Big Data in Digital Media Platforms

Introduction

According to the new Digital 2020 report jointly published by We Are Social and Hootsuite, while more than 4.5 billion people use the Internet, social media platforms have more than 3.8 billion users as of early 2020. Around 60% of the global population is online. Compared to the previous year, the number of Internet users grew by around 7%, while that of social media users by 9% (Kemp, 2020). This information makes us understand why no consensus on the definition of big data is reached despite all efforts. The common ground of all studies conducted throughout more than twenty years is that great quantities of data are produced each day owing to the digitalization of most day-to-day processes and that the most significant data instance is yet to be reached. The second point of agreement is that big data keeps records of large amounts of data and compiles various data types, creating meaningful results. Big data analytics provides a potential competitive advantage that radically changes social, cultural, political, and economic relations (Coté et al., 2016, p. 5). These data are fed by electronic transactions, applications, emails, texts, documents, videos, audios, pictures, clickstreams, public and private sector records, search terms, social network interactions, health records, scientific data, sensors, and smartphones (Gandomi & Haider, 2015). As one can see, the central pillar of digitalization is big data analytics. Increased economic efficiency is one of the most critical features of big data. This feature is seen as a significant opportunity because it provides a competitive advantage to many industries. Data and algorithms manage processes such as processing individuals’ purchasing tendencies and decisions they take in daily or business life.

In this age where data is of great importance, it is essential to increase awareness, facilitate access to data, and work on this subject. Apart from the abundance of data, what is also essential is the processability and quality of data, making the data meaningful by maintaining data security at the same time, detecting errors and deficiencies, and taking measures to eliminate such errors and deficiencies. In addition, as the naming of data changes and improves, changes occur in the techniques and technologies used. These changes also pose some problems for consumers. Some ethical values have become a matter of debate. That big data essentially cause this to be a source not only for scientific research but also for the industrial field. In particular, the fact that the information on the Internet and social media accounts of users is shared with profit-oriented organizations without users’ consent is one of the most debated topics of recent years (Custers, 2016, pp. 1-3). At this point, it is necessary to mention GDPR-EU Regulation 2016/679 as binding legislation on data protection and privacy. This legislation, which entered into force in 2016, aims to prevent data breaches. Accordingly, no personal data can be processed unless express consent is obtained from the relevant personal data owner (General Data Protection Regulation, 2016).

Constituting the main pillar of digitalization, big data storage is considered to be a significant opportunity as it provides many industries with a competitive advantage and value-generating analytics (Strohbach et al., 2016, p. 121). Like other industries, digital technology has also influenced the television industry, therefore going through a transformative process (Lippell, 2016, pp. 245-246). The most significant opportunity of digital media platforms working with the subscription system in terms of big data is directly contacting consumers to create content tailored for their requests (Mikos, 2016). The integration of television into technology occurs in many aspects ranging from content creation to distribution channels. Owing to the fact that meaningful predictions can be made by processing big data, platform users and potential users are understood better (Zhu et al., 2017). Big data leads to this outcome not only with its volume but also with its considerable data variety (Strohbach et al., 2016, p. 136). The analysis of data concerning aspects such as watching habits, number of views, repeat watching, leaving unfinished, skipping, rewinding, and pausing also helps with identify the lines of the original content to be produced.