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.
It's easy for anyone who is expecting some simple AI problems like fetching the keywords, understanding the intent, language translation, etc. to be solved from an existing database and all they need is to connect to their APIs via a subscription model. But for complex use cases, there is still room for improvement like customization of underlying AI models for a specific use case like identifying some unique identifiers with respect to industry.
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.
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.
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.
IBM Watson Assistant has been early into this market and has improved a lot over time compared to Azure AI Cortana. More documentation related to the services. But Ease of integration Azure AI ranks over IBM Watson Assistant. And again in terms of services offered under the ecosystem, Azure AI precedes IBM Watson Assitant.
Difficult to ascertain the ROI as we are a software house who have developed a module in our application using Cortana. However for companies that use our software I would say the use of sentiment analysis in our application could free up at least 1 full time resource to be used elsewhere in their organisation.
