SAS Analytics vs SAS Enterprise Miner comparison

SAS Analytics and SAS Enterprise Miner are both solutions in the Data Mining category. SAS Analytics is ranked #5 with an average rating of 8.5, while SAS Enterprise Miner is ranked #7. SAS Analytics holds a 8.0% mindshare in DM, compared to SAS Enterprise Miner’s 7.8% mindshare. Additionally, 95% of SAS Analytics users are willing to recommend the solution, compared to 93% of SAS Enterprise Miner users who would recommend it.

SAS Analytics

Read 13 SAS Analytics reviews

1,358 Views
1,344 Comparison Views

95% willing to recommend

SAS Enterprise Miner

Read 13 SAS Enterprise Miner reviews

2,033 Views
732 Comparison Views

93% willing to recommend

SAS Analytics

SAS Enterprise Miner

Comparison Buyer's Guide

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Executive SummaryUpdated on May 21, 2025

SAS Analytics and SAS Enterprise Miner compete in the data analytics domain. SAS Enterprise Miner has the upper hand in advanced data mining features, while SAS Analytics is advantageous in pricing flexibility and data visualization capabilities.

Features: SAS Analytics offers extensive statistical tools, business intelligence capabilities, and data visualization. SAS Enterprise Miner provides robust data mining functions, predictive modeling, and machine learning.

Room for Improvement: SAS Analytics could improve in its advanced data mining features, machine learning integration, and on-premise deployment options. SAS Enterprise Miner may benefit from streamlined deployment processes, enhanced pricing flexibility, and increased scalability for broader analytics needs.

Ease of Deployment and Customer Service: SAS Analytics benefits from cloud-based deployment, ensuring easy access and efficient implementation with highly regarded customer service. SAS Enterprise Miner requires more involved initial setup due to its on-premises nature but is rewarded for handling complex data mining tasks with strong customer support.

Pricing and ROI: SAS Analytics is noted for competitive pricing and substantial ROI through scalable solutions. SAS Enterprise Miner, despite higher initial costs, offers significant ROI with advanced data mining capabilities that cater to complex analytic projects, justifying its price for intensive data-driven initiatives.

To learn more, read our detailed SAS Analytics vs. SAS Enterprise Miner Report (Updated: June 2026).

Buyer's Guide

SAS Analytics vs. SAS Enterprise Miner

June 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

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

SAS Enterprise Miner

Ranking in Data Mining

7th

Average Rating

7.6

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

Data Science Platforms (23rd)

Mindshare comparison

As of June 2026, in the Data Mining category, the mindshare of SAS Analytics is 8.0%, up from 6.8% compared to the previous year. The mindshare of SAS Enterprise Miner is 7.8%, up from 4.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining Mindshare Distribution
Product	Mindshare (%)
SAS Analytics	8.0%
SAS Enterprise Miner	7.8%
Other	84.2%

Data Mining

Featured Reviews

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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reviewer1352853

Executive Head of analytics at a retailer with 5,001-10,000 employees

A stable product that is easy to deploy and can be used for structured and unstructured data mining

We use the solution for predictive analytics to do structured and unstructured data mining I like the way the product visually shows the data pipeline. The product must provide better integration with cloud-native technologies. I have been using the solution for 20 years. The product is very…

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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

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

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

"The most valuable feature is the ability to handle large data sets."

"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."

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

"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."

"I think that the cost-benefit ratio is okay, it is the right balance."

"It is able to connect to all major platforms, and all the smaller platforms that I have come across."

More SAS Analytics pros

"The most valuable feature is the decision tree creation."

"The setup is straightforward. Deployment doesn't take more than 30 minutes."

"Overall it is a good solution."

"I like the way the product visually shows the data pipeline."

"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."

"The solution is very good for data mining or any mining issues."

"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."

"Technical support has been good, and when I called them at the start of using the product with some issues they were very helpful."

More SAS Enterprise Miner pros

Cons

"This solution should be made more user-friendly. It needs a better visual output to explain logic to senior executives."

"In the graphical area much more improvement is still needed."

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

"It is relatively expensive."

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

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

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

"SAS is very expensive. The challenge with them is that you have to pay for a license with every module you buy."

More SAS Analytics cons

"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."

"The user interface of the solution needs improvement. It needs to be more visual."

"The visualization of the models is not very attractive, so the graphics should be improved."

"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."

"The ease of use can be improved. When you are new it seems a bit complex."

"Price of the product"

"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."

"Plus it is prohibitively expensive and is not available with perpetual licensing."

More SAS Enterprise Miner cons

Pricing and Cost Advice

"Licensing was rather straightforward."

"I rate the product’s pricing a one out of ten."

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

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

"Prices were comparable with alternative solutions."

"SAS is very expensive."

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

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

More SAS Analytics pricing and cost advice

"The solution must improve its licensing models."

"This solution is for large corporations because not everybody can afford it."

"The solution is expensive for an individual, but for an enterprise/institution (purchasing bulk licenses), it is not a high price for the use that will come from it."

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

University

13%

Construction Company

10%

Outsourcing Company

Financial Services Firm

21%

Construction Company

13%

Educational Organization

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	4
Large Enterprise	7

Questions from the Community

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

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Comparisons

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Compared 12% of the time

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Compared 11% of the time

Oracle Advanced Analytics vs SAS Analytics

Compared 7% of the time

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Compared 11% of the time

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Compared 7% of the time

Azure Data Lake Analytics vs SAS Enterprise Miner

Compared 7% of the time

IBM SPSS Statistics vs SAS Enterprise Miner

Compared 5% of the time

Alteryx vs SAS Enterprise Miner

Compared 4% of the time

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Product Reports

Buyer's Guide

SAS Analytics

May 2026

Download SAS Analytics product report

Buyer's Guide

Data Mining

June 2026

Download SAS Enterprise Miner product report

Also Known As

No data available

Enterprise Miner

Overview

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

SAS Enterprise Miner enables comprehensive data management and analytics, handling extensive data volumes with diverse algorithms for model creation. Its integration and flexibility in SAS code usage make it suitable for both enterprise and personal use.

SAS Enterprise Miner is recognized for its data pipeline visualization, data processing, and statistical modeling capabilities. Its user-friendly GUI and automation support data mining tasks, decision tree creation, and clustering. However, improvements are needed in its interface visualization, affordability, technical support, and integration with languages like Python and cloud-native tech. Enhanced performance, visualization, and model development auditing, along with text analytics in the main license, are desirable upgrades. Integration with Microsoft SQL and combined offerings remains a priority.

What are SAS Enterprise Miner's most important features?

Data Management: Handles large data volumes efficiently.
Analytics: Integrates diverse algorithms for accurate model creation.
Integration: Supports flexible incorporation of SAS code.
Automation: Enhances data mining tasks through automation.
Clustering: Facilitates extensive clustering operations.

What benefits or ROI should users look for in reviews?

Performance: Delivers remarkable efficiency in data processing tasks.
Compatibility: Seamless integration with other technologies.
Flexibility: Adaptability for enterprise and personal use cases.
User-Friendly: Intuitive GUI aids in simplifying complex tasks.

SAS Enterprise Miner is applied across industries like banking, insurance, and healthcare for data mining, machine learning, and predictive analytics. It aids in activities such as text mining, fraud modeling, and forecasting model creation, handling structured and unstructured data, and performing ad hoc analysis to model business processes and analyze data clusters.

SAS

Sample Customers

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

Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare

Buyer's Guide

SAS Analytics vs. SAS Enterprise Miner

June 2026

Free Report: SAS Analytics vs. SAS Enterprise Miner

Find out what your peers are saying about SAS Analytics vs. SAS Enterprise Miner and other solutions. Updated: June 2026.

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