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What is Prescriptive Analytics in Data Science?

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  • Difficulty Level : Medium
  • Last Updated : 03 Aug, 2021
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It can be determined that the purpose of any type of analytical service in the business field is to accumulate a huge stack of internally sourced data from public and other third-party resources into responsive feed to improve community operations. Prescriptive Analytics is the area of Business Analytics dedicated to searching out the best solution for day-to-day occurring problems. It is directly related to the other two comparable processes, i.e. Descriptive and Predictive Analytics. Prescriptive Analytics can be defined as a type of data analytics that uses algorithms and analysis of raw data to achieve better and effective decisions for a long as well as a short span of time.  It suggests strategy over possible scenarios, accumulated statistics, and past/present databases collected through the consumer’s community. 


Google Self-driving cars, Waymo is a preferred example showing prescriptive analytics. It showcases millions of calculations on every trip. The car makes its own decision to turn in whichever direction, to slow/speed up and even when and where to change lanes- these acts are normal like any human being’s decision-making process while driving a car.

Prescriptive Data Analytics Model


To process such a huge amount of data stacks, the analytics uses concepts of Artificial intelligence technology, machine learning computing tactics, and in most scenarios use any type of human input. Due to the scalability and reliability of technological era machines which quickly self-learn and adapt themselves of holding extra data packages and deriving well-advanced solutions as per the convenience remains advantageous. It goes beyond simple prediction options and delivers a range of potential ideas for each action. The process can be stated as much faster and even more accurate than human capacity. 

Descriptive Analytics Vs Predictive Analytics Vs Prescriptive Analytics

Descriptive analytics works over the statistical data to give us the details related to the past. It helps the business to get all relatable details regarding their performance from past stats. For Example: Analyzation of past purchasing details of consumers/customers to decide the best time for launching a new product or any sales scheme in the market. 

Predictive analytics uses a machine learning model consisting of all the relatable key trends and particular scalable patterns with the help of historical data and feeds. This model is then used in business to predict what will happen next applying the latest information. For Example: Statistics models are used by enterprises to through previous data whether how much consumers are using the services and which services are most popular among them so relatable model to check in-demand services among users.

Prescriptive analytics is used to make next-level and advanced usage of predicted data. Business enterprises use the predicted possibilities to develop and provide better services to their customers/consumers. For Example: For a successful and cost-effective delivery system transportation enterprises used algorithms and predictive models to decide the best route with minimum energy usage for saving time and increasing profits.


  • Effortlessly map Business analysis to declare out steps necessary to avoid failure and achieve success.
  • An accurate and Comprehensive form of data aggregation and analysis also reducing human error and bias.
  • Helping in decision-making threads related to problems rather than jumping to unreliable conclusions based on instincts.
  • Removing immediate uncertainties help in the prevention of fraud, limit risk, increase efficiency and create logical customers.

Business-Related Prospects

As day by day, the database is expanding for a set of enterprises in business processes, with such data analytics models it’s easier than ever to leverage information collected to drive real business value- providing optimistic approaches and curable outcomes. Trust-worthy organizations can make decisions based on analyzed facts rather than jumping to absurd conclusions directly based on instincts. Organizations can easily gain a better understanding of the likelihood of worst-case scenarios and plan accordingly. This could be the key to a flourishing business in software technology and economy department as organizations can make better predictions of worst scenarios and plan accordingly for the present as well as future too.

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