Data Theory & Fundamentals

# What Are the 4 Pillars of Data Analytics? (With Real-World Examples)

When you first step into the world of data science, the terminology can feel incredibly overwhelming. People throw around words like "machine learning," "algorithms," and "big data" like everyone knows exactly what they mean. But strip away the heavy academic jargon, and the entire industry is actually built on a very simple foundation.

To truly understand how tech giants operate, you need to understand the 4 types of data analytics. These categories represent the journey a company takes with its data—starting from simply looking at the past, all the way to predicting the future.

In this guide, we are going to break down the 4 pillars of data analytics. To make it easy, we’ll skip the boring textbook definitions and use real-world scenarios you experience every day right here in Bangalore—from Swiggy deliveries to Silk Board traffic.

## 1. Descriptive Analytics (What Happened?)

The Foundation of Data

Descriptive analytics is exactly what it sounds like: it describes what has already happened. It is the most basic, yet most essential form of analytics. Without it, you are flying blind. This pillar takes raw historical data and turns it into something humans can understand, usually in the form of a dashboard or a chart.

#### 🏍️ The Swiggy Example:

Imagine you are a regional manager at Swiggy. Descriptive analytics tells you: "Last Friday night, we completed 15,000 food deliveries in Indiranagar, and average delivery time was 42 minutes." It gives you the raw facts, but it doesn't tell you why it took 42 minutes.

## 2. Diagnostic Analytics (Why Did It Happen?)

Finding the Root Cause

Once you know what happened, you naturally want to know why. Diagnostic analytics digs deeper into the data to find correlations and root causes. Analysts use techniques like data discovery, drill-down, and data mining to connect the dots between different anomalies.

#### 🌧️ The Swiggy Example (Continued):

You notice that 42 minutes is unusually slow. You use Diagnostic analytics to dig into the data and discover that there was unexpected, heavy rainfall in Koramangala and Indiranagar that evening, causing severe traffic blocks and a shortage of active delivery partners. Now you have your reason.

## 3. Predictive Analytics (What Will Happen?)

Forecasting the Future

This is where the job gets exciting—and where salaries start to jump. Predictive analytics uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. It doesn't guarantee the future, but it gives businesses a highly educated guess.

#### 📦 The Flipkart Example:

Flipkart looks at five years of historical buying data and weather patterns. Predictive analytics tells them: "Based on approaching monsoon patterns, there is an 85% probability that the demand for umbrellas, raincoats, and power banks in Bangalore will spike by 300% next week."

## 4. Prescriptive Analytics (What Should We Do?)

The Action Plan

The final and most complex pillar. Prescriptive analytics goes beyond predicting the future; it actually suggests the best course of action to take advantage of that prediction. This requires heavy computational power, AI, and complex algorithms.

#### 🚗 The Google Maps Example:

You are driving toward Silk Board junction. Descriptive says there is a jam. Diagnostic says it's due to an accident. Predictive says you will be stuck for 40 minutes. Prescriptive analytics is the app actively rerouting you through HSR Layout 3rd Sector to save you 15 minutes. It provides the optimal solution.

### What Are the 5 Big Data Analytics?

As technology evolves, you might sometimes hear people ask about the 5 big data analytics. While the core four pillars remain the industry standard, a new fifth pillar is emerging: Cognitive Analytics (or AI Analytics).

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  Cognitive Analytics attempts to mimic human brain function to draw inferences from existing data and patterns.

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  Instead of just providing a route (like Prescriptive), a Cognitive system might learn that you prefer scenic routes over highways and automatically adjust its suggestions based on your personal emotional state and past behavior.