What Are the Three V’s of Big Data That Often Define Its Characteristics?

What are the three V’s defining Big Data? They include: Volume, Velocity, and Variety. They were all named by someone at a recent conference as part of a definition of Big Data. And yet, I wonder what is meant by these words for so many people use the term “data” to describe and analyze almost everything in our lives.

In the definition of “volume,” it means “the quantity of data that defines or describes a particular entity.” The definition is sometimes used in the context of a business or technology. “Velocity” is defined as, “the velocity with which any system moves.” When describing and predicting future data sets the “variety” is used to describe the types of elements included. This is the third V. To me, the question seems to be, what are the V’s and what do they mean to your organization?

From the viewpoint of a CEO looking for the best approach to analytics in his business, what are the three V’s of big data? According to those in attendance at the recent SIA conference who had been asked this question: “The first is velocity. It represents how quickly something changes and the second are variety. It represents the value of the data and the third is the business impact.”

In the context of a small business, the V’s may be more apparent. Say, you have one employee and ten metrics. You want to measure employee engagement. You measure productivity, training and development, and retention. Those are the metrics that you are using to define your big data V.

But for large businesses and organizations, the V’s are less obvious. Defining these V’s may require you to turn to a professional Big Data Analytics company for help. According to these consultants, the three V’s are: customer, market and value. In other words, the V’s may be more complex than what has been described here, but they are still useful for defining the characteristics of big data analytics.

In many instances, big data can bring significant business value. However, it can also be problematic. According to the data scientist definition, a problem within the business can lead to a big data problem. In other words, if the V’s are too complex or too far-fetched to use effectively by employees, then there is no point to collect and analyze the data. When there is no direct value to the business, the data may be deemed useless for analysis.

These three V’s are extremely important when defining big data. They represent the beginnings of data visualization, which is the process of combining big data with visual tools in order to interpret and create visual maps, dashboards and graphs. This allows data to scientists, managers and IT staff to visualize data in order to make informed decisions and provide insight into the data. The V’s of big data may seem overly complicated and over-defined, however this is not so. Once understood, they can be implemented easily and used effectively.

Understanding what are the three I’s of big data will help IT departments and business managers focus their attention where it matters most. When properly executed, data visualization can lead to better business intelligence solutions, increased productivity and ultimately greater profit. Just remember to ask the right questions when developing big data projects. Once the answer has been found, then implementing it becomes much easier.

V. What are the three I’s of big data that often define its characteristics? In truth, these three terms, although often used colloquially, are actually quite accurate. While not exactly numbers, these three words define what makes big data unique. V stands for volume. By calculating the amount of data stored in a physical or online medium, we come up with a definitive number representing the density of data per unit of space.

D stands for loss. While the volume can be calculated, the nature of the data itself can’t be. This represents how easily something can be lost in the haywire of data that defines big data. While it may sound like doom and gloom, it is important to understand that there will always be some sort of data loss, so don’t fret too much about the in’s at all.

A and E stand for access and availability. We want to make sure that the information that defines big data is available to people who need it right away. This is why the availability factor is such an integral part of what are the three I’s of big data. While there are certainly going to be some cases where people are more technically savvy than others, the availability factor ensures that anyone at any age, regardless of experience, can tap into the inherent power of big data.