As data science and analytics teams continue to feel pressure to deliver more value from analytics, many organizations still struggle with the processes and technology required to deploy models into production and more rapidly make data-driven decisions. When evaluating how to best undertake these activities, organizations should consider an important distinction to determine the best path forward.
Human or Machine?
The pervasive level of analytics required to reach Level 4 or 5 on IIA’s Analytics Maturity Model requires that a significant number of analytics products are integrated into business operations. These products can be used to help humans make a better decision in the context of their workflow. For example, a call center rep might see cross-sell offers prioritized based on the context of an interaction. Or, a supply chain manager might receive an alert that the forecasted price of a key ingredient has significantly changed so that they can take action.
Analytics products can also be integrated into business processes to guide the decision making of a machine. Examples include automatically underwriting a life insurance application or managing the dynamic pricing of airline seats.
The roles, processes, and technology needed to inform a human decision maker are very different than those needed to inform a …