The 23Vs of Big Data

Because 10Vs is just not enough…

When I took an online course on Big Data, the instructor instructed me that “Big Data is commonly characterized by a number of Vs.” She did not, however, specify the number, and the Vs were introduced with great suspense:

The first three are Volume, Velocity, and Variety.

Later on, she introduced two more Vs: Veracity and Valence

I was pretty excited at this point, especially since I hadn’t heard from Valence since high school chemistry class. Not a minute later, she dropped a bombshell:

Moreover, we must be sure to never forget our sixth V, Value.

With no more Vs mentioned for several lectures, I settled down with this model of Big Data, and indeed I passed the course with high marks. It wasn’t until months later, my world was turned upside-down with a shocking revelation: Big Data has no fewer than TEN Vs!

The Incognito Four are: Variability, Venue, Vocabulary, and Vagueness

Would you believe that the Incognito Four remained hidden for more than 13 years before revealing themselves to the world?! Before anyone starts thinking, “This must be a joke”, I warn you: It is not. I know this because the one who helped reveal the Incognito Four, Dr. Kirk Borne, says it is not.

As I began to think more and more about Big Data, however, I realized that ten is just not enough. Big Data is too big and important to be contained by a mere ten Vs.

Therefore, I submit to you, dear Reader, that there are no fewer than 23 Vs necessary to characterize Big Data. Here are the more recent discoveries, the Lucky Thirteen, along with the requisite explanations:

Vastness: Big Data is not just big, it is vast.

Voluminousness: Because the volume of Big Data is so big, it is voluminous, and therefore it has voluminousness.

Voluptuousness: Big Data is not just voluminous, it has an innate beauty and therefore has a certain voluptuousness. Don’t take my word for it. 93% of Data Scientists agree on this.

Voodoo-magic: If you are not employing voodoo-magic in your Big Data Analytics, you should at least tell people you are.

Victory: If Big Data doesn’t give you victory against your opponents, you’re doing it wrong.

Vegetarian: You probably never realized this, but Big Data doesn’t eat meat.

Viscosity: Big Data is like a huge tanker truck full of molasses. It has crashed, and the molasses is spilling everywhere. It is going to crush you and everything you love. But it will take time.

Venom: When Big Data is really working for you, it is a bit like venom, isn’t it? See Victory above.

Vernacular: Big Data is often expressed in the vernacular. Vernacular is, by definition, how people actually express things. Therefore, Big Data cannot be anything but vernacular, right?

Vagility: Big Data will give your company or scientific career lots of vagility. Vagility is like agility, but it starts with a V; therefore, it is better.

Vectors: Big Data has lots of vectors. It has even vectors of vectors!

Virality: Big Data has obviously gone viral. Therefore, it has the essential quality of virality.

Vorticality: Vortexes are things that spin, often out of control. They are vortical. Big Data is like this, and therefore, it has vorticality. In fact, this word was invented specifically to describe how vortical Big Data is.

Venality: Big Data will really do whatever you want it to do, no matter how good or bad your personal intentions are. This is what ontologists call venality.

(Note: If you aren’t familiar with ontologists or ontologies, see my other article “The 64 Os of Ontologies”)

There you have it. The 23Vs of Big Data. For future students of Big Data, I sure hope it is enough. But if history is any lesson, it is only a matter of time before more Vs are discovered. While 23 was enough to satisfy Dr. Pepper, it wasn’t enough for Messrs. Baskins, Robbins, or Heinz. We may even be in a period of hockey stick growth for the Vs of Big Data. For the sake of humanity, let’s hope Vs don’t contribute to global warming. I repeat, this is not a joke.

Ok……yes, it is. ;-)

The 23Vs of Big Data was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.