DataRobot vs. SAS Enterprise Miner

DataRobot can be used for risk assessment, such as predicting the likelihood of loan default. It can handle both classification and regression tasks effectively. It relies on historical data for model training. If you have limited historical data or the data quality is poor, it may not be the best choice as it requires a sufficient amount of high-quality data for accurate model building.

Incentivized

Text Miner option is very useful to uncover trending themes in very large data sets.

Incentivized

• The breadth of models available to use is helpful and allows much more analytical power than programming them all yourself.
• The built-in variable diagnostics are helpful when testing large variable sets to see which perform the best.
• Many of the adjustments on the models are easy to use/it's easy to re-run and kick off new models as you want to try new things.

Incentivized

• Developing and evaluating ensemble models.
• A very transparent interface.

Incentivized

• Further improvements to their text analysis tool, to be more like the Qualtrics text analysis tool, would be a great addition. Qualtrics has templates built into their text analysis tool for customer service, quality control, etc, and will automatically slot your text responses into categories associated with certain sub areas of those larger categories.

Incentivized

• One of the major flaws is that the tool is basically an interface to SAS/STAT code. It generates code in the background and runs it. Because of that, some errors are warning might be a little difficult to understand for users who aren't proficient with SAS code.
• R integration is nice but I would like to see the possibility to integrate even more statistical models different than SAS. That would allow for better performance optimization when really required.
• The light client is java based and a little heavy on the OS. It would be nice to get a web-based version of the tool instead of the java one.

Incentivized

DataRobot presents a machine-learning platform designed by data scientists from an array of backgrounds, to construct and develop precise predictive modeling in a fraction of the time previously taken. The tech invloved addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot utilizes parallel processing to evaluate models in R, Python, Spark MLlib, H2O and other open source databases. It searches for possible permutations and algorithms, features, transformation, processes, steps and tuning to yield the best models for the dataset and predictive goal.

Incentivized

As I am writing this report I am participating with Datarobot Engineers in an complex environment and we have their whole support. We are in Mexico and is not common to have this commitment from companies without expensive contract services. Installing is on premise and the client does not want us to take control and they, the client, is also limited because of internal IT regulations ,,, soo we are just doing magic and everybody is committed.

Incentivized

I have contacted SAS twice in the past year and they have been super responsive both times. They solved my problem. I am also registered for an in-person class next month and they called today to tell me that it will be an online-only session. They apologized for the change and registered me for the online version. Super helpful!

Incentivized

I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the model from there and fine tune further if you need.

Incentivized

For those that are used to the SAS ecosystem, SAS Enterprise Miner is a massive move in the right direction. It makes doing analytics much more enjoyable. It is more user-friendly than Spotfire or Kinesis and seems to produce better results overall. SAS Enterprise Miner seems to be written by analysts for analysts.

Incentivized

• We have been able to cut costs by not buying leads that we will not be able to sell on
• We have been able to deploy loan eligibility reporting which brought in new business
• We have been able to improve the performance of our credit providers and our partners which has helped to retain business

Incentivized

• It has a positive ROI to our business, as our sales lead rate increased after we started recommending SAS EM.
• Our business operation numbers improved after we introduced SAS EM and started using predictive analytics for our customer retention and customer chain prediction.
• The statistical modelling for the risk controls in our financial department helped to reduce the related residual risk.

Incentivized