Big
Data and its Role in Social Media: A study of Facebook
Introduction
The
concept of big data has gained application with the increase in the need for data
transfer and storage in the application of conventional information technologies
over time. Traditionally, databases performed roles that are currently associated
with this concept. The evolution of data has been through advancement in terms of
data characteristics and applicability, resulting in incremental innovation as the
needs associated with data use also revolves (Madden 2012). For instance, big data
has been described as a disruptive innovation based on its features and the benefits
that have been associated with it (Lycett 2013). Conventionally, any data is characterized
by volume, velocity, and variety. The distinction between the different types of
data and the sole driver for the innovation from databases to big data is based
on the quantity or level associated with the data features (Madden 2012).
While
databases handled data in terms of gigabytes and megabytes, Big Data handles data
in terabytes to Petabytes. This is an indication of volume expansion from databases
to Big Data. Moreover, the velocity associated with big data depends on repetitive
observations or decisions constantly being made. This implies that while databases
and small data are associated with more intermittent data access, handling and storage,
big data deals with continuous and highly dynamic data. In terms of variety, the
databases and small data are restricted in terms of variety while big data deals
with a range of data varieties (Zikopoulos et al 2012). It is because of these differences
that there is increasing shift from the conventional methods to operations based
on big data. The application of big data has transcended the confines of structure
and limitations such as exhaustibility and data resolution. The move from the traditional
storage of structured data to the present digital storage of unstructured data could
not be achieved without big data (Cote 2014). Currently, big data find application
in a wide variety of fields, from the government operations, the stock market operations,
and other financial aspects, education, social media and scientific sectors. The
benefits associated with big data are similarly diverse, ranging from improved cost
effectiveness and innovation (Vis 2013).
Despite
the wide applications, the roles played by big data on various fronts have not been
studied extensively. The benefits of big data on some sectors have been looked into
by some studies, although to a very narrow level. This clearly indicates that there
is a narrow dearth of knowledge regarding the particular roles played by big data
on these fronts. The application of big data on social media is thus a justifiable
topic of study, as it will help to expand the information available about the roles
played by big data in the communications sector. Facebook will be used as a case
study in obtaining the required information. In carrying out the study, the research
will be based on a literature review and an empirical analysis, which will be aimed
at:
Finding out the features that make big data
applicable in various sectors
Determining the scope of big data applications
in social media
Finding the roles played by big data in the
operation of Facebook
Literature Review
Introduction
Big
Data has been associated immensely with the concept of business intelligence both
in academia and in the business sector. According to Lycett (2013), big data is
the foundation of business intelligence since the greatest resource for intelligent
business operation is data. The need for intelligent data operation drives innovation
and productivity in all business quarters. Although the need for big data is critical
in all businesses, the level of need differs from one business to the other depending
on the volume and variety of data that needs to be applied and managed. As in other
business sectors, the social media has found immense application of the big data
concept due to the large volume of data that is required to be handled. It may even
be said that social media is the greatest applicant of big data. The application
of big data and benefiting from its many advantages is however hindered by lack
of comprehension of its scope and what it entails.
Non-human
assemblages in Big Data usage
Traditionally,
the characteristics associated with any type of data included volume, variety and
velocity. These features formed a basis for the suitability of data to business
applications. The volume of data handled under the big data concept is significantly
larger than the conventional data volumes. Instead of the gigabytes of data conventionally
handled in small data models, the big data concept has seen the realization of larger
volumes such as petabytes to Brontobytes (Davenport and Dyche 2013). The implication
of this has been wide application of data dependent operations such as those, which
require cloud computing. The importance of big data in such contexts is thus undeniable.
With high volumes of data handling, the concept of velocity also becomes known.
The
velocity of big data describes the frequency of data handling which was initially
intermittent. However, with the evolution of modern big data concept, data handling
has adopted a more continuous approach, dependent only on the availability of data
handling resources (Tufecki 2014). The present business environment requires
dynamic data representations, which can only be achieved with big data. As
opposed to the traditional set- up where data handling involved discreet
procedures, big data offers the opportunity to carry out streaming operations,
which result in a continuous flow of data. Moreover, due to the continuous mode
of data handling and management that is required in social media platforms
enables big data to be the mode of choice in social media businesses. The velocity
of big data is even more pressing due to the continued expansion of data variety
handled especially within the social media context.
Variety
of big data relates the types of data that can be handled independently. Database
management systems are restricted in terms of the data types they can handle. Similarly,
small data contexts also handle significantly narrow range of data types while the
big data concept enables handling a huge variety in the type and form of data. These
key features form the basis of operation and applicability of all data concepts.
Big data, offers these features and even more, which makes it the most preferred
data model in businesses (Lycett 2013).
Besides,
these conventional characteristics, other characteristics associated with big data
include: exhaustiveness, high resolution, flexibility and relational characteristic.
These characteristics enhance the benefits associated with data use, with the impact
that a wide range of applications can be achieved through big data use. In terms
of exhaustiveness, big data has the capacity to enable the recording of every single
inactivity or activity that takes place based on data (Dalton and Thatcher 2015).
This application has made big data essential particularly in the social media platforms,
which require that all records be kept every second of every day. This requires
immense storage space and large volumes of analyses. Moreover, exhaustiveness is
also associated with accuracy and precision since every record kept has to be an
accurate account of the events on the platforms. In addition, accuracy in timing
is important for effective record keeping. Despite the advantage of big data in
terms of exhaustiveness, the benefits of big data with reference to effective record
keeping are still treated with concern. This is based on ethical considerations
of the use of the data collected in record keeping. It is a question of ethical
implications in the generation and distribution of data (Zikopoulos et al 2012).
Secondly,
big data is associated with high resolution. In the contemporary data dependent
operations, the variety of data that requires handling demands that high levels
of resolution be available for effective representation and data management (Kitchin
2014). The range of data that requires handling includes reports, photos, visual
and audio files. This variety in data requires that data handling procedures should
result in the acquisition and management of data with high resolution. Big data
is fine grained hence enables the display of data to be carried out with high precision
and clarity. In the application of small data and databases, the representation
of data depended on the use of unique identifiers, which enable data collation,
sorting, monitoring, and creation of entity profiles. The unique identifiers were
use to create relations between the extant data and the profiles of various entities
who apply the data. This comes hand in hand with high data velocities, which are
also associated with big data (Cukier and Mayer-Schoenberger 2011). The major challenge
that data dependent businesses have had over the years has to do with the capacity
to hold large volumes of data and to deal with large data transfers. Big data overcomes
this challenge through an offer of flexibility and variety (Kitchin 2014).
In
social media applications, the data handled ranges from posts, images, texts, videos
and audio files, which requires high performance platforms. Traditional data models
did not offer opportunities for the combination of various data varieties in single
outputs. On the other hand, big data has overcome this challenge through the increase
in management capacity, information extraction capabilities and data processing
abilities. These capabilities have been associated with high performance big data
platform infrastructure such as hadoops (Davenport and Dyche 2013). Hadoop uses various algorithms, which are conditioned
to carry out complex instructions such as those related to text mining (Davenport
and Dyche 2013). The importance of such a platform in social media is because big
data makes it easy to create links between various data types. Big data links unstructured
data types with the structured data types. This makes it the most suitable data
model for application in social media platforms, a use which it has taken by stride
(Lycett 2013). Moreover, the applications of big data in both the social media and
in other platforms are also extensive as well as scalable. The flexibility associated
with big data makes it possible to apply it various platforms, which experience
operational changes and dynamism in data structures. In addition, big data can scale
the walls of capability in terms of its provisions.
Human
assemblages in Big Data application
While
the major benefits associated with big data result from their non-human entities,
the major challenges experienced in the application of big data arise from the human
assemblages of big data. The socio-cultural concepts in the use of big data forms
the greatest challenge as well as the greatest potential for the application of
big data. These aspects include data generation and the mediation platforms. These
assemblages contribute a lot in terms of ethical considerations in the use of big
data (Tene and Polonetsky 2013).
Secondly,
ethical issues also arise in the generation and application of big data. For instance,
the mode of continuous data generation in social media raises concerns as to the
nature of this data. The exhaustive nature of big data on the other hand, makes
the scope of data generated wide. The implication of this is that regulations have
to be laid down for restricting access to the generated data through processes such
as encryption of data relating to personal information, or that requiring confidentiality
(Beato et al n.d). In addition to this, the exposure of personal information by
various social media platforms is also restricted and only carried out after authorization
by the platform user (Hoadley et al 2009). A major concern that has been raised
in the use of social media is the identification of information that could be considered
private. This has led to the exposure of information that is to be held in confidentiality
(Tene and Polonetsky 2013).
Furthermore,
the political and economic conditions also influence the use of big data since data
is considered as intellectual property. The use of such data is therefore subject
to regulations guiding the protection of intellectual property and the relevance
to intellectual property rights (Beato et al n.d). On the other hand, the applicability
of big data in the monitoring of intellectual property information is also an important
concept in the use of big data. This implies that the aggregation of big data into
the intellectual property monitoring system can help in the creation of language
commonalities, and thus drive communication improvement between specialists in intellectual
property and owners of the IP rights (Swycher 2014).
Empirical
Analysis: Facebook
Facebook
as a social media platform has undergone immense growth since its inception more
than a decade ago. The growth has been in terms of both organic reach and ideological
expansion. Currently, Facebook has over 1.39 billion users worldwide. This implies
that in every second, there are a huge number of users who are on Facebook. In the
year 2014, it was reported that approximately 890 million users log into Facebook
on a daily basis (Sedghi 2014). This figure has however risen in the past few months
resulting in increase in the number of users per day. Moreover, these users also
cover a wide range in terms of age and intellectual characteristics. This number
of daily users can only translate into a higher need for data recording and handling
capabilities.
The
challenge in terms of capacity and capability is still inherent, needing the incessant
application of big data. With more than 200 friends per user, Facebook continues
to register immense activity in terms of interactions requiring recording, sharing
of posts, messages, videos, and audio files. All these operations indicate the need
for better data handling strategies that can only be addressed with big data. From
the number of Facebook users per day, it is indicated that each of these users spends
an average of 21 minutes each day on Facebook (Sedghi 2014). The implication of
this is that the number of users of Facebook has grown exponentially, resulting
in a significant growth of the social media platform’s net worth.
The
organic growth in Facebook has been driven even further through the variation in
the applications to which Facebook can be placed. For instance, from 2003, the increase
in organizational use of Facebook for promotional purposes has been significant.
The revenue obtained by Facebook from advertising has risen significantly as a result
of the rise in Facebook application in advertising. From a value of $2.02 per user
in 2009, the revenue has risen to an average of $8.05 per user in 2014 (Statista
2015). This means that with increasing use of Facebook in advertising, the variety
of data that needs handling also increases. For instance, while posts and messages
are mainly dependent on text data, advertisement may need the incorporation of other
data types such as videos, audios and images. The implication is an increasing need
for larger capacities and resolutions (Tufecki 2014).
Moreover,
the use of Facebook has also expanded in nature of the devices used. From the fixed
internet access that was traditionally confined to the use of Facebook. It has been
reported that access to Facebook can be enhanced through access to the internet.
It is approximately that at least 70 percent of internet users who are over 18 years
of age have access to Facebook. This percentage increases with increase with age
of the internet user. At 26 years of age, up to 84 percent of the internet users
also access Facebook (Guimaraes 2014). The implication of this is that with an increase
in age, the needs for socialization and interaction with others also rise hence
the need to use big data.
Discussion
Big
data applications in social media
From
the empirical analysis and the literature review, it has been established that the
application of big data in social media are immense. This is based on the operations
that have been described as being involved in social media. For instance, with increasing
use of social media platforms such as Facebook, the need for better data management
and analysis is required. First, in the use of Facebook, various data handling operations
are carried out. The greatest role of the Facebook employees in using their information
systems is to ensure that effective records are kept regarding the applications
of Facebook. Over 9000 employees are involved in monitoring data transfer and managing
other data related operations at Facebook. This means that the feature of big data
with reference to exhaustiveness takes centre place in the effectiveness of the
record keeping process. Big data, being exhaustive also helps Facebook to keep detailed
records of all operations, which make it possible to produce statistics such as
the revenue obtained from advertising per person annually.
These records can aid in monitoring organizational
growth and charting the way forward for improved service delivery. Besides the general
records kept to enable Facebook keep track of its operations, the company also has
to provide usage records for individual users in the process referred to as datafication
(van Dijck 2014). This implies that innovativeness and productivity in terms of
technological advancements. This is because these records are so dynamic and cannot
be followed up by individual employees.
The
dynamic nature of the Facebook operations also requires that the company should
take advantage of big data immensely. From the features of big data that have previously
been described in the literature review, it has been established that big data is
also associated with high velocities. The continuous nature of big data that
enables streaming is very applicable in the context of social media platforms such
as Facebook. This is because with the high number of users associated with these
platforms on a daily basis, serial use is impossible. This implies that the data
transfer and processing operations at Facebook have to be continuous daily and on
a yearly basis. What this requires is the application of big data, which offers
high velocity and enables a never-ending information flow. While this is critical
and very beneficial to Facebook and to other social media platforms, it is also
significantly costly in terms of management costs.
With
the rise in the net worth of Facebook as an organization, the costs of managing
big data operations need not be a challenge. This is particularly so because beside
the traditional Facebook platform, other platforms that have been acquired over
the years also contrib.ute to the revenue obtained from the Facebook operations.
With records indicating an increase in operational varieties over the years and
a subsequent increase in revenue, the costs associated with big data handling can
very effectively be covered by the revenues obtained from these operations. As has
been said previously, big data is also associated with very large volumes. This
simply implies that the volume of data applications in social media do not in any
way influence the applicability of big data. The main challenge however, may be
in terms of protecting individual information and monitoring the data transfer operations
also forms the basis of ethical considerations. The balance between the ethical
aspects associated with the application of social media and big data and the benefits
realized in terms of greater interconnectivity remains the core concern in using
big data. Facebook has also diversified in terms of user applications.
The
diversification in application brings out a core concern in terms of data types.
The data handled in advertisements is significantly of higher variety and requiring
closer monitoring and management. At Facebook, the application of big data can aid
in this concept greatly through improved resolution of the data transferred. Advertisement
requires exceptional representation, which can only be achieved through high-resolution
models. Small data and databases cannot be effective with respect to achieving high
data resolution. Moreover, the fine graining associated with big data makes it possible
for data to be collocated easily and to be sorted and monitored effectively. Sorting
and monitoring data is essential in Face book’s record keeping activities, as the
company has to produce records of revenue obtained from various operations. The
sources of Facebook’s revenues include provision of communication platforms, sale
of services and goods and advertisement (Kate 2014). In addition, collation of big
data enables the association between textual information in adverts or in posts,
which include images, and texts to be actualized. It is the role of the organization
to ensure that data handling operations can be achieved without freezing.
One
feature of big data that makes it possible for the organization to avoid freezing
is that it is variable in terms of application. As has been confirmed, Facebook
like other social media handles a wide range of data applications. The relationship
between various data and media in the social network can be created or determined
based on the application of big data. Data linking is often required in the social
media platforms particularly due to variation of the data types. For example, through
Facebook, it is possible to present various users as being related socially in real
life. This requires that data relating to different individuals be liked together
through the media. Moreover, it is also possible for individuals to create links
to other media that is not within the social media platform in use. This has been
applied widely in the advertising context where people provide links to product
adverts instead of creating the adverts on social media. While this is beneficial
to the Facebook users, it implies that greater attention has to be paid to the data
that is in both sites. This requires exceptional management and processing capabilities,
which can only be associated with big data.
An
additional application that Facebook has for big data is in the extension of its
operations. This is based on the flexibility associated with big data. This is based
on the premise that with Facebook acquiring more social media platforms, the range
of capabilities and operations also increases hence requiring a data format that
can be extended to encompass new additions, and which can scale the heights of technological
advancements.
Conclusion
The
project has been effective in achieving the research aims. The first aim was to
find out the features of big data that make it applicable to a wide range of sectors.
It has been confirmed that besides being related to large data volumes, exhibiting
high velocities and wide variety like other conventional data types, big data is
also exhaustive, flexible, fine grained and relational. These features make big
data essential in various applications, as it is possible to fine tune organizational
needs to the various deliverables of big data. However, the main limitation experienced
in the application of big data is associated with the high management costs. The
benefits linked to big data however overcome this challenge in terms of incremental
revenue. Secondly, big data has also been confirmed to be applicable in the social
media in several aspects. The needs of social media operations vary in terms of
velocity requirements, flexibility, and the need for high resolution. Big data offers
all the solutions to dealing with the challenges associated with data use in social
media. The applications of big data in the social media platforms therefore range
from record keeping of user activities to keeping of actual records for specific
users. Moreover, big data is also essential in the social media for creating relations,
accessing information from other sites through information linkage and creation
of flexible platforms that can be accessed as well as expanded at will. The growth
of the social media use in the recent times has made the need for big data even
more pressing as the variety of data applications also increase.
Finally,
the roles played by big data in social media have been identified as including:
enhancement of continuous flow of information, increase of platform flexibility,
effective, accurate, and precise record keeping, enhanced data analysis, better
data linkages between structured and unstructured data types, improvement of the
handling of a wide data type range and better monitoring of data dependent operations.
From these findings, it can be concluded that the
study has been beneficial and worthwhile in terms of knowledge provision. However,
the main limitation that was faced during the study is the availability of limited
information. It is therefore recommended that more research should be carried out
in this area, particularly with reference to the benefits that particular social
media platforms obtain from the use of big data. The need for additional
information is very important and should be addressed by the academia as soon
as possible.
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