Big data and analytics are two buzzwords that are splashed across various places. Companies like Facebook, Amazon, Apple and Google have been spending millions of dollars on big data and analytics. From a layman’s perspective, there might not be much difference in both. However, ask the experts and they will tell you that both these concepts stand on the far ends of the spectrum.

So, if you are inclined towards a career in these fields or add one more competency to your existing skill set, you need to understand how they differ from one another. A big data and analytics certification program would be good to start with. However, the below information will also give you a basic foundation in the concepts and explain their differences.

 

  1. By Definition

What is Big Data?

Big data is a huge volume of structured and unstructured data that is generated every day, around the world. And, we are talking about quintillion bytes a day! Yes, you need to do a bit of math to find out where quintillion stands. However, this data is so complex and mind-boggling that it becomes almost impossible to analyze them manually or basic data management tools and applications.

What is Analytics?

Analytics is a systematic computation and interpretation of big data using statistics, mathematics, machine learning and predictive techniques. It is a scientific process to convert raw big data into meaningful information for useful insights and decision-making.

 

  1. Inherent Difference

Big data differs from analytics on three major Vs:

Volume: The amount of data is generated from various sources such as social media, business transactions and machines.

Variety: Data comes in various formats – structured, unstructured, text, video, GPS, emails, websites, etc.

Velocity: The speed at which data is generated makes all the difference to decision-making and competitive advantage. The more real-time it is, quicker and timely the decisions can be taken.

Depending on how big data is generated on these thee V’s, analytics can define necessary metrics to measure it. Only when an organization leverages these three V’s effectively, can its analytics become more robust.

 

  1. Machines v/s People

At the end of the day, machines generate big data, but it is people who are required to do the analysis. There are data specialists who pour into spreadsheets for hours and do all number-crunching to provide insightful information to decision-makers. So, while computers and machines stay in the background mining data, data interpreters work on the front foot to analyse it.

Now that you know the difference, a data analytics certification course can further add to your knowledge and necessary skills.

 

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