IBM Smart Analytics vs SAS Analytics comparison

IBM and SAS are both solutions in the Data Mining category. IBM is ranked #12, while SAS is ranked #5 with an average rating of 8.0. IBM holds a 0.7% mindshare in DM, compared to SAS’s 6.6% mindshare. Additionally, 100% of IBM 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 IBM Smart Analytics and SAS Analytics based on real PeerSpot user reviews.

Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining.

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

Buyer's Guide

Data Mining

November 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 IBM Smart Analytics is 0.7%, up from 0.4% 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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Randall Hinds

Program Manager - Enterprise Command Center at a financial services firm with 10,001+ employees

Adding LA on top of a well deployed & working Tivoli Framework opens up a flood of native logged data points. The visual presentation layer of LA is less than cutting edge.

The IBM monitoring software products (Tivoli) are not easy to instrument and require many separate pieces of the total framework to be operationally functional and useable. That said, adding LA on top of a well deployed & working Tivoli Framework opens up a flood of native logged data points for unstructured search & query. My team had a special need to implement custom alerting on 10s of thousands of MQ channels in a short amount of time, and the traditional approach (also w a Tivoli product) would have been very costly (labor) and time consuming (requiring individual app review). As an alternative, we had a new event stream create to track all MQ channels to generate logs and then used LA to visualize the behavior trends for review, reporting and eventually alerting. The effort took longer than I hoped ~6 months, but the traditional approach would have taken 2+ yrs to review and implement app by app.

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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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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

University

Manufacturing Company

No data available

Financial Services Firm

13%

Computer Software Company

11%

Educational Organization

10%

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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.

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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...

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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...

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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...

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What needs improvement with SAS Analytics?

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

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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.

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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 12% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 12% of the time

Dataiku vs IBM SPSS Statistics

Compared 4% of the time

More IBM SPSS Statistics Competitors

No data available

KNIME vs SAS Analytics

Compared 36% of the time

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

Data Mining

November 2024

Download IBM Smart Analytics product report

Buyer's Guide

Data Mining

November 2024

Download SAS Analytics product report

Also Known As

SPSS Statistics

Smart Analytics

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IBM

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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.”

The IBM Smart Analytics System offers a wide range of analytics capabilities, enabling you to consume information in the most digestible format, gain insight, and make smarter decisions today and into the future.

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

WIdO AOK, EEKA Fashion, SSGC, GS Retail

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

Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining. Updated: November 2024.

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