4 V’s of Big Data – How to Define and Use Big Data Velocity, Veracity, Volume and Variety


Introduction to the 4 V’s of Big Data

Today finding invaluable information is supercritical for every business. This kind of information comprises large, complex unstructured, and structured data sets extracted from relevant sources and transmitted across cloud and on-premise boundaries. This is known as “web scraping for big data” where big data is a large volume of both structured and unstructured content, and web scraping is the action of extracting and transmitting this content from online sources. The importance of big data is caused by high-powered analytics leading to smart business decisions related to cost and time optimizations, product development, marketing campaigns, issue detection, and the generation of new business ideas. Let’s keep reading to discover what big data is, in what dimensions big data is broken, and how scraping for big data can help you reach your business goals.

The Big Idea Behind Big Data

Big data is content that is too large or too complex to handle by using standard processing methods. But it becomes invaluable, only if it is protected, processed, understood, and used correspondingly. The primary aim of big data extraction is to get new knowledge and patterns that can be analyzed to make better business decisions and strategic moves. Besides, the analyses of data patterns will help you overcome costly problems and predict customer behavior instead of guessing. Another advantage is to outperform competitors. Existing competitors as well as new players will use knowledge analysis to compete, innovate and get revenue. And you have to keep up. Big data enables to create new growth opportunities and most organizations build departments to collect and analyze information about their products and services, consumers and their preferences, competitors, and industry trends. Each company tries to use this content efficiently to find answers which will enable:
  • Cost savings
  • Time reductions
  • Figure out the market
  • Control brand reputation:
  • Increase customer retention
  • Resolving advertising and marketing issues
  • Product development

4 V’s of Big Data

There are 4 v’s of big data on which big data is standing – volume, variety, velocity, and veracity. Let’s review each one in more detail.


Volume is the major characteristic while dealing with a ton of information. While we measure regular info in megabytes, gigabytes, or terabytes, big data is measured in petabytes and zettabytes. In the past, content storing was a problem. But today new technologies like Hadoop or MongoDB make it happen. Without special solutions for storing and processing information, further mining would not be possible. Companies collect enormous information from different online sources, including e-mails, social media, product reviews, and mobile applications. According to experts, the size of big data will be doubled every two years, and this definitely will require relevant data management in the coming years.


The variety in massive content requires definite processing capabilities and special algorithms, as it can be of various types and includes both structured and unstructured content:
  • Structured content includes demographic figures, stock insights, financial reports, bank records, product details, etc This content is stored and analyzed with a help of traditional storage and analysis methods.
  • Unstructured content mainly reflects human thoughts, feelings and emotions and is captured in video, audio, emails, messages, tweets, status, photos, images, blogs, reviews, recordings, etc. The collection of unstructured content is done by using appropriate technologies like data scraping, which is used to browse webpages by reaching the maximum depth to extract valuable info for further analysis.
4 v's of big data from DataOx


Today information is streaming at exceptional speed, and companies must handle it in a timely manner. To use the real potential of extracted info, it should be generated and processed as fast as possible. While some types of content can be still relevant after some time, the major part requires instant reaction like messages on Twitter or Facebook posts.


Veracity is about the content quality that should be analyzed. When you deal with massive volume, high velocity, and such a large variety, for revealing really meaningful figures, you need to use advanced machine learning tools. High-veracity data provide information that is valuable to analyze, while low-veracity data contains a lot of empty figures widely known as noise.

Scraping Big Data

For most business owners to get an extensive amount of information is a time-consuming and rather embarrassing task. But with a help of web scraping, we can simplify this work. So let’s dig a little deeper to understand how to get records from web sources by using data scraping. Complex and large websites contain a lot of records that is invaluable, but before use it, it is necessary to copy to storage and save in readable format. And if we are talking about manual copy-paste, it is practically impossible to do it alone, particularly if there is over one website. For instance, you may need to export a list of products from Amazon and save it in Excel. Through manual scraping you can’t achieve the same productivity as with a help of special software tools. Besides, while scraping by yourself, you will face up a lot of challenges (legal issues, anti-scraping techniques, bot detections, IP blocking, etc) about which you don’t even know. To learn more about common challenges in web scraping, read the How to Deal With the Most Common Challenges in Web Scraping blog post. So, if you deal with a ton of information that impossible to handle manually, big data scraping solutions come to help you. Data scraping is based on using special scrapers to crawl across specific websites and look for specific information. As a result, we’ll have files and tables with structured content. When data is ready for further analysis, the following advanced analytics processes come into play:
  • Data mining, which screens data sets and searching patterns and relationships;
  • Predictive analytics, that builds patterns to predict customer behavior or any other upcoming developments;
  • Machine learning, which uses algorithms to study bid data sets and deep learning, a more advanced offset of machine learning.

Using Big Data in Business

Big data has a significant role in the world of business and to understand its impact on the business environment and create a value, it is necessary to learn a bit about data science. Here are the best business practices where big data can be used:
  • Risk Management – While businesses are looking for a strategic approach to handle risk management, the use of big data can provide predictive analytics for risk foresight.
  • Understanding Customers – By using big data extracted from social media interactions, review sites and messages on Twitter, you will create a proper customers profile or identify your buyer personas.
  • Determine Competitors – Big data enables to know your competitors, what pricing models they have, or what their customers are feeling about them. Plus, you can learn how they are working on their customer engagements.
  • Stay Tuned with Trends – Big data will help to identify trends and go on with product development by analyzing how customers’ behavior and buying patterns force on trends and how they will change over time.
  • Marketing Strategy – By understanding your customers, you can develop successful campaigns to target a specific audience and get insights to create high-converting marketing materials.
  • Talent Acquisition – Thanks to big data, you can boost company’s human resource management. You will have the complete information to hire the best people, organize actual trainings and boost staff satisfaction.

4 V’s of Big Data FAQ

What are the 4 v’s of big data?

There are 4 main characteristics that evaluate big data. They are also called 4V – Volume, Variety, Velocity, and Veracity.

What is variety in big data?

Variety in big data means many types of collected information, which can also be divided into structured and unstructured. If the structured one includes traditional statistics that can be easily placed in sheets, then unstructured information includes pictures, video, audio, etc.

What is an example of veracity in big data?

E.g., during a medical experiment, the data was collected from 1000 men and women in different age groups (specification of where you get the data from), it was collected via observation and written individual survey responses (how it was collected), and will be analyzed using analytical and statistical measures of their medical reactions (how it will be analyzed). All details related to these three factors will define the data quality, i.e., data veracity.

Conclusion to 4 V’s of Big Data

4 V’s of big data is the basis for making a smart business decision, and there are a few methods to turn this to your benefit – one of them is data scraping. For large and medium enterprises, it is recommended to get web scraping solutions that can perform all operations automatically without human intervention. Check out how DataOx can offer you a data scraping strategy tailored right for your business growth needs. Schedule a consultation with our expert and get to know more about web scraping and how it can enhance your business.
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