The Analytics Life Cycle

DataOps • Artificial Intelligence • ModelOps

The Analytics Life Cycle Graphic

Organizations have been pouring money into analytics initiatives for years, but too few are seeing a payoff because models aren't making it into production. To enable data-driven decisions at scale, the analytics life cycle must be highly operational and seamless. By connecting all aspects of the analytics life cycle – DataOps, artificial intelligence and ModelOps – SAS helps you turn your critical questions into trusted decisions and gain real value from your analytics investments.

The Analytics Life Cycle


Borrowing from agile software development practices, DataOps provides an agile approach to data access, quality, preparation and governance. It enables greater reliability, adaptability, speed and collaboration in your efforts to operationalize data and analytic workflows.​


Access data, regardless of size or complexity.


Transform raw data, including AI-powered suggestions.


View important relationships in data and share insights.


Build trust in data, understand lineage and gain transparency.

The Analytics Life Cycle

Artificial Intelligence

Data scientists use a combination of techniques to understand the data and build predictive models. They use statistics, machine learning, deep learning, natural language processing, computer vision, forecasting, optimization and other techniques to answer real-world questions.


Build models with multiple AI techniques to solve real-world problems​.


Automate manual tasks for feature engineering and model tuning​.


Enable users with different skill sets to collaborate on solving analytic problems.​


Work smarter using SAS Viya and open source analytics.

The Analytics Life Cycle


ModelOps focuses on getting AI models through validation, testing and deployment phases as quickly as possible while ensuring quality results. It also focuses on ongoing model monitoring, retraining and governance to ensure peak performance and transparent decisions.​


Ensure models will perform as expected in the real world​.


Embed models into operational systems and monitor them​.


Ensure decisions are safe and transparent over the life of the model.


Integrate business rules to ensure up-to-real-time results.

SAS® Analytics Life Cycle


SAS can help you accelerate the analytics life cycle, orchestrating the entire process – from data, to discovery, to deployment.

SAS® Analytics Life Cycle


Streamline data access and preparation – and enhance data quality – while ensuring the proper controls are in place. SAS dramatically increases the productivity and speed of your analytics resources.

Access data of any complexity, size or speed with a robust suite of data management tools.

Streamline data preparation with native access engines, integrated data quality and data preparation tools that use AI to automate time-consuming tasks.

Take advantage of traditional, structured data, as well as new formats, such as streaming sensor data.

SAS® Analytics Life Cycle


Discovery is about exploration, visualization and model building. Get an incredible breadth and depth of analytic techniques at your fingertips, accessible from SAS or open source languages such as Python, R or Lua. Analysts can use any language they want while benefiting from the most advanced computational power.

Tackle problems of any size or complexity with an extensive suite of proven analytic techniques.

Take advantage of programming-language flexibility, which gives you broader access to algorithms and a larger talent pool to hire from.

Make powerful analytics accessible to everyone across your organization, regardless of skill or experience.

SAS® Analytics Life Cycle


Deploying analytics into production is where your efforts to get and analyze data pay off, with an ROI for your time, data, technology and processes. It's also where organizations struggle the most. Deploy SAS and open source models into any environment fast.

Select the best model and govern its use – whether you're building one or thousands – with robust model governance. Evaluate and select the champion model from all competing models. Then register it in a centralized repository, with version control, for visibility and simplified compliance processes.

Get the best models into production quickly with faster, easier model deployment – no recoding needed. Deploy models in a few clicks to a range of targets – e.g., in database, streaming, a container or a REST API. Or deploy them into a decisioning/workflow engine.

Build models once and deploy them anywhere – without recoding and without an additional testing cycle. Our unified code base saves you immense time and effort.

SAS® Analytics Life Cycle


SAS goes beyond providing data, discovery and deployment capabilities by helping you orchestrate your entire analytics ecosystem. By connecting and accelerating analytics life cycle activities, you can go from data to tangible results faster.


Gain an upstream and downstream understanding of how data is used and transformed – from sources to transformations, models and reports – with a visual overview. And identify related data that has potential value.


Automatically monitor model performance over time to ensure models continue to perform as expected, regardless of the language used to create them. Track models from inception to usage to retirement. Get notified automatically when performance degrades so you can improve, replace or remove the model from production.


Improve efficiency and effectiveness throughout the analytics life cycle. Speed data preparation with intelligent tagging that uses AI to identify data type within a column. Generate models fast using templates and automated analysis. Use automated workflows to streamline operationalizing and updating models.

Connect with SAS and see what we can do for you.