Fluent Essays

Please follow the instructions and go through the attached PDF question document first and start working.Business intelligence (BI) leverages software and services to transform data into actionable insights that inform an organization’s strategic and tactical business decisions. BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence about the state of the business. Business intelligence often also refers to a range of tools that provide quick, easy-to-digest access to insights about an organizations current state, based on available data.Business intelligence is descriptive, telling you whats happening now and what happened in the past to get us to that state. Business analytics, on the other hand, is a term for data analysis techniques that are predictive — that is, they can tell you whats going to happen in the future — and prescriptive — that is, they can tell you what you should be doing to create better outcomes. (Business analytics are usually thought of as that subset of the larger category of data analytics thats specifically focused on business.)A variety of different types of tools fall under business intelligence. Here are some of the most important categories and features:DashboardsVisualizationsReportingData miningETL (extract-transfer-load —tools that import data from one data store into another)OLAP (online analytical processing)Of these tools, dashboards and visualization are by far the most popular; they offer the quick and easy-to-digest data summaries that are at the heart of BIs value proposition.There are tons of vendors and offerings in the BI space, and wading through them can get overwhelming. Some of the major players include:Tableau, a self-service analytics platform provides data visualization and can integrate with a range of data sources, including Microsoft Azure SQL Data Warehouse and ExcelSplunk, a “guided analytics platform” capable of providing enterprise-grade business intelligence and data analyticsAlteryx, which blends analytics from a range of sources to simplify workflows as well as provide a wealth of BI insightsQlik, which is grounded in data visualization, BI and analytics, providing an extensive, scalable BI platformDomo, a cloud-based platform that offers business intelligence tools tailored to various industries (such as financial services, health care, manufacturing and education) and roles (including CEOs, sales, BI professionals and IT workers)Dundas BI, which is mostly used for creating dashboards and scorecards, but can also do standard and ad-hoc reportingGoogle Data Studio, a supercharged version of the familiar Google Analytics offeringEinstein Analytics, Salesforce.com’s attempt to improve BI with AIBirst, a cloud-based service in which multiple instances of the BI software share a common data backend.What are we doing this week?Read the required reading on business intelligence and big dataThis weeks 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 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. integrated_understanding_of_big_data_and_business_intelligence.pdf Unformatted Attachment Preview 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, Sustainability 2018, 10, 3778; doi:10.3390/su10103778 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 3778 2 of 15 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]. Sustainability 2018, 10, 3778 3 of 15 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 Sustainability 2018, 10, 3778 4 of 15 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. • • • • • • 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. Sustainability 2018, 10, 3778 5 of 15 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 ... Purchase answer to see full attachment