Technology has an ability to forecast the future of enterprise and predict the chances of failure or success of any business organisation. Earlier, people have to rely on small samples of data, simple questions and other ways to collect useful information. But now, everything is possible. All the data collected by any organisation has to go somewhere. Lots of them make their ways to data lakes or some other type of data storage.
In today?s world, data collected by the organisation wouldn?t be used for quick gains but can be used in the future that the organisation is looking for. That is to make the best possible decisions. All those data are being utilised by the business organisation to describe current trends, predict whats is going around or what is going to happen next and most important, to prescribe the proper action that the organisation should take to ensure success in the best possible manner. This is only possible through prescriptive analytics. Prescriptive analytics is the last stage of data analytics and it requires lots of capital and human capabilities to handle and process the analysis. If there is any kind of uncertainty in any business organisation future, they can use prescriptive analytics to eliminate that problem.
Prescriptive analytics is the third and final stage of data analytics. Prescriptive analytics draw predictions about the future and refer a complete description of the present scenario in order to determine the best possible action to eliminate the cause of the problem. The centre of prescriptive analytics relies on the idea of optimisation. The process of optimisation considers every small factor that is required to draw a better prescription model. Prescriptive analytics finds a large application in supply chain management, Operation management and Human resources management.
Prescriptive analytics not only predict possible outcomes but also state the reason for their occurrence and time of occurrence. It also suggests the decisions options for the possible forecast. The term prescriptive analytics was first termed by IBM in their analytics magazines. The entire article was divided into three types of business analytics and how IBM conceives of prescriptive analytics.
- Optimisation, or how to achieve the best output.
- Stochastic optimisation, or how to achieve the best output and make better decisions by considering the uncertainty of real-time data.
Getting started with prescriptive analytics:
1. Identify the decision-making areas that need improvement
If your business organisation is planning to go for prescriptive analytics then you need to identify the weakness or make a suitable plan that needs improvement. Thus, after jotting these points, make an arrangement with your manager and subject matter experts across the organisation- Sales, Marketing, HR, Operation, merchandise, suppliers, IT and finance- to identify the important areas. Here are the lists of important decisions that can be improved through the use of prescriptive analytics.
Decisions not perfectly executed by the senior employee.
Decisions that are too expensive to be carried out by any employee.
Decisions influenced by multiple factors and personnel
Decisions having multiple objectives.
2. Decisions that can directly affect the relations with other business partners.
Getting started with a combination of outsourced and advanced applications:
The business organisation must think about identifying the best way to use and adapt prescriptive analytics. Some business organisation use prescriptive analytics to build their operation, while others use this application to build their relationships. Prescriptive analytics is often easy and less expensive when you use it with some packaged applications or outsource the capabilities. Although it will take some time to launch prescriptive analytics solutions in the market yet, you can opt for advanced predictive analytics tools and you can combine them with prescriptive analytics to get product or service for prescriptive analytics.
3. Transitioning through the phrases of business analytics
Business organisations have adopted prescriptive analytics with significant experience with predictive analytics. While it sounds little disturbing but it is possible to jump to prescriptive analytics from descriptive analytics. Descriptive and predictive analytics contains all the essential tools used in prescriptive analytics. They are part of a calibrated decision of making prescription and making data that are refined for the use of prescriptive modelling.
Prescriptive analytics provide huge capabilities but it is not much amazing. The same problem can be caused by descriptive and predictive analytics that is data limitations and external events which can affect prescriptive analytics as well. Many parameters such as customer behaviour, purchase contexts and customers demand changes constantly. Therefore prescription has to stay updated all the time. Moreover, data privacy is the biggest concern of every individual.
However, a large number of business organisation are using computing powers such as big data technology, Artificial Intelligence and Business Intelligence. These technologies are helping prescriptive analytics to solve instant decision-making problems. These technologies are paving the way for prescriptive analytics. As an organisation achieves greater analytics and data maturity, they want to explore more with the information they have in their hand. Hence, prescriptive analytics help them to unlock the hidden potential.