In this data-centric era we live in, it is very easy to get confused by all the terms related to business intelligence and analytics. Today, we are going to talk about the differences in predictive analytics and prescriptive analytics.
Predictive analytics do exactly that – predict what may happen in the future. By combining historical data with rules, algorithms, and sometimes external data, predictive analytics can determine the probable future outcome of an event or the likelihood of a situation occurring. A key phrase is “probable future outcome.” Predictive analytics does not guarantee a future event or outcome will occur.
Generating a credit score is an example of predictive analytics. The credit score predicted is used by the financial institution to determine the likelihood of a customer making future credit payments on time.
Why Predictive Analytics Are Important
Predictive analytics enable organizations to increase their profits and gain a competitive edge. Some of the ways predictive analytics are used include the following:
- Fraud detection – By combining various analytics procedures, organizations can improve pattern awareness and thwart criminal activities.
- Improve marketing campaigns – Utilizing predictive analytics to ascertain consumer purchases, reviews, or responses helps organizations and businesses entice, keep, and expand the most profitable customers.
- Operational improvement – Using predictive models to forecast inventory and control resources enables organizations to function more effectively and efficiently.
The goal of predictive analytics is going beyond the knowledge of what has happened to supply the very best analysis of what may happen in the future.
Prescriptive analytics can be likened to a crystal ball. Using a combination of methods and tools like algorithms, machine learning, business policies, computational procedures and more, and applying these methods against input from several differing data sets including historical and transactional data, real-time data feeds, and big data; prescriptive analytics not only provide information about what will happen but also why it will happen. Furthermore, prescriptive analytics provide recommendations that enable organizations to evaluate various possible outcomes based upon their actions.
Perhaps the most familiar example of prescriptive analytics is the Google self-driving car. During every trip, it makes multiple decisions about what to do based on predictions of future outcomes. For example, when the car approaches an intersection, it needs to determine whether to turn left or right and, based on various future possibilities, it decides. Therefore, the car must anticipate what traffic may be coming, whether pedestrians are crossing, and the effects of the decision before making it.
With the ability to continuously take in new data to re-prescribe, prescriptive analytics automatically improve the accuracy of predictions and prescribes better decision options.
If implemented correctly, prescriptive analytics can have a huge impact on business growth. Many companies are not yet using prescriptive analytics as they are very complex and may become complicated to manage. However, prescriptive analytics are the most valuable type of analysis and will become much more widely used in the next few years.
Putting it Simply
To quickly and easily differentiate between predictive analytics and prescriptive analytics, remember this:
- Predictive analytics answers, “What may happen?”
- Prescriptive analytics answers, “What should we do?”
Do you have an example of a scenario in which you would use either Predictive analytics or Prescriptive analytics or both? Can't wait to hear your thoughts!