Big Data design plan-Netflix Application 2022

Big Data design plan-Netflix
Name
Institution
Professor
Course
Date

Executive summary
The study focuses on the design of the big data plan for Netflix Inc. Company. The application
of big data in Netflix Inc. business process has made it possible to predict customer preference
and offer tailored services effectively. Big data evaluation on Netflix Inc. data approach has been
evaluated to determine the process used by the Company to share relevant content to its content
consumers’. Data big analytics approaches are used to analyze different organizational strategies
that Netflix Inc. The company uses to remain competitive in the industry. The Company makes
use of big data collection techniques and analytics approaches to analyze and predict customer
behavior before sharing movie or film content. Customer-centered business initiatives promote
organizational product/services and organizational operations have been analyzed to help Netflix
Inc. improve their service delivery. Application of big data analytics in the industry has been
highlighted to highlight organizational application in the entertainment industry. Application of
big data in the industry has been developed concerning big data business maturity model index
phases. Finally, data sources on Netflix Inc. dataset have been evaluated and challenges that are
coupled with big data design and implementation in the entertainment industry.
Contents

Executive summary...................................................................................................................................2
Introduction...............................................................................................................................................4
The objective of the study.........................................................................................................................5
Main objective........................................................................................................................................5
Specific objective...................................................................................................................................5
Study Questions.....................................................................................................................................5
Big data and analytics approach..............................................................................................................5
Netflix key business initiatives..................................................................................................................7
Untapped potentials to support business initiatives................................................................................8
Application to Big Data Business Model Maturity Index.......................................................................9
Data sources.............................................................................................................................................11
Summary..................................................................................................................................................12
Bibliography............................................................................................................................................13

Introduction
Netflix Inc. is a Company that offers internet-based subscription services in the
entertainment industry. It provides services on movie streaming, distributing movies over the
emails and Television (TV) episodes. To provide effective and quality services to its customers
across the globe, Netflix Inc. makes use of Big Data. The Big Data can be defined as a set of
extremely large sets of data which cannot be managed and analyzed using traditional tools (Ularu
et al. 2012, pp. 7-8). Due to its size, even standard data handling technologies are not able to
support big data operations. The concepts that make big data complex and difficult to handle is
its storage, analytics, sharing and data visualization. The Company has a wider scope because it
operates different sections like international streaming which covers customers outside the
United States of America (USA). Distributing DVD’s in the domestic industry over the emails,
and domestic streaming which covers movie consumers’ within the USA. The Consumers' can
watch company content as original films, documentaries, TV shows, and movie series. The
Company offer streaming services over the internet supported devices; Televisions, Computers
and Mobile phones. The choice of the content and consumer privileges on the movie content has
arrived through an algorithmic computational process (Sahatiya 2018, pp. 189). This article
seeks to outline the concept of the big data and its usefulness in the industry and provide
strategies that can be used to collect data required in business decision making. It also evaluates
different types of big data and its use in the industry and carry out an interpretation of big data
and offer recommendations to organizational decision-makers. The main goal of Netflix is to
make use of big data and its analytics process to identify customer preference which can be used
to identify and deliver relevance and quality content.

The objective of the study
Main objective
To analyze the design of the big data plan in the Netflix Company and evaluate the big
data concepts which help to deliver quality products to its customers’.
Specific objective
Understand the organizational big data concepts and analytics techniques deployed in the
industry.
To evaluate the level of Netflix customer satisfaction in the entertainment industry from the use
of the current organizational algorithms.
Determine other possible computational algorithms which can be sued by the organization to
leverage its services in the industry.
Study Questions
What are the computational efficiency concepts of big data that Netflix Inc. has implemented in
its business operations?
Can big data be associated with the operational success of the Netflix Company in the
entertainment industry?
What operational matrix is used by the organization to determine specific customer preference
on the consumption of the Netflix Inc. content?
Big data and analytics approach
Big data is a collection of different types of large data sets which are analyzed by the
Company to come up with unique organizational operational processes. The collected data by
Netflix Inc. should be organized, cleaned up and presented visually. For Netflix Inc. to collect,
analyze and harness meaningful information, it has to make use of data analytics. In this regard,
Netflix makes use of recommended algorithm to provide suggestions on television shows and

movies which is based on specific user preferences (Power et al. 2018, pp. 44). Netflix Inc.
collects data from it's over 139 million customers’ and implements data analytics models which
are meant to discover both customer behaviors and buying patterns. From the data analytics, the
Company can provide customized movies and television shows which is based on customer
preferences. It is important to note that, about 75% of the Netflix Inc. consumers’ activities are
based on customized recommendations. The Company operations are made possible by
collecting large sets of data which is used to create a specific customer profile. The customer
profile created by Netflix Inc. is very detailed to make possible deduce customer needs and
preferences on movie content. Most significantly, the Company can make decisions based on
data collected from the customer's interaction with the content and responses which are received
from television shows. Netflix can collect data such as specific date which its consumer watch a
movie or a show if the customer paused the show if it was stopped and resumed. On the same
note, the Company can determine if shows are watched completely and the time each customer
takes to compete for the show or a movie series (Iyamu 2018, pp. 7). Some movies and television
shows are watched more repetitively, the rating provided by the customer on the quality of the
content, the frequency of customer search on specific content and its availability. It is from this
data that Netflix Inc. can create a very detailed customer profile used to select and share users'
preferred content.
The Company is believed to generate over 1 billion from customer retention as 80% of
the streams are done through recommendation system platform. Before generating the content to
the user, Netflix Inc. makes use of data analytics tools to decide customer needs to consume
original content (Chen, Chiang & Storey 2012, pp. 1165). The organizational decision is based
on the several customer touches which are derived from the Netflix database. The Company also

uses big data and analytics to provide custom marketing on its products. The recommendation
system is designed in such a way that if a customer watches several related contents like "funny
jokes", most of the trailers would be based on that specific topic. In this case, when Netflix
decides to develop the content, it does not need to invest much in the advertising because it is
already privy with the potential number of customers. Despite collecting customer’s data, Netflix
Inc. relies on feedback from its content consumers’. To offer customized content, the feedback
system which makes use of either thump up/down helps the company to improve on content
selection and delivery (Sivarajah et al. 2017, pp. 265). The thumbs up/down system helped the
organization to improve on its customer engagement with a very great margin. To meet different
customer needs, the Company have developed 33 million versions of the Netflix platform. The
use of big data and analytics are very important as they have contributed greatly to the success of
the Company.
Netflix key business initiatives
The Company has leveraged maximum use of technological aspects trying to reach out to
different customers’ across the globe. However, there are some other business strategies that the
Company can implement to remain more competitive in the industry. First, being a primary
service provider on video streaming, the Company should focus more on service diversification.
Diversification helps an organization to create more business subsidiaries making possible for
the organization to reach out to potential customers. The Company is also looking forward to
developing more production studios that can produce original television shows and videos
(Mukherjee & Shaw 2016, pp. 68). The ability of Netflix to produce and co-produce own content
makes it possible for the Company to have an exclusive promotion on its online content
streaming. With such capability, the organization should be able to produce and compete
effectively with other competitors that offer streaming services. Production of attractive and

quality content would offer the organization an opportunity to retain more customers’ into the
consumption of its content.
Secondly, the organization should look at capacity building and technological strategy.
To make use of modern technologies, the organization has opted to adopt visualization and cloud
computing. Since Netflix Inc. is a software development company, it is focused on the
development of the computing platform. In the cases of the already existing application, the
Company has focused on integrating more features which improves user experience (Moorthy et
al. 2015, pp. 76-77). Thirdly, with ever-increasing privacy concerns, Netflix Inc. is focused on
anti-privacy initiative. The Company is continuously working with its partners and other security
Companies to protect data against unauthorized access and sharing. Netflix Inc. is building a
digital privacy rights and management systems which can scan and generate reports which help
the Company to protect the illegal distribution of the content. Finally, Netflix Inc. is making use
of machine learning which provides its customers’ with an automated video recommendation
according to consumption historical preferences.
Untapped potentials to support business initiatives
Adoption of untapped potentials in the industry would uncover various aspects which support its
key business initiatives. To support organization operational processes, the following questions
are very important;
1. What is the current state of business operations which Netflix Inc. seeks to improve?
2. Is there a technological measure that Netflix Inc. can adopt to improve customer
preference and quality of services?
3. Is there need to adopt new operating strategies to improve consumers’ Quality of
experience (QoE).

Some of the aspects that the organization is using to support customer initiative in the
industry are technology in artificial intelligence. Through the exploitation of big data, Netflix
Inc. has to come up with an automated system that can detect user preference from the customer
historical profile. Upon determining user preference, the system can choose relevant content
which is streamed to the customer. Sharing of the customized content with users’ help Netflix
Inc. to increase user instructiveness and quality of the content. The next potential initiative the
Company is using to support its operations involves the use of business intelligence (BI). It is the
initiative which organization has to deploy to help top management in decision making (Moorthy
et al. 2015, pp. 1134). Business intelligence is an essential tool which organization can use to
help in data interpretation and analysis to make decision making easier for the top management.
The product and service initiative that Netflix Inc. can adopt in the business processes are
technological measures. By developing new products with various features, the organization can
attract potential customers to the consumption of its product. It is only through technological
adoption that the organization can create a reliable product which makes Netflix Inc. competitive
in the entertainment industry.
Application to Big Data Business Model Maturity Index
The Netflix Inc. business initiatives can be used to model its business operations across
some phases of big data maturity model index. The customer centered-initiative is can be used in
the big data maturity model index at business monitoring phase (Comuzzi & Patel 2016, pp.
1468). In this case, user experience and quality of experience (QoE) can be used to evaluate
customer satisfaction on the use of Netflix Inc. content. Through diversification, the Company
can monitor business continuity process and customer level of satisfaction on both quality of the
content and efficient in streaming. The capacity building and technological strategy business
initiative aspect that Netflix Inc. can adopt in big data business maturity model index is on

business monetarization phase. The organizational investment focus is to generate revenue and
there is a need to evaluate business viability and feasibility of implementing some business
processes. On every service delivery, Netflix Inc. should invest in application development and
support which constitutes capital investment. As a result, the organization should evaluate
whether the proposed initiative would be economically viable (Mohammad, Rahim &
Abughazaleh 2018, pp. 2). Business insight phase is an important phase in Netflix Inc. business
operations because it is used to analyze the feasibility of the product. By evaluating the number
of customers who are interested in a certain product, the Company would be able to make a
sound decision on whether to implement the subjected product. If the business viability is not
well done, it can result in business loss of capital on investment.
Similarly, business optimization phase in the big data business maturity model index
would be used to analyze and improve Netflix Inc. operational processes. It is the phase which an
organization should focus on if there is the need to improve on service delivery. As for Netflix
Inc., there is a need to make sure its customers' get the best content with minimal cost while
Company can have a tangible return on investment (ROI). The return on investment is mainly
computed from the capital on investment against profit generation by the subjected Company.
The final big data business maturity model index applicable in Netflix Inc. is business
metamorphosis phase which defines the process that organization should take to change business
operations (Chen & Zhang 2014, pp. 318). The need to change business processes are as a result
of the need to streamline business operations and meet customer needs. It involves gradual
changes in business operations that organizations seek to make organization remain competitive
in the industry. It is within the metamorphosis phase that Netflix Inc. should be very innovative
to add more features on the existing products and come up with a new one that brings on board

prospective customers’. Important to note is that Netflix Inc. failure to consider gradual growth
in the industry would make it unable to remain competitive in the industry.
Data sources
To achieve the desired business operational initiative, Netflix Inc. The company should
focus on collecting data substantive sources. This would help the organization to collect valuable
data which provides the real market scenario in the competitive industry. A new dataset of about
45000 movies can be used by the Company to predict the market situation. From the data set, the
subjected firm can analyze the most preferred movie and television show series which has high
demand in the market. Ratings can also be used to evaluate and analyze on consumers
satisfaction on the content and feedback on the ratings can be used to offer required
improvements (Anshari et al. 2018, pp. 76). Some of the data sources that can be used are listed
in the links below. On the same note, the number of repetitive streams can be used to evaluate
customer satisfaction and attractiveness. The final dataset that can be used is Netflix Inc. internal
data which can be sued to evaluate customer satisfaction and the viability of developing a new
product in the market. From the internal datasets developed from organizational big data, Netflix
Inc. can use available data to decide which market segment to venture on and investment capital
required. On the same note, the number of downloads the movie
https://www.kaggle.com/rounakbanik/the-movies-dataset
http://academictorrents.com/details/9b13183dc4d60676b773c9e2cd6de5e5542cee9a
https://www.kaggle.com/chasewillden/netflix-shows
https://opendata.stackexchange.com/questions/1954/structured-movie-tv-dataset

Summary
It is understood that big data concepts involve the collection, storage, analysis and
implementation of outcomes from the study. Collection of big data may sometimes require
intensive computational algorithms which are resource-intensive. The resources required a range
from hardware, software, and human technical expertise. As such, it becomes very challenging to
consolidate all the required resources together to make big data implementation project a
success. Challenges that big data implementation would face are lack or inadequate skilled
personnel to implement and support Netflix Inc. business requirements. Technology is very
challenging due to the nature of the market which keeps growing over time. As a result, the
nature of the data collection by the Company may require complex computational algorithms
which may not be available in the market. To come up with the current algorithm, Netflix Inc.
had to create a competitive award-winning prize. This is a clear demonstration of the lack of
adequate skills to support the required innovation. Next, the modern world is full of uncertainties
on data privacy and confidentiality. Implementation of the big data design plan would be faced
by data storage security issues. Other competing firms would be very keen to understand the
operational concepts that Netflix is putting in place to remain successful. As a result,
unauthorized data access should be of high concern to Netflix Inc. security team. The final
challenge would be on industrial competitiveness due to many competing firms in the same
industry. Any organization must make sure its customers’ are fully satisfied to retain them.
Failing to offer a competitive product in the industry results in market phase-out which may
result in the closure of the business.

Bibliography
Anshari, M., Almunawar, M.N., Lim, S.A. and Al-Mudimigh, A., 2018. Customer relationship
management and big data-enabled: Personalization & customization of services. Applied
Computing and Informatics. 5(1), pp. 74-95
Chen, C.P. and Zhang, C.Y., 2014. Data-intensive applications, challenges, techniques, and
technologies: A survey on Big Data. Information sciences, 275, pp.314-347.
Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big
data to big impact. MIS Quarterly, 36(4), 1165-1185.
Comuzzi, M. and Patel, A., 2016. How organizations leverage big data: A maturity
model. Industrial management & Data Systems, 116(8), pp.1468-1492.
Iyamu, T., 2018. A multilevel approach to big data analysis using analytic tools and actor-
network theory. South African Journal of Information Management, 20(1), pp.1-9.
Kshetri, N., 2014. Big data׳ s impact on privacy, security and consumer
welfare. Telecommunications Policy, 38(11), pp.1134-1145.
Mohammad, S.A., Rahim, A. and Abughazaleh, Z.M., 2018. Big Data in Marketing Arena: Big
Opportunity/Benefit, Big Challenge, and Research Trends: An Integrated View. Int J Econ
Manag Sci, 7(533), p.2.
Moorthy, J., Lahiri, R., Biswas, N., Sanyal, D., Ranjan, J., Nanath, K. and Ghosh, P., 2015. Big
data: prospects and challenges. Vikalpa, 40(1), pp.74-96.
Mukherjee, S. and Shaw, R., 2016. Big data concepts, applications, challenges, and future scope.
International Journal of Advanced Research in Computer and Communication
Engineering, 5(2), pp.66-74.
Power, D.J., Heavin, C., McDermott, J., and Daly, M., 2018. Defining business analytics: an
empirical approach. Journal of Business Analytics, 1(1), pp.40-53.
Sahatiya, P., 2018. Big data analytics on social media data: a literature review. Int. Res. J. Eng.
Technol, 5, pp.189-192.

Sivarajah, U., Kamal, M.M., Irani, Z. and Weerakkody, V., 2017. Critical analysis of Big Data
challenges and analytical methods. Journal of Business Research, 70, pp.263-286.
Ularu, E.G., Puican, F.C., Apostu, A. and Velicanu, M., 2012. Perspectives on big data and big
data analytics. Database Systems Journal, 3(4), pp.3-14.