Prescriptive Analytics is used for performance optimization. This optimization is accomplished by using a variety of statistical and analytical techniques to identify the decisions that need to be taken in order to achieve the desired outcomes. The data sources used for the determination of outcomes can range from structured data (e.g., numbers, price points etc.), semi-structured data (e.g., email, XML etc.) and unstructured data (e.g., images, videos, texts etc.).
If done correctly, Prescriptive Analytics is the Holy Grail of analytics. However, if done incorrectly, it can result in misinformed decisions that can be outright dangerous. Individuals and organizations have to understand that even if the data is correlated that does not mean that there is some sort of causation. A general example of this is when in a news report, the host(s) says that survey has shown that x is correlated with y but then they go on how y was caused due to x. This is simply what I call “jumping the data gun” and organizations that are not aware of this can fall into this trap.
Another thing to be aware of is that after the Prescriptive Analytics gives you certain courses of action and you apply those actions, keep track of how well your Prescriptive Analytics is performing as well. In other words, you have to measure the performance of your performance optimization ways. The reason to do this is because over time you can see if the models presented by your Prescriptive Analytics engine is worth following, re-doing or dumping.
To get you started, here are a few questions to ask:
- Who uses prescriptive analytics within, across and outside your organization?
- What outcomes do prescriptive analytics tells you?
- Where is the data coming from for prescriptive analytics?
- When prescriptive analytics is used?
- Why prescriptive analytics matters?
When you are asking the above questions, keep in mind that Prescriptive Analytics uses data to create a model (aka a data version of the world) that is used by individuals and organizations to make real-world decisions. But if the model itself is flawed then you are bound to get answers that although might look visually appealing are completely wrong. It is not all doom and gloom though. In fact, Prescriptive Analytics is used in determining price points, expediting drug development and even finding the best locations for your physical stores. Companies like Starbucks have been using Prescriptive Analytics in the last few years to determine the best locations for their next coffee stores. Interestingly, some have claimed that wherever Starbucks goes, the real-estate prices also increase. While there is some correlation between a Starbucks coffee store opening with increased real-estate prices but this does not mean that because of Starbucks coffee stores the real-estate prices increase.
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Originally published at arsalankhan.com on February 28, 2015.