Predicting and mitigating weather risk to your business with BigQuery and Weather Source

Editor’s note: The post is part of a series showcasing our partner solutions that are Built with BigQuery.

In a world where data-driven decision-making is the key to success, have you ever considered the impact that weather can have on your organization? Weather-related economic and insured loss on an annual basis has been measured to be in excess of $600 billion. While there aren’t any solutions on the market that let us control the weather, being able to predict, mitigate and capitalize on weather risk is another story.

Most businesses don’t fully realize the effect of changing and anomalous weather patterns on their business, or lack the resources to integrate weather data into their models. And that’s no surprise. Complex weather analysis in the world of big data can be overwhelming, but done right, it can not only offer opportunities to mitigate operational or supply chain interruptions, but also uncover new opportunities that can be harnessed to give you a competitive edge. Weather Source makes weather analytics simple and accessible – by providing globally uniform data for generating insights and business intelligence that your organization can act on, fast and at scale.

By far, the biggest challenge we see organizations facing, ahead of unlocking insights from weather data, is the complexity of data sets. Real and reliable weather information management often requires high-performance distributed systems that are costly and require skilled personnel to run. This, as well as a lack of time, manpower, or expertise can hinder businesses from utilizing weather and climate data effectively. While weather data analytics can be difficult if organizations try to do it alone, Weather Source has done the heavy lifting by offering a single source of truth for all things weather and climate. 

Now, weather analytics is a breeze

Weather Source, backed by Google’s serverless enterprise data warehouse BigQuery, solves the challenges of weather data analytics with a powerful and scalable data warehousing solution fit for a wide range of industries. BigQuery handles resource provisioning, upgrades, security, and infrastructure management, allowing users to focus on data analysis, while Weather Source makes weather and climate data easily and instantly accessible to businesses globally, enabling insights and business intelligence. 

We are excited to announce we are making Weather Source datasets available in Analytics Hub and on the Google Cloud Marketplace. This will make it easier and faster for customers to find, purchase, and deploy directly into their BigQuery environment, without having to move data or maintain expensive TEL processes, all at the scale that consumers need.

The foundation of Weather Source is the proprietary OnPoint Grid, which consists of millions of grid points covering every land mass on the globe and up to 200 miles offshore. At each grid point, data is collected from a multitude of advanced weather sensing technologies including airport reporting stations, satellites, radar systems, and trusted weather models from ECMWF and NOAA. By integrating and unifying input data from these diverse sources, Weather Source is able to provide accurate and comprehensive weather information that is both temporally and spatially complete, as well as globally uniform.

Businesses across industries have found success with Weather Source by using it to quantify the historical impact of weather and climate on various business KPIs, and then create predictive models to improve business operations, increase sales, reduce waste, predict risks, and prevent future losses – just to name a few outcomes. 

Weather Source works with leading organizations across industries and use cases. A few examples include:

Healthcare
A large pharmaceutical company used Weather Source data in BigQuery to create an influenza forecast. By correlating historical weather data with historical flu transmission data, the customer was able to identify which weather parameters (temperature, humidity, and precipitation) and at what severity level, resulted in increased flu transmission. After the historical analysis was complete, a predictive flu forecast model was created that used forecast data to trigger advertising campaigns in areas that were forecast to have a flu outbreak. Its algorithms predicted when and where the flu would strike with up to 97% accuracy.

FinTech
A large quantitative firm used Weather Source historical data to quantify the impact of a snow forecast on the sale of a large ski resort’s season passes. The analysis focused on anomalous early starts or late ends to the ski season and determined that early forecasts of snow resulted in increased purchases of season passes and late season forecasts of snow resulted in higher-than-normal early purchases of season passes for the next season.  

Retail
A large tire manufacturer used Weather Source historical actuals and historical forecast data to understand how many tires would be sold on the first snow forecast, and whether purchasing behavior changed if it actually didn’t snow. Using historical weather information correlated with historical tire-sale information, the analysis revealed that 88% of annual snow tire sales were on the first snow forecast and the purchasing volume did not change if it didn’t snow. The results of the analysis also showed that the manufacturer sold considerably fewer tires — an average of 2% per month — after the initial snow forecast. As a result of the analysis, the manufacturer began to focus considerable marketing efforts on the first snow forecast in locations where it sold tires.

Energy and Utilities
A large energy company uses OnPoint Weather in BigQuery for energy-demand forecasts. The company used OnPoint Climatology (the statistical representation of weather over time) as a baseline for energy demand during average conditions. By differencing historical actuals from the climatology data, the company is able to identify the departures to normal (anomalies). By correlating the anomalies to energy demand, it can then understand historic energy demand during normal or average conditions, and more importantly during anomalous weather conditions. 

Built with BigQuery: The Weather Source differentiator 

Unlike other providers that rely solely on singular weather sensing inputs (i.e., airport reporting stations) and then use simple interpolation methods to extend the data to your location of interest, Weather Source approaches weather in a markedly different way. On a daily basis, Weather Source ingests multiple terabytes of data from thousands of weather sensing inputs, then unifies the inputs on a high resolution grid, and maps the weather information to precise business locations with low latency – and BigQuery is the enabler behind it:

BigQuery’s scalability and serverless architecture allow Weather Source to deliver petabyte-sized data at scale, meeting the needs of users on demand.

The ability to store, explore, and run queries on generated data from servers, sensors, and other devices using BigQuery provides the flexibility to transform data and gain business intelligence quickly.

Analytics Hub enables businesses to easily subscribe to Weather Source datasets and securely access the latest Weather Source data in their own BigQuery instance as a linked dataset.

From there, BigQuery gives data analysts access to ML and geospatial capabilities via SQL commands to query any location of interest (i.e., latitude / longitude coordinate, ZIP or Postal Code, etc.) and resource (i.e., past, present or forecast weather data) and combine them with their own data. This enables them to quantify the impact of weather and climate and create models to predict and mitigate risk and prevent future losses and business disruption.

Do you want to start gaining some control over the uncontrollable? With Weather Source making weather and climate data easily accessible, and BigQuery analyzing and generating insights within a secure cloud platform, the forecast for your organization looks promising. Learn more about Weather Source.

The Built with BigQuery advantage for ISVs and Data Providers

Google is helping tech companies like Weather Source build innovative applications on Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs through the Built with BigQuery initiative. Participating companies can: 

Accelerate product design and architecture through access to designated experts who can provide insight into key use cases, architectural patterns, and best practices. 

Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

BigQuery gives ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in.