Prescriptive Analytics: Examples, How It Works, & Challenges

Many organizations leverage analytics to make strategic decisions and optimize their business operations. Among the various types of analytics, prescriptive analytics is the most useful due to its ability to provide actionable recommendations that guide decision-making. Unlike descriptive analytics, which focuses on past events, or predictive analytics, which forecasts future outcomes, prescriptive analytics goes a step further. It suggests the best course of action to achieve specific business goals.

If you want to learn more about prescriptive analytics, this article has got you covered!

What Are Prescriptive Analytics?

Prescriptive analytics is an advanced type of data analytics that helps you determine what actions to take next to reach your desired outcome. This type of analytics allows you to consider possible scenarios, available resources, past performance, and current performance to recommend the best course of action.

Using prescriptive analytics, you can make decisions in any time frame, from immediate to long-term. As a result, it is useful in dynamic environments like health care, supply chain, and financial planning, where timely and effective decision-making is critical.

Types of Data Analytics

Descriptive

Being the simplest type of data analytics, descriptive analytics enables you to analyze and summarize historical data. These summarized results provide insights into what has happened.

You can use data mining techniques like clustering, association rule mining, or classification to identify patterns or trends in past events. Then, using statistical and visualization tools, you can present the findings in an easy-to-understand format. 

Diagnostic

Diagnostic analytics helps you investigate the causes behind historical events. Exploring data in greater depth allows you to recognize correlations and patterns that explain the factors or conditions that led to specific outcomes.

Using simple SQL queries, root-cause analysis, and what-if analysis, you can perform diagnostic analytics. As a result, you will be able to understand the root causes of issues, which can guide improvements in processes or decision-making.

Predictive

In predictive analytics, you can analyze past trends to identify the probabilities of future events, such as sales forecasts, customer behavior, or market trends. Predictive analytics can be performed manually or through machine learning algorithms to make predictions about future events.

Techniques like casual forecasting, regression analysis, and simulation can also help you build predictive analytics models.

Prescriptive

While each of the above-mentioned data analytics is unique, prescriptive analytics is a crucial approach for driving optimal future outcomes. This indicates that prescriptive analytics recommends the best course of strategy for what should be done next.

This type of analytics enables you to combine descriptive, diagnostic, and predictive analytics to describe the present and make predictions about the future. It then allows you to use heuristics, machine learning, and rule-based systems to provide specific suggestions based on data and probability-weighted predictions.

Benefits Of Using Prescriptive Analytics

Prescriptive analytics offers strategic benefits that go beyond simple data interpretation and monitoring:

Optimized Decision Making

Prescriptive analytics offers recommendations for the best possible decisions by analyzing historical data, real-time inputs, and predictive models. This helps you choose the most effective course of action, improving outcomes.

Improved Operational Efficiency

Prescriptive analytics allows you to streamline processes by identifying inefficiencies, bottlenecks, and areas for improvement. It enables you to reduce costs and enhance the overall efficiency of business operations.

Risk Mitigation

Prescriptive analytics helps you identify potential risks and their impact. It then provides proactive measures and recommendations on how to mitigate or eliminate risks. This ensures smoother operations, protects data assets, and reduces the likelihood of negative events.

Challenges With Prescriptive Analytics

Prescriptive analytics is a powerful technique for decision-making, but it comes with several challenges:

Difficult to Define a Fitness Function

A fitness function is essential for optimizing the solution to a problem in prescriptive analytics. However, accurately defining this function can be challenging as it requires a good understanding of the business context and goals.

Human Bias

Human biases can affect prescriptive analytics through biased model design, interpretation, or data collection methods. This can affect the reliability of the recommendations provided by the prescriptive model.

Data Quality Issues

High-quality data is required to generate accurate insights. Failing to provide consistent and complete data can result in misleading recommendations.

Real-World Examples Of Prescriptive Analytics

Here are some real-world examples showcasing how prescriptive analytics drives impactful decision-making across various businesses:

Amazon Transformed Retail Marketing

Amazon has changed the retail environment by setting an industry benchmark through its innovative use of prescriptive analytics. Traditionally, customers relied on local store inventories or waited for a long time for special orders to find rare or unique items. Amazon’s integration of prescriptive analytics has redefined this experience by making shopping faster, more convenient, and more personalized.

Key ways Amazon leads retail:

• Better Inventory Management: Amazon predicts what customers will want based on shopping trends and regional preferences. This helps them stock the right products in the right places, ensuring items are available quickly while avoiding extra storage costs.‍
• Smart Fulfillment Centers: Amazon’s warehouses are strategically placed to decide the best locations. This enables them to deliver even rare items quickly.

Uber Revolutionizes Rides

In the past, passengers had to locate a taxi or call a cab center, with no assurance of quick service. Uber revolutionized this by applying prescriptive analytics to make rides faster, easier, and more reliable.

Key innovations of Uber:

• Real-time Ride Matching: Uber uses data to quickly match riders with nearby drivers. It considers factors like traffic, driver location, and past ride patterns to suggest the best routes, reducing wait times.‍
• Predicting Demand: Uber analyzes events like concerts or sports games to predict where people will need rides. Drivers are then guided to those areas, saving time and increasing earnings.

Prescriptive Vs. Predictive Analytics

How Prescriptive Analytics Work?

Step 1: Define the Question

Well-defined questions help the prescriptive model understand the data type needed and guide it in delivering actionable insights. As a result, you must start by identifying the specific problem to solve or the question to answer.

Step 2: Integrate and Process Your Data

Prescriptive analytics draws results from various sources, including historical datasets, real-time streams, and external factors like market trends or weather conditions. Once the question is defined, you should gather data from these sources and load it into a centralized storage. You can further process it based on your analytical requirements. Once the data is ready, you can integrate the database system with a data analytics tool to perform prescriptive analytics.

‍Airbyte, a data movement platform, can help you with this step using its 400+ pre-built connectors. It also allows you to extract data from varied sources and load it into a preferred destination, including vector databases like Pinecone, Weaviate, and Milvus. These vector stores help you simplify GenAI workflows for efficient similarity searches and fast data retrieval.

Here are some of its popular features:

• AI-Assistant Connector Builder: You can also create a custom connector if you do not find one in its native collection using its AI-powered no-code Connector Builder feature. The AI assistant helps you automatically prefill the necessary fields during the connector configuration.
• ‍Automatic Detection of Dropped Records: During data integration, records can be lost due to issues like improper data flushing, parsing failures, or large datasets. Airbyte enables you to track record counts automatically and alerts you if any accidental data loss occurs.
• ‍Change Data Capture (CDC): Airbyte supports CDC, enabling you to capture the changes in the source system and replicate them into your chosen destination. This helps you maintain data consistency between the source and target system.

Step 3: Develop and Deploy Your Model

The next step is to create a prescriptive analytics model tailored to the given problem. To do this, you must train your prescriptive model using integrated historical data and ML algorithms.

You can then test the model with real or new data to verify its accuracy and effectiveness. Following this, refine the model iteratively by incorporating additional data or adjusting its parameters.

After validating the model’s performance, you can launch it in a production environment. To enhance recommendation accuracy over time, you must ensure the model adapts automatically as new data is added.

Step 4: Take Action

Finally, you can review the recommendations and decide whether to act based on human judgment or automate the decisions. Evaluate the model recommendations before implementation to ensure alignment with organizational goals. In cases requiring quick or repetitive decisions, you can automate responses based on the model’s suggestions.

Types Of Prescriptive Analytics Models

• The Forecast Model helps you predict future trends and behaviors by analyzing historical data.
• ‍Classification Model categorizes data into predefined categories or classes. Based on the input features, classification models allow you to predict which category the input belongs to.
• The Outlier Model enables you to recognize data points that deviate from the expected distribution. These models help identify unusual data that could indicate fraud or errors.
• With the Time-Series Model, you can analyze sequential data over time to identify patterns and trends. This analysis helps predict future values based on past data.
• ‍Segmentation Model allows you to divide data into groups that share similar characteristics. This is widely used in marketing to categorize customers based on their purchasing behavior or preferences.

Summary

Prescriptive analytics is a powerful data analytics type that enables you to make data-driven decisions. It does this by predicting future outcomes and recommending optimal actions. By leveraging advanced ML, optimization techniques, and statistical models, your business can enhance the decision-making process across various sectors, from banking to content marketing.

However, despite its potential, prescriptive analytics has challenges when the data is not in good quality. Overcoming these issues is crucial, as it will provide greater value, enabling your organization to refine its strategies for competitive advantage.