Shiny vs. The Weather Company

Shiny is well suited where an organisation is looking to empower their analysts to minimise time spent on repetitive analysis by deploying repeatable analytical pipelines, but also looking for them to add greater value to the organisation by utilising more advanced analytical techniques. Ideally it is well suited where IT are on board and supportive of some of the more advanced features such as deploying R Shiny dashboards.

Incentivized

Well suited:

• Forecasting for severe weather events.
• Generating overall seasonal trends in upcoming weather patterns.
• Content is easy to read and understand.
• Great for business decision-makers, c-level executives.

Not well-suited:

• Year ahead forecasts.
• Data that can be trusted as finite days before a weather event. The data and forecasts can change often, so remaining up to date is key.

Incentivized

• Data tables are appealing to look at.
• Enables us to create trend indexes in an effective way.
• Easy to integrate with the rest of my R syntax.

Incentivized

• Detailed forecasts.
• Hyper-local forecasts.
• Daily updated local and meaningful forecast for our business.

Incentivized

• Easier ways to connect to data sources
• Better access control for different roles in the organization
• Video material that allows a better learning experience

Incentivized

• Forecasts are not always accurate.
• Winter weather forecasts are now often now-casted which can be challenging for proactive planning.
• Would love to see more tips on how businesses can prepare for severe weather by the use of the Weather Company’s data.

Incentivized

Support for the Weather Company has not been an issue. The predictive analytics processes that they use allow them to remain a strong force in the world of weather patterns and forecasts. Brand recognition allows them to continue to grow and develop in a world that is not as crowded as other predictive analytics areas.

Incentivized

- Faster response working with a large amount of data. - R Studio connection and flexibility. - Scenarios modelling.

Incentivized

Insightful meteorologists that both understands the science of weather and also understands our business needs.

Incentivized

• We saw a good involvement to researchers when showing their models in shiny.
• We can have a quicker review from the user when the model is in production.
• False positives can be found easily and they help the retraining of the model.

Incentivized

• Better confidence in the weather forecast.

Incentivized