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The 4V of the Big Data


Once that we’ve already solved the differences between Big Data and Business Intelligence in earlier publications, today we will face the different characteristics of Big Data that highlight its value as informational base if it’s well treated. More specifically, as Marquez, N. (2016) explains, today we will focus on the “4V” of Big Data: Volume, Velocity, Variety and Veracity.

Volume: basically refers to the large amounts of data that are daily accumulated in a company. This is one of the main challenges for any IT department of a company: to know how to manage and process so much information. According to Gartner, about 25 billion devices will be connected to the network in 2020 (less than 4 years). The real challenge is not knowing how to collect such information, but to develop a continuous collection method and management of data.

Velocity: ??refers to the speed at which data is generated daily. It is, as we mentioned, the biggest challenge of all, and not only for the IT department, but for all other departments (financial, marketing, logistics, etc.). The key is to collect, manage, analyse and extract information about all business value in real time.

Variety: refers to the differences in the format, the origin (the main characteristics of the data), etc. Actually, the data may be collected in a structured, easy to treat (structured databases, predefined formats, etc.) way, or in a unstructured way, where data is extracted, for example, from social networking, video format, etc. The ability to know how to treat such data and transform it into an easier format to analyse it defines the ability of a company to know to manage and leverage their existing data volume.

Veracity: refers to the quality, predictability and availability of the data. It is the less uniform and hardest variable to control, due to the difficulty to make sure that data is 100% reliable. The key to tackle this facet of the data successfully is to have an impartial team helping to keep the data clean for its later analysis

In conclusion, the data has become so important (because of its easy access, its massive volume, and especially the wealth of information that can be extracted from it) that should be a priority for any business today. And the most important thing is not to debug this data seamlessly in one of its four sides, but to perform a cleaning job at all, to develop the ability to work with a lot of data, which are updated daily, which come from very heterogeneous sources, and without security to 100% of its accuracy and reliability.
Depending on your effort in the data treatment, you will have a better or worse outcome in subsequent analysis.
What do you expect to start taking care of your data?



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