Overview
What is SAS Enterprise Miner?
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
SAS Enterprise Miner is my favorite modeling software. Does the job well.
I can't live without SAS Enterprise Miner! A+++++
A great and excellent tool for complex data modelling and machine learning
SAS Enterprise Miner brings your "SAS shop" to the next level with customer insight
SAS Enterprise Miner is the easiest machine learning tool out there
Awards
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Popular Features
- Automatic Data Format Detection (5)9.393%
- Extend Existing Data Sources (5)9.090%
- Connect to Multiple Data Sources (5)8.181%
- Visualization (5)7.171%
Pricing
What is SAS Enterprise Miner?
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
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Features
Platform Connectivity
Ability to connect to a wide variety of data sources
- 8.1Connect to Multiple Data Sources(5) Ratings
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
- 9Extend Existing Data Sources(5) Ratings
Use R or Python to create custom connectors for any APIs or databases
- 9.3Automatic Data Format Detection(5) Ratings
Automatic detection of data formats and schemas
- 9MDM Integration(3) Ratings
Integration with MDM and metadata dictionaries
Data Exploration
Ability to explore data and develop insights
- 7.1Visualization(5) Ratings
The product’s support and tooling for analysis and visualization of data.
- 9.2Interactive Data Analysis(5) Ratings
Ability to analyze data interactively using Python or R Notebooks
Data Preparation
Ability to prepare data for analysis
- 7.8Interactive Data Cleaning and Enrichment(5) Ratings
Access to visual processors for data wrangling
- 8.2Data Transformations(5) Ratings
Use visual tools for standard transformations
- 8.1Data Encryption(3) Ratings
Data encryption to ensure data privacy
- 8.1Built-in Processors(3) Ratings
Library of processors for data quality checks
Platform Data Modeling
Building predictive data models
- 7.5Multiple Model Development Languages and Tools(5) Ratings
Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.
- 9.9Automated Machine Learning(3) Ratings
Tools to help automate algorithm development
- 8.6Single platform for multiple model development(5) Ratings
Single place to build, validate, deliver, and monitor many different models
- 9.2Self-Service Model Delivery(4) Ratings
Multiple model delivery modes to comply with existing workflows
Model Deployment
Tools for deploying models into production
- 7Flexible Model Publishing Options(5) Ratings
Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.
- 8.5Security, Governance, and Cost Controls(5) Ratings
Built-in controls to mitigate compliance and audit risk with user activity tracking
Product Details
- About
- Tech Details
- FAQs
What is SAS Enterprise Miner?
Business users and subject-matter experts with limited statistical skills can generate their own models using SAS Rapid Predictive Modeler. Its GUI steps them through a workflow of data mining tasks. Analytics results are displayed in easy-to-understand charts that provide the needed insights.
SAS Enterprise Miner Technical Details
Operating Systems | Unspecified |
---|---|
Mobile Application | No |
Frequently Asked Questions
Comparisons
Compare with
Reviews and Ratings
(11)Community Insights
- Business Problems Solved
- Pros
- Cons
- Recommendations
Users of the software have found it to be versatile and powerful, with a wide range of use cases. From data analysis in academic journals to risk modeling in the banking industry, this software has proven to be a valuable tool for solving various business problems. It has been used by students for lecture assignments and major projects, as well as by professionals in the financial services sector for credit risk models, propensity models, and decision trees for client segmentation.
One notable feature is the software's ability to build complex data mining and statistical models quickly and easily. Users have praised its simplicity in determining important variables for pricing models, making it ideal for researchers and analysts. Additionally, the software allows users to build multiple predictive models and efficiently compare results, helping them make informed decisions and increase their return on investment.
Furthermore, the software's flexibility in model selection and its accuracy in producing forecasts have made it a leading analytics solution in the market. The graphical analysis provided by the software is often considered cleaner compared to other products, enhancing the user experience and facilitating insights. Whether it's analyzing large volumes of claims, performing sentiment analysis, or creating scoring models for prediction, users have found this software to be a reliable and efficient tool for solving statistical problems and extracting valuable insights from data.
Overall, customers recommend this software for its performance across various industries and business functions. It has proven to be an indispensable tool in improving decision-making processes, enhancing internal efficiencies, and gaining valuable insights from data analysis.
Easy to Use and Accurate: Users found SAS Enterprise Miner easy to use and accurate, with the ability to handle multiple data sources effectively. Several reviewers mentioned this as a positive aspect of the software.
Wide Range of Algorithm Choices: Users appreciated the wide range of algorithm choices available in SAS Enterprise Miner. They mentioned that it includes various options for data mining tasks such as random, neural networks, support vectors, ensemble modeling, and more. This was stated by many users.
Top-Class Customer Support: Users praised the customer support provided by SAS for Enterprise Miner. They mentioned that it is top class and helpful when they encountered any issues or had questions about using the software. A significant number of reviewers highlighted this aspect.
Difficult to Learn: Several users have found the software difficult to learn, requiring hours of effort and training. They mentioned that it is not easy to grasp the nuances of each model, especially for someone with a stats background.
Outdated Interface: Many users expressed their dissatisfaction with the software's interface, describing it as old-school and not updated with trendy methods. They felt that the dashboard and interface were outdated, clunky, and not user-friendly.
Expensive and Difficult to Obtain: Users mentioned that the software is expensive and can be challenging to obtain, particularly for students or small businesses. Some reviewers also pointed out that it takes time to gain access to SAS tools and is costly compared to other data analysis software on the market.
Users have made several recommendations based on their experiences with SAS Enterprise Miner. First, it is recommended to request a demo session and prepare specific questions to address business problems before making a decision. This allows users to fully evaluate the software's capabilities and determine if it aligns with their needs. Second, users suggest trying both R and SAS Enterprise Miner free trials to determine which software is better suited for their data needs. This allows for a comparison of the features and functionalities offered by each option. Lastly, incorporating open source languages within SAS Enterprise Miner is advised. This recommendation aims to enhance the software's flexibility and expand its capabilities by leveraging additional tools and resources.
Attribute Ratings
Reviews
(1-5 of 5)- Developing and evaluating ensemble models.
- A very transparent interface.
- SAS Enterprise Miner is far from the fastest software out there
- Integrating SAS Enterprise Miner with other software tools is not easy.
- Connect to Multiple Data Sources
- 80%8.0
- Extend Existing Data Sources
- 90%9.0
- Automatic Data Format Detection
- 90%9.0
- MDM Integration
- 90%9.0
- Visualization
- 80%8.0
- Interactive Data Analysis
- 100%10.0
- Interactive Data Cleaning and Enrichment
- 90%9.0
- Data Transformations
- 90%9.0
- Data Encryption
- 80%8.0
- Built-in Processors
- 80%8.0
- Multiple Model Development Languages and Tools
- 80%8.0
- Automated Machine Learning
- 100%10.0
- Single platform for multiple model development
- 90%9.0
- Self-Service Model Delivery
- 100%10.0
- Flexible Model Publishing Options
- 70%7.0
- Security, Governance, and Cost Controls
- 90%9.0
- SAS Enterprise Miner is a positive ROI in the sense that it saves a ton of time coding.
- SAS Enterprise Miner is a negative ROI in that it's expensive, and perhaps makes analysts brainless.
I can't live without SAS Enterprise Miner! A+++++
- Enterprise Miner has many different data analysis options.
- It is easy to use and very reliable.
- The algorithms behind this statistical software are world class.
- SAS is not as user friendly as other stats software.
- Connect to Multiple Data Sources
- 80%8.0
- Extend Existing Data Sources
- 90%9.0
- Automatic Data Format Detection
- 100%10.0
- MDM Integration
- N/AN/A
- Visualization
- 60%6.0
- Interactive Data Analysis
- 80%8.0
- Interactive Data Cleaning and Enrichment
- 60%6.0
- Data Transformations
- 70%7.0
- Data Encryption
- N/AN/A
- Built-in Processors
- N/AN/A
- Multiple Model Development Languages and Tools
- 70%7.0
- Automated Machine Learning
- N/AN/A
- Single platform for multiple model development
- 80%8.0
- Self-Service Model Delivery
- 80%8.0
- Flexible Model Publishing Options
- 70%7.0
- Security, Governance, and Cost Controls
- 80%8.0
- It has made me a more productive scholar enabling me to interact with a wider range of academic disciplines.
- SAS is always well respected in the journal I publish in.
- It is very reasonable to get a yearly license through the university where I work.
- PITA to re-install.
- Very easy to use and intuitive.
- High performance.
- Open source integration with R.
- Amazing data science models.
- Very good data preparation and exploration toolkit.
- Still the same, very old and clunky GUI.
- For smaller organizations, it can be quite pricey.
- For less experienced users, the software can be a little overwhelming.
- Connect to Multiple Data Sources
- 90%9.0
- Extend Existing Data Sources
- 90%9.0
- Automatic Data Format Detection
- 80%8.0
- MDM Integration
- 90%9.0
- Visualization
- 50%5.0
- Interactive Data Analysis
- 90%9.0
- Interactive Data Cleaning and Enrichment
- 90%9.0
- Data Transformations
- 90%9.0
- Data Encryption
- 90%9.0
- Built-in Processors
- 90%9.0
- Multiple Model Development Languages and Tools
- 70%7.0
- Automated Machine Learning
- 80%8.0
- Single platform for multiple model development
- 80%8.0
- Self-Service Model Delivery
- 90%9.0
- Flexible Model Publishing Options
- 80%8.0
- Security, Governance, and Cost Controls
- 80%8.0
- It has a positive ROI to our business, as our sales lead rate increased after we started recommending SAS EM.
- Our business operation numbers improved after we introduced SAS EM and started using predictive analytics for our customer retention and customer chain prediction.
- The statistical modelling for the risk controls in our financial department helped to reduce the related residual risk.
- What are my customers next best action (what product would they consider)
- Who is more likely to leave for a competitor
- Who is more likely to increase their volume of savings and by how much.
- How much money can I safely lend customers based on several metrics such as credit score rating, assets, ... (credit scorecards)
- In case of recessions or the collapse of a specific industry, what is the change of global exposure at default based on the change in risk (IFRS9 stresstesting)
- What is the risk of customers with overdue payment to never pay what they owe (Bad debt)
- Is a customer doing money laundering or terrorist financing (Anti-money laundering)
- Enterprise Miner is really visual and lets you do a whole lot without actually going into the detailed options. For decent results, you should really explore the different advanced options though.
- The recent versions of Miner allow users to use R code in Miner. You can then compare several models and approach to get the best performing model.
- The resulting data is really well displayed and easy to understand (ex: the lift graph, score ranking, etc.)
- Miner has the ability to integrate custom SAS code which allows the user to add functionalities that are specific to the project.
- One of the major flaws is that the tool is basically an interface to SAS/STAT code. It generates code in the background and runs it. Because of that, some errors are warning might be a little difficult to understand for users who aren't proficient with SAS code.
- R integration is nice but I would like to see the possibility to integrate even more statistical models different than SAS. That would allow for better performance optimization when really required.
- The light client is java based and a little heavy on the OS. It would be nice to get a web-based version of the tool instead of the java one.
- Connect to Multiple Data Sources
- 90%9.0
- Extend Existing Data Sources
- 80%8.0
- Automatic Data Format Detection
- 90%9.0
- MDM Integration
- N/AN/A
- Visualization
- 70%7.0
- Interactive Data Analysis
- 70%7.0
- Interactive Data Cleaning and Enrichment
- 60%6.0
- Data Transformations
- 80%8.0
- Data Encryption
- N/AN/A
- Built-in Processors
- N/AN/A
- Multiple Model Development Languages and Tools
- 40%4.0
- Automated Machine Learning
- N/AN/A
- Single platform for multiple model development
- 70%7.0
- Self-Service Model Delivery
- N/AN/A
- Flexible Model Publishing Options
- 40%4.0
- Security, Governance, and Cost Controls
- 40%4.0
- In our organization, users were using SAS already so the learning curve was really low. Within a few weeks after the implementation, the users were already delivering models developed with SAS Enterprise Miner. It is difficult to talk about ROI as models were already being developed before. It was mostly a change of technology and it was a smooth transition.
- Going with Enterprise Miner came with migration from desktop use of SAS to a server use of SAS. This created a new role of SAS administrator. This was obviously a cost but as the use of SAS increased greatly, it was expected.
- From a methodology standpoint, Enterprise Miner helped greatly in the documentation of the model development which was a requirement in a few groups such as the risk groups. Having a visual "GUI-like" approach to development, the flowchart or diagram of the project in Miner was able to give users a good understanding of the approach the analyst took to develop the model.
It allowed users to easily prepare their data with SAS/Enterprise Guide and then use it with Enterprise Miner. The data preparation tools of SAS are really well integrated and I don't feel it was the case with SPSS (or at least from what I've heard).
For the technical support, it doesn't really require an additional person to cover that. In our case, the SAS admins support all SAS softwares and Miner is just one of them.
- The flow diagram combined with point-and-click make SAS Enterprise Miner easy to learn and apply in practice.
- The ability to convert the drag-and-drop models into actual SAS code makes customization easier, although not foolproof-easy.
- I think perhaps the most desirable aspect of SAS Enterprise Miner is its ability to create ensemble models, and then the straightforward way of evaluating ensemble modeling.
- With large data sets, SAS Enterprise Miner sometimes takes a long time to run. Sometimes you have to just leave your computer running while Enterprise Miner does its thing.
- If you want complete control over the modeling framework, you have to take what Enterprise Miner does and customize it. SAS seems to be working hard on making things easier to customize, but it's not completely there yet.
- The graphic capabilities of SAS Enterprise Miner leave a lot to be desired, especially in the era of self-service business intelligence software.
- Connect to Multiple Data Sources
- 100%10.0
- Extend Existing Data Sources
- 80%8.0
- Automatic Data Format Detection
- 90%9.0
- MDM Integration
- 80%8.0
- Visualization
- 60%6.0
- Interactive Data Analysis
- 100%10.0
- Interactive Data Cleaning and Enrichment
- 100%10.0
- Data Transformations
- 90%9.0
- Data Encryption
- 70%7.0
- Built-in Processors
- 70%7.0
- Multiple Model Development Languages and Tools
- 70%7.0
- Automated Machine Learning
- 100%10.0
- Single platform for multiple model development
- 100%10.0
- Self-Service Model Delivery
- 90%9.0
- Flexible Model Publishing Options
- 90%9.0
- Security, Governance, and Cost Controls
- 70%7.0
- SAS Enterprise Miner improved our forecasting models. I don't think competing software could have accomplished what we did with SAS Enterprise Miner in as little time.
- SAS Enterprise Miner isn't cheap. The high cost makes it harder to justify the return on investment. Luckily for us, SAS Enterprise Miner makes it so easy to perform machine learning and ensemble models that the costs were justified.
- Sometimes it takes time figuring out what SAS Enterprise Miner is doing. If you're a newbie to analytics, SAS Enterprise Miner is a great starting point as long as you're willing to figure out what SAS is doing. Taking Enterprise Miner's word for it didn't work sometimes.
- Tableau Server, QlikView, SAS Advanced Analytics, SAS Enterprise Guide, Sisense, Alteryx Analytics, Microsoft Access, Microsoft Azure, AWS Cloud9, TIBCO Spotfire and TIBCO Spotfire Data Science (previously Alpine Data)