What makes Big Data big?

Big Data - a term that has become very trendy during previous years. Everyone who works with data in some way knows the term, and yet there are still uncertainties about what Big Data actually is and how we should cope with it. 
In this blog post, we will elaborate on the reasons why the term “Big Data” not only refers to increased volumes of data but its scope. We will therefore not only address its definition but also its potential, challenges, and the reason why it will fundamentally change the way we see our world.

What is Big Data? 
In common terms, Big Data is defined by using the famous three V’s:

• Volume 
• Velocity 
• Variety​

As the term ‘Big Data’ suggests, it is defined by a big amount of data. The amount is hard to express in absolute numbers, since it is highly subjective. Simply put, Big Data has a volume that is impossible to handle with traditional methods. Volume itself is actually not a problem when data is at rest. When combining volume with a high frequency of incoming data that comes in a wide ranging variety, we are being challenged how to handle this mess. Variety means that data does not always have a regular table format (structured data), but also in semi-structured or unstructured format. We might need to cope with data such as text, e-mail, json, html, yaml, pictures and videos, just to name some examples. 
The three V’s can be extended with ‘Veracity’ and ‘Variability’, while the former refers to the varying quality of data and the latter describes the irregular, unpredictable streams of incoming data. 

What’s behind the hype?
The hype behind Big Data can be explained by looking at three main dimensions; the proceeding disaggregation of data, the increased computer processing capacity, and the more powerful data analysis methods available.

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