Dataiku vs. The Weather Company

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

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

• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

Incentivized

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

Incentivized

• End product deployment.

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

As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

Incentivized

The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

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

Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

Incentivized

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

Incentivized

• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

Incentivized

• Better confidence in the weather forecast.

Incentivized