Before joining Vaisala, Teppo Kuisma spent several years in Silicon Valley. He has vast experience in data-driven services and business models from established companies, tech startups, and consultancies. In this Data Insiders episode, Kuisma shares learnings from Vaisala Xweather’s experience building enormous data assets and sophisticated machine-learning models that are set to fight climate change and other environmental issues.

“Vaisala Xweather is everything we do on a subscription model. It is a new way for us to engage with our clients,” Kuisma explains.

Weather forecasting, which we are all familiar with as consumers, is sophisticated data science in itself. But Xweather is taking it to the next level to provide actionable insights to a range of industries from automotive to renewable energy to agriculture.

“We operate a global lightning detection network that catches nearly every lightning flash worldwide. We also provide a road weather forecast every 15 minutes for every stretch of road in the western world.”

With renewable energy set to overtake coal as the world's largest electricity source, weather has become very important in terms of achieving a sustainable society. Forecasting solar radiation and wind is crucial to energy producers and is one of the key use cases for Xweather environmental data.

Historical weather data has valuable practical uses too. For example, it helps insurance companies find out when and where lightning strikes occurred in order to assess claims for damages. Or what the road conditions were leading up to an accident claimed to have been caused by aquaplaning.

Solving the environment

Teppo Kuisma splits the use cases for Xweather into three categories – optimization, protection, and measuring the impact – and gives a few tangible examples.

You can reduce carbon emissions by finding an optimal route for long-distance trucks by avoiding the weather fronts on the way. Protection applies to safeguarding assets like data centers or power grid lines from thunderstorms and other severe weather events. Measuring and forecasting the impact on air quality can be used to optimize operations in mines and construction sites, and to manage traffic in large cities.

According to Kuisma, Vaisala Xweather helps thousands of businesses to address global environmental problems in hundreds of different ways.

And they are not limited to just this planet; the same sensor technologies that are used in Vaisala devices are on NASA’s Perseverence rover that was sent to Mars in 2020.

Working with the most demanding clients pushes the Xweather team to build innovative solutions, Kuisma tells:

“When we deliver weather forecasts via enterprise APIs to human-machine interfaces such as the BMW dashboard, we need to work closely together with the clients. Your car is a different media compared to your cell phone, as it’s sometimes offline for a little while before it comes back again. Still, you want the weather forecast to be up to date.”

Three maturity levels of data-driven business models

In addition to demanding clients that continuously challenge the Vaisala Xweather team, one of its success factors is years of open collaboration with the large developer audience, and Vaisala’s heritage of investing heavily in R&D.

“There is a particular Vaisala culture. We have always pushed ourselves to be the best in the world in our industry.”

Teppo Kuisma encourages enterprises in the Nordics that are building new data-driven business models to separate the emerging business unit or team from the core discipline, for example, placing them in a new office. Concrete actions like these force the teams into new thinking. This was also Vaisala’s approach when it started Xweather, Kuisma reminds us.

For those at the beginning of their data-driven journey, Teppo Kuisma points out that huge value can be created on top of existing structures and by learning to use the data that is already there. Kuisma gives examples of three maturity levels in the context of Xweather’s client BMW.

The first level is to layer data on top of your product, for example by providing air quality data on a dashboard. The second level is to use data to make the product act differently.

“If the car is automatically switching from combustion engine to electrical drive whenever there is a kindergarten or hospital nearby, it creates a totally different driver experience.”

The third level is enhancing your product or service by teaching it with the data, using machine learning and AI capabilities, for example, autonomous and assisted driving.

At the end of the discussion, Teppo Kuisma highlights the most important thing: there is a lot of data that we are not using yet, and if our goals are ambitious, such as Xweather’s vision to “solve the environment”, we can’t just expect the things to work in the old way.

“Have a high ambition to use the data that is available, because it is there. And keep your bar high!”

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