The Role of Big Data in Knowledge Sharing and Transfer

This literature review explores the pivotal role of big data in fostering knowledge sharing and transfer within organizations. The paper delves into the application of big data analytics, highlighting its significance in processing vast amounts of structured and unstructured data to extract valuable insights. It examines the '5 Vs' of big data: Volume, Velocity, Value, Variety, and Veracity, illustrating their impact on decision-making processes. The review further investigates the relationship between big data and knowledge management, including the distinction between tacit and explicit knowledge and how big data facilitates the capture, acquisition, and sharing of both. The paper also discusses the evolution of knowledge sharing through digital platforms and the challenges associated with knowledge transfer, concluding that the integration of big data and knowledge management is crucial for achieving competitive advantages and driving innovation.

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1ROLE OF BIG DATA IN KNOWLEDGE MANAGEMENT
Introduction
This paper’s aim is to identify the role of big data in promoting knowledge sharing
and transferring. In this paper, information relation to big data analytics and the use of it in
knowledge transferring are discussed. There exists a gap between big data and knowledge
sharing. Knowledge grid which is used as infrastructure for communication can represent,
acquire and exchange huge quantity of knowledge. However, current models of knowledge
grid focus on discovery of knowledge with the help of big data and there is shortage of model
of productive knowledge grid to distribute knowledge. This paper supplies literature on
knowledge sharing. Big data analytics is developing swiftly field that is showing early
success. Big data analytics ensures that analysis of data may be done and can be categorised
in information essential for business and can be transformed in knowledge of big data and
processes of decision-making. The objective of big data’s analysis and collection is for
developing competitive advantages with new knowledge.
Discussion
Big Data Analytics
The concept of big data analytics (BDA) is known as large quantity of unstructured
and structured data by literature, which can be accessed in real time. These data can be found
everywhere. However for the complexity, the data is not able to process by traditional
methods. In last few years, for these advantages, big data has acquired interest in business
and academia, as both recognizes strategic and high operational potential for generating
business value (Waller and Fawcett 2013). Big data is known as approach for processing,
managing and analysing data’s five dimensions, which are termed as”5 Vs”:

2ROLE OF BIG DATA IN KNOWLEDGE MANAGEMENT
 Volume: The amount of data created per day is growing exponentially with
innovation in technology. The data produced per second is more compared to entire
internet’s storage capability in last 20 years.
 Velocity: The data creation’s speed is more crucial than volume, as it has become
more competitive in the world of economy. The ability for making faster decisions is
a key factor to success (Khan and Vorley 2017). Nowadays data can be obtained in
real-time which makes the possibility for an organization to take faster decisions and
be more agile.
 Value: Extraction of economic benefits from big data available has huge importance.
There is often a link between this value and the organization’s ability for making
better decisions.
 Variety: Big data’s sources are relatively new and many. In fact, generation of data
takes place from several digital platforms. Consumers give information related to their
needs, desires and habits through the inputs provided by them on many digital
devices.
 Veracity: There should be quality in the data collected and there should be a certain
trust level of the original source (Khan and Vorley 2017). Veracity is a fact where
data can have noise or out-of-date and incomplete.
There is a rapid development in literature’s emerging stream which highlights the
positive outcomes of BDA with respect to organizations. Big data analytics is now as
crucial component in decision-making processes in several types of businesses (Agarwal
and Dhar 2014). High-level skills and analytic tools are required for big data’s
understanding that are not available widely.

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Knowledge Transferring and Sharing
Generally, knowledge is shared by users while solving the problem, certain topics are
discussed a comments are given on opinion of others. The process of gaining experience
from others can be termed as knowledge sharing. The process of knowledge sharing
involves knowledge receiving and knowledge transferring. The distribution of distinctive
ideas can be known as knowledge transferring (Paulin and Suneson 2015). Therefore,
sharing of information, expertise, suggestions and ideas related to community can be
defined as knowledge sharing. For analysing the current scenario of knowledge sharing,
the factors which affects knowledge sharing should be understood and users should be
motivated in exchanging their knowledge among themselves.
Knowledge can of two types: tacit knowledge and explicit knowledge. Tacit
knowledge is codified and implicit. It is difficult to capture such knowledge in text form
and dependent on context. Explicit knowledge is where documentation can be done and
transmission can easily be done and can be embedded in standardised policy (Tulasi
2013). Tacit and explicit knowledge are non-exclusive, however knowledge is converted
in other forms in few organizations. The knowledge conversion is a difficult task for
organizations, as systematic efforts have to be made by organizations for reaping the tacit
knowledge’s benefits (Majchrzak et al 2013). In this type of context, role of big data
becomes crucial to capture, acquire and share explicit knowledge’s large volume which
with help of big data can be interpreted with tacit insights.
Big Data in Knowledge Sharing
Big data contains the capability for capturing and utilising different sources for tacit and
explicit knowledge and a new knowledge depth is produced for more successful decision
making. Big data’s use influence the processes to absorb new knowledge received from
several sources, conversion of one form of knowledge to another form and applying

4ROLE OF BIG DATA IN KNOWLEDGE MANAGEMENT
knowledge (Pauleen and Wang 2017). The activity of exchanging of knowledge between
people, families, friends, organizations or communities is termed as knowledge sharing.
Organizations have identified that a valuable asset to create and sustain competitive
advantages can be constituted by knowledge. Activities of knowledge sharing are normally
supported by system of knowledge management. However, one of the elements which affect
knowledge sharing within organizations like organizational trust, incentives and culture is
constituted by technology.
Knowledge sharing creates a big issue in knowledge management’s field as few
employees resist to share knowledge with the remaining organization (Poldrack and
Gorgolewski 2014). Applications and websites enable knowledge sharing among individuals
or within teams within digital world. Although knowledge is treated as object, it is
appropriate in teaching as flow and thing. Knowledge as flow can relate to tacit knowledge’s
concept. While knowledge sharing’s difficulty is transfer of knowledge from one object to
other, it can be profitable to organizations for acknowledging knowledge transfer’s
difficulties. The growing quantity of data in organization is immense (Kaivo-Oja et al 2015).
Tools for big data analytics helps in identifying patterns and several non-trivial knowledge
and information from huge quantity of unstructured and structured data which may be
invisible.
Conclusion
This paper highlights big data’s role in promotion of knowledge transferring and
sharing. In this paper, how the improvement of knowledge sharing can be done and latest
model of knowledge grid is analysed. One of the strong aspects of revolution in big data
is integration of sets of huge data with latest analytics to solve the problem. Big data
might be new, however it is developing quickly as organizations are investing for new

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technologies. Big data analytics can be either an opportunity or a threat. Organizations are
looking increasingly into big data for improving several types of performances. This
might lead for a better process of decision making which locates information and the
applicable decision levels within same place. The decisions taken while considering big
data lead to better decisions and better performance. Leaders should built big data
analytics with specific capabilities in workforces. New age of innovation integration,
knowledge management and big data will be strategy for higher level of abstraction
where sustainability and economic growth will be considered.

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References
Agarwal, R. and Dhar, V., 2014. Big data, data science, and analytics: The opportunity and
challenge for IS research.
Kaivo-Oja, J., Virtanen, P., Jalonen, H. and Stenvall, J., 2015, August. The effects of the
internet of things and big data to organizations and their knowledge management practices. In
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Springer, Cham.
Khan, Z. and Vorley, T., 2017. Big data text analytics: an enabler of knowledge management.
Journal of Knowledge Management, 21(1), pp.18-34.
Majchrzak, A., Faraj, S., Kane, G.C. and Azad, B., 2013. The contradictory influence of
social media affordances on online communal knowledge sharing. Journal of Computer-
Mediated Communication, 19(1), pp.38-55.
Pauleen, D.J. and Wang, W.Y., 2017. Does big data mean big knowledge? KM perspectives
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Paulin, D. and Suneson, K., 2015. Knowledge transfer, knowledge sharing and knowledge
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Poldrack, R.A. and Gorgolewski, K.J., 2014. Making big data open: data sharing in
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Tsai, C.W., Lai, C.F., Chao, H.C. and Vasilakos, A.V., 2015. Big data analytics: a survey.
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