Organizations are increasingly adopting predictive analytics, and adopting these predictive analytics more broadly. 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.
The most powerful examples of analytic success use Decision Management to deploy analytic insight into in day to day operations helping organizations make more profitable operational decisions. 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.
Step one: understanding and solving the right problem
A key challenge in developing an effective model plan is ensuring understanding of the business problem to be solved. Some questions to think about while developing this plan:
- How will I measure success?
- What data is applicable?
- Who will use the model and what decisions will it influence?
- Where will I need to deploy this model?
An effective analytics team works in close collaboration with their business partners to answer these key questions and stays in synch through the model development and validation process.
Today, however, most organizations have ad-hoc processes for business problem definition in their analytic projects. It can take months for predictive models to be deployed and some models never get deployed at all. Other models get deployed but are not monitored and this can result in models that degrade to the point where they do more harm than good.
Understanding the problem with Decision Management
The first step in operationalizing analytics is developing an understanding of the problem, an understanding that will allow the problem to be solved. Decision Management is a proven approach that creates a shared framework and collaboration environment for the business, IT and the analytics teams. Within this framework they can come to an understanding of the problem by identifying and prioritizing the operational decisions that drive the organization’s success.
Decision Management links operational decisions to the business drivers and performance measures that have the most impact on the business. Modeling the operational decisions to be analytically improved clarifies and focuses the problem statements that drive analytic projects. This focus ensures projects show a strong and lasting ROI and effectively apply constrained analytic resources.
Collaboration between the analytics and business teams
Because it is a business oriented approach, Decision Management creates productive interaction between the analytics and business team. Linkage to the business process and existing IT systems clarifies how and where the resulting analytics will need to be deployed, helping guarantee a successful deployment once analytic models are developed.
Looking to operationalize analytics at your business? Don’t miss the next installment of this series, Improving analytics through an industrial scale process, or download the complete white paper, Operationalizing Analytics.