In our modern era, amassments of data have become easier and easier to come by–
from website cookies, to mouse click tracking, to email lists. To classify these large collections of data the terms Big Data and Big Data Analytics were coined. Big Data refers to the huge amounts of data that contain huge volume, variety, and velocity collected by large businesses and other organizations while Big Data Analytics is the analytics performed on the large data sets. Big Data is often so large in scale, fast, and complex that it is extremely onerous and demanding on traditional analytic and processing methods.
The complexity of Big Data was made simpler and was originally broken down by former
Meta Group Inc. analyst Doug Laney in 2001. Laney stated that there were three Vs that
comprised Big Data: velocity, value, and variability. He referred to the first V volume as the
amount of high volume but low-density data collected by various companies and in need of
analyzing. The second V velocity refers to the rapid rate at which data is received, collected,
and acted on. More often than not the velocity of data collected exceeds acting capacities and
thus requires it to be stored on different hard drives, discs, or memory boards. The third V
variety refers to the various different types of data that exist and can be accessed. Older and
more traditional data types were designed to fit in a relational database. However, with the
increased prominence of big data, this prefixed structure has become less relevant as data now increasingly comes in modified unstructured data types. Since 2001 when the three original Vs were originally created, several others have since been added and will continue to be added in the future. Some of the more recent additions include veracity, value, and variability.
Big Data is an integral part of today’s modern business practices and touches every
individual in some form or another. It has become a form of capital as a key part of the value
that marketing teams, tech companies, and large corporations offer comes from their Big Data collections and their Big Data Analytics which they look at to analyze and increase efficiency, product development, or economic gains.