Big Data Disruption and Value Chains

The assignment delves into the concept of big data as a disruptor within value chains, exploring its wide-ranging impact across various fields. It specifically highlights opportunities and challenges presented to design science, economic research, and the evolution of methodologies and theories due to the disruptive nature of big data.

Running head: ANNOTATED BIBLIOGRAPHY
Big Data
Name of the Student
Name of the University
Author’s Note

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1. Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience
and acquisition intention of big data analytics. International Journal of Information
Management, 34(3), 387-394.
The paper mainly reflects on information usage experience and data value management,
as well as on gaining target of big data analytics. It is opined by Kwon Lee and Shin (2014) that
searching, data mining as well as analysis is related with the big data analytics which are
generally comprehended as a new IT ability. This is quite helpful in improving the performance
of the firm. It is identified that even some of the organizations are accepting the big data
analytics for firming their competition market and for opening up various innovative trade
opportunities however it is identified that there are still number of firms those are still not
adopting the new technology due to lack of knowledge as well as improper information on big
data. The paper highlights one of the research models that are generally proposed for clarifying
the achievement of big data analytics as of various hypothetical perspective of information usage
experience as well as data quality management. The empirical investigation helps in revealing
the purpose for big data analytics that positively impact by marinating quality of the information
which is associated with corporate. In addition to this, the paper elaborates that the experience of
the firm in using internal source of data can hamper the intention of big data analytics adoption.
2. 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.
The paper mainly emphases on the growth of big data on cloud computing. According to
Hashem et al. (2015), in present days the cloud computing is considered as one of the powerful

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tool that helps in performing massive scale as well as complex computing. It generally helps in
eliminating the need of maintaining various types of expensive hardware, software as well as
dedicated space. It is identified that massive growth in big data is mainly generated with the help
of cloud computing. The paper elaborates that big data is one of the challenging as well as time-
demanding job that generally needs very large computational infrastructure for ensuring proper
analysis as well as data processing. The paper reviews the big data rise in context to cloud
computing with the intention of illustrate the characteristics, classification of big data with
respect to cloud computing. In addition to this, it is identified that the author focuses on various
types of research challenges in context to scalability, data transformation, data integrity,
regulatory issues as well as governance.
3. George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of
Management Journal, 57(2), 321-326
The paper mainly focuses on big data and management which is a major functionality for
future generation application. According to George, Haas & Pentland (2014), the emphasis on
big data is increasing as well as the rate of using business analytics and smart living environment
is also increases. The modern world organizations have jumped in to the big data and
management system for using ever increasing volumes of data. The data for big data is collected
from various data collection source such as various types of user generated content, mobile
Tran’s actions as well as social media. The data generally needs powerful computational
techniques for unveiling various patterns as well as trends between big socioeconomic datasets.
Moreover, new visions usually garnered from various information value abstraction which can
evocatively accompaniment official surveys, information as well as archival data sources.

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4. Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data
analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.
The paper mainly focuses on the trends of big data analytics which is one of the major
future generation applications. According to Kambatla et al. (2014), data repositories for big data
analytics are currently exceeding Exabyte which are mainly increasing in size. It is identified that
away from the sheer magnitude, the datasets and its various associated applications poses
different types of challenges for software development. The datasets are mainly distributed and
therefore the sizes as well as privacy are generally considered based on various types warrant
distributed methods or techniques. Data generally exists on various platforms with different
computational as well as network capabilities. Considerations of security, fault tolerance as well
as access control are found critical in different applications. It is reviewed that for most of the
emerging applications, data driven methods some points are net not known. Moreover, it is found
that data analytics is impacted by the characteristics of software stack as well as hardware
platform. The paper also elaborates some of the emerging trends that are helpful in highlighting
software, hardware as well as application landscape of big data analytics.
5. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and
Applications, 19(2), 171-209.
The paper mainly reviews on the background as well as on the state of the big data. It is
identified that the paper mainly focuses on the four different phases of the value chain that
mainly includes data centers, internet of things as well as Hadoop. It is identified that in each of
the phase, proper discussion about the background, technical challenges as well as review on
various latest trends are generally provided (Chen, Mao & Liu, 2014).The paper also examines

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several types of representative applications like internet of things, online social networks,
medical applications, smart grid as well as collective intelligence that are mainly associated with
big data. In addition to this, the paper elaborates number of challenges that are associated with
big data.
6. Bahrami, M., & Singhal, M. (2015). The role of cloud computing architecture in big data.
In Information granularity, big data, and computational intelligence (pp. 275-295). Springer
International Publishing.
The paper mainly reflects on the role of cloud computing architecture in big data. It is
identified that in the data driven society, large amount of data are generally collected from
different actions, people as well as algorithm however it is analyzed that handling of big data has
become one of the major challenge before the companies. In this paper, the challenges that the
companies faces due to handling of the architecture of big data are generally explained. The
paper also addresses the function of cloud computing architecture as one of the significant
solution for various types of issues that are associated with big data (Bahrami & Singhal, 2015).
The challenges that are related with storing, maintaining, analyzing, recovering as well as
retrieving big data are discussed. It is elaborated in this paper that cloud computing can be
helpful in providing proper explanation for big data with proper with open source as well as
cloud software tools in order to handle different types of big data issues.
7. Chen, M., Mao, S., Zhang, Y., & Leung, V. C. M. (2014). Big data: related technologies,
challenges and future prospects(pp. 2-9). Heidelberg: Springer.
The paper reflects on the technologies as well as challenges that are mainly related with
big data. It is stated by Chen et al. (2014) that the term of big data was mainly coined under the

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explosion of global data which was mainly utilized for describing various types of datasets. The
paper introduces number of features of big data as well as its various characteristics that include
velocity, value, variety as well as volume. Various challenges that are associated with big data
are also elaborated. Big data faces number of challenges which includes analytical mechanism,
data representation, redundancy reduction, data life cycle management, data confidentiality, as
well as energy management. The challenges as well as issues are explained on a detail basis so
that the issues can be resolved easily.
8. Glavic, B. (2014). Big data provenance: Challenges and implications for benchmarking.
In Specifying big data benchmarks (pp. 72-80). Springer, Berlin, Heidelberg.
The paper reflects on big data provenance which mainly elaborates information about the
origin as well as formation procedure of data. It is identified that such information are quite
useful for debugging transformation, auditing as well as evaluating the data quality. The paper
illustrates that provenance is generally studied by the workflow, database as well as distributed
system communities. The paper mainly reviews various types of approaches for large scale
provenance that helps in discussing different types of potential issues of big data benchmark that
generally aims to integrate provenance management (Glavic, 2014). Moreover, the paper
examines how the concept of big data benchmarking would get benefit from provenance
information and it is analyze that provenance are generally utilized for analyzing as well as
identifying performance bottlenecks for testing the ability of the system for exploiting
commonalities in processing as well as data. Additionally, it is identified that provenance are
generally utilized for data centric performance metrics, for computing fine grained as well as for
measuring the ability of the system for exploiting communalities of data and for profiling various
types of systems.

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9. Zhou, Z. H., Chawla, N. V., Jin, Y., & Williams, G. J. (2014). Big data opportunities and
challenges: Discussions from data analytics perspectives [discussion forum]. IEEE
Computational Intelligence Magazine, 9(4), 62-74.
The paper focuses on the opportunities as well as big data challenges. Zhou et al. (2014)
sated that the big data is one of the term that is considered as one of the major trends in the last
few years that generally enhances the rate of research as well as various types of administration
applications. It is identified that data is one of the powerful raw material that generally helps in
creating multidisciplinary research events for business and government performance. The main
goal of the paper is to share various types of data analytics opinions as well as perspectives that
are mainly related with the opportunities as well as challenges that are brought forth by the
movement of big data. It is identified that the author brings various types of diverse perspectives
that come from different geographical locations. In addition to this, it is identified that the paper
generally evokes discussion rather providing comprehensive survey of big data research.
10. Saha, B., & Srivastava, D. (2014, March). Data quality: The other face of big data.
In Data Engineering (ICDE), 2014 IEEE 30th International Conference on (pp. 1294-1297).
IEEE.
The paper reflects that in the era of big data, data is mainly generated, analyzed as well
as collected at an unprecedented scale for making data driven decisions. It is found that poor
quality of data is quite prevalent on web as well as on large databases. As poor quality of data
can create serious consequences on the outcome of data analysis it is identified that veracity of
big data is highly recognized (Saha & Srivastava, 2014).The paper elaborates that due to sheer
velocity as well as volume of data it is quite important for an individual to understand as well as

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repair in a quite scalable as well as timely manner. The paper mainly focuses on two major
dimensions that generally discover various types of quality issues for trading off accuracy which
identifies number of problems of the community. In addition to this, the paper elaborates the
factors that are helpful in discovering as well as learning various types of data quality semantics.
11. Tambe, P. (2014). Big data investment, skills, and firm value. Management
Science, 60(6), 1452-1469.
The paper reflects on big data investment, skills as well as on value of the firm. It is
identified that the paper mainly considers various factors that are helpful in shaping early returns
of investment in context to big data technologies. It generally tests various types of hypothesis
that generally returns to early investment in Hadoop (Tambe, 2014). The analysis utilizes
sources of new data like the LinkedIn that generally helps in enabling direct measurement of
firms into number of emerging technical skills which mainly include Map/reduce Apache as well
as Hadoop. The paper analyzed that evidence for the labor market generally disappears for the
investment that is made on mature data technologies like SQL database. In addition to this, it is
found that the skills are generally diffused and are mainly available through number of channels.
The findings of big data underscore the significance of corporate investment for acquisition of
various type of technical skills as well as for explaining various types of difference in
productivity growth rate.
12. Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big Data Research in Information
Systems: T varying perspectives onoward an Inclusive Research Agenda. Journal of the
Association for Information Systems, 17(2).

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The paper mainly focuses on big data research in information system. According to
Abbasi, Sarker & Chiang (2016), big data has got considerable attention due to the information
system discipline. It is identified that the paper mainly helps in presenting research topics for
highlighting some of the specific challenges that is generally posed by big data. It is identified
that number of steps on the research agenda of big data are generally discussed by focusing on
number of interplays between various characteristics of big data. The paper mainly highlights big
data as one of the disruption to the value chain that helps in creating widespread impact which
helps in limiting the way that is changed due to various types of scholarly work. The paper
reflects on proper critical discussion that is made on the opportunities as well as challenges for
design science, economics on research as well as on various types of emerging implications for
various types of methodologies and theories that generally arises due to disruptive effects within
the big data.

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References
Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big Data Research in Information Systems: T
varying perspectives onoward an Inclusive Research Agenda. Journal of the Association for
Information Systems, 17(2).
Bahrami, M., & Singhal, M. (2015). The role of cloud computing architecture in big data.
In Information granularity, big data, and computational intelligence (pp. 275-295). Springer
International Publishing.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and
Applications, 19(2), 171-209.
Chen, M., Mao, S., Zhang, Y., & Leung, V. C. M. (2014). Big data: related technologies,
challenges and future prospects(pp. 2-9). Heidelberg: Springer.
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of
Management Journal, 57(2), 321-326
Glavic, B. (2014). Big data provenance: Challenges and implications for benchmarking.
In Specifying big data benchmarks (pp. 72-80). Springer, Berlin, Heidelberg
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.
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal
of Parallel and Distributed Computing, 74(7), 2561-2573.

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ANNOTATED BIBLIOGRAPHY
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and
acquisition intention of big data analytics. International Journal of Information
Management, 34(3), 387-394.
Saha, B., & Srivastava, D. (2014, March). Data quality: The other face of big data. In Data
Engineering (ICDE), 2014 IEEE 30th International Conference on (pp. 1294-1297). IEEE.
Tambe, P. (2014). Big data investment, skills, and firm value. Management Science, 60(6), 1452-
1469.
Zhou, Z. H., Chawla, N. V., Jin, Y., & Williams, G. J. (2014). Big data opportunities and
challenges: Discussions from data analytics perspectives [discussion forum]. IEEE
Computational Intelligence Magazine, 9(4), 62-74.