The concept of big data has become an increased subject in recent times. First, before indulging in big data, one needs to know the meaning of data. Data refers to raw facts in the form of characters, quantities, or symbols manipulated and processed into meaningful information (Debnath,2019).
Big data, therefore, refers to a group of data or a large volume of data that poses a challenge in terms of handling them with software or traditional hardware (Anderson & semmerlroth,2015). This set of data arises from the expansion of raw facts from various fields, thus making it difficult to store such data.
The big data differs from data stored in databases in that it acquires different forms, from structured, semi-structured to unstructured, and its enormous. These data arise from day to day activities. For instance, from search engines in the internet and social media, to mention a few.
There are different types of big data: structured, unstructured, and semi-structured (Debnath,2019). Structured means the data presentation appears in a fixed format. Unstructured data does not appear in a particular order or form, whereas semi-structured data appear in a fixed format while some have an unknown form, for example, XML file.
Therefore, this paper seeks to delve into the characteristics of big data and the reasons why additional study must be done to determine their interactions. The characteristics of big data include; volume, velocity, and variety (Debnath,2019). The three traits assist in analyzing the big data.
Volume
It refers to the data’s size amassed from different sources and fields (Debnath,2019). This includes videos, images, files that a particular organization collects, for instance, Facebook. Such data becomes huge; hence storing them for analysis purposes overwhelms data managers and databases. Voluminous data carries multiple files in different forms and formats (Anderson & Semmerlroth,2015).
For instance, social media data contains videos, a chain of calls, images, audio files, and document files. All these data piles in one pool of database. It, therefore, means that the database has unstructured files. Unstructured files are those files that a data manager cannot deduce any meaningful information from them. It means that handling data, processing and analyzing them becomes cumbersome, thus hampers the decision-making course.
Anderson and Semmerlroth (2015) assert that enormous information received is described in terminologies that people find unique. For example, data like music files and movies once downloaded and saved in flash drives, and the sizes read 1kilobyte or 1megabyte or 1terabyte. On the other hand, quantities of the influx of data collected stores up in the measurement of petabyte or zettabyte and yottabyte.
Velocity
This implies the rate at which data flows into the storage (Anderson & Semmerlroth,2015). For example, many calls made and messages sent per minute depict the velocity of data that the big data sets gathers in communication firms.
Anderson and Semmerlroth (2015) states that data received at high speed falls under two classifications: streaming data and complex event processing. Streaming data refers to higher rate data collected while an event is underway or ongoing, for instance, online gaming. During such online gaming, numerous data downloads into the computers at a faster rate.
The higher velocity of streaming data enables the stock market exchange to operate since investors can make quick decisions. An excellent example of such times includes trading in cryptocurrency.
Complex event processing refers to manipulating data gathered to determine particular events in the future regarding certain aspects (Debnath,2019). This comes after careful analysis and interpretation of data such that it can be manipulated in relation to real-time events so that a viable decision may be arrived at.
Variety
This refers to a situation where data takes numerous forms, unlike the traditional way data presentation comes in numeric or character field (Anderson and Semmerlroth,2015). For instance, data appears as a video, an image, an email, among others.
Such data’s unrelated nature makes them incompatible; thus, storing them in one database makes the analysis difficult. This leads to difficulty when it comes to deducing information from the data to make a decision.
Variety traits allow streams of unstructured data like emails, images, videos, audio files to flow into the databases (Debnath,2019). Such files become difficult to sort out and come up with meaningful information in a short period. For example, data obtained from customers browsing from the internet, time taken while browsing, pictures, and audio files download are unstructured files that take time to analyze to come up with information about customers’ likes and interests.
Debnath (2019) observes that despite the big data having the three traits, a fourth trait known as veracity emerges. Thus, the trait veracity seeks to find the data’s precision in relation to gathering information concerning business value.
Reasons why additional study must be performed on the interactions between each big data characteristic
Big data traits help in numerous ways and are linked by data and data sources arising from activities in the real world. For instance, sensors technology, like Global Positioning Systems (GPS), provides geolocation information. The information shows that data gathered links the three major traits of big data.
The data links the three traits in these manners. First, it shows there’s voluminous data gathered about locations. Secondly, Complex Event Processing, a type of velocity trait, its function as an idea for predicting the occurrence of events led to its application in Global Positioning System to provide information about a place or landmarks that provide information to people it also shows the variety trait of big data.
The second example comes from the use of social media. Suppose a company wants to introduce a new product in the market, it first creates a social media account for advertisement and product popularization. Facebook, as one of the social media, offers a huge platform for such a company. Facebook has the like option and an organization can create a page too. From liking of the pages by people, the organization can retrieve structured data through the number of likes, unstructured by the way people are messaging in their inbox and posting on their wall about different issues and semi-structured derived from likes, messages, and postings.
Facebook itself provides data at a faster rate; the data comes in different forms and different varieties. Such data obtained from Facebook links all the three types of Big data Characteristics.
Despite all the glaring interaction between the three characteristics, less scholarly research exists to explain how the traits connect without relying on real time occurrences to derive an explanation on how the Big data traits relate to each other.
Conclusion
Big data, despite its being huge and cumbersome in handling, processing, and using it in the decision-making process, has numerous significances in the real world today. For example, information gathered from social media by advertisement companies, despite the data being enormous, with time, the company can know what the customers want and what to change about their products.
On the other hand, the Big data traits that include: velocity, volume, variety, and veracity offer and gives a deep insight into the Big Data and its importance. However, extensive scientific research has to be carried to determine how the traits interact with one another. Profound limitation emanates from the study of the traits. One can link the traits through technology or big data applications rather than how they naturally link to one another.