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pillars of big data

The technology revolution has transformed the world significantly over the last 15 years. Information Technology has connected the world and enabled sharing, storing and accessing information on the internet like never before. This has created an ocean of structured as well as unstructured data available on the web. With the help of right data scraping tools, you can unravel amazing insights which can aid important business decision and strategic moves. This ocean of information, or Big Data as we call it, is simplified by categorizing it into 4 dimensions, commonly known as the 4 pillars of big data.

The 4V’s of Big Data Decoded

The 4Vs of BIG Data stands for Volume, Variety, Velocity and Veracity. Let us discuss each of one of these in detail.

1. Data Volume

As the name suggests, the main characteristic of big data is its huge volume collected through various sources. We are used to measuring data in gigabytes or terabytes. However, big data volume created so far is in Zettabytes, which is equivalent to a trillion gigabytes. 1 zettabyte is equivalent to approximately 3 million galaxies of stars. This will give you an idea of a colossal volume of data being available for business research and analysis.

Take any sector and you can comprehend that it is flooded with data. Travel, education, entertainment, health, banking, shopping–you name it and you have it. Almost every industry today is reaping or trying to reap the benefits of big data. Data is collected from diverse sources which include business transactions, social media, sensors, surfing history, etc.

With every passing day, data is growing exponentially. According to experts, the amount of big data in the world is likely to get doubled in every two years. As the volume of the data is growing at the speed of light, traditional database technology will not suffice the need for efficient data management limited to storage and analysis. The need of the hour will be a large scale adoption of data management tools like Hadoop and MongoDB. These tools use distributed systems to facilitate storage and analysis of this enormous big data across various databases. This information explosion has opened new doors of opportunities in the modern age.

pillars of big data
Source: IBM Big Data and Analytics Hub

2. Data Variety

Big data is collected and created in a variety of formats and sources. It includes structured data as well as unstructured data like text, multimedia, social media, business reports, etc.

  • 1. Structured data such as bank records, demographic data, inventory databases, business data, product data feeds have a defined structure and can be stored and analyzed using traditional data management and analysis methods.
  • 2. Unstructured data includes captured like images, tweets or Facebook status updates, instant messenger conversations, blogs, videos uploads, voice recordings, sensor data. These types of data do not have any defined pattern. Unstructured data is most of the time reflection of human thoughts, emotions and feelings which sometimes would be difficult to be expressed using exact words.

As the saying goes, “A picture paints a thousand words”, one image or video which is shared on social networking sites and applauded by millions of users can help in deriving some crucial inferences. Hence, it is the need of the hour to understand the non-verbal clues of unstructured data.

One of the main objectives of big data is to collect all this unstructured data and analyze it using the appropriate technology. Data crawling, also known as web crawling, is a popular technology used for systematically browsing the web pages. There are algorithms designed to reach the maximum depth of a page and extract useful data worth analyzing.

Variety of data definitely helps to get insights from different set of samples, users and demographics. It helps to bring different perspective to the same information. It also allows analyzing and understanding the impact of different form and sources of data collection from a ‘larger picture’ point of view.

For instance, in order to understand the performance of a brand, traditional surveys are the primary channel of data collection. However, you can obtain real time feedback through various other forms like Facebook activity, product review blogs, and updates posted by customers on merchant and marketplace, in lot lesser time. Variety of data definitely gives a clearer perspective to your business decision-making process.

3. Data Velocity

In today’s fast-paced world, speed is one of the key drivers for success in your business as time is equivalent to money. In such scenarios, it becomes vital to collect and analyze a vast amount of disparate data swiftly, in order to make well-informed decisions in real-time. Think about it, low velocity of even high quality of data may hinder the decision making of a business.

The general definition of velocity is ‘speed in a specific direction’. In 4 V’s of big data, velocity is the speed or frequency at which data is collected in various forms and from different sources. The frequency of specific data collected via various sources defines the velocity of that data. In other terms, Data Velocity it is data in motion to be captured and explored. It ranges from batch updates to periodic to real-time flow of the data.

You can relate data velocity with the amount of trade information captured during each trading session in a stock exchange. Imagine a video or an image going viral in the blink of an eye to reach millions of users across the world. Big data technology allows you to process the real-time data, sometimes without even capturing in a database.

Streams of data are processed and databases are updated in real-time, using parallel processing of live streams of data. Data streaming helps extract valuable insights from incessant and rapid flow of data records. A streaming application like Amazon Web Services Kinesis is an example of an application that handles the velocity of data.

The higher the frequency of data collection in your big data platform in a stipulated time period, the more likely it will enable you to make an accurate decision at the right time.

4. Data Veracity

The fascinating trio of volume, variety, and velocity of data brings along a mixed bag of information. It is quite possible that such huge data may have some uncertainty associated with it. You will need to filter out clean and relevant data from the big data. In order to make accurate decisions, the data you have used as an input should be appropriately compiled, confirmed, validated, and made uniform.

There are various reasons for data contamination, like data entry errors or typos (mostly in structured data), wrong references or links, junk data, pseudo data, etc. The enormous volume, wide variety, and high velocity, in conjunction with high-end technology, hold no significance if the data collected or reported is incorrect. Hence, data trustworthiness (in other words, quality of data) holds utmost importance in the big data world.

In automated data collection, analysis, report generation, and decision-making process, it is inevitable to have a foolproof system in place to avoid any lapses. Even the most minor of slippage at any stage in the big data extraction process can cause an immense blunder. It is always advisable that you have two different methods and sources to validate credibility and consistency of the data, to avoid any bias.

It is not only about accuracy post data collection but also about determining right source and form of the data. Required amount or size of the extracted data, and the right method of analysis, all play a vital role in procuring required results. It will definitely allow you to position yourself in the market as a reliable authority and help to you to attain greater heights of success.

Parting Thoughts on 4 V’s of Big Data

These 4V’s are four pillars lending stability to the giant structure of big data and adding precious 5th “V, that is, Data Value, to the insights procured for smart decision making. Web scraping service, like PromptCloud helps enterprises meet their quality data requirements by providing data scraping solution to them.

How will you be utilizing these 4 V’s of big data in the near future? Do write in to us and let us know your thoughts.

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