What Are the 5 Types of Data Analytics?

Descriptive Analytics – What Happened?

Descriptive analytics summarizes historical data to understand past performance.

Key Features:

• Reports
• Dashboards
• KPIs
• Data aggregation

Example:

• Monthly sales report
• Website traffic summary
• Revenue growth chart

It answers:

“What happened?”

Businesses use tools like dashboards and BI platforms to visualize trends and patterns.

Diagnostic Analytics – Why Did It Happen?

Diagnostic analytics goes deeper by identifying causes behind trends.

Techniques Used:

• Drill-down analysis
• Correlation analysis
• Root cause analysis

Example:

If sales dropped:

• Was it pricing?
• Seasonality?
• Competitor activity?

It answers:

“Why did it happen?”

Diagnostic analytics helps organizations understand underlying drivers.

Predictive Analytics – What Will Happen?

Predictive analytics uses statistical models and machine learning to forecast future outcomes.

Techniques Used:

• Regression analysis
• Time series forecasting
• Classification models
• Machine learning algorithms

Example:

• Predict customer churn
• Forecast sales demand
• Estimate stock prices

It answers:

“What is likely to happen?”

Python Example: Simple Predictive Model

from sklearn.linear_model import LinearRegression
import numpy as np

# Example data (months vs sales)
months = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
sales = np.array([100, 120, 130, 150, 170])

model = LinearRegression()
model.fit(months, sales)

# Predict month 6 sales
prediction = model.predict([[6]])
print("Predicted Sales:", prediction[0])

This demonstrates basic predictive analytics using regression.

Prescriptive Analytics – What Should We Do?

Prescriptive analytics recommends actions based on predictive insights.

Techniques Used:

• Optimization algorithms
• Simulation models
• Decision trees
• Reinforcement learning

Example:

• Optimal pricing strategy
• Inventory optimization
• Marketing budget allocation

It answers:

“What should we do?”

Prescriptive analytics is commonly used in supply chain management and financial planning.

Cognitive Analytics – What Can We Learn Automatically?

Cognitive analytics uses artificial intelligence and machine learning to simulate human thinking.

Key Capabilities:

• Natural language processing
• Image recognition
• Sentiment analysis
• Conversational AI

Example:

• Chatbots understand customer intent
• AI-powered recommendation systems
• Fraud detection systems

It answers:

“How can systems learn and adapt?”

Cognitive analytics represents the most advanced stage of the 5 types of data analytics.