AccuWeather's Transformation: Predicting Business Impact of Weather with Azure Machine Learning
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Database Management & Storage
Applicable Industries
- Education
- Equipment & Machinery
Applicable Functions
- Logistics & Transportation
- Sales & Marketing
Use Cases
- Inventory Management
- Predictive Maintenance
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
AccuWeather, Inc., based in State College, Pennsylvania, is a leading global provider of weather forecasts. It provides minute-by-minute forecasts for 2.3 million locations around the world for more than 100 parameters—including temperature, humidity, rain, snow, and ice—for every hour over the next 90 days and every minute for the next two hours. AccuWeather gathers hundreds of thousands of real-time weather observations from land, ships, aircraft, satellites, and radar and crunches that data using its patented AccuWeather Forecast Engine. More than 180,000 websites, 200 television stations, 900 radio stations, and 600 newspapers feature AccuWeather forecasts. In total, nearly two billion people worldwide rely on AccuWeather to help them plan their lives, protect their businesses, and stay safe.
The Challenge
AccuWeather, a leading global provider of weather forecasts, was faced with the challenge of providing custom weather-impact predictions for business customers. Businesses wanted more than just weather data; they wanted predictions of how the weather would affect their operations. Initially, AccuWeather provided this service on an ad hoc consultative basis, which was labor-intensive. Customers would share their sales history data, and AccuWeather would have a data scientist clean the data and create a custom model. However, this process was not scalable. AccuWeather wanted to automate this service, allowing customers to upload their data online and generate an automated prediction. However, they were concerned that the analytical tools they had at the time wouldn’t allow them to provide high-quality predictions.
The Solution
AccuWeather turned to Microsoft Azure to build an automated, scalable weather prediction service for business customers. They chose Azure for its solid infrastructure as a service offering, and its rich and rapidly advancing platform as a service portfolio, including big data, machine learning, and AI capabilities. AccuWeather first moved its API business to Azure, which provided a more scalable, cost-effective way to manage the service. They then moved their big data storage and processing, using services such as Azure Blob storage, Azure Data Factory, and Azure SQL Database. To create highly accurate forecasts, they used Azure's machine learning tools, which were powerful yet customizable using R and Python code. In May 2017, AccuWeather launched AccuWeather D3 Express, a cloud-based analytics product that quantifies the impact of disruptive weather on a business. They then developed D3 Advanced, which delivers even more customized predictions using machine learning.
Operational Impact
Quantitative Benefit
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