Prescriptive analytics is a step ahead of descriptive and predictive analytics. While descriptive analytics provide insight into what has happened and predictive analytics forecast what might happen, prescriptive offers the best solution to maximize profitable growth. It enables decision-makers to not only look into the future to see the opportunities and threats but also come up with the best course of action to take advantage of the situation in a timely manner.
The prescriptive approach involves an analysis of potential decisions, the reason or reasons for the decisions, and the bearing these will have on an outcome to ultimately prescribe an optimal course of action in real time. Prescriptive analytics too cannot be termed as foolproof due to the limitations of data and impact of unforeseen events/external forces.
The success of prescriptive analytics depends on these five factors:
- Utilization of Hybrid Data
Businesses today run on structured data–numbers and categories, while unstructured data– text, image, video, and audio–account for 80 percent of data produced. Unstructured data has its importance in the decision-making process. The need for business is to take full advantage of hybrid data, a combination of unstructured and structured data. Hybrid data empowers businesses to make the best possible decisions based on all the available data.
A prescriptive analytics technology must be able to process hybrid data. A decision without incorporating hybrid data means decisions is based on just 20 percent of the available data.
- Integrated Predictions & Prescriptions
Prescriptive analytics is shaping the future based on observation. The functions – predictions and prescriptions – must work synergistically for prescriptive analytics to deliver the desired result.
- Prescriptions & Side Effects
Prescriptions, i.e., actions to improve the future are generated using several methods. The prevalent method of prescriptions is based on a guided framework of business rules. Operations Research (O.R.), the science of data-driven decision-making is a more scientific way to produce prescriptions for the betterment of the future. O.R. takes into account objectives, limitations, and other factors to produce the best course of action – a prescription – that has minimum undesirable side effects. The two branches of the O.R. – optimization and simulation technologies–can be used to generate effective prescriptions. A proper blend of both business rules and O.R. can only ensure the most effective and timely prescriptions.
- Adaptive Algorithms
As the business process is dynamic and evolves over time, the technology should be able to re-predict and re-prescribe in order to ensure that prescriptions are up to date.
The prescriptive analytics technology must be able to automatically recalibrate all its built-in algorithms, plus automatically create new algorithms. This recalibration also needs to be adaptive and continual in order to successfully assist the business process. Another important factor is the “action-ability” of the prescriptions, which differs from organization to organization. Sometimes, automatic generation of a prescription may not be beneficial if the old ones haven’t been executed properly.
- Feedback Mechanism
Today, apart from Google Car-Prescriptive automation, for example, companies with highly sophisticated prescriptive analytics software are dependent on humans to act on the prescriptions generated by the software. If a business manager ignores a prescription from the software, this inaction would at some point reflect in the incoming data that is being collected. The consequence of inaction will influence the future predictions and prescriptions. For example, inaction on a valid prescription could cause financial losses in avoiding an issue that could have been averted as a result of earlier notification via prediction.
While this may change in the near future, today a prescriptive analytics software requires human assistance to carry out these prescriptions.