What are Prescriptive Analytics?

Prescriptive analytics is the fourth and final pillar of modern analytics. Prescriptive analytics pertains to true guided analytics where your analytics is prescribing or guiding you toward a specific action to take. It is effectively the merging of descriptive and predictive analytics to drive decision making.

Existing scenarios or conditions (think your current fleet of freight trains) and the ramifications of a decision or occurrence (parts breakdown on the freight trains) are applied to create a guided decision or action for the user to take (proactively buy more parts for preventative maintenance).

Prescriptive analytics requires strong competencies in descriptive, diagnostic, and predictive analytics which is why it tends to be found in highly specialized industries (oil and gas, clinical healthcare, finance, and insurance to name a few) where use cases are well defined. Prescriptive analytics help to address use cases such as:

• Automatic adjustment of product pricing and inventory based on anticipated customer demand and external factors
• Flagging select employees for additional training based on incident reports in the field

The primary aim of prescriptive analytics is to take the educated guess or assessment out of data analytics and streamline the decision-making process.

How Do You Get Started with Prescriptive Analytics?

Prescriptive analytics is commonly considered the merging of descriptive, diagnostic, and predictive analytics. Getting started isn’t so much a step-by-step list but rather the time and effort up front to build your competencies within the analytics maturity curve.

Simply put, there is no starting point in prescriptive analytics without the requisite first three pillars of modern analytics being established first. If you’re ready for prescriptive analytics, then quantifying your call to action and the underlying criteria will be the first requirement.

For example: if the use case is to call corrective action for an employee (i.e. – additional training based on poor performance) then the factors that necessitate this action must be firmly established and the action itself must be clearly defined.

Moving through a data analytics maturity model shouldn’t be a race. Knowing how each kind of analytics helps you better understand your data and how to use it to move your business objectives forward is key to realizing the return on investment in data and analytics.

Don’t miss crucial steps that can shape your business’ growth path.

We hope you’ve enjoyed this five part series on how to make a lasting impact on your business with analytics. Contact the Ember team today to uncover opportunities like these and more to grow your business.