The document discusses the 4 Vs of data - volume, velocity, variety, and veracity. It explains that volume refers to the large amount of data being created, velocity is the speed at which data is generated and processed, variety means data comes in different unstructured and structured forms, and veracity is the trustworthiness and accuracy of the data. While these are key aspects, the document emphasizes that for data to be useful, it must create value for an organization through insights and informed decision-making.

1. Understanding 4 Vs of Data
We can determine the data as four Vs—
1. volume
2. velocity
3. variety
4. veracity
But in order for data to be useful to an organization, it must create value—a critical fifth
characteristic of big data that can't be overlooked.
Volume
The first V of big data is all about the amount of data—the volume. Today, every single minute
we create the same amount of data that was created from the beginning of time until the year
2000. We now use the terms terabytes and petabytes to discuss the size of data that needs to
be processed. The quantity of data is certainly an important aspect of making it be classified as
big data. As a result of the amount of data we deal with daily, new technologies and strategies
such as multitier storage media have been developed to securely collect, analyze and store it
properly.
Velocity
Velocity, the second V of big data, is all about the speed new data is generated and moves
around. When you send a text, check out your social media feed and react to posts on
Facebook, Instagram or Twitter or make a credit card purchase, these acts create data that
need to be processed instantaneously. Compound these activities by all the people in the world
doing the same and more and you can start to see how velocity is a key attribute of big data.
Variety
Today, data is generally one of three types: unstructured, semi-structured and structured. The
algorithms required to process the variety of data generated varies based on the type of data to
be processed. In the past, data was nicely structured—think Excel spreadsheets or other
relational databases. A key characteristic of big data is that it not only is structured data but
also includes text, images, videos, voice files and other unstructured data that doesn’t fit easily
into the framework of a spreadsheet. Unstructured data isn’t bound by rules like structured
data is. Again, this variety has helped put the “big” in data. We are able to use technology to
make sense of unstructured data today in a way that wasn’t possible in the past. This ability has
opened up a tremendous amount of data that have previously not been accessible or useful.
Veracity

2. The veracity of big data denotes the trustworthiness of the data. Is the data accurate and high-
quality? When talking about big data that comes from a variety of sources, it’s important to
understand the chain of custody, metadata and the context when the data was collected to be
able to glean accurate insights. The higher the veracity of the data equates to the data’s
importance to analyze and contribute to meaningful results for an organization.
Value
While this article is about the 4 Vs of data, there is actually an important fifth element we must
consider when it comes to big data. This is the need to turn our data into value. In fact,
organizations that have not created a data strategy to yield insights and to drive data-driven
decision-making are going to fall behind competitors. Big data that’s analyzed effectively can
provide important understanding of customers and their behaviors and desires, how to
optimize business processes and operations and to improve a nearly endless amount of
applications. Whether you use data to create a new product or service or to understand a way
to cut costs, it is incredibly important that big data creates value. This value is why
organizations of every size must have a data strategy in place in order to ensure the data
needed to achieve the business objectives they adopted are being collected and analyzed.