About the webinar

Analytically driven decisioning is driven by an efficiently performing analytics life cycle that feeds into decision making processes.

Organizations need a well performing ModelOps practice that ensures models are quickly deployed for use to drive business value.

Data scientists, accompanied by business experts, use analytics to detect potentially valuable insights.

They identify relevant data, prepare it for analytics, explore it and build models to generate accurate predictions automatically.

This is a very creative process where data scientists want a maximum degree of freedom to experiment with data, methods, and parameters.

In practical applications for a given business question at hand, this might lead to hundreds of models.

As soon as relevant data changes, models and rules are applied, and the decision is executed.

The quality of decisions needs to be monitored so that a re-adjustment of models and rules can be considered if decision quality decreases.

All this is done in an operational environment where other stakeholders than in the discovery phase are in charge: IT and business process owners.

They need transparency about what decision at a given time was based on what data, what version of the model, and what rule.

All this calls for automation, transparency, and close integration

At SAS we would enable the data scientist to multiple end to end model pipelines and the complete process of operationalizing model.