Please enlighten me, What distinguishes Dataiku from tools like DataRobot? They appear to be similar, trying to know how dataiku has an upper hand, would make it easy for placing option to customers.
Datarobot seem primarily focused on auto ml.
dss seems focused on supporting the full data science life cycle as conducted by teams of differently skilled humans. This includes some auto ml features.
Excuse my super-late reply here, but thought I would weigh on on this topic as we considered both products before eventually choosing DataIKU.
In my humble opinion DSS is a more a 'toolbox', where as DataRobot is an autoML platform. DataRobot is really good at what it does - if you have non-technical team who want to drop in data and leave everything to autoML then this may be the option for them. We chose DSS for our team as it provides much more flexibility and, most importantly for us, transparency.
For example in DSS you can always get to the code and unpick exactly what is going on, in DataRobot (at the time of writing) it is not possible to get this granular level of transparency.
This works both ways of course, when selecting our ML platform we wanted to port some of the models we had already built into a new system - with DSS this is straightforward. With DataRobot this was not possible.
As a final point, having some degree of ETL support was an important factor for this and DataRobot does not do this.
I hope that helps, if you have any more question fire away, happy to share experiences on this subject.