Database Reference
In-Depth Information
Prescriptive analytics
Prescriptive analytics is a little beyond deriving data insights and can go to an extent
of suggesting options on probable decisions to be taken. It takes advantage of the
predictive analytics and applies, for example, machine learning techniques to sug-
gest the best suited action and presents the quantifiable business implications of
each possible decision and action.
While predictive analytics stops at anticipating what will happen and when it will hap-
pen, prescriptive analytics goes a little beyond and additionally anticipates why it will
happen.
For every new occurrence of an event in business, prescriptive analytics takes ad-
vantage of the new data and uses it to improve the accuracy or confidence of the
prediction and thus provide optimal decision alternatives.
Prescriptive analytics, like any other analytic approaches, operates on data that can
be structured or unstructured in nature. It includes application of business rules and
implied mathematical models that could include machine learning and natural lan-
guage processing techniques. Here are a few important examples where prescript-
ive analytics is used for deriving business edge:
• Fluctuating gas prices can impact manufacturing costs of manufacturing
companies. Using prescriptive analytics, statistical modeling, and mathemat-
ical trending, future gas prices can be predicted and decisions on the course
of action to tap the best gas price can be taken, thus helping lower overall
manufacturing costs.
• Prescriptive analytics can be used in healthcare, helping hospitals to stra-
tegically plan the growth by analyzing economic data, population demo-
graphic trends, and population health trends.
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