Do you know the 5V’s of Big Data?

Do you know the 5V’s of Big Data?

09/29/2021 - Artificial Intelligence

Big Data has become one of the buzzwords in digital businesses and enterprises in recent years. Big data is a data collection, organization, and analysis technique. It has brought significant change for e-commerce and other companies that drive their business development through data-based decision making. But there are still aspects of big data that you may not yet know. Do you know the 5V’s of Big Data?

The 5 principles of Big Data

The 5V’s of big data refers to the five principles which underpin this data collection, storage, and sorting technique. All these principles arise from innate characteristics that all start with the letter ‘V’.

  1. Variety. Today there are multiple sources of digital data. They all come in different formats: numeric data, postal addresses, telephones, patterns, geographic locations, products of interest in a text. This wide variety in the nature of such data makes consolidation and pooling necessary so that it is easy to analyse. This process is essential to ensure that dispersion or false positives do not affect conclusions.
  2. Volume. Big data techniques are characterized by handling vast amounts of data. That is why volume is such an important dimension in its definition. In fact, this data technique allows large data exchanges to be collected and analysed, even with millions of records, which can then be used to benefit the business.
  3. Veracity. The ultimate goal of big data is to make business decisions based on real data. That is why veracity is one of the fundamental pillars of big data. One of the greatest challenges to ensuring that this goal is achieved is discarding invalid data. Data may be invalid either due to its origin, form or because the context in which it was collected does not fit the common trend.
  4. Velocity. As with volume, the speed of online data flow is exceptionally high. This constant change affects the veracity of data, so it needs to be updated as close to real-time as possible. Big data experts and their algorithms need to recognize this immediacy, to move it into log analysis, and ensure the correct conclusions are drawn.
  5. Value. Or, in other words: to get the real benefit from the correct data to focus it on the business. This means that you need to choose which records are most appropriate for your processing, be selective, and consider their actual linkage. Improving products, customizing services, or delivering better pricing are just three examples of how aggregated data can be transformed into value for the business and its potential audience.

How does Big Data help digital businesses?

Big data usage is booming precisely because of the digital age’s vast array of accessible sources and volumes of data. Social networks, online shopping, streaming, and millions of connected devices are all valuable sources of information. They allow digital businesses to know their audience better, adapt their offer to consumers’ wants and needs, and find the best approach for the benefit of both parties.

Platforms as varied as Spotify or Amazon use big data to tailor their content and product offerings to their users. Knowing consumer tastes, trends, product purchase history, and frequency of service switching, for example, are the keys that allow you to approach your customers more naturally, ensuring you are building a community.

Reactev uses big data science techniques to record and store market price data so that you can safely optimize your e-commerce pricing strategy . What are you waiting for? Request your trial!

Category: Artificial Intelligence


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Maria Jose Guerrero
Content Manager

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