Introduction to Data Analytics

Introduction

Organizations are accumulating data at an exponentially growing speed. Just a decade ago, the volume of data transferred in a year globally was estimated to be 5 zettabytes (that is approximately equivalent to 5,000,000,000,000,000 megabytes). In 2021, the volume of data transferred globally is estimated to be 79 zettabytes (Source: Statista).

But how are any of the 79,000,000,000,000,000 megabytes of data relevant to you? If you're looking to open a new retail branch as a business owner, data can provide insights on whether the new location has a high chance of success. If you're running a digital advertising campaign, you can swiftly adjust the campaign based on real-time feedback. As an auditor, you can perform control tests using structured transaction data. For every scenario we just discussed, the end goal remains the same. You want to make better decisions using data.

Data analytics is the science of deriving insights from raw data. It combines multiple disciplines from statistics, mathematics, programming, and even art! 🎨 Data analytics is further categorized into four different types of analytics.

1. Descriptive Analytics: Analyze historical and current data to describe what happened (or is happening)
2. Diagnostic Analytics: Use data to understand why something happened in the past
3. Predictive Analytics: Predicts what is likely to happen in the future 🔮
4. Prescriptive Analytics: Test potential outcomes of each decision using advanced algorithms and recommends the best course of action