Item: IBM Watson Studio on Cloud Pak for Data
Rating: 8
Author: Davide Tognon

Overall Satisfaction with IBM Data Science Experience (DSx)

Use Cases and Deployment Scope

We use IBM DSx in the whole company, mostly in the field of research; we address it to predictive and descriptive analysis.

Pros and Cons

Pros

DSx is very useful for viewing data from different points of view (via Watson Analytics)
DSx is very powerful with its Python and R ambient integrated within
DSx is a good tool for collaborative machine learning procedures

Cons

DSx ...

Return on Investment

By now we are just testing but it surely had a good impact on the time reduced for getting such powerful analysis

DSx very good for descriptive and predictive analysis. We gifted our manager with this new tool so that our equipment can be used for research purposes based upon analysis and new features that could be achieved after a predictive analysis. We use both of them, inverting the order, sometimes, testing our ideas or being inspired by a different point of view upon our data.

Using IBM Data Science Experience (DSx)

Users and Roles

5 - IT technicians that are on a path to becoming data scientists.

Support Headcount Required

2 - Programmers, mathematics, physics

Business Processes Supported

Predictive analysis
Descriptive analysis
analysis of data integrity

Innovative Uses

with DSX we have found several new points of view on the matter that concerns our analysis: it made possible to watch our data from 2 steps backwards and that allowed us to move 4 steps forward on our comprehension of the phenomena that we were studying

Future Planned Uses

we are thinking about what we call DSX over DSX which means that, according to the results we found and to the paths that lead us to them, we are modyfing our own procedures as well and they became our new field of study: the time used for 'cleanin' data has been well spent because now we can think about their structure and remodelling the whole procedure that concerns with data itself

Likelihood to Renew

because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)

Evaluating Data Science Experience and Competitors

Products Replaced

Not Sure

Key Differentiators

Product Reputation
Existing Relationship with the Vendor

Read many reviews and articles about case studies

Evaluation Lessons Learned

I would try demo version of the most credited choices

Data Science Experience Implementation

Implementation Rating

It surprised us with unpredictable case of use and brand new points of view

Implementation Details / Implementation Partner

Implemented in-house

Implementation Phases

Yes - Analysis of data, import steps, normalizing data, test and development of solution

Implementation Issues

watson analytics
predictive analysis

Data Science Experience Training

Training Types Used

Online training
In-person training
Self-taught

In-Person Training

The trainers on the job are very smart with solutions and very able in teaching

Online Training

The Platform is very handy and suggests further steps according my previous interests

Ease of IBM Watson Studio Training

Platform lead you step by step in acquiring the knowledge that one needs

Data Science Experience Support

Support Rating

I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path

IBM Watson Studio Customer Support Pros and Cons

Pros	Cons
Quick Resolution Good followup Knowledgeable team Problems get solved Kept well informed No escalation required Immediate help available Support understands my problem Support cares about my success Quick Initial Response	None

Bug Resolution

Exceptional Examples of IBM Watson Studio Support

At the beginning I was wondering how to parse my typemismatched data and they showed how DSX itself could help, via machine learning, in refining the procedures of business intelligence for parsing purposes