Characteristics of Big Data: A Guide to Big Data Analytics

Written by Coursera Staff • Updated on

Discover how big data changes, how it is organised and analysed, and how big data characteristics make it unique in data science.

[Featured Image] A big data installer is working in a server room.

Big data is no longer just a phenomenon but a global force creating job demand worldwide. In India alone, experts anticipate the big data market will reach a value of INR 253 billion by 2027, at a compound annual growth rate (CAGR) of 10.6 percent between 2023 and 2027 [1]. Big data is essential for businesses in India and worldwide as it helps increase efficiency, discover opportunities for improvement, and has the power to reveal insights that better society as a whole.

India’s future with big data looks bright, with continued job growth and strong earning potential. Understanding the characteristics of big data has become a valuable skill for many data scientists, analysts, and cloud computing professionals. This article will discuss those characteristics and examine some career paths that use big data analytics. 

Definition of big data

What is big data? India’s Department of Science and Technology defines it as “data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it” [2]. With big data comes the need for new tools, skills, and architecture to analyse it. Data continues growing, with experts expecting humans to generate 394 zettabytes by 2028, up from 149 zettabytes in 2024 [3]. For reference, one zettabyte has 21 zeros behind it and is the equivalent of a trillion gigabytes. 

Characteristics of big data

Besides the sheer size and scope of big data, various factors make it unique, namely the four “Vs” of big data. While you can find other “Vs” used to describe big data, the main four this article will focus on are:

  • Volume

  • Variety

  • Velocity

  • Variability

Each of these four driving characteristics of big data has driven markets, companies, and data scientists to create new ways of thinking about and analysing data. By understanding these characteristics, you can find new ways to think about data science. 

Volume

Named for the immense amount of data people generate daily, big data represents an enormous scope of information. As digital engagement continues rising worldwide, with users embracing online shopping, streaming videos and other content, and social media, so does the amount of data humans generate daily. As of October 2024, there were roughly 5.5 billion internet users worldwide, or 67.5 percent of the global population [4]. All of those users are expected to drive significant data growth each year.

Variety

Not only does the amount of data generated daily characterise big data, but its variety is much larger. Nearly every facet of digital life generates data in some form. From social media posts and videos to consumer data and medical records, almost every aspect of life is now tracked and stored. Data also comes in three ways: structured, semi-structured, and unstructured data sets. Structured data is an organised data set, while unstructured data is unorganised, like a flood of pictures or videos. Semi-structured data falls in between, with classifying characteristics but no rigid schema. 

Velocity

With volume and variety comes speed, another factor that makes big data unique. With smartphones and social media, humans generate data at rapid rates unprecedentedly before the onset of big data architecture. For example, every minute, global users [5]: 

  • Conduct 6.3 million Google searches

  • Send 2341 million emails

  • Stream 43 years' worth of content

  • Send 6,944 prompts on ChatGPT

  • Send 41.6 million messages on WhatsAppendf

Variability

Variability is another unique characteristic of big data. It describes the tendency for big data to change constantly, allowing other unique factors to emerge continually. That is why so many different “Vs” have been attributed to it. The three main “Vs” (volume, variety, velocity) create a landscape that constantly needs more data organisation, categorisation, and analysis by data scientists. Big data constantly changes, forcing analysts to create a new architecture to capture and glean new insights. 

Additional Vs of big data

Aside from the main four, three more notable Vs include veracity, visualisation, and value.  

  • Veracity is the quality and accuracy of collected data. The higher the quality of the data, no matter how much quantity, the more valuable it can be for analytics. 

  • Visualisation is the creation of visual guides or references so businesses, analysts, and other users can “see” their data in a digestible format. 

  • Value describes the benefits the data provides to an organisation, including its ability to aid in financial decision-making. Big data gives value through the insights analysts can glean from it. 

Big data analytics

With its unique characteristics, big data has forced new ways to think about and analyse large data sets. Here are some of the latest technologies that are blazing new paths in the field of big data analytics:

  • Cloud computing: Data storage and transfers through the internet facilitates big data. 

  • Data management: Big data is only valuable if appropriately managed and organised. Analysts must sort useful from useless data for the analysis process. 

  • Data analysis: Data analysis technology has made it possible to automate discovering patterns in large swathes of data. Without it, humans would never be able to analyse and find trends in this volume of data. 

  • Machine learning: Machine learning works in tandem with data mining and analysis. With the onset of big data, computers can learn and think like humans. Training artificial intelligence (AI) to analyse data sets and predict future probabilities is one of the unique aspects that emerge from big data analytics. 

Jobs in big data

Big data is only getting bigger as the world generates more data daily. Big data creates new job growth and demand to keep up with the need for big data organisation and analysis. Some jobs that use insights from big data analytics and their average annual salaries include:

All salaries provided by Glassdoor as of December 2024. 

  • Machine learning engineer: ₹10,75,000 

  • Data scientist: ₹11,33,513

  • SQL developer: ₹5,00,000

  • Data engineer: ₹9,00,000

  • Database administrator: ₹3,85,000

  • Data analyst: ₹6,00,000

  • Cloud solution architect: ₹18,00,000

  • Cloud engineer: ₹7,00,000

Next steps with big data

Big data is changing the way that data is analysed. To learn more about big data or to upskill, try the Big Data Specialisation offered by UC San Diego or get a certificate from IBM in their Data Engineering Professional Certificate course. 

Article sources

1

Business Wire. “India Big Data Market Analysis Report 2023, https://www.businesswire.com/news/home/20230522005517/en/India-Big-Data-Market-Analysis-Report-2023-The-Presence-of-600-and-Counting-Data-Analytics-Firms-Bodes-Well-for-the-Sector---ResearchAndMarkets.com#:~:text=The%20big%20data%20market%20in%20India%20was,~10.60%%20during%20the%202023%20%2D%202027%20period..” Accessed 5 December 2024.

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