Interview Question - What are the 43 Vs in Big Data

If you are asked to explain the Vs in Data in an interview, what will be your answer. Is it 3Vs, 4Vs, 5Vs, 7Vs or 10 Vs? You might be surprised that you have 43 Vs!!!

Like the evolution of data, number of Vs have evolved over time. 

Initially, it is 4Vs with Volume, Variety, Velocity and Veracity. Read detail at here. 

Source: https://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data

Then Value was added and it became 5Vs. Read details at here.

Source: https://www.edureka.co/blog/big-data-characteristics/

Then 5 become 8 with the additions Viscosity, Visualization, Virality, 

Source: https://twitter.com/vladobotsvadze/status/942197217473032192

It didn't take much longer to this to become 10 Vs. 

Source: http://houseofbots.com/news-detail/2819-1-the-10-v%27s-of-big-data

The latest article shows that we have 42 Vs which is not possible to draw in fancy diagrams. This article shows 42 different Vs as per in 2017. This article shows how the number of Vs have evolved over the years. 

Source: https://www.kdnuggets.com/2017/04/42-vs-big-data-data-science.html

During navigation through a Coursera course at  University of California San Diego, there is another V which is not in the 42. That is Valence. Valence in Big Data refers, to connectivity between data. When you are dealing with multiple data sets, there can be connectivity between data. When the Valence increases, you need to adapt to more complex algorithms. 

So we have 34 Vs of Properties of Big Data. Any things else you know. Surely, now this should have passed the hundred!

Think about Data Volume in Vs of Data

If you are working and learning about data, pretty much sure that you have come across Vs for properties of data. Some are saying it is 3Vs and for some, it is 4Vs or 5 Vs. Here is an image that shows the 10Vs of properties data. 

Let us stop arguing what is the actual number of Vs and let me share an interesting story on Volume, one V which no one will dispute. 

When I started my career as an implementation officer way back in 25 years ago things were a little different from what you have today. I was working for a software development company that was supporting multiple clients around the country that consists of hotels, plantations, factories etc.  Every year, I have a job to do. Once the accounts year ended or coming close to an end, these systems do not have enough disk space for the next year. So I have to visit every client's place as there is no remote connectivity support. Then I will print all the detailed reports that will take hours and hours and file them. Then take backups of the existing data and delete data so that next year's work can start.

Now think about data analysis. As you imagine nothing much of analysis can be done with one year's of data. At that time we were boasting about our reports that have the ability to compare revenue or expenses of this month to last month. 25 years ago that is surely a cracking feature to have. However, one of our clients who happens to be a Hotel said, there is no point in comparing this month to last month. In the case of the hotel, it is a seasonal business so they need to compare this month's values with last year's same month not last month. Our systems are not designed to accommodate two years of data, so we had to redesign our system.

On the other hand, our office server hard disk was upgraded to 1 GB. No, it is not a typo and you read it correctly, it is 1 GB.

Comparison of 1 GB now and Then

These stories tell you how precious was the storage back then. With that IT systems are more used for operational activities rather than analytical activities. Increase in the storage technologies has paved the way for different types of analytics such as Diagnostics, Predictive and Prescriptive. The above picture tells us a story of thousand words on how the storage technogies have increased over the years.