Big Data promises value: is hardware technology taken onboard?
| Pages | 1577-1595 |
| Date | 19 October 2015 |
| Published date | 19 October 2015 |
| DOI | https://doi.org/10.1108/IMDS-04-2015-0160 |
| Author | Wasim Ahmad Bhat,S.M.K. Quadri |
| Subject Matter | Information & knowledge management,Information systems,Data management systems |
Big Data promises value:
is hardware technology
taken onboard?
Wasim Ahmad Bhat and S.M.K. Quadri
Department of Computer Sciences, University of Kashmir, Srinagar, India
Abstract
Purpose –The purpose of this paper is to explore the challenges posed by Big Data to current trends
in computation, networking and storage technology at various stages of Big Data analysis. The work
aims to bridge the gap between theory and practice, and highlight the areas of potential research.
Design/methodology/approach –The study employs a systematic and critical review of the
relevant literature to explore the challenges posed by Big Data to hardware technology, and assess the
worthiness of hardware technology at various stages of Big Data analysis. Online computer-databases
were searched to identify the literature relevant to: Big Data requirements and challenges; and
evolution and current trends of hardware technology.
Findings –The findings reveal that even though current hardware technology has not evolved with
the motivation to support Big Data analysis, it significantly supports Big Data analysis at all stages.
However, they also point toward some important shortcomings and challenges of current technology
trends. These include: lack of intelligent Big Data sources; need for scalable real-time analysis
capability; lack of support (in networks) for latency-bound applications; need for necessary
augmentation (in network support) for peer-to-peer networks; and rethinking on cost-effective
high-performance storage subsystem.
Research limitations/implications –The study suggests that a lot of research is yet to be done in
hardware technology, if full potential of Big Data is to be unlocked.
Practical implications –The study suggests that practitioners need to meticulously choose the
hardware infrastructure for Big Data considering the limitations of technology.
Originality/value –This research arms industry, enterprises and organizations with the concise and
comprehensive technical-knowledge about the capability of current hardware technology trends in
solving Big Data problems. It also highlights the areas of potential research and immediate attention
which researchers can exploit to explore new ideas and existing practices.
Keywords Big Data, Networking, Storage, Knowledge economy, Microprocessor, Technology trends
Paper type Research paper
1. Introduction
Earlier technology only provided a platform for processing data to yield information in
order to improve the performance of existing processes, businesses and activities. With
the advancement and proliferation of technology, the digital data nowadays comes
from variety of sources (countless sensors, innumerous web applications, growing
handheld devices and so on), in various forms (text, images and videos), in huge
volumes and with high velocity. Big Data is the term used to describe this voluminous,
heterogeneous and frequent generation of semantically unstructured data (Laney,
2001). Big Data is large in volume, complex in structure and aggressive in its
production. Information and communication technology has always had a positive
Industrial Management & Data
Systems
Vol. 115 No. 9, 2015
pp. 1577-1595
©Emerald Group Publis hing Limited
0263-5577
DOI 10.1108/IMDS-04-2015-0160
Received 29 April 2015
Revised 8 September 2015
13 September 2015
Accepted 14 September 2015
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
The authors thank their colleagues from the University of Kashmir, especially Dr Ameen Parray,
who offered his invaluable expertise. The authors would also like to thank the anonymous
reviewers for their critical assessment of the manuscript, detailed insights, and valuable and
encouraging comments.
1577
Big Data
promises
value
impact on innovation activity (Kmieciak et al., 2012), performance (Bayo-Moriones et al.,
2013) and value (Ong and Chen, 2014) of an enterprise; Big Data being no exception.
Big Data provides an opportunity to academics, industry and organizations to better
understand a process or a phenomenon. If proper analysis of Big Data is performed and
correct correlations are drawn, Big Data promises generation of new ideas, innovations,
new products, higher productivity and profitability. Research has shown that
knowledge creation has the potential to act as a catalyst for innovation (Begona Lloria
and Peris-Ortiz, 2014) and Big Data not only promises creation of new knowledge but
also new kinds of knowledge, on which an entirely new economy can be founded
(Haynes and NGuyen, 2014). Big Data also promises entirely new classes of economic
activities built on insights, and the value derived from it.
Immense technology advancement and its intrusion into every aspect of our life are
the basic reasons for creation and growth of Big Data. If this proliferation and
penetration of technology continues, richer and heavier data sets would be created.
On the other hand, if advancements in technology support proper, diverse and timely
analysis of such data sets, stronger and stable knowledge economy would be created.
This implies that for a sustainable knowledge economy to be an outcome of Big Data,
technology must play its part effectively at all stages of Big Data. Although
the benefits of Big Data are being unanimously envisioned across the globe, the
challenges are still being discussed and their solutions are yet to be finalized.
One such challenge is faced by hardware technology to support Big Data creation,
growth, communication and analysis for knowledge creation. National and
international projects such as the Large Hadron Collider at CERN, and such many
other, are frequently cited for the way they will challenge the state of the art in three
main aspects of hardware technology, i.e., computation, networking and storage
(Lynch, 2008). The problem is exaggerated by the fact that current technology trends
in computation, networking and storage have not evolved keeping in view Big Data,
though same is being employed in Big Data from its generation to analysis.
Therefore, allowing technological gaps to creep in at all stages of Big Data; rendering
Big Data incomplete and its analysis inappropriate.
Academics and practitioners alike, therefore, face several questions: what are the
challenges posed by Big Data to current trends in computation,networking and storage
technology at various stages of Big Data analysis? How effectively does current
technology trends in computation, networking and storage support Big Data at various
stages? Are there any technological gaps between Big Data requirements and current
trends in computation, networking and storage technology? What are the areas of
potential research in computation, networking and storage technology with respect to
Big Data? Many studies in pasthave analyzed the general challenges posed by BigData
to technology. Also, many focussed-studies have analyzed the challenges posed by
Big Data to specific areas of technology like security (Tankard, 2012), usability
( Jianzhong and Xianmin, 2013), privacy (Kshetri, 2014), data management (Little, 2012),
cloud (Agrawal et al., 2011) and so on. Unfortunately, studies performing assessment of
effectiveness of technology employed, exploration of existing technological gaps and
identificationof areas of potential research with respectto computation, networking and
storage technology (collectively) is yet to be done. Therefore, leaving aforementioned
questions yet to be answered. Answering such questions would bridge the gap
between theory and practice, and would point toward areas of potential research,
besides significantly contributing to knowledge base and pinpointing stage-specific
effective-technology. Bearingthis research in mind, the aim of this paper is to analyzethe
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