Swinburne INF80040: Big Data & Knowledge Management Literature Review

This literature review, submitted for INF80040 at Swinburne University, examines the relationship between big data and knowledge management. The report begins with an introduction outlining the importance of decision-making processes in organizations and the role of big data in enhancing performance. The literature review explores the concept of big data, defining it as large and dynamic datasets requiring advanced analytical processes. It then delves into the core question of whether big data truly equates to big knowledge, presenting arguments from two authors. The first author emphasizes the crucial role of human knowledge in leveraging big data analytics, while the second highlights the challenges and contradictions associated with big data implementation, including data, process, and management issues. The review concludes by summarizing the complementary relationship between big data and knowledge management, emphasizing the need for careful implementation and the importance of knowledge management in governing big data usage. The report includes references in Harvard style.

Running Head: KNOWLEDGE MANAGEMENT AND ANALYTICS
KNOWLEDGE MANAGEMENT AND ANALYTICS
Name of the Student
Name of the University
Author Note

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Table of Contents
Introduction................................................................................................................................2
Literature Review.......................................................................................................................2
Big Data.................................................................................................................................2
Big Data Mean Big Knowledge.............................................................................................3
Contradictions of Big Data by Two Authors.........................................................................3
Conclusion..................................................................................................................................5
Reference....................................................................................................................................7

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Introduction
The processes of decision-making are marked by the various kinds of the elements
that are related to and made as well as aligned with the strategies of the companies. There
exist toolsets for example data repositories, analysis of decisions as well as information
system, which are used as an aid for the process of decision-making (Erickson and Rothberg
2014). An investment on big data enhances the performances of various different processes in
the organization as well as making the process of decision effective. However, there are
certain challenges of big data in respect of data, processes as the management that hinders the
implementation of big data. These big data is implemented without handing the challenges
then it would lead towards failure of the technology as well as some results that are
unfavorable (Jinet al. 2015). Hence, under this assignment, discussion will be done for
answering the question whether big data mean big knowledge.
Literature Review
Big Data
The Big data is referred as the large and the dynamic volumes of the data, which
requires the analytical processes for managing the vast amount of the data in order for
deriving the insights of real-time business that relates to profit, consumers as well as
productivity management. In the era of the big data science, it becomes critical for the
companies and the business for enabling or embracing this new facility and for accurately
integrating the bases of knowledge from the various sources into the repository of
organizational information (Liet al. 2015). There is great influence on everything that is right
from data itself as well as its collection, to processing and to the decisions that are final
extracted, from the emergence of the big data. Recently, big data term has been applied to the
databases, which grows so large that it become impossible to work by using the traditional

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systems of databases management (Schroeder and Cowls 2014). These data sets have the
sizes that are beyond the ability of the tools that are used commonly of software and the
system of storage for capturing, storing, managing and processing the data that is within the
elapsed time of tolerance.
Big Data Mean Big Knowledge
Big data in recent days has become one of the most buzzed concept in the world of
information technology, especially with the vertiginous development in driving for increasing
of the data that is encouraged by emerging of the high technologies of the storage such as
cloud computing. This helps in creating efficient solutions that are challenging in the
security, health, government and more. Apart from this it is highly used in the new era of the
decisions and the analytics. Moreover, knowledge management consists of the set of the
practices as well as strategies that is used for identifying, creating, representing, distributing
as well as enabling for creating the knowledge, which is helpful for constituting the real
immaterial capital. The evolution of the technology as well as increased multitudes of the
flowing of data in as well as out of the company on the daily basis has made it necessary for
the requirement of faster as well as efficient ways in order to do the analysis of these data
(Wambaet al. 2017). Hence, tools and the methods are aroused that are specialized for the big
data analytics and the required architecture to store as well as manage these data. However
there are different argument that are in favor as well as against the big data. Some view this
as a great tool for managing or processing the big data and some people view this as a tool
that is quite challenging (Xianget al. 2015).
Contradictions of Big Data by Two Authors
Contribution by First Author- Big Data and Knowledge Management

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According to DJ Pauleen, Knowledge is important and central for any of the
discussion around the big data because of two different reasons. The first reason is that it is
the knowledge of human, which has been developed the required capabilities of the big data
as well as analytics that means without having the knowledge of big data; there is no
existence of big data. The knowledge as well as experience of the human is responsible solely
for decisions on the collections of the data and analysis of the algorism. Hence, it becomes
impossible for negate the influences of the knowledge while discussing regarding impact of
and influences on the big data or analytics (De Mauro, Greco and Grimaldi2015). Moreover,
the second reason regarding the critical role played by the knowledge on the discussion of the
big data is that the knowledge of human generally decide that how information that is
generated from the big data would be used. The big data may be collected as well as analyzed
without having any specific objective in mind. However, in major cases, collection as well as
analysis of the big data are initiated for the automated response to the predefined problems
that are existing, motivation for exploring the new opportunities with having clear definitions
of the problems and supporting of the strategic decisions for achieving the goals of
organizations (Pauleen and Wang 2017). The example of this approach of the author is that if
the company has started using big data analytics for processing high volume of the data then
it is not only this analytics plays vital role rather it also depends on the knowledge of the
human. It is because it is human who will be using these data for making any important
decisions. In absence of sufficient knowledge of the human, decisions would be made
effectively. Therefore, human knowledge plays important role in big data analytics.
Contribution by Second Author- Big Data Analytics
According to Nasser Thabet, the big data is having the special attributes that is not
able to be managed as well as processed by existing traditional systems of software that
becomes the real problem. Despite of the various benefits of the big data, it is factual as well

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as substantial. There remains the plethora of the challenges, which should be addressed in full
for realizing big data potential. There are certain challenges such as existing analysis model
as methods as well as limitations of the current system of processing data. The other issues
relates to privacy issues as well as ethical considerations, which are relevant to mining of
these data. Building of the solution that are viable for the large as well as multifaceted data is
the big challenge that the business are learning constantly and then they are implementing the
new approaches (Bhosale and Gadekar, 2014). The example of problem in relation to big data
is the high cost of the infrastructure. The equipment of hardware is quite expensive even if
there is the availability of technologies of cloud computing. Moreover, there are three main
challenges of the big data are data, processes as well as the management. The challenge of
data is group of the challenges, which are related to the data features itself. The process
challenge is consists of all challenges that are being encountered when processing the Big
data, which starts with the step of capturing and end with presentation of the output to the
clients. Lastly, the management challenge is consists of the legal as well as ethical issues that
are related with the assessment of the data. Every layer of architecture provides the required
technologies for overcoming the different challenges. The continuing evolution of technology
necessitates the innovation of new big data analytics for digging deeper in looking of data for
much valuable insights (Nasser and Tariq 2015). The example of this approach is that when
the company installs or converges the business IT and the manufacturing IT then it would be
beneficial for the company in one aspect but in other context it would increase the cost of
installation and it also requires more training to the employees for handing the these
analytics.
Conclusion
Hence, is can be summarized from the analysis that big data mean big knowledge
because it helps in contributing towards positive benefits in creating efficient challenging

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solutions in different aspects of the economy and society. Big data requires knowledge
management and in same way knowledge management needs big data. Both the concept is
having complementary relation. Moreover, big data has gained recently interest because of its
perceived benefits as well as unprecedented opportunities. In era of the information, the large
voluminous varieties of the high velocity of the data are being daily produced and inside
them, there are inherent details that are laid down and patterns of the knowledge that is
hidden should be utilized and extracted. Moreover, it has been analyzed that there are various
challenges in the implementation of the big data, but it would be minimized if implemented
with great cautions. Lastly, it has been analyzed that knowledge management assumes the
leading role of organization in governance and management of uses of big data in the settings
of the organizational.

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Reference
Bhosale, H.S. and Gadekar, D.P., 2014. A review paper on big data and
hadoop. International Journal of Scientific and Research Publications, 4(10), pp.1-7.
De Mauro, A., Greco, M. and Grimaldi, M., 2015, February. What is big data? A consensual
definition and a review of key research topics. In AIP conference proceedings (Vol. 1644,
No. 1, pp. 97-104). AIP.
Erickson, S. and Rothberg, H., 2014. Big data and knowledge management: establishing a
conceptual foundation. Electronic Journal of Knowledge Management, 12(2), p.101.
Jin, X., Wah, B.W., Cheng, X. and Wang, Y., 2015. Significance and challenges of big data
research. Big Data Research, 2(2), pp.59-64.
Li, J., Tao, F., Cheng, Y. and Zhao, L., 2015. Big data in product lifecycle management. The
International Journal of Advanced Manufacturing Technology, 81(1-4), pp.667-684.
Nasser, T. and Tariq, R.S., 2015. Big data challenges. J ComputEngInfTechnol 4: 3. doi:
http://dx. doi. org/10.4172/2324, 9307(2).
Pauleen, D.J. and Wang, W.Y., 2017. Does big data mean big knowledge? KM perspectives
on big data and analytics. Journal of Knowledge Management, 21(1), pp.1-6.
Schroeder, R. and Cowls, J., 2014, August. Big data, ethics, and the social implications of
knowledge production. In data ethics workshop, New York, NY. Retrieved from
https://dataethics.github.io/proceedings/BigDataEthicsandtheSocialImplicationsofKnowledge
Production. pdf.
Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R. and Childe, S.J., 2017. Big
data analytics and firm performance: Effects of dynamic capabilities. Journal of Business
Research, 70, pp.356-365.

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Xiang, Z., Schwartz, Z., Gerdes Jr, J.H. and Uysal, M., 2015. What can big data and text
analytics tell us about hotel guest experience and satisfaction?. International Journal of
Hospitality Management, 44, pp.120-130.