Understanding the V’s of Big Data
In the modern world, big data is all around us. Everyone has a smartphone and is constantly creating new data whether he or she knows it or not. Big data can be described by many characteristics but the most known are the 5V’s: Volume, Value, Variety, Velocity, and Veracity.
The volume of data describes its size. As you would assume, big data has a rather large volume (hence its name). Companies like Google can collect large amounts of data that are gathered from millions of Google searches. Then, they can analyze this data and look for trends that can lead to new conclusions and innovative discoveries. The volume of big data in the past has been a huge hurdle, but with today’s modern technology, more data is able to be collected and analyzed than ever before.
Value is vital to big data because the products that big data produce can be invaluable. For example, in 2009, when H1N1 pictured in Figure 1 was being tracked by the CDC, Google was able to provide very accurate and timely information about the spread of the virus was based on their users’ search data. Google was able to use big data to track the virus faster than the CDC could because the CDC had to wait for people to go to the doctor and take a test. By the time that all happened, the data would’ve been nearly two weeks old. This is a very extreme example, but there are lots more examples of times when big data was able to save people’s lives from natural disasters.
Variety refers to the different data types that big data encompasses some examples are shown in figure 2. Big data can come from anywhere it comes in all types and forms from security camera footage to texts and emails. Big data can be unstructured, semi-structured, or structured. Big data allows analysts to effectively combine and analyze any type of data.
Velocity does not seem like it would be related to big data, but in this context, velocity refers to the speed at which data travels. Big data involves collecting a large, continuous flow of data coming in from everywhere, and that data needs to be analyzed very fast — sometimes almost in real-time. This allows the data to be used to make decisions very quickly. For example, an F1 car like the one in Figure 3 has around 300 sensors, and the data from all need to be analyzed very rapidly to not only drive at top speeds but also to make sure that the car is not about to break and put the driver’s life in danger.
Veracity refers to the quality and accuracy of the data that is collected. Veracity is very important because it can help keep the other 4 characteristics in check. If there is too much volume, the data may not arrive at the same time as some pieces of data could be delivered over a messy network. Without veracity, the data will lose its value.
The 5V’s are the 5 basic characteristics of data, but big data can be so much more. However, knowing the 5V’s — Volume, Value, Variety, Velocity, and Veracity — can be very valuable to anyone who is or is planning on attempting to use or collect big data.
