What is the exact difference between Big Data, Data Science & Data Analytics?

By Juliet D'cruz


Today’s world revolves around data. “Without data, you are just another person with an opinion,” says late American engineer W. Edwards Deming while describing the necessity of data in the contemporary business environment. In this dynamic and competitive business industry, data is considered the new oil to run a company smoothly and successfully.

Now that you have learned the importance of data, you might have encountered terms like big data, data science, and data analytics. If digits and figures attract you, you would assuredly be interested in knowing the primary differences between the three. This blog outlines the main differentiation between big data, data science, and data analytics, besides providing you with the benefits of pursuing a big data analytics program

An overview of big data, data science, and data analytics

  • Big data

In essence, big data is often a mixture of an enormous volume of structured, semi-structured, and unstructured data. Massive data emanates from various digital sources, such as social data, machine data, and transactional data, contributing to the generation of big data. The volume of big data is so huge that it cannot be processed through conventional data storing tools. Large organizations usually collect big data to mine for helpful information. Big data is generally characterized by the five V’s: Volume, Velocity, Variety, Veracity, and Value. Industries use big data to gain insights into their targeted customers and enhance decision-making for business success.

Click here – Role of Decision Making in Business administration

  • Data science

While big data is all about the technique to collect and process massive data, data science is an area of study that includes working with an enormous volume of data. This interdisciplinary field uses scientific methods, computational algorithms, mathematical techniques, and statistics to identify patterns and uncover big data correlations. The tools mainly used to prepare data for analysis and pattern identification include Hadoop, Spark, and Flink, among many others. Data science is more conceptual than technical and uses Machine Learning algorithms to uncover the event occurrence at a particular time.

  • Data analytics

After understanding big data and data science, it’s time to gain an overview of data analytics. Data analytics play a crucial role in the growth of a business. From offering improved decision-making to more effective marketing, better customer service, to more efficient operations, data analytics help companies improve business performance. In fact, data analytics is a process of analyzing raw or unstructured data to gain insights and trends. Please note that industries use data analytics to enhance business efficiency by drawing insights from raw information sources. Many people often confuse data science with data analytics, but what differentiates both is their fundamental level.

Advantages of big data analytics program

In the recent few years, new-age technology has revolutionized how we live. The ever-changing business environment requires new expertise and skills to succeed in this competitive landscape. Therefore, industries are churning out data scientists, analysts, software engineers, and statisticians in large numbers to enhance business performances. Therefore, a big data analytics program will provide you with the tools and knowledge you need to help create value for the company you will be working with in the future. So, enroll in the program right away in top Canadian institutes to gain an edge in the job market.

Click here – 7 Out-of-the-Gift-Box Ideas for Employee Gifts in 2021