SAS Viya Features

Reports interface for standard and self-service reports is intuitive and easy to use.

Requires a minimal number of steps/clicks to answer business question.

Offers a variety of attractive graph and chart formats.

Score cards visually track KPI's.

Provides business users an interface to easily design, refine and collaborate on their dashboards

Using formulas based on existing data elements, users can create and calculate new field values.

Business users have the ability to filter data in a report based on predefined or automodeled parameters.

Users can drill down and explore data to discover new insights.

Ability to search global data set to find and discover data.

Ability for users to share data and reports they have built within the BI tool and outside the tool through other collaboration platforms.

Tool automatically suggests data types, schemas and hierarchies.

Analyze current and historical trends to make predictions about future events.

Communicate complex information clearly and effectively through advanced graphical techniques.

Ability to handle large, complex, and/or siloed data sets.

Converts data formats of source data into the format required for the reporting system without mistakes.

Ability to (re)structure data in a manner that allows extracting insights fast and accurate.

Provides business users an interface to easily design and refine their dashboards and reports. (What You See Is What You Get)

Application Programming Interface - Specification for how the application communicates with other software. API's typically enable integration of data, logic, objects, etc with other software applications.

Supports a variety of scripting environments

Mines data from databases and prepares data for analysis

Applies statistical algorithms to selected data

Analyzes both structured and unstructured data

Interacts with data to prepare it for visualizations and models

Offers modeling capabilities

Creates data visualizations or graphs

Generates reports of data performance

Unifies information on a singular platform

Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript

Offers the ability for developers to drag and drop pieces of code or algorithms when building models

Provides users with pre-built algorithms for simpler model development

Supplies large data sets for training individual models

Transforms raw data into features that better represent the underlying problem to the predictive models

Offers image recognition services

Offers natural language processing services

Offers natural language generation services

Offers artificial neural networks for users

Offers natural language understanding services

Provides deep learning capabilities

Manages the intelligent application for the user, reducing the need of infrastructure

Allows users to insert machine learning into operating applications

Provides easily scaled machine learning applications and infrastructure

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Allows one to create or host applications built on top of business rules.

Provides APIs or other development tools for integrating with other applications.

Uses collected data to assist organizations in understanding what they can afford now, and in years to come.

Creates a continuous and recurrent planning process that can be calculated according to a specified and customizable timeline.

Leverages data from budgeting and forecasting processes to help corporations understand their reality and make decisions accordingly.

Offers methods of visualizing data via customizable dashboards, statistical graphs, and charts.

Scours data for actionable insights.

Allows users to customize their own reports according to business needs.

Provides pre-built reporting templates to organize data for payroll, manufacturing, and other company factions.

The ability to process large amounts of data.

The ability to process data from a wide variety of sources and formats.

The ability to work seamlessly with another software platform.

Processing data from a variety of sources in real time as it arrives.

Ensures privacy and security of customer data.

Visualizes text data through charts and graphs.

Allows for searchable communication within the tool via notes, checklists, and discussions to ensure clarity between users.

Reflects updates immediately, ensuring users have the most recent data while maintaining records of past performance.

Updates varying factors in real-time, assuring forecasts and inferences are continually made according to the most recent information.

Displays and shares company performance data to increase transparency across the board.

Provides a wide range of possible data connections, including cloud applications, on-premise databases, and big data distributions, among others

Allows businesses to easily connect to any data source

Offers API connections for cloud-based applications and data sources

Permits profiling of data sets for increased organization, both by users and machine learning

Indexes metadata descriptions for easier searching and enhanced insights

Tools to (re)structure data in a manner that enables quick and accurate insight extraction

Allows self-service joining of tables

Provides the ability to combine data sources into one data set

Allows users and administrators to easily clean data to maintain quality and integrity

Offers collaborative functionality for sharing queries and data sets

Ensures user access management, data lineage, and data encryption

Provides a wide range of possible integrations, including analytics, data integration, master data management, and data science tools

Allows businesses to easily integrate with analytics, data integration, master data management, and data science tools

Operationalizes data workflows to easily scale repeatable preparation needs

Tools to visualize and analyze data.

Analyzes and compares the impact of automated decisions.`

Provides tools for simulating rules on real or fictitious operational data, and use the results to assess and refine the behavior of the rules.

Tools to (re)structure data in a manner that allows extracting insights quickly and accurately

Analyzes data to find and recommend the highest value customer segmentations.

Tools to create and adjust workflows to ensure consistency.

Presents information and analytics in a digestible, intuitive, and visually appealing way.

Data quality consists of deduplication, cleansing, and appending your marketing database.

Allows users to select samples of data for defined procedures.

Share data across your organization.

Supports compliance with PII, GDPR, HIPPA, PCI, and other regulatory standards.

Allows administrators to set policies for security and data governance

Monitors data quality and send alerts based on violations or misuse

Curates collected data for big data analytics solutions to analyze, manipulate, and model

Facilitates the disseminating of collected big data throughout parallel computing clusters

Compile data from across all systems so that users can view relevant information easily.

Communicate complex information clearly and effectively through advanced graphical techniques.

Supports evaluation of durations, events, and reliability in relation to statistical analysis

Identify risk data attributes such as description, category, owner, or hierarchy.

Tools to analyze financial data in order to gain usable insights.

Supports analysis of variance (ANOVA) to determine observed variance.

Supports various regression methods such as ordinary least squares (OLS), weighted least squares (WLS), or generalized linear model (GLM).

Supports the analysis of time series data for predictive analytics and exploratory analysis.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Allows users to manage model artifacts and tracks which models are deployed in production.

Provide follow-up information after data cleanings through a visual dashboard or reports.

Automatically run data identification, correction, and normalization on data sources.

Schedule automated audits to identify data anomalies over time based on set business rules.

Gives a view of the entire data quality management ecosystem.

Stores the database metadata, that is the definitions of data elements, types, relationships etc.

Creates a copy of the database to maintain consistency and integrity.

Allows users to create, update and retrieve data in a database.

Defines the logical design of the data before building the schemas.

Monitors and analyzes critical database attributes like query performance, user sessions, dead lock detail, system errors etc and visualize them on a custom dashboard.

Lets users build a glossary of business terms, vocabulary and definitions across multiple tools.

Provides a built-in integrated data catalog that allows users to easily locate data across multiple sources.

Monitors and cleanses data with the help of business rules and analytical algorithms.

Visualize data flows and lineage that demonstrates compliance with reports and dashboards through a single console.

Provides an automated data lineage functionality which provides visibility over the entire data movement journey from data origination to destination.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Stores business rules in a catalog, to ensure business users can access them in their latest version.

Gives users the ability to collaborate on decision management, such as the ability to work simultaneously or annotate models.

Correctly identify inaccurate, incomplete, or duplicated data from a data source.

Utilize deletion, modification, appending, merging, or other methods to correct bad data.

Standardize data formatting for uniformity and easier data usage.

Clean data as it enters the data source to prevent mixing bad data with cleaned data.

Finds duplicates using the fuzzy logic technology or an advance search feature.

Gives user ability to import a variety of data sources for immediate use

Ability to connect the analytics platform with a wide range of connector options for common data sources, including popular enterprise applications.

Connects to enterprise data governance software, or provides integrated data governance features to avoid misuse of data

Using formulas based on existing data elements, users can create and calculate new field values

Business users have the ability to filter data in a report based on predefined or automodeled parameters.

Allows the user to combine data from multiple sources into a functioning dataset.

Analysis that creates business plans outside of finance, including sales, marketing, or HR.

Reports on departmental KPI metrics measured against targets.

Consolidates planning processes across an organization into a single solution.

Allows data movement from one database to another.

Provides data backup and recovery functionality to protect and restore a database.

Allows users to access and work on data concurrently, supporting several views of the data.

Encrypts and transforms data at the database from a readable state into a ciphertext of unreadable characters.

Allows restricted user acess to modify depending on the access level.

Authenticates and authorizes individuals to access the data they are allowed to see and use.

Helps identify and manage the roles of owners and stewards of data.

Defines, validates, and monitors business rules to safeguard master data readiness.

Control model usage and performance in production

Deploy mission-critical ML applications where and when you need them

Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance.

Provides the ability to integrate data and transform data in order to facilitate decision-making.

Allows the user to connect to variety of applications across the business.

Allows users to write and edit business rules using business language, decision tables, etc.

Confirms whether your rules are executing as expected through testing them.

Provides the ability to create or integrate with predictive models.

Provides the ability to import data from a variety of sources and in multiple data formats.

Consistently maintains, updates, and tests data sources to ensure quality.

Allows users to analyze data easily without the need to code.

Automates back-end technical manual processes.

Identifies entities such as organization, person name, location, etc

Extracts keyphrases to determine patterns and themes within text.

Automatically identifies and organizes text based on topic or subject matter.

Utilizes sentiment analysis to capture user feedback.

Identifies the language in which text was written in.

Provides the ability to identify syntax and parts of speech.

Allow capabilities to be customized (key-phrase, topics, sentiment, named entity) by adding keywords or exceptions.

Allow user to add custom functions to Analysis capabilities

User created models can be used as features/pre-built in other models

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Supports integration with multiple data sources for seamless data input.

Enhances output accuracy and speed through efficient ingestion and processing of training data.

Generates actionable insights by applying learned patterns to key issues.

Continuously improves and adapts to new data using specified algorithms.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Enables non-technical users to build dashboards through intuitive, drag-and-drop interfaces

Enables automated report generation and scheduled delivery to stakeholders

Allows dashboards and analytics to be embedded into external apps with branding flexibility

Supports integration with major data sources like cloud data warehouses, SQL/NoSQL databases, and SaaS applications

Efficiently processes large datasets with minimal lag and ensures high performance under load

Maintains performance and uptime during high traffic from multiple users or teams

Supports semantic data layers, role-based access controls, and metadata governance

Integrates with Jupyter, Python, or R for custom analytics and modeling

Provides native tools for statistical analysis, forecasting, and trend prediction

Uses AI to generate textual summaries, key takeaways, and data stories from dashboards

Allows users to query data and build reports using conversational or plain language

Detects and notifies users about KPI anomalies or significant metric changes in real time

Recommends next questions, analyses, or exploration paths using autonomous AI agents

Learns from historical user interactions to improve and personalize query results over time

Tailors dashboard views and suggestions based on user roles, access levels, and past behavior

Supports AI-driven exploration via prompts or multi-turn conversations for iterative querying

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Analyzes incoming datasets to surface missing values, distributions, outliers, and data quality issues automatically

Enables users to ingest data from diverse sources (databases, APIs, cloud storage, spreadsheets) without custom coding

Automatically alerts users when incoming data’s schema deviates from expected structure over time

Suggests or auto‑selects candidate algorithms and hyperparameters based on dataset characteristics

Allows users to insert custom code (e.g. Python, R, SQL) or custom modules into pipeline stages for flexibility

Automatically proposes or applies derived features to improve model performance