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Rigorous quality control and a scientific approach are essential for clients to have confidence in their decision-making.
Stephen Doherty – Chairman and Co-Founder, Speedwell Weather
Corporate businesses across sectors such as energy, agriculture insurance and retail base major decisions on weather data – so the quality of its analysis is of huge commercial importance.
But for an end user to find value in this data analysis, it must also identify which areas of its business are vulnerable to the impact of variations from seasonal norms – and not just extreme weather events.
This information can offer the genuine insight required to improve efficiencies and savings throughout the business and, as a result, an increasingly diverse range of companies are using it to gain commercial advantage.
Yet it is not always obvious which industries are finding most value in the latest data analysis techniques.
Agriculture might seem an obvious contender, for instance, but farmers have adapted their processes over centuries and use their historic knowledge of meteorological patterns to plan production and invest in the future, according to Oliver McEntyre, National Agriculture Strategy Director for Barclays.
“High-value crops tend to be grown in controlled conditions to minimise the impact of the weather,” he explains.
“And, while they do rely on the Met Office when making decisions about bringing in sheep from the hill or turning cows out to pasture, the nature of farming means that over time – aside from extreme weather events – there is actually a relatively steady climate in the UK. They know our summer is usually warm with some rain and winter tends to be cold with some weak sun.”
Corporates must therefore identify where they are susceptible to meteorological factors and where they can better manage supply and demand in the short-term and, looking forward, improve their production and investment planning.
Retail, by contrast, is an industry which is particularly susceptible to the ebb and flow of seasonal variations. But getting a view of such events on the horizon requires expert data analysis, which is only as robust as the information fed into it. As digital information becomes more abundant, the challenge is to collate meaningful data sets – and find expertise required to properly analyse them.
This meteorological data is available from a range of outlets, from free sources such as the Open Weather Map^1 to private sector specialists such as Speedwell Weather^2, AccuWeather^3 or the UK’s national service, the Met Office^4.
Speedwell, for example, sources and processes raw data on weather observations from over 100,000 stations around the world on a daily basis (hourly from some sites).
The value of this data to its users is such that the firm’s chairman and co-founder, Stephen Doherty, describes it as “a form of financial market data”, a point supported by how retailers share performance moves as weather-impacted announcements are made.
Deriving usable information from processed data demands the expertise of qualified meteorologists. “You just can’t do this job without having manual meteorological oversight,” explains Doherty.
Sectors such as retail tend to outsource, but larger utilities instead employ their own meteorologists, a mark of how important nuanced data can be to commercial outcomes.
A major player in the weather risk sector – an insurer, for example – may consume as many as 10,000 globally generated data sets daily to help price risk.
The depth of understanding that it gives to operational needs “readily conflates with commercial advantage”, suggests Doherty. By enabling the identification of weather-related trends specific to an industry, location and even time, it is possible to plan ahead with more certainty.
Ultimately, the aim is to maximise efficiency and operational effectiveness, says Patrick Sachon, Group Leader of Energy, Industry and Infrastructure Business for the Met Office.
Most sectors, he adds, are also crying out for accurate regional and even local data.
In the energy sector, for instance, planning for demand is an obvious need. But as less reliable renewable energy sources, such as wind, wave and solar, are increasingly added to the mix, weather data is vital in helping companies locate the best sites and manage the balance of these sources on the energy grid.
“In retail, our clients want to minimise waste through unsold products and maximise product availability,” notes Sachon.
Weather sensitivity analysis also helps businesses to form an accurate understanding of the correlation between weather and buying behaviour, he says.
This not only informs marketing and merchandising decisions, but also production and even logistics planning where inclement weather can affect the supply chain.
More specifically, an understanding of weather data can help:
Engineering firms need to know at the design stage that a construction can withstand the range of weather extremes it will face.
They must also engineer within the tolerances set by these conditions to avoid either over-engineering or having to make hugely expensive adjustments mid-project. The cost – in both monetary and reputational terms – cannot be under-estimated.
Consider, for instance, the controversy which erupted in 2013 when it became apparent that the gleaming concave southern side of the ‘Walkie Talkie’ skyscraper in London was reflecting magnified sunrays onto the street below and causing damage to cars and the pavement. Now known as the ‘Walkie Scorchie’^5, the developer has also had to cover the cost of rectifying the issue.
So, for any business, a better understanding of the operational impact of weather can inform key business decisions, remove a degree of risk and introduce cost-saving efficiencies, to ultimately deliver a stronger return on investment.
Suddenly, listening to what the weatherman says seems to make far more sense.
Stay one step ahead of the weather
Protecting your business from extreme weather
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