Call us for any questions

+98 912 505 3609

Call us for any questions

+98 912 505 36 09

What it is & why it matters

Big Data Analytics

Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making

For the most part, in popularizing the big data concept, the analyst community and the media have seemed to latch onto the alliteration that appears at the beginning of the definition, hyperfocusing on what is referred to as the “3 Vs—volume, velocity, and variety.” Others have built upon that meme to inject additional Vs such as “value” or “variability,” intended to capitalize on an apparent improvement to the definition.

  • Volume defines the huge amount of data that is produced each day by companies, for example. The generation of data is so large and complex that it can no longer be saved or analyzed using conventional data processing methods.
  • Variety refers to the diversity of data types and data sources. 80 percent of the data in the world today is unstructured and at first glance does not show any indication of relationships. Thanks to Big Data such algorithms, data is able to be sorted in a structured manner and examined for relationships. Data does not always comprise only conventional datasets, but also images, videos and speech recordings.
  • Velocity refers to the speed with which the data is generated, analyzed and reprocessed. Today this is mostly possible within a fraction of a second, known as real time. 
  • Validity is the guarantee of the data quality or, alternatively, Veracity is the authenticity and credibility of the data. Big Data involves working with all degrees of quality, since the Volume factor usually results in a shortage of quality.
  • Value denotes the added value for companies. Many companies have recently established their own data platforms, filled their data pools and invested a lot of money in infrastructure. It is now a question of generating business value from their investments.


Increased data volumes being captured and stored
Rapid acceleration of data growth
Increased data volumes pushed into the network
Growing variation in types of data assets for analysis
Alternate and unsynchronized methods for facilitating data delivery
Rising demand for real-time integration of analytical results