Table of Contents
Introduction
The evolution of Big Data technologies is completely based on large volumes of data which need to be stored, analysed and managed, and the growth of the Internet. Google has successfully developed various breakthrough technologies in order to successfully process Internet data and store huge set of processed data on distinct distributed commodity servers. High scalability and availability has been facilitated through effective storage and processing of data sets. Big Table and Google File System (GFS) can be regarded as the basic tools which were developed for initial data processing. On the other hand, the emergence of open-source Big Data can also be observed in the modern scenario. Apache, one of the open source communities, has been responsible for development of such new technologies. The Big Data technology runs on a platform where the intake layer is responsible for collection of multi-formatted information along with processing as well as storing data in desirable locations. In earlier years, television viewing was regarded as a linear approach. However, the Big Data technologies have played a vital role in transforming the traditional mechanism. The integration of interactive experiences within television is the final outcome of using Big Data. It can be stated that the modern TV application architectures has efficiently shifted from hardware-centric functionality towards a merger of both modular software services and hardware appliances. In this study, the focus will be on evaluating the ways through which ‘algorithmic turn’ in case of television production has been able to affect what audience watch.
Discussion
The algorithmic turn is closely associated with user interaction behaviour. It can be claimed that the instrumented applications have helped in enhancing user level interaction in multi-screen devices or traditional CPE. All forms of traditional events can be effectively measured with the support of such modern applications. Such events usually comprise of tuner change, user navigation, session start, content discovery, direct interaction, etc. The client applications within the emerging TV services have also helped in analysing wide range of collected events. On the basis of such detailed analysis, it becomes easier to ascertain aggregated and abstract consumption behaviour in terms of programme popularity within a specific geographical region. Tracking and monitoring of events also in turn facilitates great deal of user interaction. Digital television is accompanied with the characteristic of what users will be viewing is controlled. Big Data technologies are used to store large volume of customer related data. On the basis of such technologies, operators are able to analyse programme popularity along with determining which programme is viewed by which customer. The time, data and other similar records are maintained with the help of such in-built technologies. Netflix has been able to strengthen their position in the field of digital television. It can be argued that Netflix has adopted an algorithmic dimension which has enabled the company to keep a close track on what is being played, rated or searched. User interactions can also be efficiently tracked based on what is being searched, rated or played (Mayer and Negra, 2015). The scrolling or browsing information is also noted through evaluating user interactions. The different types of information collected can be easily fed into distinct algorithms, where each data shall be inclined towards serving a specific purpose.
It is evident that a common assumption is followed by the company where same viewing patterns represented in algorithm denotes similar kind of taste. The example of Netflix can be taken into account so as to understand how audience is simply being converted into puppets with the emergence of Big Data technologies. In general context, when a pause button is pressed while viewing a particular episode on the television, then an event is eventually created. The event being created can be easily analysed, logged and recorded. In U.S., Netflix is certainly the largest firm in context of commercial video streaming programming. Therefore, the company is able to accumulate sufficient knowledge regarding specific viewing pattern of target audience. Since 2012, digital television transformed the mechanism working behind viewing a specific TV programme. It was not simply a switch-in format; rather the algorithmic turn highlighted the way through which online programming provider can acquire viewing habits. Netflix has set forth high performance standards in context of providing an intersection between entertainment media and Big Data (Gandhi, Martinez-Smith and Kuhlman, 2017). “House of Cards” was probably the first case where the company exploited their hidden opportunities through establishing Big Data-driven strategy. Based on the collected data, the management was able to formulate the decision on whether to obtain a license on the re-launch of popular programme. It was derived that same subscribers had specific interest towards BBC production and even showed interest towards movies of Kevin Spacey.
The higher degree of engagement is facilitated in digital television through telecasting programmes which match specific habits of audience. Netflix has been able to efficiently save marketing costs through understanding viewing patterns and investing in programme which can generate higher margins. The management has already claimed that around 75% of overall subscribers are greatly influenced by what is recommended by Netflix. Big Data plays a vital role not only in promotion of digital television, but also contribute towards delivery of programme. The data is obtained from around 29 million subscribers. Nielsen and similar third party service providers play an integral role in keeping a close track on ratings data. Every TV show is linked with its highly distinct licensed data (Leonard, 2013). On the other hand, all viewers must be authenticated when it comes to viewing digital television. The logistic involved in the delivery process is more concerned about analysing every single piece of information and then successfully arriving at a conclusion. Big Data technologies have not enabled such companies to compare the differences in telecasting shows in two different channels on two separate days. Arguably, the transition in obtained information helps the company to predict the likelihood of a particular show on a Sunday afternoon. For instance, the way through which ‘algorithmic turn’ impacts what we watch revolves around correlating wide range of information. Individuals belonging to a specific ZIP code have inclination towards a particular programme being played on Sunday afternoon. The raw numbers are perfectly correlated in terms of gaining knowledge on Kevin Spacey fans, preference towards British political dramas, etc. Netflix evaluates particular areas of the content so as to arrive at a better conclusion. There is a difference between Big Data driven approach of Netflix and traditional broadcast networks. Netflix does not have to depend upon shovelling content to identify what are the specific tastes or preferences of customers. The Big Data concept provides a framework through which the company gain assurance on specific customer-centric wants.
Arguably, the emergence of the ‘algorithmic turn’ does not always ensure that creative decisions based on Big Data concept would prove to be profitable (Gandomi and Haider, 2015). Numerous customers have revealed in an interview session that Netflix is more focused on critically exploring what has been watched last night so as to recommend TV shows and movies to be watched the next very day. Prefabrication of programming helps in meeting specific wants of the target audience. In overall context, the strategic direction which has been adopted by Netflix is a sophisticated version of how Big Data can be used for successfully viewing television. The way Big Data controls what audience wants to view is more about gaining knowledge about the currently telecasted programmes and then recommending few TV shows. It can be claimed that the specificity level has been upgraded with the support of Big Data technologies. In the planet, there are thousand Exabyte of information which needs to be captured and processed. The way a company explores such huge chunk of information helps in determining whether audience are mere puppets or happy subscribers. In the past decade, it has been witnessed that television has successfully reinvented in multiple ways. The three-network system has been completely eradicated which has gradually led to evolution of multi-channel cable along with global satellite delivery. It is no more about simply watching television, rather digital systems development and technological innovations have brought forth positive changes.
A new interactive phase of television has emerged with the active integration of Big Data technologies. It is observed that the role of television is drastically changing at home and even in public places. The trend of social activism is embedded within the growth of Internet. Television’s role has also extended to arts and education. In the current scenario, with the use of Big Data technologies, individuals can effectively transform the platform as a medium for teaching (Olsson and Spigel, 2014). Modern television is based on the concept of analysing customer wants and launching programmes aligned with such wants. However, it cannot be standardised that Big Data technologies only need to be used for collection of relevant data. The combination of intelligence and data collection facilitates deriving proper conclusions. Therefore, decisions are made on remake of popular shows, whereas, less popular shows are not given the prime time-slots. The contribution of Big Data shall also continue in the future time-frame where the advanced technologies will continue to control what we watch. Technology professionals are often faced with the common challenge of regulating storage requirements. Video on demand (VOD) applications are changing the working mechanism of traditional TV. The shift between methods is being initiated through the growing rate of connectivity. Internet bandwidth improvements have been a primary cause behind the evolution of modern television. It is witnessed that the transition in content consumption has definitely proved to be advantageous in some scenarios, but has even posed challenges. A huge amount of space is constituted by high-definition content. Maximum percentage of high content data is found to be stored online which demonstrates ability of Big Data to successfully address both emerging and current viewers’ demands. When a specific content is accessed on the Internet, then the associated access data is found to be stored in a specific location. Big Data analytics explores such storage locations in order to get hold of relevant information and undertake suitable strategic decisions. IT companies are always attempting to develop network products and servers for addressing rising demand for any kind of bandwidth-hogging information (Moat et al., 2014). The modular and unlimited scalability of distinct storage capacity is a mechanism which has been adopted by companies to deal with fluctuating demand. It can be argued that the extensive data volume and cost-effective online access forms the major two benefits linked with Big Data driven strategy. The unlimited aspect is being targeted by current operators for reaching out to wider base of audience. Industry experts often argue that cable TVs are nothing but the home landline which majority possess, but do not use. For a landline, many individuals hold the perception that it is not a major requirement, however, there is no such problem in keeping it. Digital television is now bundled with social media services. Arguably, social interaction enables many operators to successfully grasp the actual market demand. Big Data technologies have facilitated the interconnectivity between social media and digital television. Google Chromecast, Apple TV, etc., has emerged in the marketplace so as to meet the varying demand. The mentioned devices are capable enough to beam the content to the TV sets.
With the new technologies, customers are also benefitted in terms of ignoring high cable costs. The inclusion of network-based technologies is one of the primary reasons behind upgrading the entertainment level. Television networks are also found to structure specific content which can be efficiently stored on the Internet for future purpose. In the coming few years, there is a high possibility that audience will shift away from cable service. The growing number of online options shall make it easier for audience to further access relevant information or their favourite channels. In earlier years, television broadcasting was more about designing creative programmes and telecasting. The emergence of Big Data technologies have transformed the standardised concept where focus was always on creative content (Vitesse Media Plc, 2015). In the present scenario, creativity has not taken a back seat, but market driven operations have gained popularity. For instance, firms are now analysing viewers’ choices prior to reflecting upon broadcasting. The rating predictions are always not beneficial for companies. Arguably, the algorithmic framework helps in critically evaluating both hidden and known facts of viewers.
Conclusion
From the above study, it can be summarised that Big Data technologies have played an important role in changing the way traditional television broadcasting used to work. With the evolution of new technologies, companies are now being able to better understand customers’ viewpoint. The high degree of customer interactivity is the main competitive advantage which has been provided by Big Data technologies to the entertainment sector. Network DVR and similar devices is helping to initiate user interaction. TV was initially considered as a medium for entertainment, but now it has even extended to educational field. The presence of modular software services tends to enhance the functionality level of TV application. An argumentative side has been adopted in this particular essay to highlight the areas where Big Data has really brought forth change. The algorithmic turn has been able to facilitate interactive experiences through analysing, acquiring and storage of particular events. Programmers and operators are able to efficiently use such productive information in order to formulate appropriate strategic advantage. For each viewer, customised applications are being provided with the support of Big Data technologies. Channel or programme popularity, or presence of active users at any specific time or date can be denoted as sources for obtaining information in order to run the in-built algorithm.
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