Dataiku vs SAS Visual Analytics comparison

The compared Dataiku and SAS solutions aren't in the same category. Dataiku is ranked #7 in Data Science Platforms , with an average rating of 7.5, and holds a 11.5% mindshare in the category. SAS is ranked #9 in DV , with an average rating of 8.5, and holds a 5.2% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 96% of SAS users who would recommend it.

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Executive Summary

We performed a comparison between Dataiku and SAS Visual Analytics based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Knime, Microsoft and others in Data Science Platforms.

To learn more, read our detailed Data Science Platforms Report (Updated: November 2024).

Buyer's Guide

Data Science Platforms

November 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

Data Science Platforms

Data Visualization

Q&A Highlights

Hakim ERRAJI

Founder & Managing Partners at OpenIn Partners

Jun 07, 2023

Which solution do you prefer: SAS Viya or Dataiku Data Science Studio?

Dataiku is my choice as it's not bulky and the learning path for people like me (noobs in ML and data science) is not steep at all, so after a couple of pieces of training I feel very confident. Also, you can check it out for free, it has the sandbox free of charge. For the guys who are tech-aware, it provides a wide variety of tools as well as flexible customization (via code notebooks, preset connectors, etc). The pricing bites but the charge depends on the scale of your needs, so you have a choice there. As for SAS - I personally didn't use it but read many reviews stating that it's kinda clumsy and not intuitive to use vs many other solutions, though I might be wrong.

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Featured Reviews

AbakarAhmat

Software Engineer at a university with 10,001+ employees

Enhancing survey analysis that provides valued insightfulness

I used traditional tools where I would prepare data, click through menus, and use SQL Server for data visualization. We switched to IBM SPSS because it offers strong certification and aligns well with the standards we prioritize in our surveys. In terms of popularity, it stands out as the top choice in the market, especially in the research and university domains. Many different organizations and institutions use SPSS for statistical analytics. While there are other tools like MCLab and similar options available, SPSS is the most renowned and widely used among them.

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Sabrine Bendimerad

Data Scientist at Electricite De France

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.

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Robert Heck

Co Owner at Hecht und Heck GmbH

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.

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Quotes from Members

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

Pros

"You can quickly build models because it does the work for you."

"SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools."

"You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use."

"I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well."

"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."

"The most valuable feature is the user interface because you don't need to write code."

"It has helped our analyst unit deliver work with more transparency and confidence, given that we can always view the dataset in totality, after each step of data transformation."

"The SPSS interface is very accessible and user-friendly. It's really easy to get information in it. I've shared it with experts and beginners, and everyone can navigate it."

More IBM SPSS Statistics pros

"The most valuable feature is the set of visual data preparation tools."

"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."

"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."

"Cloud-based process run helps in not keeping the systems on while processes are running."

"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."

"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."

"Data Science Studio's data science model is very useful."

"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."

More Dataiku pros

"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."

"The alert generation feature also helps in sending out ad hoc messages to the business users if business thresholds have been crossed."

"Great for handling complex data models."

"The speed to display charts and react to users' choices is great."

"It is a very stable solution."

"Quick deployment to dashboards and analytics features (using SAS Visual Statistics and Enterprise Guide). Easy to create a simple forecast and discover business insights using segmentation tools."

"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 with quality assurance processes."

"It's relatively simple to create basic dashboards and reports."

More SAS Visual Analytics pros

Cons

"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."

"I think the visualization and charting should be changed and made easier and more effective."

"SPSS is a tool that's been around since the late 60s, and it's the universal worldwide standard for quantitative social science data analysis. That said, it does seem a bit strange to me that the graphical output functions are so clunky after all these years. The output of charts and graphs that SPSS produces is hideous."

"I would like SPSS to improve its integration with other data-filing IBM tools. I also think its duration with data, utilization, and graphics could be better."

"IBM SPSS Statistics does not keep you close to your data like KNIME."

"I feel that when it comes to conducting multiple analyses, there could be more detailed information provided. Currently, the software gives a summary and an overview, but it would be beneficial to have specific details for each product or variable."

"Improvements are needed in the user interface, particularly in terms of user-friendliness."

"There is a learning curve; it's not very steep, but there is one."

More IBM SPSS Statistics cons

"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."

"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 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."

"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."

"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

"I think it would help if Data Science Studio added some more features and improved the data model."

"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."

More Dataiku cons

"The integration aspects of the solution could be improved."

"There are scalability issues. It depends on the data volume and number of end-users. VA requires a lot of hardware resources to move volumes of data."

"SAS Visual Analytics could improve by making it more accessible for users outside the organization."

"The licensing ends up being more expensive than other options."

"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."

"There are certain shortcomings in the tool's support services, making it an area where improvements are required."

"SAS Visual Analytics could be more user-friendly."

More SAS Visual Analytics cons

Pricing and Cost Advice

"It's quite expensive, but they do a special deal for universities."

"More affordable training for new staff members."

"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."

"The price of this solution is a little bit high, which was a problem for my company."

"The price of IBM SPSS Statistics could improve."

"I rate the tool's pricing a five out of ten."

"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."

"While the pricing of the product may be higher, the accompanying service and features justify the investment."

More IBM SPSS Statistics pricing and cost advice

"Pricing is pretty steep. Dataiku is also not that cheap."

"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."

"The cost of the solution can be expensive. There is an additional cost for users."

"I work with the tool's free version...The tool's corporate version is very expensive and requires a monthly hire."

"The product is quite expensive."

"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."

"SAS Visual Analytics is expensive, as is the rest of the platform."

"$10,000 per annum for an enterprise license."

More SAS Visual Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

University

Computer Software Company

Manufacturing Company

Financial Services Firm

18%

Educational Organization

16%

Manufacturing Company

Computer Software Company

Financial Services Firm

20%

Government

13%

Computer Software Company

10%

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about IBM SPSS Statistics?

The software offers consistency across multiple research projects helping us with predictive analytics capabilities.

See all answers

What is your experience regarding pricing and costs for IBM SPSS Statistics?

The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...

See all answers

What needs improvement with IBM SPSS Statistics?

IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...

See all answers

What is your experience regarding pricing and costs for Dataiku Data Science Studio?

It is very expensive.

See all answers

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 integratin...

See all answers

What is your primary use case for Dataiku Data Science Studio?

We use the solution for data science and machine learning.

See all answers

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...

See all answers

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.

See all answers

What needs improvement with SAS Visual Analytics?

Some capabilities are missing compared to Power BI, especially when working with spreadsheet types. Furthermore, Exce...

See all answers

Comparisons

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 14% of the time

Alteryx vs IBM SPSS Statistics

Compared 14% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 12% of the time

KNIME vs IBM SPSS Statistics

Compared 2% of the time

More IBM SPSS Statistics Competitors

Databricks vs Dataiku

Compared 38% of the time

KNIME vs Dataiku

Compared 15% of the time

Alteryx vs Dataiku

Compared 11% of the time

RapidMiner vs Dataiku

Compared 11% of the time

IBM SPSS Modeler vs Dataiku

Compared 1% of the time

More Dataiku Competitors

Tableau vs SAS Visual Analytics

Compared 25% of the time

Microsoft Power BI vs SAS Visual Analytics

Compared 21% of the time

Databricks vs SAS Visual Analytics

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs SAS Visual Analytics

Compared 5% of the time

SAS Enterprise Miner vs SAS Visual Analytics

Compared 4% of the time

More SAS Visual Analytics Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

November 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Dataiku

November 2024

Download Dataiku product report

Buyer's Guide

SAS Visual Analytics

October 2024

Download SAS Visual Analytics product report

Also Known As

SPSS Statistics

Dataiku DSS

SAS BI

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IBM

Dataiku

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SAS

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

SAS Visual Analytics is a data visualization tool that is used for reporting, data exploration, and analytics. The solution enables users - even those without advanced analytical skills - to understand and examine patterns, trends, and relationships in data. SAS Visual Analytics makes it easy to create and share reports and dashboards that monitor business performance. By using the solution, users can handle, understand, and analyze their data in both past and present fields, as well as influence vital factors for future changes. SAS Visual Analytics is most suitable for larger companies with complex needs.

SAS Visual Analytics Features

SAS Visual Analytics has many valuable key features. Some of the most useful ones include:

Data
Interactive data discovery
Augmented analytics
Chat-enabled analytics
Sharing and collaboration
Visual analytics apps
Embedded insights
Location analytics
Security and administration
In-memory engine

SAS Visual Analytics Benefits

There are many benefits to implementing SAS Visual Analytics. Some of the biggest advantages the solution offers include:

Machine learning and natural language: SAS Visual Analytics uses machine learning and natural language explanations to find, visualize, and narrate stories and insights that are easy to understand and explain. This enables you to find out why something happened, examine all options, and uncover opportunities hidden deep in your data.

Easy and efficient reporting: With SAS Visual Analytics, you can create interactive reports and dashboards so you can quickly summarize key performance metrics and share them via the web and mobile devices.

Easy to use: SAS Visual Analytics was designed to be easy to use. Its easy-to-use predictive analytics enables even business analysts to assess possible outcomes, which also helps organizations make smarter, data-driven decisions.

Self-service data: Self-service data preparation gives users the ability to import their own data, join tables, create calculated columns, apply data quality functions, and more. In turn, the solution empowers users to access, combine, clean, and prepare their own data in an agile way, which helps facilitate faster, broader adoption of analytics for your entire organization.

Reviews from Real Users

Below are some reviews and helpful feedback written by PeerSpot users currently using the SAS Visual Analytics solution.

A Senior Manager at a consultancy says, “The solution is very stable. The scalability is good. The usability is quite good. It's quite easy to learn and to progress with SAS from an end-user perspective.

PeerSpot user Robert H., Co-owner at Hecht und Heck GmbH, comments, “What I really love about the software is that I have never struggled in implementing it for complex business requirements. It is good for highly sophisticated and specialized statistics in the areas that some people tend to call artificial intelligence. It is used for everything that involves visual presentation and analysis of highly sophisticated statistics for forecasting and other purposes.

Andrea D., Chief Technical Officer at Value Partners, explains, “The best feature is that SAS is not a single BI tool. Rather, it is part of an ecosystem of tools, such as tools that help a user to develop artificial intelligence, algorithms, and so on. SAS is an ecosystem. It's an ecosystem of products. We've found the product to be stable and reliable. The scalability is good.”

Sample Customers

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

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.

Buyer's Guide

Data Science Platforms

November 2024

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