Annotated Bibliography: Big Data Analytics in Various Industries

This annotated bibliography provides a comprehensive overview of big data analytics, encompassing various perspectives and applications across different industries. It includes twelve references, each summarized to highlight the key arguments, methodologies, and findings. The bibliography covers topics such as the impact of big data analytics on the accounting profession, strategic asset management, supply chain management, and marketing strategies. It also examines the use of big data analytics in the financial industry, digital transformation, and cloud computing. The included studies explore the tools, techniques, and challenges associated with implementing big data analytics, offering insights into how organizations can leverage data to drive value, improve decision-making, and enhance their competitive advantage. The bibliography also touches upon the ethical considerations and future trends in the field, providing a well-rounded understanding of big data analytics.

Annotated Bibliography
Big Data Analytics
Student’s name
Institution Affiliation(s)

Reference 1:
Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C. (2017). Big Data Analytics:
Opportunity or Threat for the Accounting Profession?. Journal Of Information
Systems, 31(3), 63-79. doi:10.2308/isys-51805
As opposed to Frey and Osborne's (2013) forecast that the bookkeeping profession faces
eradication, we contend that accountants can, in any case, make an incentive in a universe of Big
Data analytics. To propel this position, we give a conceptual framework given
organized/unstructured data and issue-driven/exploratory examination. We contend that
accountants as of now exceed expectations at issue driven investigation of organized data, are
very much situated to assume the main part in the issue-driven examination of unstructured data,
and can bolster data researchers performing an exploratory investigation on Big Data.
Reference 2:
The Impact of Maintenance 4.0 and Big Data Analytics within Strategic Asset
Management. (2017). MPMM 2016, Maintenance, Performance, Measurement &
Management: conference proceedings, 96.
The most recent mechanical upset is showed by smart and networking hardware. Understanding
the full estimation of these apparatuses, and different business assets have turned out to be
progressively essential. Strategic asset management faces administrative, specialized and also
methodological difficulties, of which some could be lessened or overwhelmed by applying
mechanical arrangements, for example, Internet of things, distributed computing, digital
physical frameworks and big data analytics. This paper plots the effect of the developing

advancements in the zone of strategic management with extreme accentuation on the analytics
as a specialist co-op for the support capacities.
Reference 3:
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the Use of Big Data Analytics Affects
Value Creation in Supply Chain Management. Journal Of Management
Information Systems, 32(4), 4-39. doi:10.1080/07421222.2015.1138364
In spite of various tributes of first movers, the hidden instruments of associations' big data
analytics (BDA) utilization merit close examination. Our examination tends to two fundamental
research questions: (1) How does hierarchical BDA use influence esteem creation? And (2) What
are critical forerunners of authoritative level BDA utilization? We draw on powerful abilities
hypothesis to conceptualize BDA use as an exceptional data preparing capacity that conveys
upper hand to associations. Moreover, we utilize the technology– organization– condition (TOE)
framework to distinguish and guess ways through which factors impact the genuine use of BDA.
Study data gathered from 161 U.S.- based organizations demonstrate that: authoritative level
BDA utilization influences hierarchical esteem creation; how much BDA use impacts such
production is directed by environmental dynamism; mechanical factors straightforwardly impact
authentic BDA use, and hierarchical and natural factors by implication impact hierarchical BDA
use through best management bolster.
Reference 4:
Sanders, N. R. (2016). How to Use Big Data to Drive Your Supply Chain. California
Management Review, 58(3), 26-48.

Big data analytics has turned into a basis for business pioneers over each industry part.
Analytics applications that can convey an upper hand seem up and down the supply chain
choice range - from the focused area based showcasing to improving supply chain inventories to
empowering provider chance appraisal. While numerous organizations have utilized it to
remove new bits of knowledge and make new types of significant worth, different organizations
still can't seem to use big data to change their supply chain operations. This article analyzes how
driving organizations utilize big data analytics to drive their supply chains and offers a
framework for execution in light of lessons learned.
Reference 5:
Biju, S. M., & Mathew, A. (2017). COMPARATIVE ANALYSIS OF SELECTED BIG
DATA ANALYTICS TOOLS. Journal Of International Technology & Information
Management, 26(2), 2-22.
In the course of the most recent couple of years, big data has risen as an essential subject of
discourse in many firms attributable to its capacity of creation, stockpiling and handling of
substance at a reasonable cost. Big data comprises of cutting-edge devices and systems to
process vast volumes of data in associations. Interest in big data analytics has nearly turned into
a need in large measured firms, especially multinational organizations, for its exceptional
advantages, especially in the forecast and distinguishing proof of different patterns. The simple
most mainstream big data analytics programming utilized today are MapReduce, Hive, Tableau,
and Hive, while the framework Hadoop empowers simple preparing of such exceedingly
substantial data sets. The momentum inquires about endeavors to make a relative appraisal of
five such applications, in particular, IBM SPSS, IBM Watson Analytics, R, Minitab, and SAS.
The case produced results of the test was that of the elements influencing housing reasonableness

in the US. In light of the measurements got from the American Housing Survey (AHS) database,
the analyst has distinguished distinctive components affecting the reasonableness in the states.
The procedure of diminishing factors, however, Principal Component Analysis (PCA) and a
model in light of halfway minimum square relapse/polynomial relapse were fitted to check the
effect on the moderateness. The essential discoveries recommend that significantly age of the
leader of the family, salary earned were the two most important variables influencing the
estimating in the locale. Likewise, a correlation is drawn toward the finish of concentrate with an
understanding of the most and minimum compelling applications
Reference 6:
Dremel, C., Wulf, J., Herterich, M. M., Waizmann, J., & Brenner, W. (2017). How AUDI
AG Established Big Data Analytics in Its Digital Transformation. MIS Quarterly
Executive, 16(2), 81-100.
Digital transformation, which frequently incorporates setting up big data analytics capacities,
postures significant difficulties for customary assembling associations, for example, auto
organizations. Efficiently presenting big data analytics requires substantial hierarchical
transformation and new authoritative structures and business forms. Given the three-arrange
development of big data analytics abilities at AUDI, we give proposals to how customary
assembling associations can effectively present big data analytics and ace the related
authoritative transformations.
Reference 7:
Henry, R., & Venkatraman, S. (2015). BIG DATA ANALYTICS THE NEXT BIG
LEARNING OPPORTUNITY. Academy Of Information & Management Sciences
Journal, 18(2), 17-29.

The tireless gathering of data from client associations in sites has presented both an abnormal
state of many-sided quality, and also an incredible open door for organizations. Furthermore, the
pattern of interfacing individuals, as well as machines to the Internet, and then gathering data
from these machines through sensors would soon bring about an incredible storehouse of data.
This consistently expanding gathering of data, otherwise called Big Data, may be helpful on the
off chance that it can be broken down to give valuable experiences to business issues, and maybe
even to influence proposals with reference to when and where future matters to will happen
(predictive analytics) so the problems can be evaded or if nothing else alleviated. Understudies
must be readied exploit for next open doors in the field of big data analytics. In many business
programs, particularly data framework as the real, center course like database plan, office
applications, and essential writing computer programs are educated to students. A fundamental
part missing from numerous undergrad business programs are center courses concentrating on
data analytics. The US Department of Labor predicts 4.4 million open doors will exist by 2018,
working with data analytics.
Reference 8:
Metcalf, J. (2016). Big Data Analytics and Revision of the Common Rule. Communications
Of The ACM, 59(7), 31-33. doi:10.1145/2935882
The article talks about conceivable changes to the Common Rule in inquiring about morals
because of the development of big data analytics. Points incorporate a September 2015 Notice
of Proposed Rule-Making (NPRM) issued by the U.S. Branch of Health and Human Services
(HHS), the qualification amongst training and research in connection to the 1979 Belmont
Report on human subject research, and the proper class of freely accessible datasets containing
private data.

Reference 9:
Cloud computing for big data analytics in the Process Control Industry.
(2017). Mediterranean Conference on Control and Automation 2017 25th
Mediterranean Conference on Control and Automation, MED 2017, 1373.
doi:10.1109/MED.2017.7984310
The point of this article is to show a case of a novel cloud computing foundation for big data
analytics in the Process Control Industry. Most recent developments in the field of Process
Analyzer Techniques (PAT), big data and remote advances have made another condition in
which all phases of the mechanical procedure can be recorded and used, for wellbeing, as well as
for continuous enhancement. Given investigation of recorded sensor data, machine learning
based streamlining models can be produced and conveyed progressively shut control circles. Be
that as it may, in any case, the neighborhood execution of those frameworks requires an immense
interest in equipment and programming, as an immediate consequence of the big data nature of
sensors data being recorded consistently. The current mechanical progressions in cloud
computing for big data processing, open new open doors for the industry while going about as an
empowering influence for a noteworthy diminishment in costs, making the technology accessible
to plants of all sizes. The principle commitment of this article comes from the introduction of a
clench hand time ever of a pilot cloud-based engineering for the use of a data-driven
demonstrating an ideal control design for the field of Process Control. As it will be displayed,
these advancements have been conveyed in cozy association with the procedure industry and
clear a route for a summed up use of the cloud-based methodologies, towards the fate of Industry
4.0
Reference 10:

Digital Service Innovation Enabled by Big Data Analytics: A Review and the Way
Forward. (2017). Proceedings of the 50th Hawaii International Conference on
System Sciences 2017,
Service innovation is drawing in consideration with the expanding service enterprises and
economies. Joined by real improvements in ICT and real and digital advancements, the
enthusiasm for digital service innovation (DSI), both from the scholarly community and
industry, is expanding. Digitization and the going with mechanical progressions are prompting
wonders that call for extensive research in connection to service innovation; one of which is Big
data analytics (BDA). In this paper, we survey the DSI writing and investigate how BDA can
contribute along the distinctive measurements of DSI. The ex-post writing experiences the
absence of such examinations. Appropriately, we recommend an exploration plan for BDA-
empowered DSI, spurred by emerging examination holes, and also openings and directing
exploration questions.
Wedel, M., & Kannan, P. K. (2016). Marketing Analytics for Data-Rich
Environments. Journal Of Marketing, 80(6), 97-121. doi:10.1509/jm.15.0413
The creators give a basic examination of marketing analytics strategies by following their
recorded advancement, looking at their applications to organized and unstructured data produced
inside or outer to a firm, and evaluating their capability to help marketing choices. The creators
recognize headings for new systematic research techniques, tending to (1) analytics for
improving marketing-blend spending in a data-rich condition, (2) analytics for personalization,
and (3) analytics with regards to clients' protection and data security. They survey the
suggestions for associations that mean to execute big data analytics. At long last, swinging to the

future, the creators distinguish patterns that will shape marketing analytics as a train and also
market analytics instruction.
Reference 12:
Lawler, J., & Joseph, A. (2017). Big Data Analytics Methodology in the Financial
Industry. Information Systems Education Journal, 15(4), 38-51.
Firms in the industry keep on being pulled in by the advantages of Big Data Analytics. The
benefits of Big Data Analytics undertakings may not be as apparent as much of the time
demonstrated in writing. The creators of the investigation assess factors in an altered procedure
that may build the advantages of Big Data Analytics ventures. Evaluating firms in the budgetary
industry, the creators find that business and procedural elements, for example, joint effort
development of the association and Big Data Analytics administration, are more critical than the
subtleties of technology, for example, equipment and item programming of technology firms, in
starting to amplify the capability of Big Data Analytics in the organizations. The discoveries of
the paper will profit instructors in enhancing Big Data Analytics curricular projects to be present
with the examples of firms productively starting Big Data Analytics systems.