How to Go Live with the Predictive Analysis Model

After developing your predictive analysis model and successfully testing it, you’re ready to deploy it into the production environment. The ultimate goal of a predictive analytics project is to put the model you build into the production process so it becomes an integral part of business decision-making.

How to deploy the model

The model can be deployed as a standalone tool or as part of an application. Either way, deploying the model can bring its own challenges.

  • Because the goal is to make use of the model’s predictive results and act upon them, you have to come up with an efficient way to feed data to the model and retrieve results from the model after it analyzes that data.

  • Not all predictive decisions are automated; sometimes human intervention is needed. If a model flags a claim as fraudulent or high-risk, a claim processor may examine the claim more thoroughly and find it to be sound, saving the company from the loss of opportunity.

    The higher the stakes of a predictive decision, the more necessary it is to incorporate human oversight and approval into those decisions.

The model accrues real value only when it’s incorporated into business processes — and when its predictions are turned into actionable decisions that drive business growth higher. This becomes especially useful when the deployed model provides recommendations, some of them in real time, during customers’ interactions — or assesses risk during transactions, or evaluates business applications. This business contribution is especially powerful when repeated across several transactions, seamlessly.

How to monitor and maintain the model

The longer the model is deployed, the more likely it will lose its predictive relevance as the market changes. Business conditions are constantly changing, new data keeps coming in, and new trends are evolving. To keep your model relevant, monitor its performance and refresh it as necessary:

  • Run the deployed model on the newly acquired data.

  • Use new algorithms to refine the model’s output.

A model tends to degrade over time. A successful model must be revisited, revaluated in light of new data and changing conditions, and probably new retrained to account for the changes. Refreshing the model should be an ongoing part of the overall planning process.

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