Dataiku DSS vs. IBM Watson Studio on Cloud Pak for Data

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Dataiku DSS
Score 9.1 out of 10
N/A
Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.N/A
IBM Watson Studio
Score 8.8 out of 10
N/A
IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.N/A
Pricing
Dataiku DSSIBM Watson Studio on Cloud Pak for Data
Editions & Modules
Discover
Contact sales team
Business
Contact sales team
Enterprise
Contact sales team
No answers on this topic
Offerings
Pricing Offerings
Dataiku DSSIBM Watson Studio
Free Trial
YesNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Dataiku DSSIBM Watson Studio on Cloud Pak for Data
Considered Both Products
Dataiku DSS

No answer on this topic

IBM Watson Studio
Top Pros
Top Cons
Features
Dataiku DSSIBM Watson Studio on Cloud Pak for Data
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Dataiku DSS
9.1
4 Ratings
9% above category average
IBM Watson Studio on Cloud Pak for Data
8.0
22 Ratings
4% below category average
Connect to Multiple Data Sources10.04 Ratings7.922 Ratings
Extend Existing Data Sources10.04 Ratings7.922 Ratings
Automatic Data Format Detection10.04 Ratings9.721 Ratings
MDM Integration6.52 Ratings6.414 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Dataiku DSS
9.9
4 Ratings
15% above category average
IBM Watson Studio on Cloud Pak for Data
9.7
22 Ratings
13% above category average
Visualization9.94 Ratings9.722 Ratings
Interactive Data Analysis10.04 Ratings9.722 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Dataiku DSS
10.0
4 Ratings
19% above category average
IBM Watson Studio on Cloud Pak for Data
9.3
22 Ratings
12% above category average
Interactive Data Cleaning and Enrichment10.04 Ratings9.722 Ratings
Data Transformations10.04 Ratings9.721 Ratings
Data Encryption9.94 Ratings7.920 Ratings
Built-in Processors10.04 Ratings9.721 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Dataiku DSS
8.7
4 Ratings
3% above category average
IBM Watson Studio on Cloud Pak for Data
9.3
22 Ratings
10% above category average
Multiple Model Development Languages and Tools5.14 Ratings9.721 Ratings
Automated Machine Learning10.04 Ratings9.722 Ratings
Single platform for multiple model development10.04 Ratings9.722 Ratings
Self-Service Model Delivery10.04 Ratings7.920 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Dataiku DSS
9.0
4 Ratings
6% above category average
IBM Watson Studio on Cloud Pak for Data
7.8
22 Ratings
8% below category average
Flexible Model Publishing Options9.04 Ratings8.822 Ratings
Security, Governance, and Cost Controls9.04 Ratings6.922 Ratings
Best Alternatives
Dataiku DSSIBM Watson Studio on Cloud Pak for Data
Small Businesses
Saturn Cloud
Saturn Cloud
Score 9.0 out of 10
Saturn Cloud
Saturn Cloud
Score 9.0 out of 10
Medium-sized Companies
Mathematica
Mathematica
Score 8.4 out of 10
Mathematica
Mathematica
Score 8.4 out of 10
Enterprises
Alteryx
Alteryx
Score 9.1 out of 10
Alteryx
Alteryx
Score 9.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Dataiku DSSIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
10.0
(4 ratings)
7.9
(65 ratings)
Likelihood to Renew
-
(0 ratings)
8.2
(1 ratings)
Usability
10.0
(1 ratings)
9.6
(2 ratings)
Availability
-
(0 ratings)
8.2
(2 ratings)
Performance
-
(0 ratings)
8.2
(2 ratings)
Support Rating
9.4
(3 ratings)
8.2
(2 ratings)
In-Person Training
-
(0 ratings)
8.2
(1 ratings)
Online Training
-
(0 ratings)
8.2
(1 ratings)
Implementation Rating
-
(0 ratings)
7.3
(2 ratings)
Product Scalability
-
(0 ratings)
8.2
(1 ratings)
Vendor post-sale
-
(0 ratings)
7.3
(1 ratings)
Vendor pre-sale
-
(0 ratings)
8.2
(1 ratings)
User Testimonials
Dataiku DSSIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
Dataiku
Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.
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IBM
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.
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Pros
Dataiku
  • The intuitiveness of this tool is very good.
  • Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
  • The way you can control things, the set of APIs gives a lot of flexibility to a developer.
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IBM
  • 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.
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Cons
Dataiku
  • End product deployment.
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IBM
  • 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
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Likelihood to Renew
Dataiku
No answers on this topic
IBM
because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
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Usability
Dataiku
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
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IBM
The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
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Reliability and Availability
Dataiku
No answers on this topic
IBM
From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
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Performance
Dataiku
No answers on this topic
IBM
Never had slow response even on our very busy network
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Support Rating
Dataiku
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.
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IBM
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
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In-Person Training
Dataiku
No answers on this topic
IBM
The trainers on the job are very smart with solutions and very able in teaching
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Online Training
Dataiku
No answers on this topic
IBM
The Platform is very handy and suggests further steps according my previous interests
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Implementation Rating
Dataiku
No answers on this topic
IBM
It surprised us with unpredictable case of use and brand new points of view
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Alternatives Considered
Dataiku
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
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IBM
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.
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Scalability
Dataiku
No answers on this topic
IBM
It helped us in getting from 0 to DSX without getting lost
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Return on Investment
Dataiku
  • Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
  • Platform also ease tracking of data processing workflow, unlike Excel.
  • Build-in data visualizations covers many use cases with minimal customization; time saver.
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IBM
  • 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
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ScreenShots