KNIME vs SAS Analytics comparison

Knime and SAS are both solutions in the Data Mining category. Knime is ranked #1 with an average rating of 7.8, while SAS is ranked #5 with an average rating of 8.0. Knime holds a 26.1% mindshare in DM, compared to SAS’s 6.6% mindshare. Additionally, 94% of Knime users are willing to recommend the solution, compared to 94% of SAS users who would recommend it.

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Executive Summary

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

Find out in this report how the two Data Mining solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed KNIME vs. SAS Analytics Report (Updated: December 2024).

Buyer's Guide

KNIME vs. SAS Analytics

December 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of December 2024, in the Data Mining category, the mindshare of IBM SPSS Statistics is 15.1%, up from 12.7% compared to the previous year. The mindshare of KNIME is 26.1%, up from 25.9% compared to the previous year. The mindshare of SAS Analytics is 6.6%, down from 10.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining

Featured Reviews

Md Masudul Hassan

Agricultural Finance and IT Researcher at Sher-e-Bangla Agricultural University

Comprehensive data analysis capabilities with a user-friendly interface, providing an efficient and reliable platform for researchers and analysts

I believe that offering short-term SPSS licenses, perhaps when customer sourcing is available, could make it more affordable. These licenses shouldn't include features tailored for universities or large sales organizations. Instead, they could offer discounts or additional facilities for smaller entities to access the software. In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options. For example, offering basic features to the first hundred users can help them become familiar with the software and its capabilities. This approach encourages users to upgrade to higher tiers as they become more experienced and require additional functionality.

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Hansong Choi

Head of Customer Analytics at American International Group

A low-code platform that reduces data mining time by linking script

The solution allows for sharing model designs and model operations with other data analysts. Other solutions such as SAS, R, and Python consist of just the script which is difficult to share. The solution is a low-code platform which reduces data mining time and its platform includes a clickable icon for linking script.

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Tanin U

Assistant Vice President at AEON Thana Sinsap

Provides comprehensive data analysis tools and functionalities, but its higher pricing and potential stability issues may present drawbacks

SAS Analytics plays a vital role in enhancing our decision-making processes, particularly in areas such as customer segmentation and operational efficiency. While our current setup operates smoothly, certain partners still rely on another platform, specifically the virtualization dashboard. I believe it's a significant improvement compared to the traditional approach I've been using for the past ten years. Previously, when management had inquiries, they would create documents or memos, and IT would then track down the data, interpret it, and provide feedback. This process involved explaining the data structure, database, and definitions in the data warehouse. Now, with SAS Analytics, the workflow is much smoother and faster. IT is more streamlined, allowing for quicker access to data, and managers can directly access and utilize the sales reports.

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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

"The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important."

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

"One feature I found very valuable was the analysis of variance (ANOVA)."

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

"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."

"It is perfectly adequate if all you need are the results and not the trail of evidence."

"Since we are using the software as a statistical tool, I would say the best aspects of it are the regression and segmentation capabilities. That said, I've used it for all sorts of things."

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

More IBM SPSS Statistics pros

"It is a stable solution...It is a scalable solution."

"Clear view of the data at every step of ETL process enables changing the flow as needed."

"KNIME is easy to learn."

"There are a lot of connectors available in KNIME."

"The product is open-source and therefore free to use."

"It is very fast to develop solutions."

"This solution is easy to use and especially good at data preparation and wrapping."

"I've tried to utilize KNIME to the fullest extent possible to replace Excel."

More KNIME pros

"It has improved the level of efficacy and validity of our reports."

"It has also been around for an extremely long time, has a strong history, and good market penetration."

"I use it to replicate our entire financial system to verify/duplicate calculations."

"The technical support is okay."

"SAS Analytics plays a vital role in enhancing our decision-making processes, particularly in areas such as customer segmentation and operational efficiency."

"It's very easy to use once you learn it."

"I use SAS daily to analyze data, produce reports, and other outputs."

"SAS Business Intelligence is well-suited for our large corporation. We have demand for scalable and reliable insights into information which is housed in our large systems."

More SAS Analytics pros

Cons

"It would be helpful if there was better documentation on how to properly use the solution. A beginner's guide on how to use the various programming functions within the product would be so useful to a lot of people. I found that everything was very confusing at first. Having clear documentation would help alleviate that."

"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."

"The statistics should be more self-explanatory with detailed automated reports."

"Most of the package will give you the fixed value, or the p-value, without an explanation as to whether it it significant or not. Some beginners might need not just the results, but also some explanation for them."

"The solution needs to improve forecasting using time series analysis."

"Needs more statistical modelling functions."

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

"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."

More IBM SPSS Statistics cons

"The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data."

"The documentation is lacking and it could be better."

"KNIME is not scalable."

"It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end."

"In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them."

"It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME."

"One area that could be improved is increasing awareness and adoption of KNIME among organizations. Despite its capabilities, it is not as well-known as other tools. The advertising and marketing efforts to reach out to companies and universities have not been very successful."

"Sometimes, we needed more space to handle larger operations, especially since our machines had limited space and memory due to Kubernetes clusters."

More KNIME cons

"The graphing and visualization features could be enhanced, in my opinion. I would especially stress improving the visualization capabilities."

"The installation could also be easier, and the price could be better."

"The training for SAS Business Intelligence is often difficult to arrange. It is often cancelled due to not enough people being enrolled."

"There is potential for enhancement, particularly in the virtualized dashboard's capability to generate reports."

"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."

"This solution should be made more user-friendly."

"I would like to see their interface to R added to either Base SAS or SAS Analytics."

"Support at universities used to be limited, but I hear this is changing."

More SAS Analytics cons

Pricing and Cost Advice

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

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

"We think that IBM SPSS is expensive for this function."

"More affordable training for new staff members."

"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."

"The price of IBM SPSS Statistics could improve."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

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

More IBM SPSS Statistics pricing and cost advice

"Scaling to the on-premises version requires a licensing fee per user that is a bit expensive in comparison to R, Python, and SAS."

"This is a free open-source solution."

"KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required."

"While there are certain limitations in functionality, you can still utilize it efficiently free of charge."

"KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website."

"KNIME offers a free version"

"It's an open-source solution."

"KNIME is a cost-effective solution because it’s free of cost."

More KNIME pricing and cost advice

"SAS is very expensive."

"I think that the cost-benefit ratio is okay."

"Licensing was rather straightforward."

"It is relatively expensive. It is not an easy software to afford."

"Our licensing covers the usage for around 50 data analysts."

"Setup costs were quite reasonable."

"The pricing tends to be higher, and there are additional expenses involved."

"The cost for SAS Business Intelligence can prove to be a little prohibitive."

More SAS Analytics pricing and cost advice

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824,053 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

University

Manufacturing Company

Financial Services Firm

13%

Manufacturing Company

12%

Computer Software Company

Educational Organization

Financial Services Firm

13%

Computer Software Company

11%

Educational Organization

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 do you like most about KNIME?

Since KNIME is a no-code platform, it is easy to work with.

See all answers

What is your experience regarding pricing and costs for KNIME?

I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.

See all answers

What needs improvement with KNIME?

The current UI is primarily in English. Analyzing data in local languages might present challenges or issues.

See all answers

What do you like most about SAS Analytics?

I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile...

See all answers

What needs improvement with SAS Analytics?

The natural language querying and automated preparation of dashboards should be improved. The cost and accessibility ...

See all answers

What is your primary use case for SAS Analytics?

This is an application I use for data prep, data exploration, BI reporting, and some basic automated analytics.

See all answers

Comparisons

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 16% of the time

Alteryx vs IBM SPSS Statistics

Compared 13% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 11% of the time

Anaconda vs IBM SPSS Statistics

Compared 2% of the time

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RapidMiner vs KNIME

Compared 27% of the time

Microsoft Power BI vs KNIME

Compared 17% of the time

Dataiku vs KNIME

Compared 12% of the time

Alteryx vs KNIME

Compared 10% of the time

H2O.ai vs KNIME

Compared 1% of the time

More KNIME Competitors

Weka vs SAS Analytics

Compared 21% of the time

IBM SPSS Modeler vs SAS Analytics

Compared 14% of the time

More SAS Analytics Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

December 2024

Download IBM SPSS Statistics product report

Buyer's Guide

KNIME

December 2024

Download KNIME product report

Buyer's Guide

Data Mining

November 2024

Download SAS Analytics product report

Also Known As

SPSS Statistics

KNIME Analytics Platform

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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.”

KNIME is an open-source analytics software used for creating data science that is built on a GUI based workflow, eliminating the need to know code. The solution has an inherent modular workflow approach that documents and stores the analysis process in the same order it was conceived and implemented, while ensuring that intermediate results are always available.

KNIME supports Windows, Linux, and Mac operating systems and is suitable for enterprises of all different sizes. With KNIME, you can perform functions ranging from basic I/O to data manipulations, transformations and data mining. It consolidates all the functions of the entire process into a single workflow. The solution covers all main data wrangling and machine learning techniques, and is based on visual programming.

KNIME Features

KNIME has many valuable key features. Some of the most useful ones include:

Scalability through data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
High extensibility via a well-defined API for plugin extensions
Intuitive user interface
Import/export of workflows
Parallel execution on multi-core systems
Command line version for "headless" batch executions
Activity dashboard
Reporting & statistics
Third-party integrations
Workflow management
Local automation
Metanode linking
Tool blending
Big Data extensions

KNIME Benefits

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

Integrated Deployment: KNIME’s integrated deployment moves both the selected model, and the entire data model preparation process into production simply and automatically, allowing for continuous optimization in production and also saving time because it eliminates error.

Elastic and Hybrid Execution: KNIME’s elastic and hybrid executions helps you reduce costs while covering periods of high demand, dynamically.

Metadata Mapping: KNIME enables complete metadata mapping of all aspects of your workflow. In addition, KNIME offers blueprint workflows for documenting the nodes, data sources, and libraries used, as well as runtime information.

Guided Analytics: KNIME’s guided analytics applications can be customized based on reusable components.

Powerful analytics, local automation, and workflow difference: KNIME uses advanced predictive and machine learning algorithms to provide you with the analytics you need. In combination with powerful analytics, KNIME’s automation capabilities and workflow difference prepare your organization with the tools you need to make better business decisions.

Supports enterprise-wide data science practices: The deployment and management functionalities of KNIME make it easy to productionize data science applications and services, and deliver usable, reliable, and reproducible insights for the business.

Helps you leverage insights gained from your data: Using KNIME ensures the data science process immediately reflects changing requirements or new insights.

Reviews from Real Users

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

An Emeritus Professor at a university says, “It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional. It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.”

Benedikt S., CEO at SMH - Schwaiger Management Holding GmbH, explains, “All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function. Technical support has been extremely responsive so far. The solution has a very strong and supportive community that shares information and helps each other troubleshoot. The solution is very stable. The initial setup is pretty simple and straightforward.”

Piotr Ś., Test Engineer at ProData Consult, says, “What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.”

SAS was founded in 1976 and actually began as a project at North Carolina State University to analyze agriculture research. It has since become a global company that is recognized for its innovation in data analytics and business intelligence. SAS is redefining what's possible with data analytics through greater efficiency, strong information value chains, effective collaboration tools, and state-of-the-art visualization software. SAS Analytics is designed for use in a variety of industries including government, manufacturing, higher education, defense & security, banking, automotive, communications, and much more. SAS Analytics is a business intelligence (BI) solution that has the ability to reveal patterns and anomalies in data, identify relationships and different variables, and predict future outcomes. Users of SAS Analytics will benefit from making more sound, better informed business decisions based on company data and market trends. Data mining, data visualization, text analytics, forecasting, statistical analysis, and more are all available through SAS Analytics. Staples, which boasts $27 billion in sales across the globe, has a business philosophy that prioritizes customer loyalty and satisfaction. In order to better engage their customers, Staples utilizes SAS Analytics to plan finely tuned marketing campaigns. Through forecasting and advanced analytics, Staples has been able to rely on fewer contractors, and cut their marketing budget, while improving their customer retention rate.

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

Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG

Aegon, Alberta Parks, Amway China, Axel Springer, Bank of America, Belgium Special Tax, CAP Index, CareSource, CBE Group, Cemig, Center for Responsible Lending, CESCE, Ceska sporitelna, Chantecler, Chico's, Chubb Group of Insurance Companies, CIGNA Thailand, City of Wiesbaden, Germany, Confused.com, Creditreform, Des Moines Area Community College, Deutsche Lufthansa, Directorate of Economics and Statistics, DIRECTV, Dow Chemical Company, Dow Chemical Company, Dun & Bradstreet, EDF Energy, Electrabel GDF SUEZ, ERGO Insurance Group, Erste Bank Croatia, Farmers Mutual Group, Finnair, Florida Department of Corrections, Geneia, Generali Hellas, Genting Malaysia Berhad, Grameenphone, Grandi Salumifici Italiani, HealthPartners, Highmark, Hong Kong Efficiency Unit, HP, Hyundai Securities, Illinois Department of Healthcare and Family ServicesInc Research, ING-DiBa, Institut Pertanian Bogor, InterContinental Hotels Group (IHG), IOM, Kelley Blue Book, Lenovo, Lillebaelt Hospital, Los Angeles County, Maspex Wadowice Group, National Bank of Greece, New Zealand Ministry of Health, New Zealand Ministry of Social Development, Nippon Paper, NMIMS, North Carolina Department of Transportation, North Carolina Office of Information Technology Services, Northern Virginia Electric Cooperative (NOVEC), Oberweis Dairy, ODEC, Ohio Mutual Insurance Group, Oklahoma State University, OneBeacon, Orange Business Services, Orange County Child Support Services, Organic, Orlando Magic, OTP Bank, Plano Independent School District, Project Odyssey, Royal Society for the Protection of Birds, RSA Canada, SCAD, Scotiabank, Singapore National Library Board, Sobeys Inc., SRA International, Staples, Statistics Estonia, Swisscom, SymphonyIRI Group, Telecom Italia, Telef‹nica O2, Town of Cary, Transitions Optical, TrueCar, Turkcell Superonline, UniCredit Bank Serbia, University of Alabama, University of Missouri, USDA National Agricultural Statistics Service

Buyer's Guide

KNIME vs. SAS Analytics

December 2024

Free Report: KNIME vs. SAS Analytics

Find out what your peers are saying about KNIME vs. SAS Analytics and other solutions. Updated: December 2024.

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