Researching of Big Data Analysis

Subject: Tech & Engineering
Pages: 2
Words: 579
Reading time:
3 min
Study level: Master

Big data is a term first coined in the 1990s. Nonetheless, it gained traction and relative fame in 2011, eliciting major debates on what it is and its importance in propagating effective product and service delivery. In the first instance, many people take big data at face value, defining it using one aspect of its provisions, a large volume of information. However, it is important to distinguish between various factions of big data to elicit its true form.

It is important to state that the article does not claim that volume is not a part of the subject matter; it is one of the major facets of big data. Volume in big data is relative as companies hold massive amounts of structured and unstructured data. The latter takes up 95% of the information, while structured data accounts for 5% of organizations’ information (Gandomi & Haider, 2015). In this way, varying organizations use different database management systems to manage data. For instance, an organization such as Facebook holds approximately 20 petabytes of data in unstructured form. In the recent past, one terabyte of information (1 petabyte=1024 terabyte) was considered a large volume of information (Gandomi & Haider, 2015). While this remains true, large companies such as Facebook process millions of pictures per second, necessitating large data storage designs. Volume is relative as it will continue to grow due to technological advancement.

Velocity is another component of big data and helps define its use in large and small organizations. It is important to consider that the social media age is volatile and tumultuous for organizations gathering information on client behavior (Gandomi & Haider, 2015). A large volume of unstructured and sentimental data is elicited every second through platforms such as Facebook and Twitter. Companies need to sift through such data before it disappears to develop a semblance of accurate data. It is important to note the large volume of information gained by organizations within a short period encompasses its velocity (Gandomi & Haider, 2015). Companies use specialized database systems to incorporate large volumes of this data as it elicits greater accuracy when using certain keywords to group information, aiding businesses in determining customer preferences and behavior.

Furthermore, information exhibits variety, a different but key component of big data. Technology allows businesses to use structured, unstructured, and semi-structured data. Organizations hold large volumes of varying data in either of these forms. However, it is important to discern that database management systems are necessary to use this information to guide decision-making within these businesses (Gandomi & Haider, 2015). Additionally, businesses have to account for new information and determine how to incorporate it for revenue generation. Clickstream information is integral to developing effective decision-making processes in online businesses, providing them with audio, video, and textual information to guide these assertions (Gandomi & Haider, 2015). Facial recognition technologies and user reviews help brick-and-mortar businesses to determine their customers’ trends and purchase behavior (Gandomi & Haider, 2015). In this way, a variety of information is constant in big data and used to analyze customer behavior.

Conclusively, big data is a large field that incorporates various major tenets such as volume, velocity, and variety to aid business decision-making. Large and small organizations use specialized database management systems and depend largely on unstructured data to determine effective promotion and product placement for their customers. In this way, big data elicits a game-changing capacity for organizations to make accurate decisions and offer customer-oriented products and services with relative ease.


Gandomi, A., & Haider, M. (2015). Beyond the hype: Big Data Concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.