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Dataiku vs SAS Visual Analytics comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Dataiku
Average Rating
8.2
Reviews Sentiment
7.1
Number of Reviews
12
Ranking in other categories
Data Science Platforms (6th)
SAS Visual Analytics
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
40
Ranking in other categories
Data Visualization (7th)
 

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. Dataiku is designed for Data Science Platforms and holds a mindshare of 12.3%, up 10.3% compared to last year.
SAS Visual Analytics, on the other hand, focuses on Data Visualization, holds 3.4% mindshare, down 5.4% since last year.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Dataiku12.3%
Databricks14.5%
KNIME Business Hub12.3%
Other60.9%
Data Science Platforms
Data Visualization Market Share Distribution
ProductMarket Share (%)
SAS Visual Analytics3.4%
Tableau Enterprise20.6%
Apache Superset9.3%
Other66.7%
Data Visualization
 

Q&A Highlights

HE
Jun 07, 2023
 

Featured Reviews

RichardXu - PeerSpot reviewer
The platform organizes workflows visually and efficiently
One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.
Danna Nemeth - PeerSpot reviewer
Provides ease of use and has advanced data visualization capabilities
The initial setup can be complex and depends on the specific environment and licensing. It typically requires some time and effort to configure properly. It can be deployed as a SaaS solution or on-premises. It typically requires an installer and someone knowledgeable in business analytics and data management. Generally, it does not require extensive maintenance, though occasional upkeep might be necessary.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."
"I believe the return on investment looks positive."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"The most valuable feature is the set of visual data preparation tools."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"Traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another."
"Data Science Studio's data science model is very useful."
"It's a stable, reliable product."
"The flexibility of the configuration is valuable to me."
"Simplifies report designs and quickly displays tables and graphs."
"It's quite easy to learn and to progress with SAS from an end-user perspective."
"The visualization capabilities and the email functionality are most beneficial."
"I like SAS Visual Analytics for its ability to provide an initial understanding of data through exploration, even before deep analytics."
"Visual Analytics is very easy to use. I use Visual Analytics for all the typical use cases except text mining. I used it to analyze data and monitor statistics, not text mining. I also use it for data visualization as well as creating interactive dashboards and infographics."
"It is a very stable solution."
 

Cons

"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"I think it would help if Data Science Studio added some more features and improved the data model."
"The technical support from Dataiku is not good. The support team does not provide adequate assistance, and there are concerns about billing requests."
"One area for improvement is the need for more capabilities similar to those provided by NVIDIA for parallel machine learning training. We still encounter some integration issues."
"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."
"We still encounter some integration issues."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"The deployment isn't smooth. Deploying Visual Analytics on the cloud takes a lot of work, or you can use some providers that give you SAS as a service. For example, there is a provider called SaasNow. They host SAS Visual Analytics and the license. You can buy the license and deploy it there without the hassle of installation because deploying the software isn't easy."
"There are a few little things that are predefined and can be done out of the box immediately. There is no business intelligence application that is predefined, which is something some customers or prospects would love to have. Small and mid-sized companies would struggle with it because they prefer something standard that has been predefined by somebody else."
"Some capabilities are missing compared to Power BI, especially when working with spreadsheet types."
"In Brazil, there are few documents, courses, and other resources for studying and implementing the tool."
"The charts and tables could use better sorting, primarily using other variables than the ones on the figure. If they could implement views like in the older version (previous to Viya), it would be very nice."
"Better connectivity with other data origins, better visualization, and the ability to create KPIs directly would all help."
"The product is expensive and needs the integration of more languages."
"The solution is a little weak at the front end."
 

Pricing and Cost Advice

"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."
"Pricing is pretty steep. Dataiku is also not that cheap."
"SAS Visual Analytics is expensive, as is the rest of the platform."
"$10,000 per annum for an enterprise license."
"Licensing is simple."
"It's approximately $114,000 US dollars per year."
"The cost of the solution can be expensive. There is an additional cost for users."
"The product is quite expensive."
"The product is expensive."
"I work with the tool's free version...The tool's corporate version is very expensive and requires a monthly hire."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Manufacturing Company
10%
Computer Software Company
9%
Energy/Utilities Company
6%
Financial Services Firm
20%
Government
10%
Computer Software Company
9%
University
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise1
Large Enterprise7
By reviewers
Company SizeCount
Small Business13
Midsize Enterprise8
Large Enterprise19
 

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?
I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is a pricey solution and I primarily recommend it to bigger companies.
What needs improvement with Dataiku Data Science Studio?
There is room for improvement in terms of allowing for more code-based features. I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend ...
What is your primary use case for Dataiku Data Science Studio?
My company sells licenses for both Dataiku and Alteryx, and we have clients who use them. I engage with several companies in telecommunications, retail, and energy to assess how our clients are uti...
What do you like most about SAS Visual Analytics?
The most solution's notable aspect, in my view, is the ability to integrate various data sources and harness advanced technologies such as machine learning and artificial intelligence. This helps w...
What is your experience regarding pricing and costs for SAS Visual Analytics?
It's about an average of five. It's easy to scale, but it comes with cost.
What needs improvement with SAS Visual Analytics?
In terms of configuration, I would like to see AI capabilities since many applications are now integrating AI. It may be that our current subscription does not include AI-enabled features, but I wo...
 

Also Known As

Dataiku DSS
SAS BI
 

Overview

 

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
Staples, Ausgrid, Scotiabank, the Australian Institute of Health and Welfare, the Blue Cross and Blue Shield of North Carolina, Oklahoma Gas & Electric, Xcel Energy, and Triad Analytics Solutions.
Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Knime and others in Data Science Platforms. Updated: August 2025.
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