Dataiku vs. VizualSurvey

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

Understanding customer experience, purchase behaviour, deciding factors, pricing points, delivery schedule, delivery speed, eagerness, acceptance, reciprocation to offers, reciprocation to price increases, results of an offer, results of market changes, factors behind purchase decision, and also employee feedback. All these various scenarios are well suited for the use of VisualSurvey.

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

• Being able to visualize our data gives us better insight into what works, and what doesn't
• Allows us to create dashboards branded specifically to our individual clients
• Super fast learning curve, easy to get everyone on board

Incentivized

• End product deployment.

Incentivized

• Connecting dashboards and templates.
• Prepping software data.
• Need more how-to videos.

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

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

Google Data Studio has several problems with modifying data and the way we collect data is very basic and can be outdated sometimes. The visualizations are limited and the formatting is very restrictive. VizualSurvey is much better with data visualization and survey mechanisms where people respond better and we get a higher response rate.

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

• Higher success rates with overall customer satisfaction leads to return customers
• Customers are more trusting on larger projects based on the satisfaction of other customers
• Able to more efficiently meet clients needs, leading to faster closings

Incentivized