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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
8.0
Reviews Sentiment
7.2
Number of Reviews
9
Ranking in other categories
Data Science Platforms (7th)
SAS Visual Analytics
Average Rating
8.2
Reviews Sentiment
6.9
Number of Reviews
39
Ranking in other categories
Data Visualization (8th)
 

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. Dataiku is designed for Data Science Platforms and holds a mindshare of 12.1%, up 7.8% compared to last year.
SAS Visual Analytics, on the other hand, focuses on Data Visualization, holds 5.0% mindshare, down 6.6% since last year.
Data Science Platforms
Data Visualization
 

Q&A Highlights

HE
Jun 07, 2023
 

Featured Reviews

Sabrine Bendimerad - PeerSpot reviewer
Saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms
One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to use GitHub with Dataiku, in practice, it was difficult to manage our code effectively and push it from Dataiku to GitHub. Another limitation was its ability to handle different types of data. While Dataiku is powerful for working with structured data, like regular or geospatial data, it struggled with more complex data types such as text and image. In addition to the challenges with GitHub integration, the limited support for diverse data types was another feature lacking at that time.
Robert Heck - PeerSpot reviewer
A great solution for big organizations, complex business requirements, and highly sophisticated and specialized statistics
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. For instance the system does not come with a pre-defined accounting, budgeting or planning model for a particular industry. Some competitors come with such a model (e.g. for retail companies) which makes the implementation of course easier if the customer can comproise with this predefined model. SAS does not provide such models but does not demand customers to comply with a foreign business model.

Quotes from Members

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

Pros

"I rate the overall product as eight out of ten."
"The solution is quite stable."
"Traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another."
"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."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"Data Science Studio's data science model is very useful."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The product is stable, reliable, and scalable."
"It's a stable, reliable product."
"Great for handling complex data models."
"The speed to display charts and react to users' choices is great."
"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."
"We've found the product to be stable and reliable."
"The alert generation feature also helps in sending out ad hoc messages to the business users if business thresholds have been crossed."
"I use Visual Analytics for enterprise reporting."
 

Cons

"I think it would help if Data Science Studio added some more features and improved the data model."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"The ability to have charts right from the explorer would be an improvement."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"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."
"The license is very expensive."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"The reason we haven't rolled it out across the board is due to the fact that the licensing is so expensive."
"There are certain shortcomings in the tool's support services, making it an area where improvements are required."
"The licensing ends up being more expensive than other options."
"The solution should improve its graphics."
"There is a need for coding when it comes to digital reporting which can be intimidating."
"There is room for improvement in anti-money laundering prevention and operation monitoring, as well as operation monitoring surveillance."
"The integration aspects of the solution could be improved."
"Colours used on report objects"
 

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."
"It's approximately $114,000 US dollars per year."
"It was licensed for corporate use, and its licensing was on a yearly basis."
"Licensing is simple."
"The cost of the solution can be expensive. There is an additional cost for users."
"The product is expensive."
"The product is quite expensive."
"Visual Analytics is expensive for a small company like mine. You also need to deploy it on a server or cloud, so you pay for the license as well as the cost of the cloud or the server that you will deploy on."
"SAS Visual Analytics is expensive, as is the rest of the platform."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Educational Organization
16%
Manufacturing Company
9%
Computer Software Company
8%
Financial Services Firm
20%
Government
13%
Computer Software Company
10%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What needs improvement with Dataiku Data Science Studio?
One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to us...
What is your primary use case for Dataiku Data Science Studio?
We use the solution for data science and machine learning.
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?
Some capabilities are missing compared to Power BI, especially when working with spreadsheet types. Furthermore, Excel is more customizable compared to SAS Visual Analytics, which can be quite rigi...
 

Also Known As

Dataiku DSS
SAS BI
 

Learn More

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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.
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