Operationalizing Analytics

Because data alone doesn't drive your organization. Decisions do.

Get From Data to Impact Faster

Every day you make decisions that affect your business. Because analytically driven decisions are better decisions, incorporating analytics into your decision making processes enables you to make the best choices every time – even when making thousands or millions of them each day. This requires operationalizing analytics at scale – and SAS can help you go the last mile to derive value from your data science efforts.


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.


Less than 50% of the best models get deployed


90% of models take more than three months to deploy


44% of models take over 7 months to be put into production.  


The Challenges

Organizations often struggle with operationalizing analytics because they lack a structured process for coordinating resources across analytics, IT and the business.

  • Manual processes limit scalability.
  • Model performance can't be monitored automatically.
  • Data features must be manually recreated in production.
  • Lack of integrated technology prevents connecting discovery with deployment.
  • Models must be manually recoded in another language for deployment.
  • Models are too complex to perform at scale.
  • Despite degradation, model retraining rarely occurs.
  • Lack of governance and documentation lead to rework.


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

While organizations have been pouring money into analytics initiatives for years, too few are seeing a payoff because they are getting stuck at the last mile, with models not making it into production. To enable data-driven decisions at scale, the analytics life cycle must be highly operational and connected to a decisioning process. SAS can help you break the cycle and cross the last mile to get real value from your analytics investments.

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Accelerate the Analytics Life Cycle With ModelOps

Accelerating and scaling the analytics life cycle requires collaboration between IT and analytics teams, accomplished through a ModelOps approach, similar to how DevOps fosters collaboration in application development.  Taking a ModelOps approach to analytics enables you to smoothly, efficiently and continuously develop and deploy analytic models. SAS can help you build and enable a ModelOps culture so you can cross the last mile and get value from your analytics investments.


Model Development

The insights revealed during the data and discovery phases of the analytics life cycle uncover potential value, but those insights must be put into action to ensure that value is not only realized, but it is also repeatable and scalable.   

How SAS Can Help


SAS enables you to access virtually any data from a trusted source and align it to privacy and security standards. 

Model Building

Build models using SAS or your preferred open source language, keeping a deployment scenario in mind to avoid re-work.    


Model Deployment

Deployment is one of the most underestimated phases of operationalizing analytics. It's where your return on investment of time, data, technology and processes can rise exponentially – if you have the tools and processes to enable and facilitate model deployment. SAS can help you get models into production quickly. Once models are in production, over time they will start to lose effectiveness or give incorrect recommendations. SAS enables you to automatically monitor models continually and retrain them when performance decays. 

How SAS Can Help

Fast & Easy Model Deployment

The SAS Platform helps you deploy models rapidly into any environment, using automation to boost speed and agility. SAS also saves you significant time, enabling you to build models once, then deploy anywhere without additional testing cycles.

Model Governance

SAS expedites model deployment with built-in model governance, no matter what language you use to build your models. Lineage capabilities track changes to models and data to ensure compliance with internal analytical processes.

Model Monitoring & Improvement

The SAS Platform tracks models from inception to usage to retirement. Models are monitored automatically over time to ensure they continue to perform as expected, regardless of the language they were created in.



Deploying models into production isn’t the end. SAS helps you connect the analytics life cycle to your decisioning process so you can go that last mile – making the right analytically driven decisions at the right time, every time, creating tangible business value.

How SAS Can Help

Decision Management

SAS enables you to design, manage and govern decisions with capabilities for decision testing, change management, auditing and validation through rules fired analysis and full version control across all decision flows, business rules and individual decision components.

Business Rule & Analytical Model Execution

Easily bring together all necessary business processes, data sets, rules and analytical models from across multiple teams and systems into a single decision engine.

High Volume Interaction Automation

SAS enables you to automate high-volume interactions, ingesting millions of events per second, while delivering decisions on more than 5,000 real-time interactions per second and achieving response times of under 10 milliseconds per transaction.

Look Who's Operationalizing Analytics With SAS®

Why SAS for operationalizing analytics?

Scale to enterprise level

  • Run and manage SAS and open source models using the same high-performance engine designed to operate robustly at scale.
  • Automate process execution through workflows using portable code you can deploy anywhere.
  • Deploy models faster to increase returns on your analytics investments.
  • Determine best model, regardless of language, via champion/challenger tests. 

Monitor model effectiveness & decay

  • Create routine analysis of model performance and continuously monitor model health.
  • Accelerate model retraining to maintain optimal performance.

Centralize analytic asset governance

  • Control user access to data and models across the entire organization.
  • Track model history (e.g., usage and performance) to ensure correct deployment in business operations.

Integrate analytics into decisioning processes

  • Adapt to constantly changing market conditions and business objectives.
  • Execute as fast as real time to drive the best decisions in the moment.
  • Scale analytically driven decision making using data in motion, at the edge and at rest.

Explore More on Operationalizing Analytics and Beyond


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