In this modern world, where data is involved in almost everything, businesses across the world are increasingly analyzing and using this data to make informed decisions and predict future trends. One important part of data analytics that helps with this process is prescriptive analytics. Prescriptive analytics goes beyond just understanding what has happened or what might happen; it provides specific recommendations on what actions should be taken next to overcome the following problem. In this article, we will explore what prescriptive analytics is, how it works, and different prescriptive analytics examples to help you understand its practical applications and usage.
So, without wasting much of our time let us begin with the article and understand this concept in detail.
What Is Prescriptive Analytics?
Prescriptive analytics is a branch of data analysis that uses advanced tools and methods to analyze data and suggest the best actions that can be taken on that data. It helps in deciding the next steps by answering the question, “What should we do?” In other words, it guides us on the best way to move forward based on the data we have.
In the past, setting up prescriptive analysis needed expensive equipment and specialized data science skills to create custom algorithms. But now in recent times, cloud data warehouses provide the storage and speed you need at a lower cost. Plus, new Auto ML tools make it easy to build, train, and use custom machine learning models without needing deep technical knowledge.
The 4 Types Of Data Analytics
Prescriptive analytics are basically built on the other three types of data analytics, which help us to understand the present and predict the future. It uses methods like heuristics analysis, machine learning, and rules to give specific recommendations based on data and predictions. Let us understand what each type of analytics has and why they are specifically used with the help of the table below-Â
| The 4 Types Of Data Analytics | |
| Analytics Types | Questions Answered |
| Descriptive Analytics | Why has happened? |
| Diagnostic Analytics | What did it happened? |
| Predictive Analytics | What will happen? |
| Prescriptive Analytics | What should we do? |
How Does Prescriptive Analytics Work?
The basic process of how prescriptive analytics work will depend on your specific needs and the type of data you are working with, but here is a general overview that will help you to understand the normal process being followed during the prescriptive analytics task:
- Define the Question: Like any data analytics or data science project, start by clearly defining the problem you want to solve or the question you need to answer. This will guide you in gathering the right data and help your model produce useful results.
- Integrate Your Data: This step involves collecting and preparing the data you need. Make sure to include data that covers all relevant factors. For machine learning projects, ensure your data is properly labeled and formatted, avoid errors like data leakage, and clean it to remove any missing or inconsistent data. After importing the cleaned data, review your dataset again for better accuracy.
- Choose the Right Tools: If you are working with large amounts of data, you will need powerful tools. Cloud data warehouses are the place that can provide the storage, speed, and power needed for analytics at a reasonable cost.
- Develop Your Model: Now you can build, train, evaluate, and deploy your model. You can either hire a data scientist to create a custom model or use an Auto ML tool to build one yourself. The model will process structured data, unstructured data, and business rules. Techniques like simulation, optimization, and game theory are often used here. You will need to alter and test the model multiple times to ensure it gives the right and best recommendations.
- Deploy Your Model: Once you are satisfied with the model’s performance, you can deploy it. For a one-time project, batch processing may be sufficient. For ongoing processes that require quick predictions, real-time deployment is better. The model should update automatically as new data comes in, this will improve its recommendations over time.
- Take Action: Review the model’s recommendations, decide if they make sense, and take action. In some cases, you will need to use your own judgment alongside the model’s suggestions. In other cases, the model’s recommendations can trigger actions automatically, especially if it is integrated into a larger process.
Prescriptive Analytics Examples
Now, after understanding the working of prescriptive analytics models. Let us understand this topic more clearly by looking at some prescriptive analytics examples. Understanding these real-life prescriptive analytics examples will help you to get deeper insights into the topic and provide you a better understanding to implement it well.
1. Financial Services Â
In the financial services industry, prescriptive analytics can help companies to reduce risk by analyzing the likelihood of a loan default. For example, when a bank receives a loan application, prescriptive analytics can examine the applicant’s credit history, income, employment status, and other relevant data. The model then suggests whether to approve or deny the loan based on the risk. This analysis helps banks to make better decisions, reducing the chances of lending to someone who may not be able to repay the loan.
2. Healthcare Â
In healthcare, prescriptive analytics can enhance patient care by forecasting patient admissions and readmissions. Hospitals can use these predictions to allocate resources like staff, beds, and equipment more effectively. For example, if the model predicts an increase in patient admissions next month, the hospital can prepare by hiring more staff or increasing the availability of certain medical supplies. This leads to better care for patients and ensures the hospital is not over burdened suddenly.
3. Energy Utilities Â
Energy companies use prescriptive analytics to predict peak demand cycles and ensure consistent service. For example, during extreme weather conditions, energy demand might spike a bit. By analyzing historical data and weather patterns, the model can predict when these peaks will occur and suggest ways to manage the supply accordingly. This generally helps companies to prevent blackouts and ensures that customers have a reliable power supply operator.
4. Life Sciences Â
In the life sciences sector, prescriptive analytics helps companies to determine the most efficient and effective territory alignment for their sales teams. For example, a pharmaceutical company might use analytics to decide which sales representative should cover which regions based on factors like doctor availability, patient demographics, and historical sales data. This ensures that each representative is working in the area where they can be most effective, leading to better sales and service.
5. Public Sector Â
Governments can also use prescriptive analytics to optimize investments in transportation infrastructure. For example, by analyzing population density and traffic patterns of a certain area, the model can suggest the best locations for new roads or public transportation routes. This helps to ensure that investments are made where they are most needed, improving transportation efficiency and serving the public in a better way.
6. Travel Hospitality Â
In the travel and hospitality industry, prescriptive analytics can help companies to segment their customer base and promote the best packages and pricing. For example, a hotel chain may use analytics to identify different customer segments, such as business travelers and vacationers. The model can then suggest tailored promotions and pricing for each group, which can lead to higher customer satisfaction and increased revenue.
Prescriptive Analytics Vs Predictive Analytics
After understanding prescriptive analytics techniques in detail along with their real-life examples and definitions. You must be wondering about what is the difference between this and predictive analytics. Both of them sound quite similar and are used for analytics purposes as well. Still, these two terms are quite different from each other and have different roles to play in their day-to-day life. Let us understand their basic difference with the help of a table written below:
| Prescriptive Analytics Vs Predictive Analytics | ||
| Aspect | Predictive Analytics | Prescriptive Analytics |
| Output | It predicts possible outcomes but does not provide advice on what to do. | It gives specific advice on what actions to take based on predictions. |
| Scope | It focuses on specific areas, which might improve one part but harm others. | It considers all parts of the business to give balanced recommendations. |
| Models | Uses set scenarios with limited choices. | Uses machine learning to consider all factors and outcomes, offering a more holistic picture. |
| Human Bias | Needs human decisions because it doesn’t give clear advice. | Reduces human involvement by offering data-driven recommendations. |
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Prescriptive Analytics FAQs
How does Prescriptive Analytics differ from Predictive Analytics?
Predictive Analytics generally forecasts possible future outcomes whereas Prescriptive Analytics goes a step further by suggesting the best course of action to achieve desired results.
Why is Prescriptive Analytics important for businesses?
Prescriptive Analytics is crucial because it helps businesses in not only understanding potential future scenarios but also guides them on the best actions to take, leading to better decision-making and optimized outcomes.
What are some examples of Prescriptive Analytics?
Examples of Prescriptive Analytics include recommending the best pricing strategy for products, suggesting the most efficient supply chain routes, or advising on the optimal inventory levels to maintain.
What tools are used in Prescriptive Analytics?
Tools that are commonly used in Prescriptive Analytics include machine learning algorithms, optimization models, and simulation software, which help to analyze data and generate actionable recommendations based on that.
