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.
  • Best practice templates (basic, intermediate or advanced) help users get started quickly with machine learning tasks.
  • Interpretability reports.
  • 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.

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 Juypter 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 and YOLOv2 and Tiny YOLO.
  • 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

Accessible, web-based, centralized and secure repository for managing analytical models

  • Access all models in the model repository – whether they’re located in a folder or project.
  • Access models and model-score artifacts using open REST APIs.
  • Support for SAS model registration from SAS Visual Analytics, SAS Visual Statistics, SAS Studio, and Model Studio for SAS Visual Text Analytics and SAS Visual Data Mining and Machine Learning.
  • Set up, maintain and manage separate versions for models:
    • Champion model is automatically defined as a new version when the model is set as champion, updated or published in a project.
    • Only one champion model is produced per project. New versions are automatically created when new model projects are registered from the Model Studio environment in SAS Visual Data Mining and Machine Learning and SAS Visual Text Analytics.
    • Choose challenger models to the project champion model.
    • Monitor and publish challenger and champion models.
    • Integration of champion models with SAS Event Stream Processing, including automated notifications when model project champion is updated.
  • Monitor performance of champion models for all projects using performance report definition and execution.
  • Publish SAS models to SAS Cloud Analytic Services (CAS), Hadoop, SAS Micro Analytic Service or Teradata.
  • Python code publishing support for SAS Micro Analytic Service execution target.
  • Provides accounting and auditability, including event logging of major actions, including model creation, project creation and versioning.
  • 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).
  • Import models from the SAS Platform, including training code, score logic, estimate tables, target and input variables and output variables, using SAS package files (.SPK), PMML and ZIP format files.
  • 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.
  • Import code snippets/models from any code base (C, C++, Java, Python, etc.) into the managed inventory.
  • Create DATA step score code for PMML models on import for inclusion in scoring tasks, reporting and performance monitoring.
  • Model repository can be searched, queried, sorted and filtered by attributes used to store models – such as type of asset, algorithm, input or target variables, model ID, etc. – as well as user-defined properties and editable keywords.
  • Register, compare, report, score and monitor models built in R or Python (classification and prediction).
  • Compare two or more models using automatically calculated model fit statistics to easily understand model differences through plots and analytical metrics.
  • Provides secure, reliable model storage and access administration, including backup and restore capabilities, overwrite protection, event logging and user authentication.

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.
  • Provides 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.

Scoring logic validation before models are exported to production

  • Define test and production score jobs for SAS and Python models using required inputs and outputs.
  • Define and execute scoring tasks, and specify where to save the output and job history.
  • Publish model updates to different scoring channels and notify subscribers via message queues.
  • Create model input and output variables from the score.sas file to generate missing metadata from model variables.
  • Integration with SAS Scoring Accelerator for in-database model deployment.
  • Integration with SAS Micro Analytic Service – for SAS and Python code testing and result validation.

Model performance monitoring and reporting during test and production

  • Integrated retraining for data mining and machine learning models using Model Studio: 
    • Retrain data mining and machine learning models when performance reporting threshold metrics are reached.
    • Automated, configured registration after model retraining is completed from Model Studio. No need to import separately.
  • Model performance reports produced for champion and challenger 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.
  • SAS Visual Analytics provides a wide range of model comparison reports.
  • Performance results are prepared and made available to SAS Visual Analytics for simplified access to a wide range of model comparison reports.
  • Ability to specify multiple data sources and time-collection periods when defining performance-monitoring tasks.

Distributed, accessible and cloud-ready

  • Runs on SAS Viya, a scalable and distributed in-memory engine of the SAS Platform.
  • Distributes analysis and data tasks across multiple computing nodes.
  • Provides fast, concurrent, multiuser access to data in memory.
  • Includes fault tolerance for high availability.
  • Lets you add the power of SAS Analytics to other applications using RESTful APIs. 

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 and 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.

    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 on 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 and 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 and 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 and 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

    Self-service data discovery

    • Interactive data discovery enables business users and analysts to easily identify relationships, trends, outliers, etc. 
    • Autocharting automatically chooses the graph best-suited to display selected data.
    • Analytical visualizations include box plots, heat maps, animated bubble charts, network diagrams, correlation matrices, line charts with forecasting, parallel coordinates plot, donut charts, decision trees and more.
    • Geographical map views provide a quick understanding of geospatial data. 
    • Network diagrams enable you to display networks across a map. 
    • Any visualization can be published as a report object. 
    • Custom calculations let you combine functions, operators and existing data items to formulate values specific to your needs. 
    • A resizable overview bar lets you interactively subset a portion of visuals with many data points. 
    • Dashboard objects can be connected to discovery objects, which enables interaction between them. 
    • You can bring your own custom interactive visualizations (e.g., D3.js graphs, C3 visualizations or Google charts) into SAS Visual Analytics, so they are all driven by the same data.    
    • New key value visualization allows you to display important metrics (numeric or categorical values) in an infographic style for quick reference.
    • Sankey diagrams let you perform path analysis in order to visualize relationships between distinct sequences of events. Path analysis displays the flow of data from one event (value) to another as a series of paths.

    Self-service analytics

    • Descriptive statistics – such as min, max and mean – provide an overall sense of a particular measure. 
    • Users can create new calculated measures and add them to any view. 
    • Forecasts that include forecasting confidence intervals can be generated on the fly. 
    • The most appropriate forecasting algorithm for specific data is selected automatically. 
    • 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 trees graphically depict likely outcomes. An expert level allows you to modify certain influencing parameters for the tree generation. 
    • 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 and YouTube comments.

    Interactive reporting & dashboards

    • Precise and responsive layout capabilities give you flexible report layout and design options. You can stack or group items, and more.
    • A variety of graph objects or charts are included: bar with multiple lines, pie, donut, line, scatter, heat map, bubble, animated bubble, treemap, dot, needle, numeric series, schedule chart, vector, etc.
    • Add web content (e.g., YouTube videos, web apps) and images (e.g., logos) to your reports.
    • A variety of prompt controls enable report creators and consumers to better interact with the report.
    • Custom sort capability lets you sort category data items in a table or graph.
    • Automatic filtering (e.g., one-way, bidirectional) and linked selections reduce the amount of time you spend manually linking content (e.g., visualizations, reports).
    • Text objects can include date-driven or system-generated text for relevant context.
    • Users can collaborate easily across mobile devices and the web by adding comments to a report.
    • Synchronize selection and filters across all visualizations in a report or dashboard.
    • Link different reports (e.g., link a sales report to an inventory report).
    • Report consumers can change parameters used in calculations and display rules using controls, filters, etc., to see information that is most relevant to them.
    • Create alerts for a report object to notify subscribers via email or text message when the threshold condition is met.
    • Securely distribute reports via PDF or email, either once or at recurring intervals (e.g., daily, weekly or monthly).
    • Recover reports you are editing when your sessions ends unexpectedly. Reports are automatically saved every five seconds after an edit is made.
    • Administrators can configure support for guest access to view reports or visualizations. Guest users can view the insights that are available to the public.
    • Drillable hierarchies can be created in a self-service manner without the need to predefine user paths. 

    Mobile BI

    • Native iOS, Windows 10 and Android support for tablets and smartphones uses native gestures and capabilities to provide a rich and responsive user experience. 
    • Reports can be created once and then viewed anywhere. 
    • Securely access content on mobile devices, both online and offline. 
    • Live screen sharing enables remote users to share their mobile screens with colleagues in the office.
    • Support for collaboration includes the ability to annotate, comment, share and email reports to others.
    • 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.
    • Works with third-party MDM vendors, such as Good Technologies, MobileIron and AirWatch. 

    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.
    • 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 needs dictate.
    • Geographic point clustering makes it easier to visualize high-volume location data and identify areas of interest. You can get more or less details at different zoom levels.
    • Travel-time and travel-distance analysis is available through a premium Esri ArcGIS Online license (purchased separately from Esri).
    • Geoenrichment lets you visualize demographic and other types of data from Esri (requires Esri ArcGIS Online license from Esri) in a different context to reveal new insights.
    • Choropleth maps make it easy to visualize measurement variances over a geographical area.

    Self-service data preparation

    • Import data from a variety of sources – databases, Hadoop, Excel spreadsheets, clipboard, social media, etc.
    • Network diagrams enable you to view lineage.     
    • Use basic data quality functions, including:
      • Change case.
      • Convert, rename, remove, split columns.
      • Create calculated columns and transformations using custom code.
    • New table and column profiling enable you to understand data immediately.      
    • Prep data using append, join, filter and transpose functions.
    • Reuse, schedule and monitor jobs.

    Administration & management

    • 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. 
    • Support governance with:
      • User authentication and content authorization.
      • Object-level security (folders, reports, etc.) and data security (table and row level).
      • Rules-mapping application capabilities for users and groups.
    • Seamlessly integrate with corporate identity directories, such as LDAP.
    • Determine authorization for SAS Mobile BI by whitelisting or backlisting mobile devices.
    • Monitor system health and key activities using a near-real-time dashboard.
    • Add and delete distributed processing nodes. 
    • Scriptable APIs let you perform administrative tasks in batch, including management of security, libraries, users groups and configurations.
    • Customize monitoring and performance reports.
    • Perform environmentwide log exploration, job scheduling and monitoring.

    SAS® Viya® in-memory 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 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 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. 
    • A 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.       
    • The SAS Viya engine supports legacy SAS code and direct interoperability with SAS 9.4 M5 clients.        
    • Includes support for multitenancy deployment, allowing for a shared software stack to support isolated tenants securely.

    SAS® procedures (PROCs) & 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 or Lua programmers or IT staff can access data and perform basic data manipulation against a CAS server or execute CAS actions using PROC CAS. 
    • With REST APIs, you can integrate and add the power of SAS to other applications.

    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.

    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.

    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: 
      • Computes measures of variable importance. 
      • 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 autotuning.
    • Linear regression:
      • Influence statistics.
      • Supports forward, backward, stepwise and lasso 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.
      • 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.

    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.
      • SAS managed software as a service (SaaS).
      • Cloud Foundry platform as a service (PaaS) to support multiple cloud providers. 

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