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be trusted to perform accurately on future unseen data. Segregation. The third component to trust is risk segregation. Whereas accuracy and stability are metrics that data scientists use to verify the validity of a model, segregation can be thought of as the “proof is in the pudding” view of trust in the model. In short, there is an understanding that a model is not perfect. That is, the model is predicting the future, not reading the future. It is understood that there can and will be errors in predictions. The final part of trusting
a model is to accept the errors of that model.
Easy Hazard Identification
Once the model is trusted, organizations must use it to identify the risks. Many times, companies struggle because they have too many sites/areas/departments to oversee and not enough resources to support them—which is why the goal of this pillar is to allow orga- nizations to proactively focus their extremely limited resources to the areas where it is needed most.
For example, a predictive platform can share a snapshot of risk is high level and depicts (1) the current state, (2) the trend towards current state, and (3) top areas for focus. The snapshot is meant to support the high-level decisions that are happening at the executive level. This is not about getting into the details and nitty gritty of managing risk. At this point, it is about try- ing to understand, at a broad level, the organization’s risk and where that risk exists.
Effective Risk Mitigation
Once there is an understanding of where risk is present, and be- fore actions are taken to mitigate risk, it is necessary to first under- stand why risk is present—which is identified through many charts within any predictive platform. Once your team has reviewed and identified where the risk is occurring, it’s important it determine gather the right stakeholders together to determine an action plan to mitigate this risk. After the right team has been assembled, go out in the field and ask questions to understand why the identified risk may be happening—is your team on they on a tight deadline? Do they not have the right tools to be successful? By asking ques- tions to people actually conducting the work, you are proving or disproving any initial assumptions.
From there, discuss and strategize with your team to come up with the best plan to ensure the risk is actually mitigating the risk. If it’s a tight deadline, what resources could your team bring in to sup- port them. If it’s lack of proper tools, how can you get the right tools for them? Once this strategy is defined, communicate the findings to the organization and set clear metrics that track the progress to ensure the plan implemented is having a positive impact.
Nick Bernini is the Director of predictive analytics and lead data scientist at Predictive Solutions Corp. He has spent the last 10 years building predictive models across the Marketing, Education, Retail, Finance, and Governmental sectors.
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