Correlating Weather and Location Data to Score and Manage Asset Risks

by   |   November 19, 2013 11:08 am   |   0 Comments

Riskpulse screenshot 650x474 Correlating Weather and Location Data to Score and Manage Asset Risks

A screenshot from the Riskpulse application shows a company’s assets as they relate to the progress of Hurricane Sandy in 2012. Colored circles represent risk levels, with orange designating higher risk.

 Correlating Weather and Location Data to Score and Manage Asset Risks

Matt Wensing of Stormpulse.

Risk managers and business continuity planners have long watched the weather for signs of trouble. In recent years, the proliferation of weather and other datasets has created opportunities to correlate those signs with the location of business assets.

On this episode of the Data Informed podcast, Matt Wensing, the cofounder and CEO of Stormpulse, discusses location analytics and risk management.

Stormpulse offers a location analytics application that enables users to map their warehouses, factories, offices and other assets, and then layer weather data on top of that to assess risks related to current and forecasted weather patterns.

The company’s latest tool, called Riskpulse, uses government weather data as well as proprietary data to analyze asset locations and assign a relative risk score to those assets, so that managers can make decisions about facilities in harm’s way, or make plan for facilities that are not in trouble now but are located in a historically risky region, depending on the company’s business.

In the podcast, Wensing discusses the use cases for businesses correlating weather data with asset locations. He also describes the challenges of integrating existing datasets that a company has, and the importance of user training for the map visualizations that applications like Stormpulse generates.

Wensing is a software developer who started dabbling with government weather data feeds in 2004 so he could stay informed about the conditions affecting family and friends in South Florida during an active hurricane season. He started a company and built a weather database programmed in Python after his data feeds and online map mashups drew interest from the media, government agencies and businesses.

Michael Goldberg is the editor of Data Informed. Email him at Follow him on Twitter: @MGoldbergatDI.

Related articles and podcasts at Data Informed:

• Risk Managers Visualize Fresh Correlations by Analyzing Location Data

• Podcast: Esri’s Jack Dangermond on Marrying Maps with Big Data Analytics

• Geofeedia Structures Twitter, Social Media Data by Location and Time

• Focus On: Location Analytics

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