Big Data Analytics with Business Intelligence Article Essay This week’s article provided a case study approach which highlights how businesses have integra | Course Hero

Big Data Analytics with Business Intelligence Article Essay This week’s article provided a case study approach which highlights how businesses have integrated Big Data Analytics with their Business Intelligence to gain dominance within their respective industry. Search the UC Library and/or Google Scholar for a “Fortune 1000” company that has been successful in this integration. Discuss the company, its approach to big data analytics with business intelligence, what they are doing right, what they are doing wrong, and how they can improve to be more successful in the implementation and maintenance of big data analytics with business intelligence.

Your paper should meet the following requirements:

• Be approximately 3-5 pages in length, not including the required cover page and reference page.

• Follow APA guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.

• Support your response with the readings from the course and at least five peer-reviewed articles or scholarly journals to support your positions, claims, and observations. The UC Library is a great place to find resources.

• Be clear with well-written, concise, using excellent grammar and style techniques. You are being graded in part on the quality of your writing. sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin
and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; yutajin002@gmail.com
* Correspondence: hjkim@assist.ac.kr; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018

Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and continuously evolving methods of
extracting valuable information through big data and big data analysis (BDA) for business intelligence
(BI) to make better decisions. The term “big data” refers to large amounts of information or data at
a certain point in time and within a particular scope. However, big data have a short lifecycle with
rapidly decreasing effective value, which makes it difficult for academic research to keep up with their
fast pace. In addition, big data have no limits regarding their type, form, or scale, and their scope is
too vast to narrow them down to a specific area of study.
Big data can also simply refer to a huge amount of complex data, but their type, characteristics,
scale, quality, and depth vary depending on the capabilities and purpose of each company.
The same holds for the reliability and usability of the results gathered from analysis of the data.
Previous studies generally agree on three main properties that define big data, namely, volume,
velocity, and variety, or the “3Vs” [1–4], which have recently been expanded to “5Vs” with the addition
of veracity/verification and value [5–10].
There are numerous multi-dimensional methods for choosing how much data to gather and how
to analyze and utilize the data. In brief, the methodology for extracting valuable information and
taking full advantage of it could be more important than the data’s quality and quantity. A substantial
amount of research has been devoted to establishing and developing theories concerning big data,
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BDA, and BI to address this need, but it is still challenging for a company to find, understand, integrate,
and use the findings of these studies, which are often conducted independently and cover only select
aspects of the subject.
BDA refers to the overall process of applying advanced analytic skills, such as data mining,
statistical analysis, and predictive analysis, to identify patterns, correlations, trends, and other useful
techniques [11–15]. BDA contributes to increasing the operational efficiency and business profits,
and is becoming essential to businesses as big data spreads and grows rapidly.
BI is a decision support system that includes the overall process of gathering extensive data,
extracting useful data, and providing analytical applications. In general, BI has three common
technological elements: a data warehouse integrating an online transaction processing system;
a database addressing specific topics; online analytical processing that is used to analyze data in
multi-dimensions in order to use those data; and data mining, which involves a series of technological
methods for extracting useful knowledge from the gathered data [16–20].
Some areas of BI and BDA, such as data analysis and data mining, overlap. This is to be expected,
as the raw data in BI have recently expanded to become big data in volume and scope. This has
necessitated reorganization of the field and concepts of BI to provide business insights and enable
better decision making based on BDA [21]. Although BI and BDA are generally studied independently,
it is challenging and often unnecessary to distinguish between the two concepts when performing
business tasks.
Given the cost of gathering and analyzing big data, it is important to identify what data to collect,
the range of the data, and the most cost-effective purpose of the data using BI. For this purpose, it is
effective to understand and apply the methodology based on experiences of companies shared through
a case study. Therefore, the present study has the following aims. First, we explore the meaning of BI,
big data, and BDA through a literature review and show that they are not separate methods, but rather
an organically connected and integrated decision support system. Second, we use a case study to
examine how big data and BDA are applied in practice through BI for greater understanding of the
topic. The case study is conducted on a large and rapidly growing courier service in the logistics
industry, which has a long history of research. In particular, we examine how the company efficiently
allocates vehicles in hub terminals by collecting, analyzing, and applying big data to make informed
decisions quickly, as well as uses BI to enhance productivity and cost-effectiveness.
The rest of the paper proceeds as follows. Section 2 reviews the research background and literature
related to BI, big data, and BDA. Section 3 presents the case study for the company and industry and
discusses the case in detail. Finally, Section 4 concludes by discussing the implications and directions
for future research.
2. Literature Review
Big data have become a subject of growing importance, especially since Manyika et al. pointed out
that they should be regarded as a key factor to increase corporate productivity and competitiveness [22].
Many researchers have shown interest in big data, as the rapid development of information and
communication technology (ICT) generates a significant amount of data. This has led to lively
discussions about the collection, storage, and application of such data. In 2012, Kang et al. argued that
the value of big data lies in making forecasts by recognizing situations, creating new value, simulating
different scenarios, and analyzing patterns through analysis of the data on a massive scale [23]. In 2011,
only 38 studies related to big data and BDA were listed in the Science Citation Index Expanded (SCIE),
Social Science Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), and Emerging Sources
Citation Index (ESCI), but in 2012, this number increased to 92, and then rapidly increased to 1009 in
2015 and 3890 in 2017 [24].
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2.1. Toward an Integrated Understanding of Big Data, BDA, and BI
The research boom regarding big data has led to the development of BDA, through which
valuable information is extracted from a company’s data. Companies are well aware of the increasing
importance and investment need for BDA, as shown by Tankard [25], who claimed that a company can
secure higher market share than its rivals and has the potential to increase its operating profit margin
ratio by up to 60% by using big data effectively [25,26]. In the logistics industry, big data are used
more widely than ever for supporting and optimizing operational processes, including supply chain
management. Big data have been instrumental in developing new products and services, planning
supply, managing inventory and risks, and providing customized services [26–29].
BI has a longer history of research than that of big data. In 1865, Richard Millar Devens mentioned
the concept in the Cyclopaedia of Commercial and Business Anecdotes [30], after which Luhn began
using it in its modern meaning in 1958 [31]. Thereafter, Vitt et al. defined BI as an information system
and method for decision making that incorporates the four-step cycle of analysis, insight, action,
and performance measurement [32]. Solomon suggested a framework of BI and argued that research
in the area was necessary [20]. Then, Turban et al. [33] expanded the scope of research to embrace
data mining, warehousing and acquisition, and business analysis, and a growing number of studies
followed. Miškuf and Zolotová studied BI using Cognos—a BI solution system adopted by IBM—and
the case of U.S. Steel to ascertain how to best apply enterprise/manufacturing intelligence to manage
manufacturing data efficiently [30]. Van-Hau pointed out the lack of a general framework in BI that
would allow businesses to integrate results and systematically use them, as well as discussed issues
that needed to be researched further [34]. In summary, the concept of BI has been expanding with
regard to application systems and technologies that support enterprises in making better choices by
gathering, storing, analyzing, and accessing data more effectively [35].
Previous research has dealt mostly with management and decision support systems and
applications in BI, as well as technological aspects such as algorithms and computing for big data
and BDA. However, the research areas are broadening, and topics are becoming more diverse
based on different macroeconomic environments, pace of technological progress, and division of
the research field. Therefore, many studies on BI, big data, and BDA have been conducted separately.
More importantly, big data research has a relatively short history, as it only started attracting significant
attention since around 2012, when rapid development of ICTs led to discussions on how to gather
and use the unprecedented amount of data generated. On the other hand, BI has long been a point of
interest among researchers.
The boundaries between these concepts—big data, BDA, and BI—are often unclear and ambiguous
for companies. Generally, BI consists of an information value chain for gathering raw data,
turning these data into useful information, management decision making, driving business results,
and raising corporate value [36]. However, considering that “raw data” have been expanded to “big
data” owing to the development of ICT and data storage, it is safe to say that BI and big data/BDA are
presently not independent methods but organically coexist as an integrated decision support system,
incorporating all processes from data gathering to management decision making in business.
As research interest in big data began to grow since 2012, Chen et al. grouped previous works
in the literature into BI and analytics and divided the evolution process of the subject into stages to
examine the main characteristics and features of each stage [37]. Subsequently, Wixom et al. proposed
the necessity of studying BI—including big data/BDA—and business analytics to address changes
in the field, since there was increasing awareness about the use and need of big data after the BI
Conference of the Communications of the Association for Information Systems in 2009 and 2010 [38].
Fan et al. studied BI in the marketing sector in a big data environment and concluded that big data
and BDA are disruptive technologies that reorganize the processes of BI to gain business insights for
better decision making [21]. In addition, Bala and Balachandran defined cloud computing and big
data as the two of the most important technologies in recent years and explored the improvement
of decision-making processes through BI by integrating these two key technologies for storing and
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distributing data using cloud computing [39]. These cases illustrate that an increasing number of
researchers are approaching BI and big data/BDA as an integrated concept.
2.2. In-Depth Research through Case Studies
The growing interest in big data/BDA and rapid development in this area have strengthened
BI as a decision support system, thereby promoting corporate management and enhancing business
value by providing more valuable information to generate innovative ideas for new products and
services. This has led to a rise in customer satisfaction, improved inventory and risk management,
improved supply chain risk management, creation of competitive information, and provision of
real-time business insights [26–29,40–42].
Considering the short lifecycle of big data and their use in companies, there are numerous,
multi-dimensional methods for deciding how much data to gather and how to analyze and utilize the
data speedily and effectively. As David et al. emphasized in The Parable of Google Flu: Traps in Big Data
Analysis, the essential element is turning data into valuable information, not the quantity of data or
new data itself [43]. It is thus important to establish a database of integrated convergent knowledge
and continue to develop this by accumulating knowledge and experiences through case studies based
on practical use that apply the principals of BI and big data/BDA effectively. Below, we list examples
of successful studies on the use and application of big data/BDA in practice.
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Zhong et al. examined a big data approach that facilitates several innovations that can guide
end-users to implement associated decisions through radio frequency identification (RFID) to
support logistics management with RFID-Cuboids, map tables, and a spatiotemporal sequential
logistics trajectory [44].
Marcos et al. studied both the environment and approaches to conduct BDA, such as data
management, model development, visualization, user interaction, and business models [45].
Kim reported several successful cases of big data application. Examples include analysis of
competing scenarios through 66,000 simulated elections conducted per day to understand the
decisions of individual voters during the 2012 reelection campaign of former US president Barack
Obama and delivery routes and time management based on vehicle and parcel locations adopted
by UPS, a US courier service company [46].
Wang et al. redefined big data business analytics of logistics and supply chain management as
supply chain analytics and discussed its importance [47].
Queiroz and Telles studied the level of awareness of BDA in Brazilian companies through surveys
conducted via questionnaires and proposed a framework to analyze companies’ maturity in
implementing BDA projects in logistics and supply chain management [48].
Hopkins analyzed the impact of BDA and Internet of things (IoT), such as truck telematics and
geo-information in supporting large logistics companies to improve drivers’ safety and operating
cost-efficiency [49].
The above examples of big data/BDA used by governments or corporations, as well as entities
dealing with methods in either specific or general areas, make it clear that there is an abundance of
studies on the need for and efficiency of big data. However, big data and BDA have not been studied
until recently, and few studies use real corporate examples—especially in the logistics industry—that
provide valuable business insights through detailed methods and results.
Researchers should endeavor to provide second-hand experience through specific case studies
using big data/BDA-based BI, and then accumulate and integrate such case studies to establish a
database of integrated convergent knowledge. This could enable corporations to adjust to changing
environments and improve the productivity and efficiency of the organization.
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3. Practical Business Application
The present study aims to examine the overall status of the logistics industry (an industry with
continuously growing demand and prominence) and the courier service industry (an industry used
by more consumers than any other logistics market segment) as well as business applications related
to big data/BDA and BI. The final aim is to assist corporations in reducing trial-and-error periods in
management, establishing long-term strategies, and enhancing cost-effectiveness of the corporations.
3.1. Courier Service Overview
Given consumers’ increasing focus on personal service and convenience in consumer products, as
well as global economic development, the manufacturing sector is converting from mass production of
limited items to multi-item, small-scale production. This is rapidly increasing the volume and sales of
courier services as more consumers buy online. Increased online purchases are also a result of ICT
advances. According to the Korean Statistical Information Service, Korea’s e-retail sales amounted
to KRW 79,954,478 million in 2017, an increase of 21.85% from KRW 65,617,046 million in 2016,
and a massive 107.69% increase from 2013 [50]. The courier service industry has become the biggest
beneficiary of this dramatic increase in the volume of goods transported and is a suitable yardstick to
measure the growth of the logistics industry [51,52]. Traditionally, logistics was considered a support
industry for manufacturing and consumption and was mainly perceived as a cost, but it has since
emerged as the main industry connecting producers and consumers. Manufacturing corporations
regard supply expansion based on ICT to meet consumers’ demands as a key growth strategy, and the
courier service industry has shown remarkable growth owing to the sharp increase in the need for
parcel transportation [53].
A courier service is generally defined as comprising the entire process of transportation,
from receiving a parcel to packaging, transporting, and delivering the parcel to the final destination
under the transporter’s responsibility and at the customer’s request [54,55]. The courier service
industry usually faces oligopolistic market competition, as it is an enormous service system
that requires huge initial investment. Courier service companies are normally large operational
organizations that deal with large amounts of cargo, hub terminals, general information systems, and a
wide range of transportation vehicles and consist of a complicated network of labor and equipment [51].
Davis previously examined the usefulness of courier services by using information technology
in the logistics industry [56]. DeLone and McLean showed that a successful information system
environment is a significant factor influencing user satisfaction as it models its influences on
individuals and organizations [57]. Kim et al. focused on the use of transportation routes,
freight distribution centers, and brokerage points for efficient parcel transportation via main roads [58].
Visser and Lanzendorf [59] analyzed the effects of business-to-consumer (B2C) e-commerce for cargo
transportation, revealing that an increase in the demand for courier services leads to changes in
freight per ton, distance, size, and fill rate of trucks. The authors illustrated the relationship between
consolidation and transportation routes in courier companies [59]. Jeong et al. discus…
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