DataRobot vs. Microsoft R Open / Revolution R Enterprise

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
DataRobot
Score 8.2 out of 10
N/A
The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to drive business outcomes, that is available on the user's cloud platform-of-choice, on-premise, or as a fully-managed service. The solutions include tools providing data preparation enabling users to explore and…N/A
Microsoft R Open / Revolution R Enterprise
Score 8.9 out of 10
N/A
Microsoft R Open and Revolution R Enterprise are big data R distribution for servers, Hadoop clusters, and data warehouses. Microsoft acquired original developer Revolution Analytics in 2016. Microsoft R is available in two editions: Microsoft R Open (formerly Revolution R Open) and Revolution R Enterprise.N/A
Pricing
DataRobotMicrosoft R Open / Revolution R Enterprise
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
DataRobotMicrosoft R Open / Revolution R Enterprise
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
DataRobotMicrosoft R Open / Revolution R Enterprise
Features
DataRobotMicrosoft R Open / Revolution R Enterprise
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
DataRobot
-
Ratings
Microsoft R Open / Revolution R Enterprise
5.3
Ratings
45% below category average
Connect to Multiple Data Sources00 Ratings6.10 Ratings
Extend Existing Data Sources00 Ratings6.00 Ratings
Automatic Data Format Detection00 Ratings6.00 Ratings
MDM Integration00 Ratings3.00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
DataRobot
-
Ratings
Microsoft R Open / Revolution R Enterprise
7.0
Ratings
18% below category average
Visualization00 Ratings7.00 Ratings
Interactive Data Analysis00 Ratings7.00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
DataRobot
-
Ratings
Microsoft R Open / Revolution R Enterprise
4.8
Ratings
52% below category average
Interactive Data Cleaning and Enrichment00 Ratings5.10 Ratings
Data Transformations00 Ratings5.00 Ratings
Data Encryption00 Ratings3.00 Ratings
Built-in Processors00 Ratings6.00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
DataRobot
-
Ratings
Microsoft R Open / Revolution R Enterprise
6.0
Ratings
33% below category average
Multiple Model Development Languages and Tools00 Ratings5.00 Ratings
Automated Machine Learning00 Ratings5.00 Ratings
Single platform for multiple model development00 Ratings8.00 Ratings
Self-Service Model Delivery00 Ratings6.00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
DataRobot
-
Ratings
Microsoft R Open / Revolution R Enterprise
6.5
Ratings
27% below category average
Flexible Model Publishing Options00 Ratings6.00 Ratings
Security, Governance, and Cost Controls00 Ratings6.90 Ratings
Best Alternatives
DataRobotMicrosoft R Open / Revolution R Enterprise
Small Businesses
InterSystems IRIS
InterSystems IRIS
Score 7.7 out of 10
Jupyter Notebook
Jupyter Notebook
Score 9.4 out of 10
Medium-sized Companies
InterSystems IRIS
InterSystems IRIS
Score 7.7 out of 10
Posit
Posit
Score 10.0 out of 10
Enterprises
InterSystems IRIS
InterSystems IRIS
Score 7.7 out of 10
Posit
Posit
Score 10.0 out of 10
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User Ratings
DataRobotMicrosoft R Open / Revolution R Enterprise
Likelihood to Recommend
8.6
(0 ratings)
6.0
(0 ratings)
Likelihood to Renew
6.3
(0 ratings)
7.0
(0 ratings)
Usability
-
(0 ratings)
7.0
(0 ratings)
Support Rating
8.2
(0 ratings)
8.0
(0 ratings)
User Testimonials
DataRobotMicrosoft R Open / Revolution R Enterprise
Likelihood to Recommend
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.
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Revolution Analytics is a very compelling product for Big Data Analytics. It allows distributed computing over multiple hadoop nodes thus allowing HDFS to do its role cleanly i.e. cheap massive storage and it does good job of running algorithms using R or similar programming language on Hadoop. It would be definitely advantage for the organization who uses either R or SAS as their statistical model development tool as Rev-R support both the platforms. Overall, very positive experience with Rev-R.
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Pros
  • 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.
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  • Parallel processing
  • Integration with R
  • Open-source
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Cons
  • 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.
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  • Very high learning curve
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Likelihood to Renew
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.
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In general, Revolution Analytics brings a lot of value to the organization. The renewal decision would be based on return on investment in terms of quantified actionable insights that are getting generated against the cost of the product. Additionally, market brand of the tool and reputation risk in terms of possible acquisition and its impact to overall organizational analytic strategy would be considered as well.
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Usability
No answers on this topic
It is good, easy to use, improvements are being made to the product and more info being shared in the community. It just needs some more time to become more integrated to other platforms and tools/data out there.
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Support Rating
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.
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Generally support comes through the forums and user generated channels which are helpful, easy to access, quickly turned around and provided by knowledgeable users. However the support channels are not employees and the channels are often used as a way to learn quick difficult elements of R. Better design, users interface and tutorial options would alleviate the need for this sort of interaction.
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Alternatives Considered
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.
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R is decent for our needs but in the end didn't quite solve all of our needs so moved on. It is a good tool so far. its been a couple months since we last touched it so with changes continuing and more wide spread use and more info being published this tool will improve. Depending upon your needs this can be very easy for you to setup, use, and maintain when compared to other tools out there. My suggestion is to ensure you fully understand your use cases first with data sources identified to ensure this tool can meet your needs.
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Return on Investment
  • 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
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  • Better forecasting for resource allocation has saved our organisation hundreds of thousands in conjunction with other strategies.
  • Better visualisation options has allowed smoother internal marketing and internal comms strategies when preparing teams for seasonality.
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ScreenShots

DataRobot Screenshots

Screenshot of Decision FlowsScreenshot of No Code App BuilderScreenshot of AI AppsScreenshot of Automated Time SeriesScreenshot of MLOpsScreenshot of Model Insights