<h2 id="heading-how-to-classify-data-as-big-data">How to classify data as Big Data?</h2>
<ol>
<li>Data can be classified as Big Data based on 5 v
<ul>
<li>Velocity- refers to the speed at which data is generated
</li>
<li>Volume: the size and amounts of big data that companies manage and analyze
</li>
<li>Value: the most important “V” from the perspective of the business, the value of big data usually comes from insight discovery and pattern recognition that lead to more effective operations, stronger customer relationships and other clear and quantifiable business benefits
</li>
<li>Veracity: the “truth” or accuracy of data and information assets, which often determines executive-level confidence
</li>
</ul>
</li>
<li>Additionally, later 2 extras v's where added
<ul>
<li>Variability
 Variability is different from variety. A coffee shop may offer six different blends of coffee, but if you get the same blend every day and it tastes different every day, that is variability. The same is true of data. If the meaning constantly changes, it can significantly impact your data homogenization.
</li>
<li>Visualization
 Visualization is critical in today’s world. Using charts and graphs to visualize large amounts of complex data is much more effective in conveying meaning than spreadsheets and reports chock-full of numbers and formulas.
</li>
</ul>
</li>
</ol>

## How to classify data as Big Data?

1. Data can be classified as Big Data based on 5 v
    
    * **Velocity**\- refers to the speed at which data is generated
        
    * **Volume:** the size and amounts of big data that companies manage and analyze
        
    * **Value:** the most important “V” from the perspective of the business, the value of big data usually comes from insight discovery and pattern recognition that lead to more effective operations, stronger customer relationships and other clear and quantifiable business benefits
        
    * **Veracity:** the “truth” or accuracy of data and information assets, which often determines executive-level confidence
        
2. Additionally, later 2 extras v's where added
    
    * **Variability**
        
        Variability is different from variety. A coffee shop may offer six different blends of coffee, but if you get the same blend every day and it tastes different every day, that is variability. The same is true of data. If the meaning constantly changes, it can significantly impact your data homogenization.
        
    * **Visualization**
        
        Visualization is critical in today’s world. Using charts and graphs to visualize large amounts of complex data is much more effective in conveying meaning than spreadsheets and reports chock-full of numbers and formulas.

Big Data

Table of contents

How to classify data as Big Data?