What Is Big Data?

What is big data? The term has been around for some time, but there’s still a lot of confusion about what it actually means. Read on to learn how big data works and how it benefits organizations. 

Understanding Big Data

Big data refers to the vast amount of complex data sets rapidly generated and transmitted from various sources. These data sets are so voluminous and complex that traditional data processing software can’t store or process them efficiently. 

Big data can be gathered from publicly shared comments on websites and social networks, voluntarily gathered from personal devices and applications, through electronic check-ins, product purchases and questionnaires. The presence of sensors and other inputs in smart devices, such as smartphones, allows data collection across a broad spectrum of situations. 

Big data is typically stored in computer databases and analyzed using programs designed to handle vast data sets. Many software-as-a-service (SaaS) businesses specialize in managing this kind of complex data.

Why Is Big Data Important?

Companies and organizations use big data in their systems to improve their operations, develop personalized marketing campaigns, provide better customer services, and make various moves that increase profits and revenues. Companies that use it effectively hold a competitive edge over those that don’t, as they’re able to make faster, more informed business decisions. 

For example, big data provides valuable insights about clients that organizations can use to refine their promotions, advertising, and showcasing to boost customer commitment and conversion rates. Both continuous and historical data can be broken down to evaluate the preferences of corporate buyers or consumers, empowering companies to become more responsive to customer needs and wants. 

Big data is also frequently used by clinical researchers to determine disease signs and risk factors, as well as by physicians to assist with diagnosing ailments and illnesses in patients. Moreover, a blend of data from social media sites, electronic wellbeing records, the web and various sources provides government agencies and healthcare organizations with forward-thinking data on outbreaks of infectious disease threats. 

Here are some more examples of how organizations use big data:

  • Retail companies: Data gathered from credit card transactions, loyalty programs, social media and email engagement, website behavior, mobile applications, IP addresses, purchase histories and user log-ins gives retail companies a 360-degree view of their customers.
  • Manufacturing companies: Big data analytics in manufacturing allow companies to gain end-to-end visibility into supply chain metrics, production processes and environmental conditions that affect deliverables and productivity. 
  • Educational organizations: Big data is being used by educational organizations to identify and improve teaching strategies to help students succeed academically. It’s used to measure teacher performance and monitor when students log into online learning platforms and how they progress through their coursework. 
  • Financial institutions: Financial decisions are placed in the hands of AI, which uses machine learning solutions to assess potential investments, process loan applications and calculate risk. For instance, big data analytics can assess stock prices alongside economic factors, social trends and the political landscape that might impact the stock market. 

Types of Big Data

There are three types of big data:

  • Unstructured data: This refers to data that’s unorganized and doesn’t fall into a predetermined format or model. It includes data collected from social media sources, which help organizations gather information on customer needs. 
  • Structured data: This includes data already managed by the company or organization in spreadsheets and databases. It’s usually numeric in nature.
  • Semi-structured data: This is a combination of unstructured and structured data. It includes information that can be easily organized and data that’s hard for a machine to sort. For example, the text within an email message is unstructured data, while the sender’s email address, time sent, and recipient’s name are structured data.

Characteristics of Big Data

The characteristics that define big data are variability, velocity, variety and volume. These characteristics are commonly referred to as the four Vs. Here’s a look at each of these characteristics:


The amount of data matters. With big data, organizations need to process high volumes of unstructured data. This can be data of unknown value, such as clickstreams on a mobile app, web page, or Twitter data feeds. For some companies, this may be tens of terabytes of data, while for others, it might be hundreds of petabytes. 


Variety refers to the spectrum of sources from which an organization can acquire big data and the type of formats it can appear. This includes places like stock ticker data, social media chatter, in-house devices, smartphones, and data from financial transactions. The source should be particularly relevant to the nature of the business for which the data is gathered. For instance, a retail company should be tuned in to what people are saying on social media about its recently launched product line. 

A variety of data can also extend to help companies understand customer personas and profiles. For example, a business would find it helpful to know not just the number of people who opened their newsletter but also the reason they opened it and the distinguishing characteristics of the audience. 


Velocity refers to the speed new data is generated and moves around. When you make a credit card purchase, check out your social media feed, send a text or react to a post on Twitter, Instagram or Facebook, you create data that needs to be processed instantly. If you compound these activities with all the people across the globe doing the same and more, you can start to see how velocity is a crucial attribute of big data. 


Data has intrinsic value. However, it isn’t useful until that value is discovered. Value is being able to, for example, predict how many clients will renew insurance policies, how many new members will join the website or how many orders to expect. Value is knowing who one’s best clients are and who will fall off the map in a few months or weeks, never to return.

Organizations gain value through their ability to monetize the insights big data provides. They get to know their clients better and continue providing more relevant services or products. 

Data is changing the world and the way people live or do business. If big data can do all of this today, just imagine what it will be capable of in the future. The amount of data available to people is only going to increase, and analytics solutions will become more advanced. 

Take Your Big Data Skills to the Next Level

There has never been a better time to improve your big data skills. Big data skills are invaluable assets that equip you with the tools to provide insightful, accurate and actionable data. The Data Incubator offers an immersive data science bootcamp where data science experts teach you the skills you need to succeed in the world of data. 

Here are some of the programs we offer to help you turn your dreams into reality:

  • Data Science Essentials: This program is perfect for you if you want to augment your current skills and expand your experience. 
  • Data Science Bootcamp: This program provides you with an immersive, hands-on experience. It helps you learn in-demand skills so you can start your career in data science. 
  • Data engineering bootcamp: This program helps you master the skills necessary to effortlessly maintain data, design better data models, and create data infrastructures. 


We’re always here to guide you through your journey in data science. If you have any questions about the application process, consider contacting our admissions team.


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