What exactly is prescriptive analytics? | 7wData

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Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn’t that what all analytics should be about? A hearty “yes” to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics.

Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term “prescriptive” as the goal of all these analytics — to make more effective decisions — rather than a specific analytical technique. Forrester formally defines prescriptive analytics as:

"Any combination of analytics, math, experiments, simulation, and/or Artificial Intelligence used to improve the effectiveness of decisions made by humans or by decision Logic embedded in applications."

Prescriptive Analytics Inform And Evolve Decision Logic Whether To Act (not not act) And What Action To Take

Prescriptive analytics can be used in two ways:

■ Inform decision logic with analytics. Decision logic needs data as an input to make the decision. The veracity and timeliness of data will insure that the decision logic will operate as expected. It doesn’t matter if the decision logic is that of a person or embedded in an application — in both cases, prescriptive analytics provides the input to the process. Prescriptive analytics can be as simple as aggregate analytics about how much a customer spent on products last month or as sophisticated as a predictive model that predicts the next best offer to a customer. The decision logic may even include an optimization model to determine how much, if any, discount to offer to the customer.

■ Evolve decision logic. Decision logic must evolve to improve or maintain its effectiveness. In some cases, decision logic itself may be flawed or degrade over time. Measuring and analyzing the effectiveness or ineffectiveness of enterprises decisions allows developers to refine or redo decision logic to make it even better. It can be as simple as marketing managers reviewing email conversion rates and adjusting the decision logic to target an additional audience. Alternatively, it can be as sophisticated as embedding a machine learning model in the decision logic for an email marketing campaign to automatically adjust what content is sent to target audiences.

Because "prescriptive analytics" is a focused moniker for data and analytics that are specifically designed and used to improve the effectiveness of decision logic there are many technologies that enterprises can use to improve decisions:

■ Descriptive analytics. Descriptive analytics enables analytics users within an enterprise to query data integrated from multiple applications to create reports, dashboards, or aggregated data that firms access through applications either directly or through APIs. Descriptive analytics can inform decision logic either with aggregate variables specific to customers or with historical data that has been integrated from multiple application systems. For example, a simple aggregate analytic of the current sales for the day can plug into pricing logic in other applications to raise or lower the price.

■ Predictive analytics. Predictive analytics is about creating predictive models — models that can predict an outcome with a significant probability of accuracy.Data scientists can use a combination of techniques including statistical algorithms, machine learning, and forecasting to create predictive models. These models can either provide a variable that feeds into the decision logic or make a probabilistic decision themselves. For example, an application could use extensive decision logic to approve a loan based on many factors, including a credit risk variable — which a predictive model can derive.