Operationalizing Analytics

Drive unlimited value from analytics using ModelOps.

Get From Data to Decisions Faster

Every day you make decisions that affect your business. Analytically driven decisions help you make the best choice every time. With SAS, you can operationalize analytics to drive innovation and value from your data science ambitions.

Did you know?

Many organizations build powerful analytic models, but most don't see the light of day as organizations struggle to cross the last mile to operationalize them.


Fewer than 53% of the best models get deployed.


90% of models take more than three months to deploy.


44% of models take over seven months to be put into production.

Operationalizing Analytics

The Challenges

  • Putting models into production involves multiple manual steps and processes.
  • Without a structured process for coordinating resources across analytics, IT and the business, it's impossible to deliver relevant, interactive, automated decisions at scale.
  • The lack of proper monitoring and governance of AI assets reduces transparency and trust.

The Benefits

  • Seamlessly move to production by deploying SAS or open source models in batch, streaming, cloud or edge devices.
  • Execute on the best decision every time with explainable outcomes and complete visibility of the analytics life cycle.
  • Ensure transparency with centralized governance and monitoring of all analytics assets – including open source.

Why operationalize analytics?

Gain faster, greater business value by conquering analytics' last mile.

The Analytics Life Cycle

DataOps • Artificial Intelligence • ModelOps

Operationalize Analytics at Speed With ModelOps

ModelOps is a holistic approach for rapidly and iteratively moving models through the analytics life cycle for faster deployment to deliver expected business value. A ModelOps approach gets analytics out of the lab and into use, enabling you to conquer analytics' last mile.


ModelOps focuses on getting AI models through validation, testing and deployment 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​.


Make sure decisions are safe and transparent over the life of the model.


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

Look Who's Working Smarter With SAS

  • Programming in multiple interfaces

    An automotive dealer fosters collaboration on model development by enabling people to program in multiple interfaces in the language of their choice.

  • Deploying and monitoring 1,900 models

    A large manufacturer of paper goods increases efficiency by using SAS to deploy and monitor 1,900 models multiple times a second.

  • Optimizing the use of medical resources

    A medical facility monitors and adjusts models in real time to optimize the use of medical resources like ventilators and hospital beds.

  • Creating superior real-time customer experiences

    A telecommunications company creates efficient, superior, real-time customer experiences via the customer's preferred channel.

    Explore More on Operationalizing Analytics and Beyond


    Getting Started With ModelOps

    Discover how ModelOps can help you cross the infamous last mile of analytics by redefining how you deploy models.


    Brilliant decision?

    Learn how you can automate decision making to improve customer experience and drive profitability in a rapidly changing world.


    Drive Analytic Innovation Through SAS and Open Source Integration

    Whether you are a SAS user interested in dabbling in open source or an open source user who wants to use SAS, this e-book will help you get started.

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