IBM Watson Studio on Cloud Pak for Data vs. SAS Enterprise Guide

It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.

Incentivized

SAS Enterprise Guide is good at taking various datasets and giving analyst/user ability to do some transformations without substantial amounts of code. Once the data is inside SAS, the memory of it is very efficient. Using SAS for data analysis can be helpful. It will give good statistics for you, and it has a robust set of functions that aid analysis.

Incentivized

• Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
• SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
• Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
• Estimator validation lets data scientists test and prove different models.

Incentivized

• Ability to load an AutoExec when opening a session ensuring everyone has the same global variables.
• Formatting with Ctrl I. If you're reading someone else's code and it's not formatted correctly you can highlight the area and hit Ctrl I.

Incentivized

• The cost is steep and so only companies with resources can afford it
• It will be nice to have Chinese versions so that Chinese engineers can also use it easily
• It takes a while to learn how to input different kinds of skin defects for detection

Incentivized

• Process time of data is a bit long. It depends on the size of your data and complexity of your project tree.
• There is not enough online free training videos.
• While working with the project tree sometimes the links between the modules are broken or the order for running the modules get mixed up. You should know your project tree by heart.

Incentivized

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

Incentivized

On account of current user experience and the organization-wide acceptance.

The UI flawlessly merges this offering by providing a neat, minimal, responsive interface

Incentivized

It's not all bad, but I don't believe that an enterprise purchase of SAS is worth the expense considering the widely available set of tools in the data analytics space at the moment. In my company, it's a good tool because others use it. Otherwise, I wouldn't purchase a new set of it because it doesn't have some of the better analytical functions in it.

Incentivized

From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected

Incentivized

Never had slow response even on our very busy network

Incentivized

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

Incentivized

Although I use SAS support for information on functions, these are SAS related and haven't really come across anything that is specifically for SAS EG.

Incentivized

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

Incentivized

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

Incentivized

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

Incentivized

I've not worked hands-on with the implementation team, but there were no escalations barring a few hiccups in the deployment due to change in requirement & adoption to our company's remote servers.

The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.

Incentivized

Why I prefer SAS EG: Data processing speed is much faster than that R Studio. It can load any amount of data and any type of data like structured or unstructured or semi-structured. Its output delivery system by which we have the output in PDF file makes it very comfortable to use and share that file to clients very easily. Inbuilt functions are very powerful and plentiful. Facility of writing macros makes it far away from its competitors.

It helped us in getting from 0 to DSX without getting lost

Incentivized

• Could instantly show data driven insights to drive 20% incremental revenue over existing results
• Still don't have a real use case for unstructured data like twitter feed
• Some of the insights around user actions have driven new projects to automate mundane tasks

Incentivized

• Positive (cost): SAS made a bundle that include unlimited usage of SAS/Enterprise Guide with a server solution. That by itself made the company save a lot of money by not having to pay individual licences anymore.
• Positive (insight): Data analysts in business units often need to crunch data and they don't have access to ETL tools to do it. Having access to SAS/EG gives them that power.
• Positive (time to market): Having the users develop components with SAS/EG allows for easier integration in a production environment (SAS batch job) as no code rework is required.