What is Prescriptive Analytics in Data Science?
Last Updated : 29 May, 2023
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
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 more effective decisions for a long and short span of time. It suggests strategy over possible scenarios, accumulated statistics, and past/present databases collected through the consumer community.
Example
Google’s Self-driving car, 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 every day like any human being’s decision-making process while driving a car.
Prescriptive Data Analytics Model
Working
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 the technological era machines that quickly self-learn and adapt themselves to holding extra data packages and deriving well-advanced solutions as per convenience remain 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.
Implementation Approach in Prescriptive Analytics
Suppose A company wants to optimize its supply chain network by determining the most cost-effective locations in the city for warehouses and distribution centers to minimize transportation costs while meeting customer supply and demand for goods.
Prescriptive Analytics Approach
Step 1 Data Collection: Gather data for a customer’s locations, their requirement, company warehouses, and transportation
Step 2 Mathematical Modeling: We will create mathematical models that will handle supply chain data like customer location, time, warehouse location, and routes, we will also finalize an optimization function that will minimize company cost and delivery time
Step 3 Optimization: We will use an optimization approach like linear programming or differential calculus to solve mathematical models and find optimal locations.
Step 4 Scenario Analysis: We will perform a scenario analysis for our assumptions variables about the models.
Step 5 Decision Support: Based on our data modeling and business knowledge that we got from the raw data we will create dashboards and visualization graphs that will stakeholders in taking decisions.
Step 5 Implementation: The Final and most important part after doing all the five steps is to implement it with changes that maximizes the company’s revenues
Descriptive Analytics Vs Predictive Analytics Vs Prescriptive Analytics
Descriptive analytics works over the statistical data to give us 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 a 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.
Advantages of Prescriptive Analytics
- 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 reduces human error and bias.
- Helping in decision-making threads related to problems rather than jumping to unreliable conclusions based on instincts.
- Removing immediate uncertainties helps in the prevention of fraud, limits risk, increases efficiency, and creates 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 the software technology and economy department as organizations can make better predictions of worst scenarios and plan accordingly for the present and future.
FAQs In Prescriptive Analytics
Q1. What is prescriptive Analytics in Data Science
Ans- Prescriptive analysis is a type of data analysis that uses algorithm and and analysis of raw data to achieve better and more effective decisions for a long and short span of time
Q2. What is the main goal of prescriptive analytics in data science?
Ans – The main goal of prescriptive analysis is to analyzes data and provides instant recommendations on how to optimize business practices to suit multiple predicted outcomes.
Q3. What are the techniques used in prescriptive analytics ?
Ans – Some commonly used techniques in prescriptive analytics are simulation , optimization , decision Tress , Machine learning , game theory , Heuristics , Prescriptive Analytics Platforms.
Q4. How can we optimize pricing strategies to maximize revenue or profit ?
Ans – Prescriptive analytics can recommend optimal pricing strategies by considering factors such as demand elasticity, competitive landscape, and cost structures.
Q5. How can we optimize inventory levels to meet customer demand while minimizing costs using Prescriptive analytics? Ans – Prescriptive analytics assists in determining the optimal inventory levels, considering demand patterns, lead times, costs, and service level requirements.
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