IBM Smart Analytics vs SAS Analytics comparison

IBM and SAS are both solutions in the Data Mining category. IBM is ranked #8, while SAS is ranked #5 with an average rating of 8.5. IBM holds a 4.5% mindshare in DM, compared to SAS’s 7.8% mindshare. Additionally, 100% of IBM users are willing to recommend the solution, compared to 95% of SAS users who would recommend it.

IBM Smart Analytics

Read 1 IBM Smart Analytics review

389 Views
389 Comparison Views

100% willing to recommend

SAS Analytics

Read 13 SAS Analytics reviews

1,534 Views
1,519 Comparison Views

95% willing to recommend

IBM Smart Analytics

SAS Analytics

Comparison Buyer's Guide

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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 IBM, Knime, Weka and others in Data Mining.

To learn more, read our detailed Data Mining Report (Updated: July 2026).

Buyer's Guide

Data Mining

July 2026

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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

IBM Smart Analytics

Ranking in Data Mining

8th

Average Rating

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

SAS Analytics

Ranking in Data Mining

5th

Average Rating

9.2

Reviews Sentiment

5.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Data Mining category, the mindshare of IBM Smart Analytics is 4.5%, up from 1.1% compared to the previous year. The mindshare of SAS Analytics is 7.8%, up from 7.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining Mindshare Distribution
Product	Mindshare (%)
SAS Analytics	7.8%
IBM Smart Analytics	4.5%
Other	87.7%

Data Mining

Featured Reviews

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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Sanjay Charitesh Makam

Student at Rochester Institute of Technology

Data management has become faster and clearer but integration and large data handling still need work

SAS Analytics needs to improve for entry-level people in my college because buying licenses is somewhat costly for small businesses. SAS Analytics is also somewhat complex for students who are entering the data field. Additionally, SAS Analytics is not good at integration with other environments, so it should improve on that, and some syntaxes are not good. SAS Analytics has an older architecture in some areas, so it should enhance the UX side and performance in some aspects. SAS Analytics documentation should improve to be clearer for students who are currently using or trying to use SAS Analytics, so the documentation should be simplified. Customer service is pretty good as of now, as they respond in two to four hours on business days, but faster response times may solve many problems and attract more users. I rate SAS Analytics seven or eight out of ten. SAS Analytics should improve in massive data handling, as I mentioned earlier. SAS Analytics struggles when it handles massive data, and it should also get top-tier support. The support should improve from two to four hours down to one hour or even thirty minutes. SAS Analytics should also offer more low-code options for those who do not know how to code or to save more time, and they should consider reducing their pricing. Currently, SAS Analytics has limited flexibility and very limited integration, so improvements in those areas are necessary. SAS Analytics student version does not handle long or lengthy data well right now, so it should improve on that point. As I have mentioned, it struggles with larger data sets, but for current small or medium data sets, it is very good. Customer support should improve to fifteen to thirty minutes or immediate live agents during business hours. The two to four hours wait can sometimes take more than one day, and that does not make sense. We have to stop all our work until they reply if we encounter critical problems, so they should improve for certain.

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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

"Log Analytics (LA) allows a user to see patterns of behavior and isolate issues quickly, without the need to manually access individual systems and parse logs manually."

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

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

"The most valuable aspect of SAS Analytics is the ability to automate work using macros without heavily relying on technology teams, allowing me to create functions that operate independently and streamline workflows efficiently."

"This solution has helped us materially grow multiple business categories."

"Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features."

"The technical support is okay."

"It has improved the level of efficacy and validity of our reports, and this has also extended to the area of customer service and relations."

"They have provided virtually everything we have needed to accomplish our task, as well as continuously improving our accuracy."

More SAS Analytics pros

Cons

"The indexing engine (proprietary build of LogStash) is well... very LogStash'ish... It requires more work to normalize the log feeds than competing products."

"It is relatively expensive."

"They could enhance the AI capabilities of the product."

"Once a SAS figure is produced one would like to modify things, such as titles, legends, and incorporate risk sets as a footer on the plots."

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

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

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

"SAS Analytics could improve in areas such as handling stress tests and workloads, as well as predicting financial movements, specifically profit and loss movements."

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

More SAS Analytics cons

Pricing and Cost Advice

Information not available

"Licensing was rather straightforward."

"Setup costs were quite reasonable."

"Prices were comparable with alternative solutions."

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

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

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

"SAS is very expensive."

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

More SAS Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

27%

University

12%

Construction Company

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	11

Questions from the Community

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What needs improvement with SAS Analytics?

With SAS Analytics, I find that the graphs are very poor, and since I use Power BI, I think SAS Analytics needs to enhance the visualization options to do better visualizations. Many other software...

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What is your primary use case for SAS Analytics?

I work with SAS Analytics solutions, specifically with SAS Data Mining, because I extract information from the core system of the bank via ODBC connections and use SAS Base on the servers. I am the...

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What is your experience regarding pricing and costs for SAS Analytics?

As a student, I do not pay anything for pricing; my college gives me credentials, and SAS Analytics is free for students, so the licensing is also free. Thus, SAS Analytics for students is very good.

See all answers

Comparisons

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Compared 12% of the time

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Compared 11% of the time

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Compared 12% of the time

IBM SPSS Modeler vs SAS Analytics

Compared 10% of the time

SAS Enterprise Miner vs SAS Analytics

Compared 10% of the time

IBM Watson Explorer vs SAS Analytics

Compared 7% of the time

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Product Reports

Buyer's Guide

Data Mining

July 2026

Download IBM Smart Analytics product report

Buyer's Guide

SAS Analytics

July 2026

Download SAS Analytics product report

Also Known As

Smart Analytics

No data available

Overview

IBM Smart Analytics is designed for businesses needing robust analytics to drive decision-making. It harnesses data from multiple sources, offering insights and enhancing business operations through advanced analytics capabilities.

IBM Smart Analytics offers customizable and scalable analytics solutions, supporting various business sectors. It integrates with existing systems, allowing users to extract actionable insights, improve efficiencies, and effectively address business challenges. With a focus on flexibility, it supports diverse analytical needs and adapts to changing business dynamics. Utilizing predictive analytics, it aids in forecasting and business performance monitoring.

What are the key features of IBM Smart Analytics?

Data Integration: Seamlessly connects different data sources, enabling comprehensive data analysis.
Predictive Analytics: Uses advanced algorithms to forecast trends and outcomes.
Dashboard Creation: Provides interactive dashboards for easy data visualization and analysis.
Scalability: Accommodates growing data needs, ensuring continuous performance and storage capacity.

What benefits should users expect from IBM Smart Analytics?

Improved Decision Making: Informed decisions based on comprehensive data analysis.
Operational Efficiency: Streamlined processes and reduced operational costs through data-driven insights.
Enhanced Forecasting: Accurate predictions supporting strategic planning.
Competitive Advantage: Insights leading to better market positioning and customer understanding.

IBM Smart Analytics finds applications across industries like finance, healthcare, and retail. In finance, it aids in risk management and fraud detection. Healthcare sectors use it for patient data analysis and improving treatment outcomes. Retail businesses leverage IBM Smart Analytics for customer behavior analysis and marketing strategy development.

IBM

SAS Analytics offers a powerful suite of tools for statistical analysis, predictive analytics, and data handling, making it ideal for industries requiring robust data-driven decisions. Its extensive capabilities cater to professionals familiar with SQL and demand forecasting needs across sectors.

With a strong presence in analytics, SAS Analytics provides a seamless experience for data preparation, exploration, and reporting. Users benefit from its ability to handle large data sets, generate interactive reports, and integrate with multiple platforms. Despite its high costs and need for improved visualization and natural language querying, SAS Analytics remains a favored choice for those requiring comprehensive statistical modeling and risk analytics. Enhancing self-service analytics and accelerating support response times are areas of needed improvement. Companies use it extensively for business intelligence and demand forecasting, particularly in sectors like banking and financial services.

What are the key features of SAS Analytics?

Interactive Reports: Embeds intuitive and engaging reports.
Extensive Analytics Functions: Offers a wide range of analytics capabilities.
SQL Compatibility: Streamlines use for SQL-proficient users.
Predictive Analytics: Enhances analysis with SAS E-Miner.
Automation: Utilizes macros for automated tasks.
Large Data Handling: Manages extensive data effectively.

What benefits can users look for in reviews?

Increased Productivity: Saves time with automation and efficient data processing.
Enhanced Efficiency: Direct data source connections streamline workflows.
Versatility: Adapts to diverse analytical and reporting requirements.
Comprehensive Resources: Provides extensive help and support materials.

SAS Analytics is widely implemented in industries for tasks like national auto insurance pricing, financial replication, and marketing analytics. Teams in banking and financial services apply it for quantitative analyses, risk assessments, and generating detailed operational reports, demonstrating its adaptability and strength in handling complex data scenarios.

SAS

Sample Customers

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 IBM, Knime, Weka and others in Data Mining. Updated: July 2026.

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