Prescriptive Analytics: The Crystal Ball of Modern Business

What is Prescriptive Analytics?

Prescriptive analytics is a type of data analytics. It uses tools and advanced processes for analyzing content and data in order to provide quick recommendations for optimizing business practices. The prescriptive analysis offers an answer to the question- "what should be done?"

Prescriptive analytics leverages information about past performance, current performance, scenarios or possible situations, and available resources and gives suggestions for a strategy or a course of action. It is used for making decisions for long-term or immediate actions.

How Prescriptive Analytics Works?

Prescriptive analysis gives recommended actions. Prescriptive analysis techniques depend on their specific use case and data type. Mentioned below is an overview of a high-level prescriptive analytics workflow.

Define the Question

Firstly, you must be clear with your problems or questions that you are finding a solution to. This will Mention the data requirements and will let the prescriptive model generate actionable results.

Integrate your Data

Secondly, collect the data you require and create your dataset. For an accurate model, correct data that represents each factor you can consider. While preparing data for machine learning projects, consider the points listed below:

• Make sure your dataset is correctly formatted and labeled.
• Avoid training-serving skew and data leakage.
• Clean any missing, inconsistent, or incomplete data
• Ensure accuracy by reviewing the dataset post-importing

It would help if you found the right tools, as you might be working in real time with big data. Cloud data warehouses prove cost-effective and offer you the power, speed, and storage you need.

Develop your Model

Now, you can easily build, evaluate, deploy, and train your prescriptive model. With the help of a data scientist, you can code one from the start or use any auto ML tool for creating a custom ML model. This algorithm-based model requires a combination of structured data, business rules, and various analytical techniques like simulation graphs, heuristic optimization, and game theory. It's essential to iterate and fine-tune the model to ensure accurate results. Regular testing with new data is crucial to evaluate if the recommendations align with expectations.

Deploy your Model

Your prescriptive model is ready for use once you are sure about its performance. This can be a single-time project or a part of some ongoing process. An asynchronous batch recommendation is appropriate for a single-time project. However, if your model requires a larger process, a synchronous deployment is the best option. As you add new data, your model must adjust automatically. It improves the accuracy of the recommendations.

Take Action

Go through the recommendation and see if it makes enough sense, and then proceed towards actions. Some situations need human intervention and judgment, and prescriptive analysis must be considered as decision support instead of decision automation.

Advantages and Disadvantages of Prescriptive Analytics

Prescriptive analytics has its own benefits and limitations. Refer to the points below to acknowledge the same.

Advantages:

• Prescriptive analytics shapes itself according to the changing conditions and sudden uncertainties. 
• It increases efficiency, prevents fraud, reduces risk, creates loyal customers, and aligns with the goals of a business. 
• With its effective application, it assists businesses in making informed decisions with the help of highly analyzed facts rather than making instinct-based, uninformed decisions.
• Prescriptive analytics simulates multiple outcomes and shows the probability of each, which helps organizations clearly understand the levels of risk they might face
• Businesses that use prescriptive analysis statistics can understand the worst-case scenarios in a better way and plan accordingly.

Disadvantages:

• Prescriptive analytics isn't foolproof. Hence, only if the organizations know the right questions to ask and give the appropriate reaction to the answers can they be effective.
• It is effective only with valid inputs. In case of invalid inputs, the results will be inaccurate.
• Not completely reliable for long-term solutions.
• Only a few big data providers offer results. However, others might not be able to deliver concrete results.

Examples of Prescriptive Analytics

Some of the common prescriptive analytics examples are:

• Financial Services: Automatically analyze loan or credit risk to reduce the overall risk.
• Healthcare: With patient admission and readmission, forecasting provides better care to the patients.
• Energy Utilities: Provide consistent service with peak demand cycle prediction.
• Retail Consumers: Automate price setting and marketing messages for increased customer repurchase propensity.
• Life Sciences: Acknowledge the most effective and efficient territory alignment.
• Public Sector: According to the population density, optimize your investments in transportation infrastructure.
• Travel Hospitality: Promote pricing and packages and segment customer base.
• Manufacturing: With accurate demand forecasting, enhance your ability to fulfill orders.

Several data-intensive government agencies and businesses benefit from prescriptive analytics applications. It includes multiple companies from the healthcare and financial sectors where the probability of human errors is high.

Predictive vs Prescriptive Analytics

Both descriptive and predictive analytics include the usage of modeling and statistics for determining upcoming performance according to historical and current data. However, prescriptive analysis takes a step ahead by utilizing the combination of algorithms, machine learning, and business rules To simulate multiple approaches for a specific business problem. The table below compares prescriptive vs. predictive analytics.

Conclusion

Prescriptive analytics need not be unsettling. With the correct foundation, it can prove to be a powerful tool helping to formulate strategies, achieve organizational goals, and optimize processes. If prescriptive analytics is something new or unknown to your organization, this is the time to acknowledge its impact on your decision-making process. You can start by seeking a single question's answer or any process you are willing to optimize. Collect data that surrounds that particular process or question and take a step through each type of analytics for beneficial outputs.

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FAQs

1. How does Prescriptive Analytics impact decision-making in businesses?

Prescriptive analytics assist businesses in making informed and better decisions. It helps in answering questions about what must be done in order to make something happen in the future.

2. Are there any prerequisites for implementing Prescriptive Analytics in an organization?

There are four prerequisites for implementing prescriptive analytics in an organization.

1. Appropriate sources of data
2. Data cleanliness and usefulness
3. Automation and machine learning
4. Meeting business objectives

3. What are some examples of predictive analytics?

Some of the examples of predictive analytics are listed below:

• Predict consumer behavior in retail.
• Image recognition on computers
• Illness detection in health care
• Fraudulent financial transactions detection.