Operationalizing Analytics

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 decisionmaking
and in operational, production systems. Models are used to improve customer treatment by selecting the next best action to develop a customer, to make loan or credit pricing decisions that reflect the future risk of a transaction, to predict the likelihood of equipment failure to drive proactive maintenance decisions, or to 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.

The first step in most analytic projects is developing a model plan. A key challenge in
developing an effective model plan is ensuring understanding of the business problem
to be solved. How will I measure success? What data is applicable? Who will use this
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. Working closely with the IT team also matters and improves
data access while easing the path to deployment. However, there is often a gap in
understanding, tools and collaboration environment between the business, analytic
and IT teams.

Deploying models into operational systems is central to generating significant business value. But organizations regularly find that 50-60% of models aren’t deployed and that deploying the remainder takes 30, 90, even 120 days or more. Businesses cannot bear the opportunity cost of not deploying and using these analytic insights and organizations cannot afford this level of inefficiency in their use of constrained analytic resources.

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