SAS® Unified Insights MM Features

Market-leading data mining & machine learning

  • Provides GUI-based data mining and machine learning via a single, collaborative and highly scalable environment.
  • Provides open source integration with R, Python, Java and Lua models.
  • Lets you use model competition to identify and deploy the most effective model.

View more market-leading data mining & machine learning features

Interactive programming in a web-based development environment

  • Visual interface for the entire analytical life cycle process.
  • Drag-and-drop interactive interface requires no coding, though coding is an option.
  • Supports automated code creation at each node in the pipeline.
  • Choose best practice templates (basic, intermediate or advanced) to get started quickly with machine learning tasks or take advantage of our automated modeling process.
  • Interpretability reports such as PD, LIME, ICE, and Kernel SHAP.
  • Explore data from within Model Studio and launch directly into SAS Visual Analytics.
  • Edit models imported from SAS Visual Analytics in Model Studio.
  • View data within each node in Model Studio.
  • Run SAS® Enterprise Miner 14.3 batch code within Model Studio.
  • Provides a collaborative environment for easy sharing of data, code snippets, annotations and best practices among different personas.
  • Create, manage and share content and administer content permissions via SAS Drive.
  • The SAS lineage viewer visually displays the relationships between decisions, models, data and decisions.

Intelligent automation

  • Public API to automate many of the manual,complex modeling steps to build machine learning models – from data wrangling, to feature engineering, to algorithm selection, to deployment.
  • Automatic Feature Engineering node for automatically cleansing, transforming, and selecting features for models.
  • Automatic Modeling node for automatically selecting the best model using a set of optimization and autotuning routines across multiple techniques.

Natural language generation

  • View results in simple language to facilitate understanding of reports, including model assessment and interpretability.

Embedded support for Python and R languages 

  • Embed open source code within an analysis, and call open source algorithms within Model Studio.
  • The Open Source Code node in Model Studio is agnostic to Python or R versions.

Deep learning with Python (DLPy)

  • Build deep learning models for image, text, audio and time-series data using Jupyter Notebook.
  • High level APIs are available on GitHub for:
    • Deep neural networks for tabular data.
    • Image classification and regression.
    • Object detection.
    • RNN-based tasks – text classification, text generation and sequence labeling.
    • RNN-based time-series processing and modeling.
  • Support for predefined network architectures, such as LeNet, VGG, ResNet, DenseNet, Darknet, Inception, ShuffleNet, MobileNet, YOLO, Tiny YOLO, Faster R-CNN and U-Net.
  • Import and export deep learning models in the ONNX format.

SAS® procedures (PROCs) and CAS actions

  • A programming interface (SAS Studio) allows IT or developers to access a CAS server, load and save data directly from a CAS server, and support local and remote processing on a CAS server.
  • Python, Java, R, Lua and Scala programmers or IT staff can access data and perform basic data manipulation against a CAS server, or execute CAS actions using PROC CAS.
  • CAS actions support for interpretability, feature engineering and modeling.
  • Integrate and add the power of SAS to other applications using REST APIs.

Highly scalable, distributed in-memory analytical processing

  • Distributed, in-memory processing of complex analytical calculations on large data sets provides low-latency answers.
  • Analytical tasks are chained together as a single, in-memory job without having to reload the data or write out intermediate results to disks.
  • Concurrent access to the same data in memory by many users improves efficiency.
  • Data and intermediate results are held in memory as long as required, reducing latency.
  • Built-in workload management ensures efficient use of compute resources.
  • Built-in failover management guarantees submitted jobs always finish.
  • Automated I/O disk spillover for improved memory management.

Model development with modern machine learning algorithms

  • Decision forests:
    • Automated ensemble of decision trees to predict a single target.
    • Automated distribution of independent training runs.
    • Supports intelligent autotuning of model parameters.
    • Automated generation of SAS code for production scoring. 
  • Gradient boosting:
    • Automated iterative search for optimal partition of the data in relation to selected label variable.
    • Automated resampling of input data several times with adjusted weights based on residuals.
    • Automated generation of weighted average for final supervised model.
    • Supports binary, nominal and interval labels.
    • Ability to customize tree training with variety of options for numbers of trees to grow, splitting criteria to apply, depth of subtrees and compute resources. 
    • Automated stopping criteria based on validation data scoring to avoid overfitting.
    • Automated generation of SAS code for production scoring.
  • Neural networks:
    • Automated intelligent tuning of parameter set to identify optimal model.
    • Supports modeling of count data.
    • Intelligent defaults for most neural network parameters.
    • Ability to customize neural networks architecture and weights.
    • Techniques include deep forward neural network (DNN), convolutional neural networks (CNNs), recurrent neural networks (RNNs) and autoencoders.
    • Ability to use an arbitrary number of hidden layers to support deep learning.
    • Support for different types of layers, such as convolution and pooling.
    • Automatic standardization of input and target variables.
    • Automatic selection and use of a validation data subset.
    • Automatic out-of-bag validation for early stopping to avoid overfitting.
    • Supports intelligent autotuning of model parameters.
    • Automated generation of SAS code for production scoring.
  • Support vector machines:
    • Models binary target labels.
    • Supports linear and polynomial kernels for model training.
    • Ability to include continuous and categorical in/out features.
    • Automated scaling of input features.
    • Ability to apply the interior-point method and the active-set method.
    • Supports data partition for model validation.
    • Supports cross-validation for penalty selection.
    • Automated generation of SAS code for production scoring.
  • Factorization machines:
    • Supports the development of recommender systems based on sparse matrices of user IDs and item ratings.
    • Ability to apply full pairwise-interaction tensor factorization.
    • Includes additional categorical and numerical input features for more accurate models.
    • Supercharge models with timestamps, demographic data and context information.
    • Supports warm restart (update models with new transactions without full retraining).
    • Automated generation of SAS score code for production scoring.
  • Bayesian networks:
    • Learns different Bayesian network structures, including naive, tree-augmented naive (TAN), Bayesian network-augmented naive (BAN), parent-child Bayesian networks and Markov blanket.
    • Performs efficient variable selection through independence tests.
    • Selects the best model automatically from specified parameters.
    • Generates SAS code or an analytics store to score data.
    • Loads data from multiple nodes and performs computations in parallel.
  • Dirichlet Gaussian mixture models (GMM):
    • Can execute clustering in parallel and is highly multithreaded.
    • Performs soft clustering, which provides not only the predicted cluster score but also the probability distribution over the clusters for each observation.
    • Learns the best number of clusters during the clustering process, which is supported by the Dirichlet process.
    • Uses a parallel variational Bayes (VB) method as the model inference method. This method approximates the (intractable) posterior distribution and then iteratively updates the model parameters until it reaches convergence.
  • Semisupervised learning algorithm:
    • Highly distributed and multithreaded.
    • Returns the predicted labels for both the unlabeled data table and the labeled data table.
  • T-distributed stochastic neighbor embedding (t-SNE):
    • Highly distributed and multithreaded.
    • Returns low-dimensional embeddings that are based on a parallel implementation of the t-SNE algorithm.

Analytical data preparation

  • Feature engineering best practice pipeline includes best transformations.
  • Distributed data management routines provided via a visual front end.
  • Large-scale data exploration and summarization.
  • Cardinality profiling:
    • Large-scale data profiling of input data sources.
    • Intelligent recommendation for variable measurement and role.
  • Sampling: 
    • Supports random and stratified sampling, oversampling for rare events and indicator variables for sampled records.

Data exploration, feature engineering and dimension reduction

  • T-distributed stochastic neighbor embedding (t-SNE).
  • Feature binning.
  • High-performance imputation of missing values in features with user-specified values, mean, pseudo median and random value of nonmissing values.
  • Feature dimension reduction.
  • Large-scale principal components analysis (PCA), including moving windows and robust PCA.
  • Unsupervised learning with cluster analysis and mixed variable clustering.
  • Segment profiles for clustering.

Integrated text analytics

  • Supports 33 native languages out of the box:
    • English
    • Arabic
    • Chinese
    • Croatian
    • Czech
    • Danish
    • Dutch
    • Farsi
    • Finnish
    • French
    • German
    • Greek
    • Hebrew
    • Hindi
    • Hungarian
    • Indonesian
    • Italian
    • Japanese
    • Kazakh
    • Korean
    • Norwegian
    • Polish
    • Portuguese
    • Romanian
    • Russian
    • Slovak
    • Slovenian
    • Spanish
    • Swedish
    • Tagalog
    • Turkish
    • Thai
    • Vietnamese
  • Stop lists are automatically included and applied for all languages.
  • Automated parsing, tokenization, part-of-speech tagging and lemmatization.
  • Predefined concepts extract common entities such as names, dates, currency values, measurements, people, places and more.
  • Automated feature extraction with machine-generated topics (singular value decomposition and latent Dirichlet allocation).
  • Supports machine learning and rules-based approaches within a single project.
  • Automatic rule generation with the BoolRule.
  • Classify documents more accurately with deep learning (recurrent neural networks).

Model assessment

  • Automatically calculates supervised learning model performance statistics.
  • Produces output statistics for interval and categorical targets.
  • Creates lift table for interval and categorical target.
  • Creates ROC table for categorical target.
  • Creates Event Classification and Nominal Classification charts for supervised learning models with a class target.

Model scoring

  • Automatically generates SAS DATA step code for model scoring.
  • Applies scoring logic to training, holdout data and new data.

SAS® Viya® in-memory engine

  • CAS (SAS Cloud Analytic Services) performs processing in memory and distributes processing across nodes in a cluster.
  • User requests (expressed in a procedural language) are translated into actions with the parameters needed to process in a distributed environment. The result set and messages are passed back to the procedure for further action by the user.
  • Data is managed in blocks and can be loaded in memory and on demand.
  • If tables exceed memory capacity, the server caches the blocks on disk. Data and intermediate results are held in memory as long as required, across jobs and user boundaries.
  • Includes highly efficient node-to-node communication. An algorithm determines the optimal number of nodes for a given job.
  • Communication layer supports fault tolerance and lets you remove or add nodes from a server while it is running. All components can be replicated for high availability.
  • Support for legacy SAS code and direct interoperability with SAS 9.4M6 clients.
  • Supports multitenancy deployment, allowing for a shared software stack to support isolated tenants in a secure manner.

Deployment options

  • On-site deployments:
    • Single-machine server to support the needs of small to midsize organizations.
    • Distributed server to meet growing data, increasing workloads and scalability requirements.
  • Cloud deployments:
    • Enterprise hosting.
    • Private or public cloud (e.g., BYOL in Amazon) infrastructure.
    • SAS managed software as a service (SaaS).
    • Cloud Foundry platform as a service (PaaS) to support multiple cloud providers.

Streamlined model deployment

  • Streamlines the process of creating, managing, administering, deploying and monitoring your analytical models.
  • Provides a framework for model registration, validation, monitoring and retraining.
  • Enables you to assess candidate models to identify and publish the champion model.
  • Ensures complete auditability and regulatory compliance.

View more streamlined model deployment features

Model registration

  • Provides secure, reliable, versioned storage for all types of models, as well as access administration, including backup and restore capabilities, overwrite protection and event logging.
  • Once registered, models can be searched, queried, sorted and filtered by attributes used to store them – type of asset, algorithm, input or target variables, model ID, etc – as well as user-defined propertied and editable keywords.
  • Add general properties as columns to the listing for models and projects, such as model name, role, type of algorithm, date modified, modified by, repository location, description, version and keywords (tags).
  • Access models and model-score artifacts using open REST APIs.
  • Directly supports Python models for scoring and publishing. Convert PMML and ONNX (using dlPy) to standard SAS model types. Manage and version R code like other types of code.
  • Provides accounting and auditability, including event logging of major action – e.g., model creation, project creation and publishing.
  • Export models as .ZIP format, including all model file contents for movement across environments.
  • Easily copy models from one project to another, simplifying model movement within the repository.

Analytical workflow management

  • Create custom processes for each model using SAS Workflow Studio:
    • The workflow manager is fully integrated with SAS Model Manager so you can manage workflows and track workflow tasks within the same user interface.
    • Import, update and export generic models at the folder level – and duplicate or move to another folder.
  • Facilitates collaboration across teams with automated notifications.
  • Perform common model management tasks, such as importing, viewing and attaching supporting documentation; setting a project champion model and flagging challenger models; publishing models for scoring purposes; and viewing dashboard reports.
  • Transparency and analytics governance provides visibility into your analytical process with a centralized model repository, life cycle templates and version control. Ensures complete traceability and analytics governance.

Model scoring

  • Place a combination of Python, SAS or other open source models in the same project for users to compare and assess using different model fit statistics.
  • Set up, maintain and manage separate versions for models:
    • The champion model is automatically defined as a new version when the model is set as champion, updated or published in a project.
    • Choose challenger models to the project champion model.
    • Monitor and publish challenger and champion models.
  • Define test and production score jobs for SAS and Python models using required inputs and outputs.
  • Create and execute scoring tasks, and specify where to save the output and job history.
  • Compare models side-by-side to quickly evaluate and select the champion model from all competing models (SAS and open source) for a specific business problem.

Model deployment

  • Depending on the use case, you can publish models to batch/operational systems – e.g., SAS server, in-database, in-Hadoop/Spark, SAS Cloud Analytic Services (CAS) Server, or to on-demand systems using Micro Analytic Score (MAS) service.
  • Publish Python and SAS models to run time containers with embedded binaries and score code files. Promote run time containers to local Docker, AWS Docker and Amazon EKS (elastic kubernetes service) environments.
  • New Azure container publishing destination for open source models.

Model monitoring

  • Monitor the performance of models with any type of score code. Performance reports produced for champion and challenger R, Python and SAS models include variable distribution plots, lift charts, stability charts, ROC, K-S and Gini reports with SAS Visual Analytics using performance-reporting output result sets.
  • Built-in reports display the measures for input and output data and fit statistics for classification and regression models to evaluate whether to retrain, retire or create new models. Performance reports for champion and challenger analytical models involving Python, SAS, R, etc., with different accuracy statistics are available.
  • Monitor performance of champion models for all projects using performance report definition and execution.
  • Schedule recurring and future jobs for performance monitoring.
  • Specify multiple data sources and time-collection periods when defining performance-monitoring tasks.
  • Generate custom performance reports, and create and monitor custom business KPIs with access to model performance data.

Self-service data preparation

  • Provides an interactive, self-service environment for data access, blending, shaping and cleansing to prepare data for analytics and reporting.
  • Fully integrates with your analytics pipeline.
  • Includes data lineage and automation.

View more self-service data preparation features

Data & metadata access

  • Use any authorized internal source, accessible external data sources and data held in-memory in SAS Viya.
    • View a sample of a table or file loaded in the in-memory engine of SAS Viya, or from data sources registered with SAS/ACCESS, to visualize the data you want to work with.
    • Quickly create connections to and between external data sources.
    • Access physical metadata information like column names, data types, encoding, column count and row count to gain further insight into the data.
  • Data sources and types include:
    • Amazon S3.
    • Amazon Redshift.
    • DNFS, HDFS, PATH-based files (CSV, SAS, Excel, delimited).
    • DB2.
    • Hive.
    • Impala.
    • SAS® LASR.
    • ODBC.
    • Oracle.
    • Postgres.
    • Teradata.
    • Feeds from Twitter, YouTube, Facebook, Google Analytics, Google Drive, Esri and local files.
    • SAS® Cloud Analytic Services (CAS).

Data provisioning 

  • Parallel load data from desired data sources into memory simply by selecting them – no need to write code or have experience with an ETL tool. (Data cannot be sent back to the following data sources: Twitter, YouTube, Facebook, Google Analytics, Esri; it can only be sourced form these sites).
    • Reduce the amount of data being copied by performing row filtering or column filtering before the data is provisioned.
    • Retain big data in situ, and push processing to the source system by including SAS In-Database optional add-ons.

    Guided, interactive data preparation

    • Transform, blend, shape, cleanse and standardize data in an interactive, visual environment that guides you through data preparation processes.
    • Easily understand how a transformation affected results, getting visual feedback in near-real-time through the distributed, in-memory processing of SAS Viya.

    Machine learning & AI suggestions

    • Take advantage of AI and machine learning to scan data and make intelligent transformation suggestions.
    • Accept suggestions and complete transformations at the click of a button. No advanced or complex coding required.
    • Automated suggestions include:
      • Casing.
      • Gender analysis.
      • Match code.
      • Parse.
      • Standardization.
      • Missing value imputation for numeric variables.
      • One hot encoding.
      • Remove column.
      • Whitespace trimming.
      • Convert column data type.
      • Center and scale.
      • Dedupe.
      • Unique ID creation.
      • Column removal for sparse data.

    Column-based transformations

    • Use column-based transformations to standardize, remediate and shape data without doing configurations. You can:
      • Change case.
      • Convert column.
      • Rename.
      • Remove.
      • Split.
      • Trim whitespace.
      • Custom calculation.
    • Support for wide tables allows for the saving of data plans for quick data preparation jobs.

    Row-based transformations

    • Use row-based transformations to filter and shape data.
    • Create analytical-based tables using the transpose transformation to prepare the data for analytics and reporting tasks.
    • Create simple or complex filters to remove unnecessary data.

    Code-based transformations

    • Write custom code to transform, shape, blend, remediate and standardize data.
    • Write simple expressions to create calculated columns, write advanced code or reuse code snippets for greater transformational flexibility.
    • Import custom code defined by others, sharing best practices and collaborative productivity.

    Multiple-input-based transformations

    • Use multiple-input-based transformations to blend and shape data.
    • Blend or shape one or more sets of data together using the guided interface – there’s no requirement to know SQL or SAS. You can:
      • Append data.
      • Join data.
      • Transpose data.

    Data profiling

    • Profile data to generate column-based and table-based basic and advanced profile metrics.
    • Use the table-level profile metrics to uncover data quality issues and get further insight into the data itself.
    • Drill into each column for column-level profile metrics and to see visual graphs of pattern distribution and frequency distribution results that help uncover hidden insights.
    • Use a variety of data types/sources (listed previously). To profile data from Twitter, Facebook, Google Analytics or YouTube, you must first explicitly import the data into the SAS Viya in-memory environment.

    Data quality processing

    (SAS® Data Quality in SAS® Viya® is included in SAS Data Preparation)

    Data cleansing

    • Use locale- and context-specific parsing and field extraction definitions to reshape data and uncover additional insights.
    • Use the extraction transformation to identify and extract contact information (e.g., name, gender, field, pattern, identify, email and phone number) in a specified column.
    • Use parsing when data in a specified column needs to be tokenized into substrings (e.g., a full name tokenized into prefix, given name, middle name and family name).
    • Derive unique identifiers from match codes that link disparate data sources.
    • Standardize data with locale- and context-specific definitions to transform data into a common format, like casing.

    Identity definition

    • Analyze column data using locale-specific rules to determine gender or context.
    • Use identification analysis to analyze the data and determine its context, which is particularly valuable if the data or source of data is unfamiliar.
    • Use gender analysis to determine the gender of a name using locale-specific rules so the data can be easily filtered or segmented.
    • Create a unique ID for each row with unique ID generator.
    • Identify the subject data in each column with identification analysis.
    • Identify, find and sort data by tagging data with columns and tables.

    Data matching

    • Determine matching records based upon locale- and context-specific definitions.
    • Easily identify matching records using more than 25 context-specific rules such as date, address, name, email, etc.
    • Use the results of the match code transformation to remove duplicates, perform a fuzzy search or a fuzzy join.
    • Find like records and logically group together.

    System & job monitoring

    • Use integrated monitoring capabilities for system- and job-level processes.
    • Gain insight into how many processes are running, how long they’re taking and who is running them.
    • Easily filter through all system jobs based on job status (running, successful, failed, pending and cancelled).
    • Access job error logs to help with root-cause analysis and troubleshooting. (Note: Monitoring is available using SAS Environment Manager and the job monitor application.)

    Data import & data preparation job scheduling

    • Create a data import job from automatically generated code to perform a data refresh using the integrated scheduler.
    • Schedule data explorer imports as jobs so they will become an automatic, repeatable process.
    • Specify a time, date, frequency and/or interval for the jobs.

    Data lineage

    • Explore relationships between accessible data sources, data objects and jobs.
    • Use the relationship graph to visually show the relationships that exist between objects, making it easier to understand the origin of data and trace its processing.
    • Create multiple views with different tabs, and save the organization of those views.

    Plan templates & project collaboration

    • Use data preparation plans (templates), which consist of a set of transformation rules that get applied to one or more sources of data, to improve productivity (spend less time preparing data).
    • Reuse the templates by applying them to different sets of data to ensure that data is transformed consistently to adhere to enterprise data standards and policies.
    • Rely on team-based collaboration through a project hub used with SAS Viya projects. The project’s activity feed shows who did what and when, and can be used to communicate with other team members.

    Batch text analysis

    • Quickly extract contents of documents, and perform text identification and extraction.

    Cloud data exchange

    • Securely copy data from on-site repositories to a cloud-based SAS Viya instance running in a private or public cloud for use in SAS Viya applications – as well as sending data back to on-site locations.
    • Preprocess data locally, which reduces the amount of data that needs to be moved to remote locations.
    • Use a Command Line Input (CLI) interface for administration and control.
    • Securely and responsibly negotiates your on-site firewall.  

    Visual data exploration & insights development

    • Provides bi-modal support for both governed and self-service exploration and visualization.
    • Enables self-service discovery, reporting and analysis.
    • Provides access to easy-to-use predictive analytics with “smart algorithms.”
    • Enables report sharing via email, web browser, MS Office or mobile devices.
    • Provides centralized, web-based administration, monitoring and governance of platform.

    View more visual data exploration & insights development features

    Data

    • Import data from a variety of sources: databases, Hadoop, Excel spreadsheets, social media, etc.
    • Drag an Excel file, CSV or SAS data set onto your workspace, and quickly start building reports or dashboards.
    • Use standard data quality functions like change case; convert, rename, remove and split columns; and create calculated columns and transformations using custom code.
    • Prep data using append, join, filter and transpose functions.
    • Reuse, schedule and monitor jobs.
    • View lineage with network diagrams.
    • Quickly view descriptive statistics on measures to help you see the characteristics of your data.
    • Create calculated, aggregated or derived data items.
    • Create drillable hierarchies in a self-service manner without the need to predefine user paths.

    Discovery

    • Interactive data discovery enables business users and analysts to easily identify relationships, trends, outliers, etc.
    • Precise and responsive layout capabilities give you flexible layout and design options. You can stack or group items, and more.
    • A variety of graph objects or charts are included:
      • Bar.
      • Pie.
      • Donut.
      • Line.
      • Scatter.
      • Heat map.
      • Bubble.
      • Animated bubble.
      • Treemap.
      • Dot.
      • Needle.
      • Numeric series.
      • Schedule chart.
      • Vector.
      • Key value infographics.
      • And many more with flexible graph building capabilities.
    • Add content from the web (e.g., YouTube videos, web apps) and images (e.g., logos) to your report.
    • Custom sort allows you to rank order category data items in a table or graph by characteristics (e.g., products, customers). The characteristics that are most important to your organization will be displayed first.
    • One-click filtering (e.g., one way, bidirectional) and linked selections will allow you to spend less time manually linking content (e.g., visualizations, reports).
    • Text objects include date-driven or system-generated text for relevant context.
    • Synchronize selection and filters across visualizations in a report or dashboard.
    • Link different reports (e.g., link a sales report to an inventory report).
    • Report consumers can change calculation parameters and display rules using controls, filters, etc. to see information that is most relevant to them.
    • Report consumers can switch measures and change chart type and formatting all on the fly allowing them to make critical business decisions instantly.
    • Set refresh rates for individual objects, pages or your entire report.
    • Analytical visualizations include:
      • Box plot.
      • Heat map.
      • Animated bubble chart.
      • Network diagram.
      • Correlation matrix.
      • Forecasting.
      • Parallel coordinates plot.
      • Decision tree.
      • And many more with flexible graph building capabilities.
    • Geographical map views provide a quick understanding of geospatial data, including travel time and travel distance, demographics data enrichment with Esri integration.
    • Network diagrams enable you to display networks across a map.
    • Bring your own custom interactive visualizations (e.g., D3.js graphs, C3 visualizations or Google charts) into SAS Visual Analytics, so they’re all driven by the same data.
    • Key value visualization allows you to display important metrics (numeric or categorical value) in an infographic style for quick reference.
    • Perform path analysis (Sankey diagrams) to visualize relationships between a distinct sequence of events.
    • Add cell visualizations, like bars and heat maps, to your tables to quickly identify problem points and see trends in your data.
    • Generate forecasts on the fly with forecasting confidence intervals included.
    • The most appropriate forecasting model is automatically selected after running multiple models against data.
    • Scenario analysis lets you see how changes in different variables would affect forecasts.
    • Goal seeking enables you to specify a target value for your forecast, and then determines the values of underlying factors that would be required to achieve the target value.
    • Decision tree graphically depicts likely outcomes.
    • Custom binning moves continuous data into a small number of groups for better interpretation and presentation of results.
    • Text analysis capabilities enable you to automatically find topics and understand sentiment from text sources, including Facebook, Twitter, Google Analytics, YouTube comments and more.
    • Recover reports you are editing when your session ends unexpectedly. Reports are automatically saved every five seconds after an edit is made.
    • Pick up where you left off from a prior session on all your devices.

    Augmented analytics

    • Autocharting automatically chooses the graph best-suited to display selected data.
    • Automated Explanation determines which variables contribute to an outcome and provides a simple natural language explanation that is easy to understand.
    • Quickly detect and highlight patterns and outliers in your data with Automated Explanation.
    • Automated Explanation determines the key difference between the top and bottom cases in data. For example, what best differentiates the lowest risk and the highest risk cases?
    • The steps taken to automatically explain your data are displayed for transparency.
    • Use Automated Explanation to identify interesting groups based on factors you select.
    • Automatically builds an interactive analytical story based on all your data, ready to be published.
    • Suggested insights automatically derived from your data allows you to quickly build informative reports and dashboards.
    • Related measures highlighted within the measure list so users can quickly identify potential interactions.

    Sharing & collaboration

    • Reuse and share report modifications, such as filters, calculations, hierarchies and report element formatting.
    • Collaborate across mobile devices and the web by adding comments to a report.
    • Create alerts for a report object so that subscribers are notified via email or a text message when the threshold condition is met.
    • Distribute reports as PDFs or email in a secure manner. Distribute reports once or at recurring intervals, such as daily, weekly or monthly.
    • Playable dashboards let you put your report in slideshow mode.
    • Administrators can configure support for guest access to view report or visualization.
    • Guest users can view the insights that are available to the public.
    • Users can see, organize and collaborate on their work using SAS Drive:
      • Users can favorite, share, preview and tag their content from one place.
      • Create projects that share data, content and other resources with project members.

    SAS® Visual Analytics Apps

    • Available for free from:
      • App Store for iOS iPhone and iPads.
      • Google Play for Android phones and tablets.
      • Microsoft Store for Windows 10 devices.
    • The app lets you connect and interact with your SAS Visual Analytics reports and dashboards using gestures native to your devices.
    • Interact with your SAS Visual Analytics app for iOS using voice commands.
    • Reports created once in SAS Visual Analytics can be viewed anywhere.
    • Gain secure access to content on mobile devices, both online and offline.
    • Annotate, comment, share and email reports to others for increased collaboration.
    • Screenshots can be captured and comments shared with others.
    • Notifications alert business users when a report is updated, data is changed or the application is updated.

    Embedded insights

    • Create your own mobile apps using the SAS SDK for iOS and SAS SDK for Android to create embedded insights:
      • Personalize your mobile app with embedded SAS Visual Analytics content, your corporate logo and name of your choosing.
      • Preconfigure your mobile app to connect to SAS servers and subscribe to specified reports.
      • Develop completely customized mobile apps that embed SAS Visual Analytics content (e.g., GatherIQ).
      • Manage and secure your mobile app and data by integrating with mobile device management (MDM) service (via new APIs).       
    • Embed full reports or individual objects in websites and web apps using the SAS Visual Analytics SDK:
      • Combine insights from multiple reports in one location.
      • User selections within an embedded SAS Visual Analytics object can drive other elements anywhere on the webpage.

    Location analytics

    • Geographical maps are enabled through Esri ArcGIS Online or OpenStreetMap. 
    • You can lasso data points on geographical maps to select specific data for further analysis.
    • Geographical maps make it easy to visualize measurement variances over a geographical area.
    • Access to all Esri basemaps and geosearch is available through Esri ArcGIS Online at no additional charge.
    • Custom polygons (e.g., sales territories, voting districts, floor plans, seating charts) will let you see the world just as your business demands for it. These polygons can be animated to show how key metrics change over time.
    • Geographic point clustering makes it easier to visualize high-volume location data and identify areas of interest. Get more or less details at different zoom levels.
    • Add map pins to mark points of interest and insights on a map.
    • With Esri ArcGIS Online license, you can enrich your data with Esri demographics data:
      • Start from a pin, and select the area that can be traveled based on travel distance or provided travel time.
      • Create travel routes between points.
      • Understand how location affects outcomes by geocoding your data – adding latitude and longitude columns to your data based on location information in your data (country, state, zip code, city, street).

    Security & administration

    • SAS Environment Manager provides easy-to-use, web-based centralized administration and monitoring of your BI and analytics environment, including users, data, content, servers, services and security.
    • User authentication and content authorization support governance.
    • Object-level security (folders, reports, etc.) and data security (table and row level) support governance.
    • Seamless integration with corporate identity directories such as LDAP.
    • Rules-mapping application capabilities for users and groups support governance.
    • Whitelist or blacklist mobile devices to determine authorization for SAS Visual Analytics apps.
    • Near-real-time dashboard for monitoring system health and key activities.
    • Distributed processing node addition and deletion.
    • Scriptable APIs perform administrative tasks in batch, including management of security, libraries, users groups and configurations.
    • Customizable monitoring and performance reports.
    • Environmentwide log exploration, job scheduling and monitoring.

    SAS® Viya® in-memory engine

    • CAS (SAS Cloud Analytic Services) performs processing in memory and distributes processing across nodes in a cluster.
    • User requests (expressed in a procedural language) are translated into actions with the parameters needed to process in a distributed environment. The result set and messages are passed back to the procedure for further action by the user.
    • Data is managed in blocks and can be loaded in memory and on demand.
    • If tables exceed memory capacity, the server caches the blocks on disk. Data and intermediate results are held in memory as long as required, across jobs and users.
    • Includes highly efficient node-to-node communication. An algorithm determines the optimal number of nodes for a given job.
    • Communication layer supports fault tolerance and lets you remove or add nodes from a server while it is running. All components can be replicated for high availability.
    • Support for legacy SAS code and direct interoperability with SAS 9.4M5 clients.
    • Supports multitenancy deployment, allowing for a shared software stack to support isolated tenants in a secure manner.

    Deployment flexibility

    • On-site deployments:
      • Single-machine server to support the needs of small to midsized organizations.
      • Distributed server to meet growing data, increasing workloads and scalability requirements.
    • Cloud deployments:
      • Enterprise hosting.
      • Private or public cloud (e.g., BYOL in Amazon) infrastructure.
      • Cloud Foundry platform as a service (PaaS) to support multiple cloud providers.

    Descriptive & predictive modeling

    • Explore and evaluate segments for further analysis using k-means clustering, scatter plots and detailed summary statistics.
    • Use machine learning techniques to build predictive models from a visual or programming interface.

    View more descriptive & predictive modeling features

    Visual data exploration & discovery (available through SAS® Visual Analytics) 

    • Quickly interpret complex relationships or key variables that influence modeling outcomes within large data sets.
    • Filter observations and understand a variable’s level of influence on overall model lift. 
    • Detect outliers and/or influence points to help you determine, capture and remove them from downstream analysis (e.g., models). 
    • Explore data using bar charts, histograms, box plots, heat maps, bubble plots, geographic maps and more. 
    • Derive predictive outputs or segmentations that can be used directly in other modeling or visualization tasks. Outputs can be saved and passed to those without model-building roles and capabilities.
    • Automatically convert measure variables with two levels to category variables when a data set is first opened.

    Visual interface access to analytical techniques

    • Clustering:
      • K-means, k-modes or k-prototypes clustering.
      • Parallel coordinate plots to interactively evaluate cluster membership.
      • Scatter plots of inputs with cluster profiles overlaid for small data sets and heat maps with cluster profiles overlaid for large data sets.
      • Detailed summary statistics (means of each cluster, number of observations in each cluster, etc.).
      • Generate on-demand cluster ID as a new column.
      • Supports holdout data (training and validation) for model assessment. 
    • Decision trees: 
      • Supports classification and regression trees. 
      • Based on a modified C4.5 algorithm or cost-complexity pruning. 
      • Interactively grow and prune a tree. Interactively train a subtree. 
      • Set tree depth, max branch, leaf size, aggressiveness of tree pruning and more. 
      • Use tree map displays to interactively navigate the tree structure. 
      • Generate on-demand leaf ID, predicted values and residuals as new columns. 
      • Supports holdout data (training and validation) for model assessment.
      • Supports pruning with holdout data.
      • Supports autotuning with options for leaf size.
    • Linear regression: 
      • Influence statistics.
      • Supports forward, backward, stepwise and lasso variable selection.
      • Iteration plot for variable selection.
      • Frequency and weight variables.
      • Residual diagnostics.
      • Summary table includes overall ANOVA, model dimensions, fit statistics, model ANOVA, Type III test and parameter estimates.
      • Generate on-demand predicted values and residuals as new columns.
      • Support holdout data (training and validation) for model assessment.
    • Logistic regression:
      • Models for binary data with logit and probit link functions.
      • Influence statistics.
      • Supports forward, backward, stepwise and lasso variable selection.
      • Iteration plot for variable selection.
      • Frequency and weight variables.
      • Residual diagnostics.
      • Summary table includes model dimensions, iteration history, fit statistics, convergence status, Type III tests, parameter estimates and response profile.
      • Generate on-demand predicted labels and predicted event probabilities as new columns. Adjust the prediction cutoff to label an observation as event or non-event.
      • Support holdout data (training and validation) for model assessment.
    • Generalized linear models:
      • Distributions supported include beta, normal, binary, exponential, gamma, geometric, Poisson, Tweedie, inverse Gaussian and negative binomial.
      • Supports forward, backward, stepwise and lasso variable selection.
      • Offset variable support.
      • Frequency and weight variables.
      • Residual diagnostics.
      • Summary table includes model summary, iteration history, fit statistics, Type III test table and parameter estimates.
      • Informative missing option for treatment of missing values on the predictor variable.
      • Generate on-demand predicted values and residuals as new columns.
      • Supports holdout data (training and validation) for model assessment.
    • Generalized additive models:
      • Distributions supported include normal, binary, gamma, Poisson, Tweedie, inverse Gaussian and negative binomial.
      • Supports one- and two-dimensional spline effects.
      • GCV, GACV and UBRE methods for selecting the smoothing effects.
      • Offset variable support.
      • Frequency and weight variables.
      • Residual diagnostics.
      • Summary table includes model summary, iteration history, fit statistics and parameter estimates.
      • Supports holdout data (training and validation) for model assessment.
    • Nonparametric logistic regression:
      • Models for binary data with logit, probit, log-log and c-log-log link functions.
      • Supports one- and two-dimensional spline effects.
      • GCV, GACV and UBRE methods for selecting the smoothing effects.
      • Offset variable support.
      • Frequency and weight variables.
      • Residual diagnostics.
      • Summary table includes model summary, iteration history, fit statistics and parameter estimates.
      • Supports holdout data (training and validation) for model assessment.

    Programming access to analytical techniques

    • Programmers and data scientists can access SAS Viya (CAS server) from SAS Studio using SAS procedures (PROCs) and other tasks.
    • Programmers can execute CAS actions using PROC CAS or use different programming environments like Python, R, Lua and Java.
    • Users can also access SAS Viya (CAS server) from their own applications using public REST APIs.
    • Provides native integration to Python Pandas DataFrames. Python programmers can upload DataFrames to CAS and fetch results from CAS as DataFrames to interact with other Python packages, such as Pandas, matplotlib, Plotly, Bokeh, etc.
    • Includes SAS/STAT® and SAS/GRAPH® software.
    • Principal component analysis (PCA):
      • Performs dimension reduction by computing principal components.
      • Provides the eigenvalue decomposition, NIPALS and ITERGS algorithms.
      • Outputs principal component scores across observations.
      • Creates scree plots and pattern profile plots.
    • Decision trees:
      • Supports classification trees and regression trees.
      • Supports categorical and numerical features.
      • Provides criteria for splitting nodes based on measures of impurity and statistical tests.
      • Provides the cost-complexity and reduced-error methods of pruning trees.
      • Supports partitioning of data into training, validation and testing roles.
      • Supports the use of validation data for selecting the best subtree.
      • Supports the use of test data for assessment of final tree model.
      • Provides various methods of handling missing values, including surrogate rules.
      • Creates tree diagrams.
      • Provides statistics for assessing model fit, including model-based (resubstitution) statistics.
      • Computes measures of variable importance.
      • Outputs leaf assignments and predicted values for observations.
    • Clustering:
      • Provides the k-means algorithm for clustering continuous (interval) variables.
      • Provides the k-modes algorithm for clustering nominal variables.
      • Provides various distance measures for similarity.
      • Provides the aligned box criterion method for estimating the number of clusters.
      • Outputs cluster membership and distance measures across observations.
    • Linear regression:
      • Supports linear models with continuous and classification variables.
      • Supports various parameterizations for classification effects.
      • Supports any degree of interaction and nested effects.
      • Supports polynomial and spline effects.
      • Supports forward, backward, stepwise, least angle regression and lasso selection methods.
      • Supports information criteria and validation methods for controlling model selection.
      • Offers selection of individual levels of classification effects.
      • Preserves hierarchy among effects.
      • Supports partitioning of data into training, validation and testing roles.
      • Provides a variety of diagnostic statistics.
      • Generates SAS code for production scoring.
    • Logistic regression:
      • Supports binary and binomial responses.
      • Supports various parameterizations for classification effects.
      • Supports any degree of interaction and nested effects.
      • Supports polynomial and spline effects.
      • Supports forward, backward, fast backward and lasso selection methods.
      • Supports information criteria and validation methods for controlling model selection.
      • Offers selection of individual levels of classification effects.
      • Preserves hierarchy among effects.
      • Supports partitioning of data into training, validation and testing roles.
      • Provides variety of statistics for model assessment.
      • Provides variety of optimization methods for maximum likelihood estimation.
    • Generalized linear models:
      • Supports responses with a variety of distributions, including binary, normal, Poisson and gamma.
      • Supports various parameterizations for classification effects.
      • Supports any degree of interaction and nested effects.
      • Supports polynomial and spline effects.
      • Supports forward, backward, fast backward, stepwise and group lasso selection methods.
      • Supports information criteria and validation methods for controlling model selection.
      • Offers selection of individual levels of classification effects.
      • Preserves hierarchy among effects.
      • Supports partitioning of data into training, validation and testing roles.
      • Provides variety of statistics for model assessment.
      • Provides a variety of optimization methods for maximum likelihood estimation.
    • Nonlinear regression models:
      • Fits nonlinear regression models with standard or general distributions.
      • Computes analytical derivatives of user-provided expressions for more robust parameter estimations.
      • Evaluates user-provided expressions using the ESTIMATE and PREDICT statements (procedure only).
      • Requires a data table that contains the CMP item store if not using PROC NLMOD.
      • Estimates parameters using the least squares method.
      • Estimates parameters using the maximum likelihood method.
    • Quantile regression models:
      • Supports quantile regression for single or multiple quantile levels.
      • Supports multiple parameterizations for classification effects.
      • Supports any degree of interactions (crossed effects) and nested effects.
      • Supports hierarchical model selection strategy among effects.
      • Provides multiple effect-selection methods.
      • Provides effect selection based on a variety of selection criteria.
      • Supports stopping and selection rules.
    • Predictive partial least squares models:
      • Provides programming syntax with classification variables, continuous variables, interactions and nestings.
      • Provides effect-construction syntax for polynomial and spline effects.
      • Supports partitioning of data into training and testing roles.
      • Provides test set validation to choose the number of extracted factors.
      • Implements the following methods: principal component regression, reduced rank regression and partial least squares regression.
    • Generalized additive models:
      • Fit generalized additive models based on low-rank regression splines.
      • Estimates the regression parameters by using penalized likelihood estimation.
      • Estimates the smoothing parameters by using either the performance iteration method or the outer iteration method.
      • Estimates the regression parameters by using maximum likelihood techniques.
      • Tests the total contribution of each spline term based on the Wald statistic.
      • Provides model-building syntax that can include classification variables, continuous variables, interactions and nestings.
      • Enables you to construct a spline term by using multiple variables.
    • Proportional hazard regression:
      • Fit the Cox proportional hazards regression model to survival data and perform variable selection.
      • Provides model-building syntax with classification variables, continuous variables, interactions and nestings.
      • Provides effect-construction syntax for polynomial and spline effects.
      • Performs maximum partial likelihood estimation, stratified analysis and variable selection.
      • Partitions data into training, validation and testing roles.
      • Provides weighted analysis and grouped analysis.
    • Statistical process control: 
      • Perform Shewhart control chart analysis.
      • Analyze multiple process variables to identify processes that are out of statistical control. 
      • Adjust control limits to compensate for unequal subgroup sizes.
      • Estimate control limits from the data, compute control limits from specified values for population parameters (known standards) or read limits from an input data table.
      • Perform tests for special causes based on runs patterns (Western Electric rules).
      • Estimate the process standard deviation using various methods (variable charts only).
      • Save chart statistics and control limits in output data tables.
    • Independent component analysis:
      • Extracts independent components (factors) from multivariate data.
      • Maximizes non-Gaussianity of the estimated components.
      • Supports whitening and dimension reduction.
      • Produces an output data table that contains independent components and whitened variables.
      • Implements symmetric decorrelation, which calculates all the independent components simultaneously.
      • Implements deflationary decorrelation, which extracts the independent components successively.
    • Linear mixed models:
      • Supports many covariance structures, including variance components, compound symmetry, unstructured, AR(1), Toeplitz, factor analytic, etc.
      • Provides specialized dense and sparse matrix algorithms.
      • Supports REML and ML estimation methods, which are implemented with a variety of optimization algorithms.
      • Provides Inference features, including standard errors and t tests for fixed and random effects.
      • Supports repeated measures data.
    • Model-based clustering:
      • Models the observations by using a mixture of multivariate Gaussian distributions.
      • Allows for a noise component and automatic model selection.
      • Provides posterior scoring and graphical interpretation of results.

    Descriptive statistics

    • Distinct counts to understand cardinality.
    • Box plots to evaluate centrality and spread, including outliers for one or more variables.
    • Correlations to measure the Pearson’s correlation coefficient for a set of variables. Supports grouped and weighted analysis.
    • Cross-tabulations, including support for weights.
    • Contingency tables, including measures of associations.
    • Histograms with options to control binning values, maximum value thresholds, outliers and more.
    • Multidimensional summaries in a single pass of the data.
    • Percentiles for one or more variables.
    • Summary statistics, such as number of observations, number of missing values, sum of nonmissing values, mean, standard deviation, standard errors, corrected and uncorrected sums of squares, min and max, and the coefficient of variation.
    • Kernel density estimates using normal, tri-cube and quadratic kernel functions.
    • Constructs one-way to n-way frequency and cross-tabulation tables.

    Group-by processing

    • Build models, compute and process results on the fly for each group or segment without having to sort or index the data each time.
    • Build segment-based models instantly (i.e., stratified modeling) from a decision tree or clustering analysis.

    Model comparison, assessment & scoring

    • Generate model comparison summaries, such as lift charts, ROC charts, concordance statistics and misclassification tables for one or more models.
    • Interactively slide the prediction cutoff for automatic updating of assessment statistics and classification tables.
    • Interactively evaluate lift at different percentiles.
    • Export models as SAS DATA step code to integrate models with other applications. Score code is automatically concatenated if a model uses derived outputs from other models (leaf ID, cluster ID, etc.).

    Model scoring

    • Export models as SAS DATA step code to integrate models with other applications. Score code is automatically concatenated if a model uses derived outputs from other models (leaf ID, cluster ID, etc.).

    SAS® Viya® in-memory runtime engine

    • SAS Cloud Analytic Services (CAS) performs processing in memory and distributes processing across nodes in a cluster.
    • User requests (expressed in a procedural language) are translated into actions with necessary parameters to process in a distributed environment. The result set and messages are passed back to the procedure for further action by the user.
    • Data is managed in blocks and can be loaded in memory on demand. If tables exceed the memory capacity, the server caches the blocks on disk. Data and intermediate results are held in memory as long as required, across jobs and user boundaries.
    • An algorithm determines the optimal number of nodes for a given job.
    • A communication layer supports fault tolerance and lets you remove or add nodes from a server while it is running. All components in the architecture can be replicated for high availability. 
    • Products can be deployed in multitenant mode, allowing for a shared software stack to support securely isolated tenants.

    Deployment options

    • On-site deployments:
      • Single-machine mode to support the needs of small to midsize organizations.
      • Distributed mode to meet growing data, workload and scalability requirements.
    • Cloud deployments:
      • Enterprise hosting.
      • Private or public cloud (e.g., BYOL in Amazon) infrastructure.
      • Cloud Foundry platform as a service (PaaS) to support multiple cloud providers. 

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