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Scraping Weather Forecast Data

July 7, 2019Category : Blog
Scraping Weather Forecast Data

Last Updated on by Tarun

It’s very interesting to see how there has been an investment of so much time, effort and money to predict the weather for mankind. In the twenty-first century, the weather forecast is not only about survival, but also one of the key factors influencing the bottom line of different businesses. Some of the major industries directly affected by the weather are airlines, agriculture, fishing, energy, and insurance. Other major industries like manufacturing, retail, health care, and media are indirectly affected by the weather. Businesses are becoming more analytical and data-driven in nature thus the weather forecast data is playing a major role in different businesses.

For example, a retail business can not only predict retail needs by analyzing weather data but can also manage all backend operations like the supply chain, pricing, product demand, inventory, and others. In most of the cases, airlines fly between two or more different geographic locations with different weather conditions where turbulence is one of the major reasons for injuries to cabin crews and passengers which can damage a cost up to $100 million a year for an airline. This can be avoided to a certain level by analyzing weather forecast data and planning accordingly. 

The weather impacts on the profitability of the insurance industry. Weather forecast data can help insurance companies to identify and minimize the risks, optimize the processes, mitigate losses as well as gaining customer retention and moderate costs.

There are many more intelligent uses of weather forecast data form mobile phone applications to travel business and so on. 

Whether the business is affected directly by weather or indirectly, it needs to keep a close eye on this data to plan and avoid unwanted loses.

For a local and limited area based business, weather forecast data can be collected manually from available weather forecast websites and applications but for those businesses which cover a huge geographical location, need to have proper data acquisition mechanism which can crawl different sources and provide usable data to analyze better. Depending on business type, frequency of crawl may vary from hourly to weekly or even monthly. This setup can be done in-house which may not justify the return on investment. It can also be outsourced to a fully managed and customized web scraping service like PromptCloud.

Conclusion

Depending on the business type, scraping weather forecast data can help companies in different ways and help them serve their consumers more efficiently. It is always recommended to have a deep understanding of the needs and the expertise level of any web scraping service providers before outsourcing the data acquisition service.


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