Big Data analyzes, systematically extracts, and processes large and complex data sets for future use in many areas of human life. The amount of information generated by a person is increasing every year, and, accordingly, managing structured and unstructured data with the help of modern technologies is becoming essential. Based on the analysis of data, new approaches and solutions are developed in a variety of areas – from production to healthcare. The film industry is one of the fields that introduce modern research into work to improve productivity. It uses the valuable results of Big Data analytics to identify trends and interests of viewers and to optimize expenses. The purpose of this essay is to discuss the impact Big Data analytics has on the film industry, and also to consider personal interest in this area.
Big Data Analytics
The introduction of Big Data technologies by media companies around the world reflects an attempt to compete in the media, film, and entertainment industries, as well as to create content based on the received Big Data analytics. Nowadays, directors and producers are worried about how to achieve the success of a film, show, or any other television project. To reach this goal, all people involved in the film industry need to know what Big Data is, how to use it, and how it can become the key to producing profitable movies. Thus, professional data analytics specialists are in high demand in this industry, since the success of the entire project depends on them in many respects. The widespread use of Big Data analytics began during the rapid development and application of digital information technology (Arsenault, 2017). Now, such business analytics is automated and allows filmmakers to perform more sophisticated algorithms for processing a massive amount of data.
Data analytics performs many organizational roles applied to different fields, including the film industry. It creates a discussion between business and data, learns technologies, and practices them in an organization’s plans. The analytics analyze data sets and report results, and also use various technologies to develop models that convert data into actionable insights. There are some unique functions, for example, analysis of statistical data based on the results of surveys of moviegoers and fans. They also include the formation of a film production strategy according to search queries in a browser and drawing up an algorithm for formulating high-quality video recommendations (Xu et al., 2016). The specialists have full access to a wealth of information online and use Big Data to compare work with similar released projects to find out the size of the film’s audience, and film feedback. These comparisons help determine the pros and cons of the upcoming project.
Methods and the Impact of Big Data on the Film Industry
The financial success of a movie is mostly uncertain, and it is impossible to predict the popularity of a project with absolute accuracy. However, many researchers took on the task of determining the likelihood of triumph and profitability of a film using various approaches and methods. For example, text analytics refers to methods that extract information from textual data such as social networks, emails, blogs, and online forums. Methods for answering questions implemented in the film industry show the frequency and number of questions asked by users about movie premieres, main characters, and more. Sentiment analysis methods analyze opinionated text that contains people’s thoughts about different films, genres, or actors (Gandomi &Haider, 2015). All this helps to compile statistics on people’s interest in specific projects and put the data into practice.
Big Data Analytics uses the analysis of different data from social networks and searches queries in browsers. Thus, content-based data focuses on information posted by users on social media platforms (e.g., reviews, images, posts, tweets, and videos). For example, Netflix analyzes millions of real-time data points that its viewers make, thereby helping the firm determine if a pilot will be a successful new show (Xu, Frankwick, & Ramirez, 2016). Also, HBO, YouTube, and Netflix have an algorithm to formulate high-quality recommendations for films or videos. There are processes concerned with modeling and evaluating the influence of actors and connections in a social network. Text, audio, and video analytics can be applied to get an idea of ​​such data.
Scale, Effects, Impacts, and Disruptions
One of the main issues in the production of films is to predict the profitability of the project using data available only at the stage of preparation. Its accuracy largely depends on such parameters as analysis of the audience, release, and genre features of the film (Lash & Zhao, 2016). Thus, popular movies are becoming noticeably longer and filled with 3D effects thanks to digital film production, which allows directors to work simultaneously faster and cheaper than before. Film distribution networks and film industry leaders generally did not approve of long films, as they reduce the dynamics of distribution. However, through research, surveys, and evaluation of similar movies, analysts found that moviegoers were pleased with such elongation (Lash & Zhao, 2016). So, filmmakers stubbornly create works of more than two hours duration and get large sums at the box office.
From Big Data analytics, features such as genre, rating, number of parts of a film, and plot are often included in the criteria for predicting success. Researchers also use scripting texts and reviews of leading actors (Lash & Zhao, 2016). For example, now films about superheroes are trendy; millions of people around the world are fans of everything connected with superheroes. It is beneficial for directors to make films on this subject, as well as to create sequels, prequels, and crossovers of favorite stories. In addition, many famous films are divided into several parts to reveal the plot in more detail. It is done to increase the profit from a project, and often, a story is dull, inconspicuous, and worse than the first part.
Personal Interest
The film industry is an area in which I want to build and develop a career. My goal is to create a video or advertising company, and I am very interested in how predictive analytics of Big Data can help me grow my business. Nowadays, we can see movies and TV shows are trending to use Big Data to improve budgeting and marketing. Moreover, it can predict audiences’ preferences to achieve impressive box office. According to audience age, education level, interests, and geographical distribution, companies produce a specific type of movie for the target market. Therefore, the discussion of this issue has particular importance to me and allows learning how to benefit from scientific data for creating high-quality and profitable films in the future.
Conclusion
To sum up, data science is a mixture of statistics, mathematics, programming, and subject knowledge. Digital information technologies and analytical data not only radically change the graphic component of modern films, but also have a very tangible effect on the film production process. There are many ways Big Data identifies the trends and interests of viewers to predict the success of their future projects. Thereby, Big Data Analytics evaluates the information, looks at the relationships, and links them into a single whole picture to optimize movie production and increase profits.
References
Arsenault, A. H. (2017). The datafication of media: Big data and the media industries. International Journal of Media & Cultural Politics, 13(1-2), 7-24.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Lash, M. T., & Zhao, K. (2016). Early predictions of movie success: The who, what, and when of profitability. Journal of Management Information Systems, 33(3), 874-903.
Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562-1566.