If you search over the internet, the term data is defined as qualities, characters or symbols on which operations are performed by a computer which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical or mechanical recording media.
In this post, we will show what big data is and how it is classified.
In simple words, we can say that all the facts and figures which can be stored in digital format can be termed as data. All the text, numbers, images, audios, videos stored in our phones or computers are some examples of data. They are digitally stored and comprise of zeros and ones.
The concept of Big Data denotes copious amounts of data which are too large to be processed and analyzed by traditional methods.
More than 2.5 trillion bytes of info are generated everyday through various devices. There are millions of data generated on the internet per minute. Example could be millions of search queries on google, snaps shared on snapchat, millions of log ins on Facebook or videos watched on YouTube. So, there is a lot of data created or being used. The question is how they are classified?
How do you classify any data as Big Data?
Classification is essential for the study of any subject. Therefore, Big Data is classified into three main types: structured, semi-structured and unstructured.
Structured Data
refers to the data which is already stored into databases in an organized way. It is used in most of the programming languages and computer-related activities. There are two types of structured data: Machine-Generated Data and Human-Generated Data.
Machine-generated data comprises all the data received from sensors, weblogs and financial systems. Devices such as GPS data, medical and data of usage statistics apprehended by servers and applications and the huge amount of data that usually moves through trading platforms are examples of machine-generated data. On the other hand, human-generated structured data mainly includes all the data that humans input into computers, such as names or other personal details. When a person makes a click on the internet or even makes a move in the game, data is created. This can be used by companies to figure out their customer behavior and make the appropriate recommendations and modifications.
Unstructured Data
has no clear format in storage. Most of the data a person encounters belong to this category and until recently there is not much to do, except storing it or analyzing it manually.
Unstructured data is also classified based on its source which could be: machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets on technology. Human-generated unstructured data is found in abundance across the internet, since it includes social media, mobile data and website content. This means that the pictures we upload to Facebook or Instagram, the videos we watch on YouTube and even the text messages we send, it all contributes to the enormous heap that is unstructured data.
Semi-Structured Data
Even though, it appears at a glance that is similar with unstructured data, semi-structured data differs in a way that it stores information that is not in a traditional database format but contains some organizational properties which make it easier to process. For example, NoSQL documents are considered to be semi-structured since they contain keywords that can be used to process the document easily. Also, data used in an XML file is an example of semi-structured data.
Big Data Analysis
Big Data analysis is important as it can help companies or various industries achieve cost reductions and dramatic growth. It denotes the study of huge amounts of stored data in order to extract behavior patterns. An example can be of Netflix. Netflix collects user behavior data from its millions of customers. This data helps Netflix in understanding what every individual customer wants to see. Based on the analysis, it recommends movies or TV shows which the viewer will love to watch. As a result, both sides profit as the customer is happy because he is getting what he likes without even searching for it and Netflix receives higher customer retention.
So, what are the features of Big Data?
Big Data Features
There are four important features of Big Data.
- Volume: which relates to enormous volume of data that is represented.
- Velocity: the high speed that the data is generated with.
- Variety: the immense variety of typology they encompass. As beforementioned: structured, semi-structured, or unstructured like excel records, log files or NoSQL data.
- Veracity: the degree of accuracy and trustworthiness to handle and manage the data effectively.
In general, the significance of Big Data is that it helps companies utilize their data and use it in their own way which leads to a better operational efficiency, customer retention and acquisition by recommending their preferences, cost savings or better marketing insights.
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