched computational neuroscience as a C.V. Starr fellow at Princeton University and earned a Ph.D. in Physics from the University of Pennsylvania.
Panjiva maps the network of global trade using over one billion shipping records sourced from 15 governments around the world. We perform large-scale entity extraction and entity resolution from this raw data, identifying over 8 million companies involved in international trade, located across every country in the world. Moreover, we track detailed information on the 25 million+ relationships between them, yielding a map of the global trade network with unprecedented scope and granularity. We have developed a powerful platform facilitating search, analysis, and visualization of this network as well as a data feed integrated into S&P Global’s Xpressfeed platform.
We can explore the global supply chain graph at many levels of granularity. At the micro level, we can surface the close relationships around a given company to, for example, identify overseas suppliers shared with a competitor. At the macro level, we can track patterns such as the flow of products among geographic areas or industries. By linking to S&P Global’s financial and corporate data, we can understand how supply chains flow within or between multinational corporate structures and correlate trade volumes and anomalies to financial metrics and events.