Big data is the data that is almost impossible to process using traditional methods, like a single computer, because there’s so much of it and generated so quickly, in many different formats. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks.
The elements used to define Big Data are Volume, Variety and Velocity.
Volume refers to the vast amount of data being generated every second. Companies working with Big Data find ways to process, store and analyse data coming in, at massive volumes that surpass traditional methods process and store data.
The second characteristic that defines Big Data is Velocity, which refers to the speed at which new data is generated and the speed at which data moves around. Some examples of data velocity are social media post going viral in seconds, the speed at which credit card transactions are checked for fraudulent activities. In just a matter of seconds, your credit card company received information about your purchase, was able to compare it to usual purchases you make and decide whether or not to flag this as a fraudulent transaction. Velocity is very important factor in terms of deciding big data. It is the need of an hour
Finally, Variety is also used to define Big Data. It refers to the many different types of data that exist today such as credit card transactions, legal contracts, biometric data and geographic information, just to name a few. Companies working with big data find ways to use different types of data together such as an organization might want to extract data insights from a combination of social media posts, customer transaction records and real-time product usage.
Big knowledge veracity refers to the biases, noise and abnormality in knowledge. is that the data that’s being keep, and deep-mined significant to the matter being analyzed. In context feel veracity in data analysis is that the biggest challenge once compares to things like volume and speed. In scoping out your massive data strategy you wish to possess your team and partners work to assist keep your data clean and processes to stay ‘dirty data’ from accumulating in your systems.
Types of Big Data
Big data is divided into 3 types such as structured, unstructured, semi-structured
The term structured data refers to any data that conforms to a certain format or schema. A popular example of structured data is a spreadsheet. In a spreadsheet, there are usually clearly labelled rows and columns, and the information within those rows and columns follows a certain format.
By contrast, unstructured data is often referred to as “messy” data, because it isn’t easily searched compared to structured data. Example, imagine that instead of providing you with a spreadsheet of sales, I ask you to review camera footage that shows customers buying products and ask you to tell me how much money was made. This task would be much harder to do than using our spreadsheet.
Finally, we have semi-structured data. Semi-structured data fits somewhere in-between structured and unstructured data. Semi-structured data does not reside in a formatted table, but it does have some level of organization. Example of semi-structured data is HTML code.