Big data is a set of tools that receives a large volume and variety of data that, after analyzed and interpreted, are used by companies in business strategy.Due to its gigantic volume and a great variety, this data cannot be interpreted and processed by conventional software.It was for this reason that big data emerged, for having agility and the ability to interpret data in large volumes and of different types.
What is Big Data for?
Big data was created with the purpose of receiving, identifying and interpreting as much data as possible .
The result of this process allows companies to use the information collected in the creation of new products, customer loyalty, interpretation of the target audience’s interest, among other circumstances.
Companies like Netflix and Facebook use big data to assess the interest of their users and send content that is in line with their choices, for example.
As a result, they retain a large number of customers for being attentive to the wishes of those who use their services.
What types of data are found in Big Data?
In Big Data we can find some different types of data. Some of them are:
Structured data
These are traditional databases, organized in tables, columns and rows. These types of data are those that are easy to interpret, such as texts and numbers;
Semi structured data
It is data that follows several different patterns.
Unstructured data
This is the most common data type today, accounting for more than 80% of the data in big data. They are, for example, images, videos and documents that have a wide variety of sources, so they are not standardized and easy to interpret, with structured data.
Social networks are largely responsible for the production of unstructured data.
The 5 V’s of big data
We can characterize Big data in 3 V’s: volume, speed and variety.
Volume
There are countless types of transactions made on the internet, such as sending e-mails, e-commerce purchases and payments, banking transactions, interactions on social networks, among others. All of this information arrives in great volume in big data.
The volume, therefore, is the amount of data present in big data which, in this case, is estimated to have around 1 billion terabytes stored in 2020.
Variety
Previously, the data was mostly structured and easy to analyze and interpret. As instant interactions on the internet have grown, data types have changed rapidly as well. As seen previously, big data needs to analyze, in addition to structured data, semi and unstructured data.
With a huge variety of data, companies that manage to capture this variety add value to their business.
velocity
It is already known that big data has a large volume of data every day, however, another important characteristic is the speed that this data reaches the tools.
As interactions and transactions are most often instantaneous, the speed of analysis and interpretation of this data must be immediate, even for companies to resolve issues in real time, obtaining a competitive advantage in the market.
Veracity
With such a large amount of data from different structures, it is important to identify which ones are useful and reliable. Veracity is one of the biggest and most important characteristics of big data.
The veracity of big data is related to its ability to separate useful data from those that have no value for companies and their strategic businesses.
Something important about veracity is that this data needs to be truthful and match the time it was collected. Data referring to events that have already passed are also of no value.
Value
Value is one of the most important characteristics within big data. It does not help if the speed is instantaneous and if the data are true, for example, if they do not make sense, that is, they do not add value to companies and their strategies.A company that adheres to big data, for example, must understand that, if the data collected does not make any sense to its business, it is not worth maintaining a big data system in the company.
If the data collected is not in accordance with the company’s objectives, its type of business and strategy, these data have no value.In conclusion, data with value are those that add value, that is, they mean something useful for what the company wants.
