descriptive predictive prescriptive

Guri Bee

2 min read

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21-10-2024

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From What Is to What Will Be: Understanding Descriptive, Predictive, and Prescriptive Analytics

In the ever-evolving world of data, understanding how to glean actionable insights from raw information is crucial. This is where analytics comes in. But not all analytics are created equal. Different approaches provide different levels of understanding, leading to distinct outcomes.

This article explores three key types of analytics – descriptive, predictive, and prescriptive – and their crucial roles in making informed decisions.

1. Descriptive Analytics: The "What Happened?"

• Question: What happened in the past?
• Focus: Summarizing past data to understand what has occurred.
• Tools: Dashboards, reports, basic statistical measures (mean, median, mode, etc.).

Example: A retail store manager might use descriptive analytics to review past sales data. They can see which products sold best, identify peak sales periods, and understand customer purchasing patterns. This information is valuable but doesn't necessarily offer insights into future trends.

2. Predictive Analytics: The "What Might Happen?"

• Question: What will happen in the future?
• Focus: Using historical data to build models that predict future trends.
• Tools: Statistical modeling, machine learning algorithms (regression, classification, etc.).

Example: A bank can use predictive analytics to estimate the likelihood of loan defaults based on borrowers' credit history, income, and other factors. This enables them to make informed decisions about loan approvals and risk management.

3. Prescriptive Analytics: The "What Should We Do?"

• Question: What actions should we take based on our predictions?
• Focus: Providing actionable recommendations based on predictive models.
• Tools: Optimization algorithms, simulation models, decision trees.

Example: A healthcare provider might use prescriptive analytics to analyze patient data and predict the likelihood of readmission. Based on this, they can then recommend specific interventions and care plans to prevent unnecessary readmissions.

Connecting the Dots: A Practical Scenario

Imagine a marketing team at a clothing company.

• Descriptive Analytics: They identify that sales of a specific type of jacket peak during the winter months.
• Predictive Analytics: They use historical data to predict that sales of this jacket will increase by 20% next winter.
• Prescriptive Analytics: They recommend increasing production of that jacket in advance, adjusting marketing campaigns to target winter shoppers, and offering incentives for early purchases.

The Power of Synergy

The three types of analytics work in tandem, each building upon the previous. Descriptive analytics provides the foundation, predictive analytics offers insights into future trends, and prescriptive analytics guides action.

Key Takeaways:

• Descriptive analytics: Provides an understanding of the past.
• Predictive analytics: Forecasts future outcomes.
• Prescriptive analytics: Recommends optimal actions.

By harnessing the power of these different types of analytics, organizations can gain a comprehensive understanding of their data, anticipate future events, and make informed decisions that drive success.