Big data is a term that describes a wide range of data sets, including structured, unstructured, and semi-structured, that are constantly being generated at high speeds and volumes. But it’s not just the type or amount of data that matters, it’s what businesses do with it. Big data can be analyzed for insights that improve decisions and give confidence in making strategic business moves. An increasing number of companies are now using this data to uncover meaningful insights and improve decision-making processes.
History of Big Data
In the early 2000s, analyst best linkedin inmail subjects Doug Laney wrote an article that is now the most well-known definition of big data, and Doug distilled his idea into the 5 Vs that we will cover in the next section. The increasing amount of available data and just-in-time business models have made it necessary to have a way to analyze large amounts of data in real time.
What are the Components (5 Vs) of Big Data?
We can divide the concept of big data into 5: Volume, Velocity, Variety, Veracity and Value.
1. Volume
With 2.5 quintillion pieces of data this type of marketing! as i like to call it! being created every day—from social media, website and blog interactions, purchase history, clicks, and even tracking leads and customers—volume is the starting point for understanding big data.
2. Velocity
Speed is the rate at which data canada cell numbers is generated and processed. Take the transportation sector for example. A single car connected to the internet with telematics generates and transmits 25 gigabytes of data per hour at a nearly constant rate. Much of this data needs to be processed in real time or near real time.
3. Variety
It is a vector that shows the diversity of big data. This data is not just about structured data that exists as rows and columns in relational databases. It comes in all kinds of forms that differ from one application to another, and most of the big data is unstructured. For example, a simple social media post may contain some text information, videos or images, etc.
4.Veracity
Veracity is a measure of how accurate and reliable data is and how much value it brings. If data is incomplete or inconsistent, the accuracy of the analytics process decreases. Therefore, data accuracy is often classified as good, bad, and undefined. This is quite helpful when dealing with diverse data sets such as medical records. Where any inconsistency or ambiguity can have detrimental effects.
5.Value
With such a large amount of data, you’ll probably lose track of everything when you really need it. A because it’s hard to connect and transform information across platforms. A so it’s necessary to link and correlate elements together.