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PRESCRIPTIVE ANALYTICS- UNLOCK THE TRUE POTENTIAL OF YOUR ORGANIZATIONAL IT

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It is now widely accepted that the speed of doing business has increased by many folds, so much so that, it is becoming really tough for the managers to keep up the pace. A huge amount of business data is generated every moment, which can be often misleading if not managed properly. There can be a problem of duplication of efforts, arising out of incoherent data, which needs to be sorted out. In order to gain some sort of competitive advantage and established authentic decision-making machinery, it is important to have the ability of prescriptive analytics tools. To have advance risk mitigation capabilities organizations are investing considerably to boost their analytical capabilities.

Need for Prescriptive Analytics

There are different types of analytics, which are used extensively by smart organizations, for different stages of problem-solving. For example, descriptive analytics deals with the question, “what has happened?”, diagnostic analytics says, “why it happened?”, predictive deals with, “what will be the outcome of the event?” and finally prescriptive analytics deals with the best possible solutions to prevent the occurrence of the problem. Therefore, we see that analytics are basically a subset of BI (Business Intelligence), which the organizations have at their disposal to gain a competitive edge over their peers.

Let us have a look at some other need for prescriptive analytics, like-

  • Competitive differentiator which provides individual customer experience.
  • Cost effective organizational processes.
  • Highly pervasive nature, which takes into account the Big Data.
  • Cope easily with changing customer demographics.

Prescriptive analytics is described by its own value chain, which has the following stages-

  1. Curate Data
  • Collect
  • Store
  • Process
  • Clean
  1. Summarize
  • Provides informative tables and charts for a better depiction of the situation.
  1. Describe
  • Identifies patterns in the data, which helps in further analysis.
  1. Predict
  • Captures the relationships amongst data, which helps in the better predictability of the future scenarios.
  1. Prescribe solutions

Key components of the prescriptive analysis

The prescriptive analysis is the final phase of business analytics, which encompasses descriptive and predictive analysis. This analytical study has the following branch of science as components-

  • Machine learning
  • Operations research
  • Computer vision
  • Natural language processing
  • Applied statistics
  • Signal processing
  • Image processing and enhancer
  • Mathematics

Also Read about: Carving Insights From Data: State of Analytics in 2018

Prescriptive analytics has the ability to take in a large amount of new data to re-predict and prescribe the best possible solution to a given situation. It takes in hybrid data, a combination of structured or unstructured numbers, to predict the future business event. In other words, the prescriptive analysis predicts the consequences of the actions and also assesses the value of these consequences. Prescriptive analysis also uses methods of simulation and optimization to arrive at the best possible solution. It is also used for problem structuring, which is used for justification of the selected solution. Since computer hardware is becoming increasingly smaller, IT investments on hardware and its support are going down. Organizations are increasingly using a system based on increasing demands for powerful decision making.

It is a fact that the user expectation is exploding day by day and to keep up the pace, it’s extremely important to have a robust decision-making system, which can only be obtained from prescriptive analysis tools. Prescriptive analytics finds its usage right from sports to heavy engineering. It is important to have a reliable tradeoffs system, which can point out correctly the proper business choices. In other words, the system should be able to conduct a sensitivity analysis, which is like a backbone for any organization. It is important for the organization to have the capability of taking the future opportunity or mitigate risk. Prescriptive analysis can automatically and continuously process a huge amount of business information, to predict the incoming threat. Not only a prediction, but it also analyzes the potential decisions along with its interactions amongst each other. A large part of the analysis is based on game theory and mathematics simulation, which are a boon for today’s organization. According to research prescriptive analysis market will reach over 1.6 billion USD by 2021. Today it is no longer enough to just predict the future, but managers should also be able to recommend the best possible course of action.

 Author Bio:

Ashwin Patil is a passionate content marketer who writes on technology, tech trends, tech reviews. Also, I work with people, organizations and the community to deliver solutions which are driven by Big Data, Internet of Things, Machine Learning, Deep Learning & Artificial Intelligence.

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I am an international Digital Marketing SEO and content expert, helping brands and publishers grow through search engines. I am Outbrain's former SEO and Content Director and previously worked in the gaming, B2C and B2B industries for more than a decade.
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