SAS Analytics vs SAS Enterprise Miner comparison

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945 views|752 comparisons
94% willing to recommend
SAS Logo
491 views|402 comparisons
93% willing to recommend
Comparison Buyer's Guide
Executive Summary

We performed a comparison between SAS Analytics and SAS Enterprise Miner 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 SAS Analytics vs. SAS Enterprise Miner Report (Updated: May 2024).
772,422 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"It has facilitated timely analysis results with quality work and meaningful output.""They have provided virtually everything we have needed to accomplish our task, as well as continuously improving our accuracy.""All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team.""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.""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.""I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app.""It has also been around for an extremely long time, has a strong history, and good market penetration."

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"Good data management and analytics.""The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them.""The most valuable feature is the decision tree creation.""The technical support is very good.""he solution is scalable.""I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks.""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.""I like the way the product visually shows the data pipeline."

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Cons
"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.""​Support at universities used to be limited, but I hear this is changing.​""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.""They could enhance the AI capabilities of the product.""The graphing and visualization features could be enhanced, in my opinion. I would especially stress improving the visualization capabilities.""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."

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"The ease of use can be improved. When you are new it seems a bit complex.""Technical support could be improved.""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.""The solution needs an easier interface for the user. The user experience isn't so easy for our clients.""Virtualization could be much better.""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 solution is much more complex than other options."

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Pricing and Cost Advice
  • "It is relatively expensive. It is not an easy software to afford."
  • "​Setup costs were quite reasonable."
  • "Prices were comparable with alternative solutions."
  • "Licensing was rather straightforward."
  • "​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."
  • "Our licensing covers the usage for around 50 data analysts."
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  • "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."
  • "The solution must improve its licensing models."
  • More SAS Enterprise Miner Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app.
    Top Answer:The natural language querying and automated preparation of dashboards should be improved. The cost and accessibility should be improved. For a $900 USD/user/annual license you have to set up a $12,000… more »
    Top Answer:This is an application I use for data prep, data exploration, BI reporting, and some basic automated analytics.
    Top Answer:I like the way the product visually shows the data pipeline.
    Top Answer:The solution must improve its licensing models. It bundles all the products into smaller products. We can only have a subset of the functionality available according to our license. I rate the pricing… more »
    Top Answer:The product must provide better integration with cloud-native technologies.
    Ranking
    5th
    out of 18 in Data Mining
    Views
    945
    Comparisons
    752
    Reviews
    3
    Average Words per Review
    317
    Rating
    8.0
    6th
    out of 18 in Data Mining
    Views
    491
    Comparisons
    402
    Reviews
    2
    Average Words per Review
    310
    Rating
    8.5
    Comparisons
    Also Known As
    Enterprise Miner
    Learn More
    Overview
    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.
    SAS Enterprise Miner is a solution to create accurate predictive and descriptive models on large volumes of data across different sources in the organization. SAS Enterprise Miner offers many features and functionalities for the business analysts to model their data. Some of the business applications are for detecting fraud, minimizing risk, resource demands, reducing asset downtime, campaigns and reduce customer attrition.
    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
    Top Industries
    REVIEWERS
    Financial Services Firm27%
    Healthcare Company18%
    Insurance Company9%
    Retailer9%
    VISITORS READING REVIEWS
    Financial Services Firm12%
    University11%
    Educational Organization9%
    Computer Software Company9%
    REVIEWERS
    Financial Services Firm44%
    Retailer22%
    University22%
    Media Company11%
    VISITORS READING REVIEWS
    Financial Services Firm23%
    University13%
    Educational Organization8%
    Insurance Company7%
    Company Size
    REVIEWERS
    Small Business29%
    Midsize Enterprise14%
    Large Enterprise57%
    VISITORS READING REVIEWS
    Small Business19%
    Midsize Enterprise12%
    Large Enterprise69%
    REVIEWERS
    Small Business21%
    Midsize Enterprise29%
    Large Enterprise50%
    VISITORS READING REVIEWS
    Small Business19%
    Midsize Enterprise10%
    Large Enterprise71%
    Buyer's Guide
    SAS Analytics vs. SAS Enterprise Miner
    May 2024
    Find out what your peers are saying about SAS Analytics vs. SAS Enterprise Miner and other solutions. Updated: May 2024.
    772,422 professionals have used our research since 2012.

    SAS Analytics is ranked 5th in Data Mining with 11 reviews while SAS Enterprise Miner is ranked 6th in Data Mining with 13 reviews. SAS Analytics is rated 9.0, while SAS Enterprise Miner is rated 7.6. The top reviewer of SAS Analytics writes "Provides comprehensive data analysis tools and functionalities, but its higher pricing and potential stability issues may present drawbacks". On the other hand, the top reviewer of SAS Enterprise Miner writes "A stable product that is easy to deploy and can be used for structured and unstructured data mining". SAS Analytics is most compared with KNIME, IBM SPSS Statistics, Weka and IBM SPSS Modeler, whereas SAS Enterprise Miner is most compared with SAS Visual Analytics, IBM SPSS Modeler, RapidMiner, Microsoft Azure Machine Learning Studio and SAS Visual Data Mining and Machine Learning. See our SAS Analytics vs. SAS Enterprise Miner report.

    See our list of best Data Mining vendors.

    We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.