The 4 V’s of Big Data for Yielding Invaluable Gems of Information
The technology revolution has transformed the world significantly over the last 15 years. Especially, IT 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. With the help of right tools of analysis, you can unravel amazing insights which can aid important business decision and strategic moves. This ocean of information is known as Big Data.
The concept of big data is simplified by categorizing it into 4 dimensions, commonly known as 4v’s of big data. Let us discuss each of one of these in detail:
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, according to various studies, 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 colossal volume of data being available for business research and analysis.
Take any sector and you can comprehend that it is flooded with loads of data. Travel, education, entertainment, health, banking, shopping – each and every sector can benefit immensely from the Big data advantage. Data is collected from diverse sources which include business transactions, social media, sensors, surfing history etc.
With every passing day, data is growing exponentially. Thousands of TBs worth data is created every minute worldwide via Facebook, tweets, instant messages, email, internet usage, mobile usage, product reviews etc. Every minute, hundreds of twitter accounts are created, thousands of applications are downloaded, and thousands of new posts and ads are posted. According to experts, the amount of big data in the world is likely to get doubled every two years. This will definitely provide immense data in coming years and also calls for smarter data management.
As the volume of the data is growing at the speed of light, traditional database technology will not suffice the need of efficient data management i.e. storage and analysis. The need of the hour will be a large scale adoption of new age tools like Hadoop and MongoDB. These 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.
Big data is collected and created in various formats and sources. It includes structured data as well as unstructured data like text, multimedia, social media, business reports etc.
- 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.
- 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 this non-verbal language to unlock some secrets of market trends.
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 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 one of the forms of data collection. This is done by selecting a sample, mostly from panels. The advantage of this approach is that you get direct answers to the questions. However, we can obtain real time feedback through various other forms like Facebook activity, product review blogs, and updates posted by customers on merchant websites like Flipkart, Amazon, and Snapdeal. A combination of these two forms of data definitely gives a data-backed, clearer perspective to your business decision making process.
In today’s fast paced world, speed is one of the key drivers for success in your business as time is equivalent to money. Fast turn-around is one of the pre-requisites to stay alive in this fierce competition. Expectations of quick results and quick deliverables are pressing to a great extent. In such scenarios, it becomes vital to collect and analyze vast amount of disparate data swiftly, in order to make well-informed decisions in real-time. 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 big data, Velocity is the speed or frequency at which data is collected in various forms and from different sources for processing. The frequency of specific data collected via various sources defines the velocity of that data. In other terms, it is data in motion to be captured and explored. It ranges from batch updates, to periodic to real-time flow of the data.
The frequency of Facebook status updates shared, and messages tweeted every second, videos uploaded and/or downloaded every minute, or the online/offline bank transactions recorded every hour, determine the velocity of the data. You can relate velocity with the amount of trade information captured during each trading session in a stock exchange. Imagine a video or an image going viral at 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 into your big data platform in a stipulated time period, the more likely it will enable you to make accurate decision at the right time.
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 big data, to provide insights that power up your business. In order to make accurate decisions, the data you have used as an input should be appropriately compiled, conformed, validated, and made uniform.
There are various reasons of 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, holds no significance if the data collected or reported is incorrect. Hence, data trustworthiness (in other words, quality of data) holds the highest 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 immense blunder.
Any reports generated based on a certain type of data from a certain source must be validated for accuracy and reliability. It is always advisable that you have 2 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 data, and the right method of analysis, play a vital role in procuring impeccable results. Integrity in any field of business life or personal life holds highest significance and hence, proper measures must be put in place to take care of this crucial aspect. 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.
These 4v’s are like 4 pillars lending stability to the giant structure of big data and adding precious 5th “V” – value, to the information procured for smart decision making. Check out here on how you can zero in on the right big data strategy, tailored exclusively for your organization’s growth needs.
How will you be utilizing these 4 V’s of data in the near future? Do write in to us and let us know your thoughts.