In this digital world, everyone leaves a trace. From our travel habits to our workouts and entertainment, the increasing number of internet connected devices that we interact with on a daily basis record vast amounts of data about us. There’s even a name for it: Big Data.
Definition:
Ernst and Young offers the following definition:
“Big Data refers to the dynamic, large and disparate volumes of data being created by people, tools, and machines. It requires new, innovative, and scalable technology to collect, host, and analytically process the vast amount of data gathered in order to derive real-time business insights that relate to consumers, risk, profit, performance, productivity management, and enhanced shareholder value.”
Properties of Bigdata:
There is no one definition of Big Data, but there are certain elements that are common
across the different definitions, such as
- velocity,
- volume,
- variety,
- veracity, and
- value.
These are the V's of Big Data.
Velocity:
Velocity is the speed at which data accumulates. Data is being generated extremely fast, in a process that never stops. Near or real-time streaming, local, and cloud-based technologies can process information very quickly.
Volume:
Volume is the scale of the data, or the increase in the amount of data stored. Drivers of volume are the increase in data sources, higher resolution sensors, and scalable infrastructure.
Variety:
Variety is the diversity of the data. Structured data fits neatly into rows and columns, in relational databases while unstructured data is not organized in a pre-defined way, like Tweets, blog posts, pictures, numbers, and video. Variety also reflects that data comes from different sources, machines, people, and processes, both internal and external to organizations. Drivers are mobile technologies, social media, wearable technologies, geo technologies, video, and many, many more.
Veracity:
Veracity is the quality and origin of data, and its conformity to facts and accuracy. Attributes include consistency, completeness, integrity, and ambiguity. Drivers include cost and the need for traceability.
With the large amount of data available, the debate rages on about the accuracy of data in the digital era.
Value:
Value is our ability and need to turn data into value. Value isn't just profit. It may have medical or social benefits, as well as customer, employee, or personal satisfaction. The main reason that people invest time to understand Big Data is to derive value from it. Let's look at some examples of the V's in action.
