As a decision support tool, analytics should clarify the right, best, or optimal actions to take in various circumstances
Analytics is an overarching concept: In the lexicon of normative adjectives applied to the noun "analytics", prescriptive" is a bit closer to the imperative end of the normative spectrum in terms of meaning. Does that mean prescriptive analytics is akin to actionable?
BI/Analytics Evolutionary Journey: In last thirty years, BI/Analytics is well on its evolutionary journey: Descriptive analytics - vast majority of traditional BI - provides insight into what has happened. Predictive analytics helps model and forecast what might happen in the future. Prescriptive analytics goes beyond Predictive analytics and seeks to determine the optimal solution among various choices, given the known constraints and parameters. The descriptive/prescriptive/predictive categories are another attempt to try and differentiate advanced/strong analytics from general BI - and that is certainly worthwhile.
Prescriptive is about finding the optimal solutions under a set of conditions or varies circumstances. Prescriptive analytics go far beyond descriptive analytics (what has happened) and predictive analytics (what might happen) to what action you should take based on all the available facts past, present and future. Think about it as finding a route on a
GPS device with
traffic information. The system knows where you are and where you want to go.
It knows the likelihood you will run into traffic and how long it will delay
you. And then depending on what your preference is (most use of highways,
shortest time, and least use of highways) it recommends a route. That is
prescriptive...finding the optimal answer under a set of conditions.
Prescriptive analytics is akin to "actionable".
analytics should enable action: Without the capability for action, it's a
worthless activity. In that sense, all analytics should aim to be prescriptive.
Further, predictive analytics needs to convey some sort of qualitative value in
conjunction with the results. There remains a fine line, then, between
'predictive' & 'prescriptive' analytics. Select the optimal qualitative
value from predictive analytics, and you have the 'prescriptive' equivalent.
As a decision support tool, analytics should clarify the right, best, or optimal actions to take in various circumstances, as borne out by objective descriptions about what has happened in the past and predictive models about what is likely under various future scenarios. In terms of guidance, you can distinguish between "prescriptive" (here's what you should do) vs. "proscriptive" or "interdictive" (here's what you should avoid doing).
Prescriptive Analytics may include performance management/score-carding: Some say prescriptive analytics is a re-branding of operations research, in terms of focusing on constraint-based optimization. But prescriptive analytics has also been stretched to include performance management, scorecarding, and other guided analytics in the BI context. In the final phase of analytics, the predictive models are applied and business decisions are made. Then company performance is tracked so that actual outcomes can be compared to predicted outcomes to improve the predictive quality of the model.
Bottom line: All analytics should enable action. Whether you need descriptive, predictive or prescriptive analytics is a question of picking the right tool for the job well done.