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Big Data is the birthchild of the Information Age. Since computers became a household item, and cellular device on each person, the sheer amount of data generated by the billions of people on the planet gave data the worth it has today.

It was in the early 2000s when Industry Analyst Doug Laney conceptualized and put forth 7 V’s of Big Data terminologies. These helped define the concept of Big Data which led to a massive push to the Data momentum.

It is essential to understand the different characteristics of data engineering that help companies to process, read, manage and analyze data more efficiently.

Here are the 7 V’s of Big Data defined:

Volume

The first attribute of data is sheer size. A humungous amount of data is collected from a variety of sources, including transactions, smart devices, telecommunications, videos, images, audio, social media, and much much more. There are two ways the data is stored by companies, either on their premise using servers or cloud storage. The place where all the unprocessed data is stored is called data lakes.

Velocity

The flow of data inside the system happens at an unprecedented rate and has to be controlled promptly. Technologies like the Internet of Things (IoT) like RFID tags, sensors, smart meters, and QR code scanners create data quickly in real-time.

Variety

Businesses generate and receive a variety of data in all types of formats. These might range from structured or numeric data, which are part of traditional databases to unstructured data like text documents, emails. videos, audios, activity logs, stock portfolios, and other financial transactions.

Virality

The thing about data is that you can analyze and find trends but the market decides what goes viral. This is one of the most challenging aspects of data. The flow of data is unpredictable. All businesses must be ready to manage daily, seasonal, and event-triggered peak data loads.

Veracity

The attribute defines the quality of data. It is crucial to maintain clean data for processing purposes. But data comes into the system from so many sources that it becomes difficult to standardize the inputs. Businesses need to extract, transform and load data using correlated relationships, hierarchies, and multiple data linkages.

Visualization

One of the objectives of Big Data is to gain insights into the market and the audience. This can only be done when the data is translated into a picture that can be read and understood by the users of an organization. It becomes essential to transform the raw data that is relevant and actionable using visual aid.

Value

Businesses focus on the Return on Investment with every step. Big Data is a must investment but the step must be taken with a strategy and well-defined objectives. While dealing with data it is crucial to know the worth and the value it will create over time for your company. Value is the end game.

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