Business Knowledge

The Core Concepts of Business Analytics

Kieran F. Noonan
Business Analytics Data Analysis Decision Making Data Science

Summary

Business analytics is the discipline of using data and quantitative analysis to make better, more informed business decisions. It’s a broad field that encompasses everything from simple reporting to complex predictive modeling. To navigate this landscape, it’s helpful to understand the four core types of business analytics: Descriptive, Diagnostic, Predictive, and Prescriptive. These types build on each other, moving from a simple understanding of what happened to recommending what should happen next. This guide provides a clear overview of these foundational concepts.

The Concept in Plain English

Imagine you are a doctor treating a patient. Your process would follow a clear path:

  1. First, you check the vital signs. What is the patient’s temperature? What is their blood pressure? This is Descriptive Analytics (What happened?).
  2. Next, you figure out what’s wrong. Why does the patient have a fever? You run tests to find the cause. This is Diagnostic Analytics (Why did it happen?).
  3. Then, you predict what might happen. Based on the diagnosis, what is the likely course of the illness? Will the fever get worse? This is Predictive Analytics (What will happen?).
  4. Finally, you prescribe a treatment. You recommend a course of action (e.g., take a specific medicine) to achieve the best possible outcome. This is Prescriptive Analytics (What should we do about it?).

Business analytics follows the exact same logic, using data instead of medical tests to keep a company healthy and growing.

The Four Types of Business Analytics

These four types represent a spectrum of complexity and value, with each level building on the one before it.

1. Descriptive Analytics: What happened?

This is the most common and fundamental type of analytics. It involves summarizing historical data to provide a clear picture of the past.

  • Questions Answered: How many sales did we have last quarter? Which of our web pages are most popular? What was our employee turnover rate last year?
  • Techniques: Data aggregation, summary statistics (mean, median, mode), and data visualization (dashboards, charts, reports).
  • Example: A retail company’s weekly sales report showing total revenue by product category.

2. Diagnostic Analytics: Why did it happen?

This type of analytics drills down into the data to understand the root causes of past events. It moves from “what” to “why.”

  • Questions Answered: Why did sales dip in the northeast region last month? What factors are causing customer complaints to increase? Why are our website visitors leaving without making a purchase?
  • Techniques: Data mining, correlation analysis, and drill-down/OLAP (Online Analytical Processing).
  • Example: After seeing a dip in sales (Descriptive), the retailer discovers a correlation between the sales dip and a new competitor’s marketing campaign in that specific region (Diagnostic).

3. Predictive Analytics: What will happen?

This type uses statistical models and machine learning techniques to forecast future outcomes based on historical data.

  • Questions Answered: Which customers are most likely to churn in the next six months? What will our sales be next quarter? Which marketing leads are most likely to convert?
  • Techniques: Regression analysis, forecasting models, and predictive machine learning models (e.g., decision trees, neural networks).
  • Example: The retailer builds a model that predicts which customers are at high risk of churning based on their past purchase behavior.

4. Prescriptive Analytics: What should we do about it?

This is the most advanced form of analytics. It goes beyond predicting an outcome to recommend the best course of action to achieve a desired goal.

  • Questions Answered: What is the best discount to offer a specific customer to maximize both the chance of a sale and our profit margin? What is the optimal inventory level for each store? Which marketing campaign should we run to maximize ROI?
  • Techniques: Optimization, simulation, and A/B testing.
  • Example: The retailer’s system uses the churn prediction (Predictive) to automatically recommend a specific, personalized offer (e.g., “15% off your next purchase”) to each at-risk customer to maximize the probability they will be retained.

Risks and Limitations

  • Data Quality is Paramount: All four types of analytics depend on accurate, complete, and timely data. “Garbage in, garbage out” applies at every level.
  • Correlation is Not Causation: A common pitfall in diagnostic analytics is assuming that because two things happened at the same time, one must have caused the other.
  • Predictions are Probabilities, Not Certainties: Predictive models are never 100% accurate. They provide a likely outcome, but there is always a margin of error.
  • Complexity and Cost: As you move up the analytics maturity curve from descriptive to prescriptive, the cost, complexity, and skill requirements increase significantly.
  • Business Analytics: Applied Frameworks: Structured processes like CRISP-DM for managing projects that utilize these four types of analytics.
  • Data Visualization: A critical skill for communicating the insights from all four types of analytics, especially descriptive.
  • Machine Learning: The underlying technology that powers many predictive and prescriptive analytics solutions.