Dataiku vs. Explorium

Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.

Incentivized

We need to constantly measures costs in our health business and we forecast pricing acoording to several values and conditions. Explorium works quite good analysing simple datasets, but when hierahies start to increase, meaning 6-10 olap variables, the system start to slow down quite a bit until was no longer to retrieve the info we required. This is why we test several tools, because even world-class solutions we purchase, don´t do the job we need. Explorium is a good tool, but complexity will be a minus in some scenarios.

Incentivized

• Allows users to collaborate and monitor individual tasks
• Caters to both types of analysts, coders and non-coders, alike
• Integrate graphs and plots with visualization tools such as Tableau

Incentivized

• Data ready for consumption.
• Good enough relationships between entities.
• Nice integration with other players. That's a good thing.

Incentivized

• The integrated windows of frontend and backend in web applications make it cumbersome for the developer.
• When dealing with multiple data flows, it becomes really confusing, though they have introduced a feature (Zones) to cater to this issue.
• Bundling, exporting, and importing projects sometimes create issues related to code environment. If the code environment is not available, at least the schema of the flow we should be able to import should be.

Incentivized

• relationships between entities could be better. Would be great to have scenarios.
• The data normalized needs improvement. Works pretty good, but it needs more refinement when using AND-OR Formulas that came from various datasets.

Incentivized

The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level

Incentivized

The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.

Incentivized

Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by even other kinds of users.

Incentivized

The simplicity of the tool is an advantage. The integrations as well work quite well. All these solutions have worked well until some point and what we have discovered over the years is that we need to combine various solutions. There is no such thing as one tool ruling them all. Explorium works quite well until we start testing more advanced relations, and here, the tool is promising but requires a little work.

Incentivized

• Customer satisfaction
• Timely project delivery

Incentivized

• Positive. The time save in analysis per hour of internal consulting was the best compared to our SAP and Cognos solutions.
• In term of objetives works as stated, but when using multi-conditions of dataset, start the issues.

Incentivized