The Evolution of Data-Driven Decisions: Prescriptive Analytics

By: Katana Graph

March 21, 2022

The Evolution of Data-Driven Decisions: Prescriptive Analytics

The term “analytics” encompasses an entire realm of thought about processing data. There are several different kinds of analytics contained within the overarching term, but all analytics strive to gain some sort of understanding from data. Modern analytical methods tend to be lumped into four groups: descriptive, diagnostic, predictive, and prescriptive.

Descriptive analytics chronicle events that have taken place, diagnostic analytics determine why those events have taken place, and predictive analytics considers all known information about a given subject to make predictions about future events. Prescriptive analytics suggest how we might mitigate the effects of an event, how to determine what actions ought to be taken, and how actions influence future events.

Descriptive analytics chronicle events that have taken place, diagnostic analytics determine why those events have taken place, and predictive analytics considers all known information about a given subject to make predictions about future events. Prescriptive analytics suggest how we might mitigate the effects of an event, how to determine what actions ought to be taken, and how actions influence future events.

Prescriptive analytics typically include a normative element since it addresses the question of what should be – we should stop at stop signs to produce safer driving conditions, to maintain a reasonable flow of traffic, and to increase the likelihood of individual vehicle integrity. Prescriptive analytics is tightly coupled with predictive analytics, the primary difference being the inclusion of a desired outcome, thereby allowing the generation of specific recommendations. Prescriptive analytics tells us what we should do or what the correct action is. Those types of answers are generally driven by subjective value-based interpretations.

For example, some insurance companies involve the use of an app installed on one's phone to track via GPS one's driving habits to influence the customer's insurance rate. Does the customer travel at the speed limit, stop at stop signs, or use other apps while moving? Personalized rates determined by prescriptive analytics are attractive to both insurance companies, who can make a more accurate risk assessment on a customer, and to the customer, who would prefer a lower rate.

This combination of statistical knowledge with deterministic knowledge influences transportation and logistics. Knowledge of our location from GPS combined with our desired destination and live traffic data allows predictive analytics to compute arrival times based on an array of possible routes. Prescriptive analytics might include the values and preferences of a particular driver or the driver’s company, such as, to some degree, favoring arrival time accuracy over shortest transit time.

“To some degree” in this example, might entail a complex set of nonlinear relationships. For example, variances under an hour might be of no concern at all, but the knowledge that a shipment will arrive on a day when the Receiving Department is open might eclipse a one-day reduction in average delivery time.

Another example of prescriptive analytics recently in the news is the determination of the most attractive venture capital opportunities by potential investors. Decision algorithms incorporate predictions of future performance of a startup and the investment profiles and risk profiles of investors. Experiments with algorithmic decision-making in this realm have highlighted the complementary roles of predictive analytics and the modeling of experience, subjective judgments, and preferences.

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