Comprehensive Report: Big Data in Digital Entertainment Industry

This report delves into the application of big data within the digital entertainment sector. It begins with an introduction to the concept of big data and its challenges, followed by a discussion of the pre-big data entertainment industry. The report then explores how big data is utilized to understand customer trends, optimize content scheduling, increase acquisition and retention, and target advertisements. It also examines the importance of big data analytics, predictive analytics techniques, and major issues associated with big data. Furthermore, the report analyzes the needs of the media and entertainment industry, focusing on services, suppliers, customers, processes, and infrastructure. The report concludes by emphasizing the benefits of big data analytics and its role in shaping the future of the entertainment industry. This report is a comprehensive analysis of how big data is revolutionizing the creation, marketing, and distribution of entertainment content.

Running head: BIG DATA IN DIGITAL ENTERTAINMENT
BIG DATA IN DIGITAL ENTERTAINMENT
Name of student:
Name of university:
Author’s note:

1BIG DATA IN DIGITAL ENTERTAINMENT
Introduction
This report aims to discuss the topic Big data in digital entertainment. A brief
discussion of the industry of entertainment before the implementation of big data is provided
in this report. A detailed discussion of the application of big data in the sector of
entertainment and media is provided in this report. A comprehensive discussion of the
analysis of the needs of industries in the sector of media and entertainment is provided in this
report. A brief discussion of the importance of big data analytics is provided in this report. A
discussion of the techniques of predictive analytics is provided in this report. The major
issues of big data is provided in this report. Lastly, this report concludes with an appropriate
conclusion for this report.
The term big data refers to the datasets that are significantly large or complicated for
the conventional software application of data-processing for adequately deal with (Swan,
2013). The data with several cases provide greater power of statistics, while the data with
higher complexity can lead to the higher discovery rate that is false. The challenges of big
data includes the data capturing, storage of data, analysis of data, search , transfer, sharing,
visualisation, querying, privacy of information and updating (Provost & Fawcett, 2013). It
also refers to the utilisation of predictive analytics, analytics of user behaviour, or several
other advanced methods of data analytics that can extract important value from data1, and
produce data to a specific size of data set.

2BIG DATA IN DIGITAL ENTERTAINMENT
Discussion
The publishers, organisations of news, broadcasters and the companies of gaming in
the entertainment and media industry are now facing new models of business for the methods
by which the creation, marketing, and the distribution of the content is executed (John
Walker, 2014). This is happening because of the present search and access content of
consumers from anywhere, at any time and on any device. This leads to the increased
pressure for executing innovative digital production and the advertising that is done multi-
channel and strategies of distribution that relies on a particular understanding of the
preferences and the behaviours of the consumption of media of the consumers (Wu et al.,
2014). And, due to the interest shift of the consumers from the analog to the media that is
digital, there are substantial opportunities for monetising the content and for identifying
innovative services and products.
Industry of entertainment before Big data
In the age of digital media, there are several connected devices for the automation
tasks (Lazer et al., 2014). These devices produce a huge amount of information that can
provide huge information about the customers. The information varies in formats and
frequently altering the result in the formation of the big data. When the organisations of
media and entertainment do not leverage this kind of technology, they can miss on a number
of opportunities (Raghupathi & Raghupathi, 2014). One of the primary losses in the losing
out on the actionable insights. Commonly, the conventional entertainment authorities
focusses on the decision taking that are based on the patterns that are long-established for the
decisions that are not working in their respective favour (Murdoch & Detsky, 2013). While
undertaking such decisions that are based on the traditional model can usually land the
organisations with huge debts or it can also suffer enormous losses when there is a failure in
the strategy.
Application of big data for the entertainment and media sector
With the increase of the consumers of digital media from thousands to millions, the
industry of media and entertainment are in a unique position of leveraging the assets of big
data for increasing the profitable engagement of customer (Gandomi & Haider, 2015). The
usage of big data in the entertainment industry helps in obtaining the insights of the trends of

3BIG DATA IN DIGITAL ENTERTAINMENT
customers, the schedules of shows and the trends of advertisements. The utilisation of smart
devices is rising in the present times. According to a research, 29% of the people all over the
world spend approximately 4-6 hours on their smartphones daily (Hashem et al., 2015). The
viewing of entertainment has been changed with the increase of smartphones users. As a
broad platform of entertainment is provided by the smartphones, the companies that are
dealing with the media and entertainment are also turning towards the technological
platforms, which can help them in experiencing the transformation. As enormous amount of
information are generated by the smart devices, when all the big data is analysed, it can assist
in the obtaining of actionable insights about the information that can assist them in the
addition of strength to the processes of decision-making (Chen, Mao & Liu, 2014). The
aspect of big data in the industry of entertainment can be a technology that can assist the
houses of media in experiencing the analytics innovation. Some of the examples how
entertainment and media companies can gain benefits from the applications of big data.
Several organisations tend to schedule the shows at the times and the days according
to the customers for preventing the losses of TRP and the revenue loss. With the analytics of
big data, the CIOs and the CTOs of the companies have the information of the slots of time
when their shows can generate the highest TRPs (Chen & Zhang, 2014). With the help of the
available platforms of social media, finding the likes and the dislikes of the customers poses a
challenge for the houses of media, as these companies have the ability of analysing the
collected data from these sources for knowing the preferences of the customers. Actionable
insights can help the companies in the process of planning the shows in a manner, which can
attract the attention of the customers.
With the help of the analytics of big data, the companies can also strategize the
methods of tweaking the subscription offers for generating increased profits (Sagiroglu &
Sinanc, 2013). The CTOs and CIOs of the house of media and entertainment should also
study the success of other companies for generating plans for development. Apart from the
learning processes, the companies can also focus on the training of the employees for
working with the software that can assist them in the insight obtaining from the information
in their possession.
Predicting the preferences of the customers
The scope of the collected big data by the industry of media and entertainment and for
potentially mining it for understanding the preferences of content, shows, music and the

4BIG DATA IN DIGITAL ENTERTAINMENT
movies of the customers (Kitchin, 2014). The viewing history, reviews, searches, ratings,
location and clickstreams, device data, log files and the sentiment of social media are some of
the common sources of data that can help in the identification of the interest of the audience.
Optimisation scheduling
With the insights from the big data, the companies of the industry of entertainment
and media gained the capability of understanding the most likely schedule of content viewing
and predicting the device from which the customers view the content (Marx, 2013). This
information can be analysed by conducting scalability of the big data at the ZIP code level
that is granular for localised distribution.
Acquisition and retention increasing
The companies of media and entertainment can perform the development of best
strategies of promotion and product creation for attracting and retaining customers with the
help of big data for understanding the pattern of subscribing and unsubscribing any content
(Fan & Bifet, 2013). The unstructured sources of big data are best handled by the applications
of big data like the records of call detail, sentiment of social media and email reveals some of
the commonly overlooked factors that drives the interest of the customers.
Targeting of advertisements
The use of big data makes it possible for understanding the consumption of digital
media and entertainment and the behaviour that can be utilised with the conventional
demographic data for providing the advertising that are personalised in the correct context, at
right time and in correct place (Kitchin, 2014). The applications of big data can help in
improving the targeting of advertisements in the increasingly complicated behaviour of
consumption of content. For example, as the consumers access the media and entertainment
on several devices at one instance, its beneficial for using the insights of big data for
understanding the situations when any customers is using second screen for the optimisation
of the campaigns across devices (Ward & Barker, 2013). The companies of digital media and
entertainment can increase the rates of digital conversion with offering micro-segmentation
of the customers for the advertising of exchanges and networks.
Monetisation of content and development of innovative products
Big data can assist the companies of media and entertainment in generating additional
revenue sources by suggesting innovative methods of incentivising the behaviour of

5BIG DATA IN DIGITAL ENTERTAINMENT
customers, revealing the true value of content in the market, or identify any new service or
product opportunity.
Analysis of the needs of industries in the sector of media and entertainment
Data has been generated from the sector of media in the form of research, customer
databases, sales, log files and several others. Similarly, the huge majority of the broadcasters
and publishers have continuously faced the requirement of competing right from the early
days of the publishing of newspapers in the eighteenth century (Jagadish et al., 2014). Even
the government or the media bodies that are privately funded have to continuously prove the
relevance to their audiences for staying relevant in the world of extensive choices and for
securing the funding in the future. But the mind-set of big data, the technical solutions and
the strategies offers the ability of managing and disintegrating the data at quick speed and at
scales that have existed before.
There are three major areas where the big data has the potential of disrupting the current
status and help in the economic growth in the sector of media and entertainment. These major
areas are:
1. Services and products: The businesses that are driven by big data have the ability of
publishing content in a more complicated method. Human expertise in curation, psychology
and the editorial intelligence can be complemented with the quantitative insights that are
derived from the analysis of large and heterogeneous datasets (Kaisler et al. 2013). But, with
the use of the analysis tools of big data, the prediction is done easily which is easy to use for
the business users and the data scientists.
2. Suppliers and customers: Big data will be utilised by the ambitious companies of media for
discovering about the customers, like the preferences, attitudes and the profile and this
information will be used for building relationships that are more engaged. Media companies
get content from the individuals who have become suppliers with the usage of social media
tools and the capturing of data (Bettencourt, 2014). Without the applications of big data, the
approach will be random and wasteful for discovering the content that is most interesting.
3. Process and infrastructure: While the SMEs and the startups can function efficiently with
the open source and the infrastructure of cloud, this is a challenge for the bigger companies as
the upgrading of the infrastructure of IT is significantly difficult for them. The standards and
the legacy products still needs to be supported in the transition for big data method of
working and thinking. The culture of organisation and the process might also need to be

6BIG DATA IN DIGITAL ENTERTAINMENT
updated with the expectation of the features of big data (George, Haas & Pentland, 2014).
The failure of transforming the culture and the skillset of the staff could affect the companies
who are more profitable in the present times but they cannot adapt to the business models that
are driven by data.
Importance of analytics of big data
Big data can prove to be beneficial to any organisation when it is used with the
predictive analytics that enables the businesses in making swift strategic decisions. It is the
roadmap to developing better business. Predictive analytics is the technology that is driven by
data and the statistical methods that examines the large data sets for discovering patterns,
reveal new information and also predict the points in failure and the future outcome
(Xiaofeng & Xiang, 2013). The usage of big data can be a huge advantage to any
organisation when it is used with the predictive analytics that enables the businesses for
making swift strategic decisions. The importance are big data analytics are as follows:
Provide rigorous customer insight and improve the customer relationship: It is easy to
predict the customer spending habits of every customer by conducting analysis of all data
related to the behaviour of customers such as the transactions, activity on social media,
browsing of the web, demographics, interests and then transform these into meaningful trends
Identify the key issues in the business processes: For avoiding the inefficiencies that costs
the company several customers and revenues, the methods of predictive analysis can be used
for providing focus to the processes of the business. These analyses might help in
determining the areas of problem from the beginning to the end of the work cycle and then
optimise the processes (Lee, Kao & Yang, 2014). This data can be backed up with the help of
the feedback from the customers with the reviews and social media.
Improve the networks of suppliers: The methods of predictive analysis are extensively
becoming more advantageous for the management of supply chain as this makes the
processes more accurate, reduced cost and more reliable. As the management of supply chain
is a continuous and cohesive process, any kind of failure in the system will result in the
causing of insufficient execution (Katal, Wazid & Goudar, 2013). This is the main reason
why predictive analysis must be applied in each step such as the discovery of the demand
data and the effort of calculating the demands in the future, and converting it for forecasting
the requirements of production and backwards in the requirements of procurement and
logistics.

7BIG DATA IN DIGITAL ENTERTAINMENT
Techniques of predictive analytics
The techniques and the approaches that can be utilised for conducting predictive
analytics are widely categorised into regression methods and the techniques of machine
learning.
Regression techniques
The regression methods are the most common method of predictive analytics. In this
method, the focus lies on the establishment of an equation of mathematics as the structure for
representing the connections among the various considered variables. There are several
methods of performing the predictive analytics depending on the instance. Some of the
methods are as follows:
Linear regression models: This model analyses the relationship among the dependent
or the response variable and any set of the independent or else the predictor variables. This
connection is expressed as the equation, which can predict the response variable as the linear
function of these parameters. Adjustment of parameters is done for measuring the
optimisation of a fit. Huge amount of effort in the fitting of model is basically focussed on the
minimisation of the residual size and the ensuring, which is randomly distributed depending
on the predictions of the model.
Discrete choice models: The method of multiple regression is used in the situations
when the variable of response is found to be continuous and consists of a range that is
unbounded. Sometimes the variable of response might not be continuous but it can be
discrete. Although mathematically, multiple regression can be applied for the dependent
variables that are discretely ordered, bulk of the assumptions on the multiple linear regression
theory are outdated and there are several other methods like the models of discrete choices
that are most preferred for this analysis kind (Waller & Fawcett, 2013). When the dependent
variable is found to be discrete, the methods of logistic regression, the models of probit, and
multinomial logit are superior.
Logistic regression: Within any setting of classification, allocating the probabilities of
outcome to the observations might be obtained by utilising the model of logistic that is
fundamentally a technique that performs information transformation about the dependent
variable that is binary into a variable that is continuous and unbounded and it guesses a model
of regular multivariate.

8BIG DATA IN DIGITAL ENTERTAINMENT
Multinomial logistic regression: this is an extension of the model of binary logit for
the cases where the considered variable that is dependent consists of more than two groups is
the model of multinomial logit. The multinomial logit model is the most suitable technique in
such cases, particularly when the ordering of the dependent variable is not done into
categories.
Issues of big data
Even though the utilisation of big data is growing to be extensive in the recent times,
there are several issues related to the use of big data. Some of the key issues while using big
data are:
Breach of privacy: This is the biggest issue in the use of big data. This refers to the release of
the private information to the unauthorised persons who must have no access to the data,
whether it is done deliberately or mistakenly. The breaches of privacy can occur when any
business deploys weak measure of security. Even though any hacker is majorly liable for this
kind of act, it can be prevented with the implementation of strict tools and extensive security
measures. For combatting the privacy breaches, investment must be done on software of anti-
malware that provide an entry point and then employ the connections that are secure from the
system of data collection to the system of data storage.
Issue of anonymity: The identification of the individuals with the help of anonymised data in
the datasets that are public is now possible. Even though this process is not easy, but it
provides the opportunity of identity theft.
Accuracy of analytics: In any kind of research, there is an aspect of error margin or the
possibility of miscalculations, exceptions and several other factors that causes a
comparatively dismissible error amount. The analysis of big data possess this factors as it
consists the analysis of significantly huge amount of data (Townsend, 2013). As it is
extensively difficult to verify the analysis manually, the best possibility is that the analytics
will not offer huge comprehensive inaccurate data by utilising a data analysis tool that is
trusted, which guarantees the greatest accuracy level.
Conclusion
Therefore, the use of big data in the sector of digital entertainment is advantageous for
the companies. The term big data denotes to the datasets that are significantly large or
complicated for the conventional software application of data-processing for adequately deal

9BIG DATA IN DIGITAL ENTERTAINMENT
with. The data with several cases provide greater power of statistics, while the data with
higher complexity can lead to the higher discovery rate that is false. The publishers,
organisations of news, broadcasters and the companies of gaming in the entertainment and
media industry are now facing new models of business for the methods by which the creation,
marketing, and the distribution of the content is executed. In the age of digital media, there
are several connected devices for the automation tasks. These devices produce a huge amount
of information that can provide huge information about the customers. With the increase of
the consumers of digital media from thousands to millions, the industry of media and
entertainment are in a unique position of leveraging the assets of big data for increasing the
profitable engagement of customer. The scope of the collected big data by the industry of
media and entertainment and for potentially mining it for understanding the preferences of
content, shows, music and the movies of the customers. With the scalability of big data, this
information can be analysed at the ZIP code level that is granular for the distribution that is
localised.

10BIG DATA IN DIGITAL ENTERTAINMENT
References
Bettencourt, L. M. (2014). The uses of big data in cities. Big Data, 2(1), 12-22.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and
technologies: A survey on Big Data. Information Sciences, 275, 314-347.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and
applications, 19(2), 171-209.
Fan, W., & Bifet, A. (2013). Mining big data: current status, and forecast to the future. ACM
sIGKDD Explorations Newsletter, 14(2), 1-5.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and
analytics. International Journal of Information Management, 35(2), 137-144.
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The
rise of “big data” on cloud computing: Review and open research issues. Information
systems, 47, 98-115.
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan,
R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of
the ACM, 57(7), 86-94.
John Walker, S. (2014). Big data: A revolution that will transform how we live, work, and
think.
Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013, January). Big data: Issues and
challenges moving forward. In System sciences (HICSS), 2013 46th Hawaii
international conference on (pp. 995-1004). IEEE.
Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and
good practices. In Contemporary Computing (IC3), 2013 Sixth International
Conference on (pp. 404-409). IEEE.
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data &
Society, 1(1), 2053951714528481.

11BIG DATA IN DIGITAL ENTERTAINMENT
Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-
14.
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps
in big data analysis. Science, 343(6176), 1203-1205.
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry
4.0 and big data environment. Procedia Cirp, 16, 3-8.
Marx, V. (2013). Biology: The big challenges of big data.
Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health
care. Jama, 309(13), 1351-1352.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-
driven decision making. Big data, 1(1), 51-59.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), 3.
Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In Collaboration Technologies
and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
Swan, M. (2013). The quantified self: Fundamental disruption in big data science and
biological discovery. Big data, 1(2), 85-99.
Townsend, A. M. (2013). Smart cities: Big data, civic hackers, and the quest for a new
utopia. WW Norton & Company.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a
revolution that will transform supply chain design and management. Journal of
Business Logistics, 34(2), 77-84.
Ward, J. S., & Barker, A. (2013). Undefined by data: a survey of big data definitions. arXiv
preprint arXiv:1309.5821.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE
transactions on knowledge and data engineering, 26(1), 97-107.
Xiaofeng, M., & Xiang, C. (2013). Big data management: concepts, techniques and
challenges [J]. Journal of computer research and development, 1(98), 146-169.