KNIME vs Oracle Advanced Analytics vs SAS Analytics comparison

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Knime Logo
3,037 views|2,059 comparisons
93% willing to recommend
Oracle Logo
603 views|396 comparisons
50% willing to recommend
SAS Logo
945 views|752 comparisons
94% willing to recommend
Comparison Buyer's Guide
Executive Summary

We performed a comparison between KNIME, Oracle Advanced 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: April 2024).
771,170 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's a very powerful and simple tool to use.""I've tried to utilize KNIME to the fullest extent possible to replace Excel.""Clear view of the data at every step of ETL process enables changing the flow as needed.""The solution is good for teaching, since there is no need to code.""It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea.""Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing.""Valuable features include visual workflow creation, workflow variables (parameterisation), automatic caching of all intermediate data sets in the workflow, scheduling with the server.""It is a stable solution...It is a scalable solution."

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"Ability to pull together multiple sources of information.""The dashboard interface is intuitive and the user is able to interact with it to receive good results from the analytic.""When needed, we will work closely with Oracle support and implement their workaround in our application."

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"It has facilitated timely analysis results with quality work and meaningful output.""Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features.""I use it to replicate our entire financial system to verify/duplicate calculations.""The most valuable feature is the ability to handle large data sets.""It has also been around for an extremely long time, has a strong history, and good market penetration.""All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team.""I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app.""It has improved the level of efficacy and validity of our reports."

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Cons
"Data visualization needs improvement.""Not just for KNIME, but generally for software and analyzing data, I would welcome facilities for analyzing different sorts of scale data like Likert scales, Thurstone scales, magnitude ratio scales, and Guttman scales, which I don't use myself.""The predefined workflows could use a bit of improvement.""The overall user experience feels unpolished. In particular: Data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best.""It's very general in terms of architecture, and as a result, it doesn't support efficient running. That is, the speed needs to be improved.""I would prefer to have more connectivity.""The documentation is lacking and it could be better.""The pricing needs improvement."

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"There are some transactions we have not been able to find through the dashboard.""Could use some refinement getting things that are not standard cloud applications, but more customized.""The performance, scalability and queries should be addressed, as well as the data distribution of certain data techniques."

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"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.""I would like to see their interface to R added to either Base SAS or SAS Analytics.""They could enhance the AI capabilities of the product.""The installation could also be easier, and the price could be better.""This solution should be made more user-friendly.""​Support at universities used to be limited, but I hear this is changing.​"

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Pricing and Cost Advice
  • "It is free of cost. It is GNU licensed."
  • "KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required."
  • "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."
  • "The price of KNIME is quite reasonable and the designer tool can be used free of charge."
  • "It's an open-source solution."
  • "The price for Knime is okay."
  • "At this time, I am using the free version of Knime."
  • "This is an open-source solution that is free to use."
  • More KNIME 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."
  • More SAS Analytics Pricing and Cost Advice →

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    Comparison Review
    Anonymous User
    I’m part of a small group of mathematics enthusiasts in Kansas City who meet about once a month on Saturday mornings to drink coffee and discuss mathematics. This past weekend it was my turn to do a presentation to the rest of the group and I chose to speak on the mathematical foundations of the Support Vector Machine algorithm in Oracle Data Mining. While I wasn’t surprised that some in the group had a better handle on Vapnik-Chervonenkis theory than I and gently “guided” me a few times, I was somewhat surprised at their positive reaction to my characterization of the “Oracle” approach to data mining in contrast with the “SAS” approach. While gross simplifications are always “gross”, here is my take on what I believe to be very different philosophies. Let’s use classification as an example since we’re talking about SVMs. I think of the “SAS” approach to be similar to that of a “statistician” or classic data scientist. That is, there is a desire to understand the algorithm in context of the data set. The main objective is to identify and understand the source(s) of error in the model and to characterize the algorithm through the use various coefficients and ratios. A good deal of effort is spent in the evaluation process of the algorithm and in understanding the impact of different choices in methodology. The SAS perspective emphasizes understanding the data preparation and the algorithm. The more detail, the better. The “Oracle” approach to data mining is characterized by a… Read more →
    Questions from the Community
    Top Answer:Since KNIME is a no-code platform, it is easy to work with.
    Top Answer:We're using the free academic license just locally. I went for KNIME because they have a free academic license. And to… more »
    Top Answer:KNIME is not good at visualization. I would like to see NLQ (Natural language query) and automated visualizations added… more »
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    Earn 20 points

    Top Answer:I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile… more »
    Top Answer:The natural language querying and automated preparation of dashboards should be improved. The cost and accessibility… more »
    Top Answer:This is an application I use for data prep, data exploration, BI reporting, and some basic automated analytics.
    Ranking
    1st
    out of 18 in Data Mining
    Views
    3,037
    Comparisons
    2,059
    Reviews
    21
    Average Words per Review
    501
    Rating
    7.9
    7th
    out of 18 in Data Mining
    Views
    603
    Comparisons
    396
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    5th
    out of 18 in Data Mining
    Views
    945
    Comparisons
    752
    Reviews
    3
    Average Words per Review
    317
    Rating
    8.0
    Comparisons
    Also Known As
    KNIME Analytics Platform
    OAA
    Learn More
    Overview

    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.”

    Oracle Advanced Analytics 12c delivers parallelized in-database implementations of data mining algorithms and integration with open source R. Data analysts use Oracle Data Miner GUI and R to build and evaluate predictive models and leverage R packages and graphs. Application developers deploy Oracle Advanced Analytics models using SQL data mining functions and R. With the Oracle Advanced Analytics option, Oracle extends the Oracle Database to an sclable analytical platform that mines more data and data types, eliminates data movement, and preserves security to anticipate customer behavior, detect patterns, and deliver actionable insights. Oracle Big Data SQL adds new big data sources and Oracle R Advanced Analytics for Hadoop provides algorithms that run on Hadoop. 

    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
    Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
    Orbitz, Marriott, SGS Life Science, Masdar, AlliantEnergy Corporation, British Standards Institute, Skybox Security, Triple PointTechnology, and Coca Cola.
    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
    Top Industries
    REVIEWERS
    University25%
    Comms Service Provider17%
    Retailer14%
    Government8%
    VISITORS READING REVIEWS
    Manufacturing Company12%
    Financial Services Firm11%
    Computer Software Company9%
    Educational Organization8%
    VISITORS READING REVIEWS
    Government15%
    Manufacturing Company13%
    University13%
    Computer Software Company12%
    REVIEWERS
    Financial Services Firm27%
    Healthcare Company18%
    Insurance Company9%
    Retailer9%
    VISITORS READING REVIEWS
    Financial Services Firm12%
    University10%
    Computer Software Company10%
    Educational Organization9%
    Company Size
    REVIEWERS
    Small Business28%
    Midsize Enterprise26%
    Large Enterprise46%
    VISITORS READING REVIEWS
    Small Business19%
    Midsize Enterprise14%
    Large Enterprise67%
    REVIEWERS
    Small Business70%
    Midsize Enterprise20%
    Large Enterprise10%
    VISITORS READING REVIEWS
    Small Business21%
    Midsize Enterprise11%
    Large Enterprise68%
    REVIEWERS
    Small Business29%
    Midsize Enterprise14%
    Large Enterprise57%
    VISITORS READING REVIEWS
    Small Business19%
    Midsize Enterprise12%
    Large Enterprise69%
    Buyer's Guide
    Data Mining
    April 2024
    Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining. Updated: April 2024.
    771,170 professionals have used our research since 2012.