Operationalizing Analytics

The most powerful examples of analytic success use Decision Management to deploy analytic insight into day to day operations making more profitable operational decisions.

Organizations are increasingly adopting predictive analytics, and adopting these predictive analytics more broadly. Many are now using dozens or even thousands of predictive analytic models. These models are increasingly used in real-time decision-making and in operational, production systems. Predictive models are used to:

  • Improve customer treatment by selecting the next best action to develop a customer
  • Make loan or credit pricing decisions that reflect the future risk of a transaction
  • Predict the likelihood of equipment failure to drive proactive maintenance decisions
  • Detect potentially fraudulent transactions so they can be routed out of the system before they hit the bottom line.

Examples like these deliver high ROI from analytics.

However, many analytic teams rely on approaches and tools that will not scale to this level of adoption. These teams need a repeatable, effective, efficient process for creating and deploying predictive analytic models into production. They must operationalize analytics.

Operationalizing analytics has three elements:

  • A collaborative environment and shared framework for problem definition to ensure that the analytics created are solving the right problem.
  • A repeatable, industrial-scale process for developing the dozens or even thousands of predictive analytic models needed.
  • A reliable architecture for deploying and managing predictive analytic models in production systems.

The solution to operationalizing analytics involves the effective combination of a Decision Management approach with a robust, modern analytic technology platform. Such a combination focuses analytics on the right problems and effectively integrates analytical results directly into operational systems for faster and more profitable decisions.

This paper discusses both how to use a focus on decisions to ensure the right problem gets solved and what such an analytic technology platform looks like.

Sponsored by SAS.

Operationalizing Analytics