Prescriptive Analytics Definition

Prescriptive analytics is a statistical method used to generate recommendations and make decisions based on the computational findings of algorithmic models.
For learning analytics, this could range from simple automated recommendations made to employees who are taking online training, to recommendations that indicate how instructors or course designers can improve the design of a course or program.
At present, prescriptive analytics is not widely used in the field of learning & development due to the complex requirements needed in the field of machine learning. It can be found in adaptive learning and also within some learning experience platforms (LXP).

Benefits

Mitigate risks

Gain insight into how decisions can have business-wide impacts and hedge against data uncertainty.

Improve operations

Optimize product planning, reduce inefficiencies, drive smarter operational decision-making.

Manage resources more efficiently

Better utilize: capital, personnel, equipment, vehicles and facilities.

How does prescriptive analytics work?

Generating automated decisions or recommendations requires specific and unique algorithmic models and clear direction from those utilizing the analytical technique. A recommendation cannot be generated without knowing what to look for or what problem is desired to be solved. In this way, prescriptive analytics begins with a problem.

Example:The accuracy of a generated decision or recommendation, however, is only as good as the quality of data and the algorithmic models developed. What may work for one company’s training needs may not make sense when put into practice in another company’s training department.