But the implications of the acquisition go well beyond just that.
To be effective, intelligent systems need to be able to handle complex questions and marshal vast amounts of data. IBM now has weather data and analytics it can use in multiple industries and it gained an Internet of Things (IoT) platform that collects information from many sources, including airplanes and even mobile phones. IBM plans to extend that platform to gather data from any source and sensor-enabled device.
"When you are talking about A.I. (artificial intelligence systems), they need two things: They've got to be good at understanding questions, and they have to know about the world," said Andrew Moore, the dean of Carnegie Mellon's School of Computer Science and a one-time vice president at Google who oversaw several A.I.-driven projects.
Moore believes IBM made a smart move, and expects other tech vendors will be following its lead to similarly enhance their own systems with stores of data.
But why weather Everyone knows the weather, right
IBM believes that weather data isn't very well integrated into decision making. The evidence is on the news almost nightly, played out in scenes of people trapped in vehicles on flooded streets or stranded on icy highways.
If weather data can be coupled with sensor data from culverts, such a system could accurately predict which culverts will overflow, when that will happen and to what extent. With better insights, emergency managers might be able to predict the consequences of an extreme weather event more precisely and act confidently to keep people out of harm's way, said Joel Cawley, general manager of IBM's Information and Insight as a Service.
In other areas, such as the retail industry, weather data and analytics capabilities could help with things such as inventory management and staffing. "Accuracy in the retail environment means you can do the forecast down to the store location," said Cawley.
IBM has designed its system to deliver "actionable" insights, not "entertaining" ones, he said.
Around the world, the number of data sources "is exploding," said Cawley, thanks to declines in the costs of sensors. Some data, such as census information, is static. But other data sources, such as a Twitter feed, have a lot of "noise," or information of little relevance.
The trick is extracting the true signals from the noise. That's an area IBM's data scientists are focused on as they add new data sources, Cawley said.
According to Moore, noise in data can give faulty alarms or alerts. And as you gather more data, "the chances of something going wrong goes up dramatically."
Beyond the Weather Company move, IBM is also working with Twitter in the development of its product set.
If IBM combines weather data with Twitter, there may be value to it, said Ryan Fogt, an assistant professor of meteorology at Ohio University.
Social media reports of storm damage are now used to assess the impact of weather events, but a lot of that analysis arrives after the fact. It may be hard to know, at first, what's what. For instance, social media sources might blame a tornado for knocking down an old barn, when a high wind was really to blame.
If IBM can couple social media reports about storms with the weather data in real-time, it could provide useful information, said Fogt.