Leveraging a Faster Model Development Environment for Data Analytics

Moving from data to insights is no easy feat for today’s businesses. First, it requires finding the right mix of data sources. Then, the business must cleanse and unify the data so it can actually be used. To do this efficiently, businesses need tools that can handle large data sets and cloud computing. Those tools can quickly create decision strategies that can be seamlessly deployed with applications across various industries and use cases. With this type of analytics, businesses can better understand their customers. For example, what are they thinking? How are they behaving? And what are they going to do next?

 

“Often times, we see businesses that are building great attributes and models, but they end up waiting six to eight months to get them into production,” says Ryan Baltes, Analytics Product Manager, Equifax. “These companies need to leverage a faster model development environment where data is curated and a path to production is streamlined. These types of environments allow you to increase model performance through use case configuration, machine learning techniques, the use of alternative and trended data, and cloud computing.”

Employing an Automation Tool

  When data scientists build a model from scratch, businesses expect their model risk management program reviewer to understand and replicate the custom build. Each time analysts build a new model, the reviewer repeats this process. As a result, this creates a tedious and time-consuming production cycle.

“The day the model is finalized is the day it is most predictive,” says Baltes. “So each day the model is prolonged due to challenges with deployment and governance cycles, it loses value and the performance deteriorates. Employing an automation tool allows you to leverage a standardized framework that expedites the model governance process. In this case, the reviewer already understands the methodology and choices, and must simply review the outputs of the process. It’s a faster approach because the data is already there and the review/governance cycles are shortened.”

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