Five steps to build better predictive analytics applications
An outline approach to planning and manage predictive analytics applications initiatives to help ensure that they don’t miss the mark on meeting business needs.
Citizen data science is on the rise as modern analytics tools empower an increasingly diverse community of data analysts to implement predictive models. Yet a byproduct of the democratization of predictive analytics is confusion about which algorithms to use for what, and whether a particular type of predictive model will perform better than others.
While the available tools provide a rich palette of analytics methods to work with, the plethora of choices can stun inexperienced users into a near-catatonic state as they ponder where, to begin with, predictive analytics applications. As a result, it’s helpful to devise a set of processes and best practices that can guide analysts to match the right methods, algorithms, and models to the business challenges with which they’re presented.
Here are some guidelines on what that might look like at a high level:
Specify the business opportunity and objectives, describe the desired analytics outcomes, and set measurable goals for success. This will also help to define performance metrics that can be used later to evaluate how well different analytics methods work. Continue reading
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