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DSx, more for experimenting than experience.
April 06, 2018

DSx, more for experimenting than experience.

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with IBM Data Science Experience (DSx)

DSX is being used to explore and mature ideas but has not yet been broadly adopted. It is being used to manage the analytics process of new ideas and theories as well as provide a common platform for the team to leverage and align on.
  • Allows for people with various technology backgrounds to use a common platform.
  • Easy implementation due to the cloud availability.
  • The DSX platform allows for junior and citizen data scientists to perform complex actions without needing to have deep knowledge of some of the underlying configuration and setup that generally come with standalone/local analytics tools.
  • Interfacing with non-IBM technologies is often cumbersome and sometimes restrictive.
  • The interface has undergone a number facelifts which often causes some lost productivity when you need to "find" where things have moved to.
  • At this time the product is still being evaluated so ROI is not yet fully determined.
The learning curve for DSX is smaller compared to other tools. The data science user base often has preferred tools that they have used previously which are often not DSX which makes adoption of DSX by trained data scientists harder than new users.
Data science ideation and POC is definitely a sweet spot in my opinion of DSX. It is easy to get up and running and can elevate people that have the business knowledge but lack some of the senior science skills to be proficient analytics users.

Moving the models developed to a production ready model is not an easy path and often taking the analytics idea to a product involves translating the method and approach to other tools.