The previous two posts in this series discussed solving the right problem and creating an industrial-scale process as the first two steps toward operationalizing analytics. This installment will cover the third step in the process: implementing a reliable deployment architecture.
Today, the results of the predictive model development process are often captured in a document describing the model, in a report or dashboard, or in code generated by the modeling system. For strategic decisions, a report is often sufficient as the person who needs the insight can review the analysis and make their decision. But to harness the full value of predictive analytics, analytic insights need to be deployed in operational systems.
Taking predictive analytics to the next step
With an industrial-scale model development process addressing the right business problems, Decision Management takes predictive analytics to the next step by enabling a reliable architecture for deploying predictive analytic models into operational systems. Making faster and better decisions requires the integration of decision logic and analytics in operational systems.
Decision management ensures that when a model is built and validated all the teams involved, the business, the analytics and the IT teams, know where and how the model will be deployed. The teams can now focus on the point at which operational decisions are being made and on the systems used to make those decisions.
Using an in-database infrastructure
One way to make predictive analytic models available in operations is to use in-database scoring infrastructure. These take analytic models and push them directly into the core of your operational data stores. Once deployed, the models are available as a function or as SQL and can be included in views or stored procedures. This allows operational systems direct access to the result of the model while ensuring that this is calculated live, when requested, and not based on a potentially out of date batch run. The business rules for a decision can then access the predictive analytic result like any other piece of data.
Direct analytic deployment
Predictive analytic models can also be deployed directly into operational environments using decision services. Decision Services are the implementation of a decision — how other systems will find out what the decision is. A Decision Service also makes the decision reusable and widely available. Decision Services are essentially business services in a Service Oriented Architecture (SOA) that deliver an answer to a specific question. These services generally do not update information — they just answer questions. Because they don’t make any permanent changes, they can be used to answer questions whenever they come up without worrying about potential side effects.
Staying predictive and effective
Reliable deployment architecture is only as effective as its ability to stay in synch with the changing business environment. In addition to model monitoring and tuning, the application of performance management techniques and technologies to the monitoring of decisions is critical. Tracking and reporting on this information will help the business owners understand and thus manage their decisions more effectively.
Regardless of how the predictive analytics are deployed into the Decision Service, they should be used in real-time. This ensures that the most up-to-date data is always used. Recognizing that modern operational systems are real-time is critical for analytic teams.
Predictive Analytics can deliver insight, predictions, and resolve business uncertainty into profitable probabilities. Decision Management combined with a robust and modern technology platform delivers:
- A collaborative environment and shared framework for problem definition to ensure the analytics is solving the right problem.
- A repeatable, industrial-scale process for developing the dozens or hundreds of predictive analytic models that will be needed.
- A reliable architecture for deploying predictive analytic models into production systems.
Decision Management focuses on decision effectiveness and more effective decisions reduce fraud, improve customer interactions and spot opportunity.
How is your business operationalizing analytics? For more detail on the necessary previous steps to consider, download the complete white paper, Operationalizing Analytics.