Defining properties of Big Data Analytics

Big Data Analytics gives remarkable business insights to make faster and better decisions by examining large amount of data and varied data sets to uncover hidden patterns, market trends, correlations and many other insights in an organised business environment.

It is important to know the defining properties of Big Data Analytics as it provides the perceived value of data i.e, it provides a platform to businesses The defining properties of Big Data Analytics are Volume, Velocity, Variety and Veracity.

1. Volume - Big Data infers large amount of data. This need not be in measurements of petabytes. The volume of data is very huge with the usage of machines and networking over the years. Analysing this large set of data is a task to be considered.
2. Velocity is the speed at which the large incoming data is analysed and processed. Social media sites get enormous amount of incoming data. If the velocity of such large flow of data is handled it helps businesses provide strategic outputs.Streaming apps like Netflix is an example.
3. Variety - Big data is obtained from variety of sectors such as  Government, Healthcare, Retail, Telecom, Education and media. Both structured and unstructured data can be described as variety. Structured data such as spreadsheets, information in databases is increased by unstructured data such as photos,media files in twitter. The variety of unstructured data has to be stored, maintained and analysed.
4. Veracity is mostly the data in doubt, uncertainty due to data inconsistency and incompleteness. In other words, varacity is data ambiguity. To achieve data variety is a challenge. A partial set of techniques are cleansing of data, estimation of confidence, stochastic optimisation methods.

When the four V's of Big Data are measured, one can broaden the outcomes of the data to influence upsell, cross-sell, retention, customer acquisition and other revenue indicating generators. Analysing Big Data with the four V's brings value to the business. Big data initiates association with what is not known with what might lead to newer possibilities.